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

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

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Service-oriented computing

Technical foundations introduction software reliability and system reliability the operational profile software reliability modelling survey model evaluation and recalibration techniques practices and experiences best current practice of SRE software reliability measurement experience measurement-based analysis of software reliability software fault and failure classification techniques trend analysis in validation and maintenance software reliability and field data analysis software reliability process assessment emerging techniques software reliability prediction metrics software reliability and testing fault-tolerant SRE software reliability using fault trees software reliability process simulation neural networks and software reliability. Appendices: software reliability tools software failure data set repository.

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Handbook of software reliability engineering

When DevOps started gaining momentum in the software industry, one of the first service-based architectural styles to be introduced, be applied in practice, and become popular was microservices. Migrating monolithic architectures to cloud-native architectures such as microservices reaps many benefits, such as adaptability to technological changes and independent resource management for different system components. This article reports on experiences and lessons learned during incremental migration and architectural refactoring of a commercial MBaaS (mobile back end as a service) to microservices. It explains how adopting DevOps facilitated a smooth migration. Furthermore, the researchers transformed their experiences in different projects into reusable migration practices, resulting in microservices migration patterns. This article is part of a theme issue on DevOps. The Web extra at https://youtu.be/MF3-dKTCQ88 is an audio recording of Brian Brannon speaking with author Pooyan Jamshidi and James Lewis, principal engineer at ThoughtWorks, about DevOps and microservices architecture.

Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture

Migration to the cloud has been a popular topic in industry and academia in recent years. Despite many benefits that the cloud presents, such as high availability and scalability, most of the on-premise application architectures are not ready to fully exploit the benefits of this environment, and adapting them to this environment is a non-trivial task. Microservices have appeared recently as novel architectural styles that are native to the cloud. These cloud-native architectures can facilitate migrating on-premise architectures to fully benefit from the cloud environments because non-functional attributes, like scalability, are inherent in this style. The existing approaches on cloud migration does not mostly consider cloud-native architectures as their first-class citizens. As a result, the final product may not meet its primary drivers for migration. In this paper, we intend to report our experience and lessons learned in an ongoing project on migrating a monolithic on-premise software architecture to microservices. We concluded that microservices is not a one-fit-all solution as it introduces new complexities to the system, and many factors, such as distribution complexities, should be considered before adopting this style. However, if adopted in a context that needs high flexibility in terms of scalability and availability, it can deliver its promised benefits.

Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

Microservices migration patterns

A mobile robot should be able to analyze what it is seeing in real time rate and decide accordingly. Fast and reliable analysis of image data is one of the key points in soccer robot performance. In this paper we suggest a very fast method for object finding which uses the concept of perspective view. In our method, we introduce a set of jump points in perspective on which we search for objects. An object is estimated by a rectangle surrounding it. A vector based calculation is introduced to find the distance and angle of a robot from objects in the field. In addition we present a new color model which takes its components from different color models. The proposed method can detect all objects in each frame and their distance and angle in one scan on the jump points in that frame. This process takes about 1/50 of a second. Our vision system uses a commercially available frame grabber and is implemented only in software. It has shown a very good performance in RoboCup competitions.

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Abbas Heydarnoori

Papers

Microservices Architecture Enables DevOps: Migration to a Cloud-Native Architecture

Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

Microservices migration patterns

A Fast Vision System for Middle Size Robots in RoboCup