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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

The lion's share of bacteria in various environments cannot be cloned in the laboratory and thus cannot be sequenced using existing technologies. A major goal of single-cell genomics is to complement gene-centric metagenomic data with whole-genome assemblies of uncultivated organisms. Assembly of single-cell data is challenging because of highly non-uniform read coverage as well as elevated levels of sequencing errors and chimeric reads. We describe SPAdes, a new assembler for both single-cell and standard (multicell) assembly, and demonstrate that it improves on the recently released E+V-SC assembler (specialized for single-cell data) and on popular assemblers Velvet and SoapDeNovo (for multicell data). SPAdes generates single-cell assemblies, providing information about genomes of uncultivatable bacteria that vastly exceeds what may be obtained via traditional metagenomics studies. SPAdes is available online ( http://bioinf.spbau.ru/spades ). It is distributed as open source software.

SPAdes, a new genome assembly algorithm and its applications to single-cell sequencing ( 7th Annual SFAF Meeting, 2012)

This paper provides an overview of the Internet of Things (IoT) with emphasis on enabling technologies, protocols, and application issues. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. This paper starts by providing a horizontal overview of the IoT. Then, we give an overview of some technical details that pertain to the IoT enabling technologies, protocols, and applications. Compared to other survey papers in the field, our objective is to provide a more thorough summary of the most relevant protocols and application issues to enable researchers and application developers to get up to speed quickly on how the different protocols fit together to deliver desired functionalities without having to go through RFCs and the standards specifications. We also provide an overview of some of the key IoT challenges presented in the recent literature and provide a summary of related research work. Moreover, we explore the relation between the IoT and other emerging technologies including big data analytics and cloud and fog computing. We also present the need for better horizontal integration among IoT services. Finally, we present detailed service use-cases to illustrate how the different protocols presented in the paper fit together to deliver desired IoT services.

Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications

Future network should be able to efficiently serve packet-based networks, such as the Internet. In this paper, based on results from COST 266, we explore characteristics of optical burst switching (OBS) and optical packet switching (OPS). Both node design and metropolitan area network (MAN) are discussed. A unique joint comparative performance evaluation of contention resolution in OBS and OPS are presented, as well as methods of quality of service (QoS) differentiation in OBS/OPS networks, and their performance.

Optical burst and packet switching: node and network design, contention resolution and quality of service

IEEE 802.11p hardware is hard to find. Previous research efforts often relied on project-specific prototype implementations which are characterized by a high cost and are not always available to the entire research community. Commercially available turnkey implementations are rare and quite expensive compared to commercial of-the-shelf (COTS) IEEE 802.11a/b/g hardware. However, the difference between the IEEE 802.11p amendment and the other IEEE 802.11 standards is quite small. It can be seen as a combination of the IEEE 802.11a and IEEE 802.11e standards, with some specific adjustments. This paper presents how an approximation of the IEEE 802.11p standard can be implemented using COTS IEEE 802.11a hardware and some specific software adjustments. This way, vehicular test infrastructures can be established in a much more cost effective manner, and existing IEEE 802.11 wireless testbeds can be used to support VANET research.

/pdf/approximation-of-the-ieee-802-11p-standard-using-commercial-2nj1vnw7hn.pdf

Approximation of the IEEE 802.11p standard using commercial off-the-shelf IEEE 802.11a hardware

https://biblio.ugent.be/publication/369728/file/577243.PDF

Design of CASP: an open enabling platform for context aware office and city services

The concept of content distribution networks (CDNs) has recently been introduced to enhance the delivery of bandwidth-intensive multimedia content to end users. In a CDN architecture, the content is replicated from the origin server to so-called surrogate servers at the edge of the Internet, to improve the quality of service and optimise network bandwidth usage. The introduction of peer-to-peer (P2P) architectures, where all nodes fundamentally play equal roles, enables self-organisation of the CDN and automatic recovery in case of node failures. To optimise the distribution of the content over the different surrogate servers, replica placement algorithms (RPAs) have been developed. In this paper, we present two distributed RPAs for CDNs. We will demonstrate that they further improve CDN performance by reducing the server load and the bandwidth usage. The introduction of link costs allows these algorithms to additionally support load balancing on the network links.

Load balancing through efficient distributed content placement

As Massively Multiplayer Online Games enjoy a huge popularity and are played by tens of thousands of players simultaneously, an efficient software architecture is needed to cope with the dynamically changing loads at the server side. In this paper we discuss a novel way to support this kind of application by dividing the virtual world into several parts, called microcells. Every server is assigned a number of microcells and by dynamically redeploying these microcells when the load in a region of the world suddenly increases, the platform is able to adapt to changing load distributions. The software architecture for this system is described and we also provide some evaluation results that indicate the performance of our platform.

/pdf/a-platform-for-dynamic-microcell-redeployment-in-massively-3v2c03psjk.pdf

Piet Demeester

Papers

Optical burst and packet switching: node and network design, contention resolution and quality of service

Approximation of the IEEE 802.11p standard using commercial off-the-shelf IEEE 802.11a hardware

Design of CASP: an open enabling platform for context aware office and city services

Load balancing through efficient distributed content placement

A platform for dynamic microcell redeployment in massively multiplayer online games