From the Publisher:
Written by the author of the best-selling HyperText & HyperMedia, this book provides an excellent guide to the methods of usability engineering. Special features: emphasizes cost-effective methods that will help developers improve their user interfaces immediately, shows you how to avoid the four most frequently listed reasons for delay in software projects, provides step-by-step information about which methods to use at various stages during the development life cycle, and offers information on the unique issues relating to informational usability. You do not need to have previous knowledge of usability to implement the methods provided, yet all of the latest research is covered.

Usability Engineering

This paper reports on the development of social network analysis, tracing its origins in classical sociology and its more recent formulation in social scientific and mathematical work. It is argued...

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Social Network Analysis

This book introduces the non-specialist reader to the principal ideas, nature and purpose of social network analysis. Social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals achieve their goals. Social network theory maps these relationships between individual actors. Though relatively new on the scene it has become hugely influential across the social sciences. Assuming no prior knowledge of quantitative sociology, this book presents the key ideas in context through examples and illustrations. Using a structured approach to understanding work in this area, John Scott signposts further reading and online sources so readers can develop their knowledge and skills to become practitioners of this research method. A series of Frequently Asked Questions takes the reader through the main objections raised against social network analysis and answers the various queries that will come up once the reader has worked their way through the book.

What is social network analysis

2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet [12] on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13A— actual speedup over AlexNet while maintaining comparable accuracy.

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ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

Similarity search finds application in specialized database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data-parallel tasks, prior approaches are bottlenecked by algorithms that expose less parallelism, such as k-min selection, or make poor use of the memory hierarchy. 
We propose a design for k-selection that operates at up to 55% of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5x faster than prior GPU state of the art. We apply it in different similarity search scenarios, by proposing optimized design for brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation enables the construction of a high accuracy k-NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility.

Billion-scale similarity search with GPUs

Similarity search finds application in database systems handling complex data such as images or videos, which are typically represented by high-dimensional features and require specific indexing structures. This paper tackles the problem of better utilizing GPUs for this task. While GPUs excel at data parallel tasks such as distance computation, prior approaches in this domain are bottlenecked by algorithms that expose less parallelism, such as $k$ k -min selection, or make poor use of the memory hierarchy. We propose a novel design for $k$ k -selection. We apply it in different similarity search scenarios, by optimizing brute-force, approximate and compressed-domain search based on product quantization. In all these setups, we outperform the state of the art by large margins. Our implementation operates at up to 55 percent of theoretical peak performance, enabling a nearest neighbor implementation that is 8.5 × faster than prior GPU state of the art. It enables the construction of a high accuracy $k$ k -NN graph on 95 million images from the Yfcc100M dataset in 35 minutes, and of a graph connecting 1 billion vectors in less than 12 hours on 4 Maxwell Titan X GPUs. We have open-sourced our approach for the sake of comparison and reproducibility.

Billion-Scale Similarity Search with GPUs

Species in plant communities generally shown an aggregated distribution at one or more spatial scales. This, and the fact that competition between sessile organisms occurs chiefly between neighbours, suggests that the spatial configuration of plants should affect the process and outcome of interspecific competition. Cellular automaton models were constructed to simulate the competitive interaction of five grass species, Agrostis stolonifera, Holcus lanatus, Cynosurus cristatus, Poa trivialis and Lolium perenne, based on experimentally determined rates of invasion. A model with a random initial starting arrangement showed a very rapid loss of species compared to initial arrangements in which species occurred in monospecific bands (...)

http://www.jstor.org/stable/pdfplus/2260696.pdf

Cellular Automaton Models of Interspecific Competition for Space--The Effect of Pattern on Process

A description is given of the Xerox 8010 Star information system, which was designed as an office automation system. The idea was that professionals in a business or organization would have workstations on their desks and would use them to produce, retrieve, distribute, and organize documentation, presentations, memos, and reports. All of the workstations in an organization would be connected via Ethernet and would share access to file servers, printers, etc. The distinctive features of Star are identified, and changes to the original design are examined. A history of Star development is included. Some lessons learned from designing Star are related. >

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Jeff Johnson

Papers

Billion-scale similarity search with GPUs

Billion-Scale Similarity Search with GPUs

Cellular Automaton Models of Interspecific Competition for Space--The Effect of Pattern on Process

The Xerox Star: a retrospective

The Xerox Star: a retrospective