Ten Simple Rules for Responsible Big Data Research

The use of big data research methods has grown tremendously over the past five years in both academia and industry. As the size and complexity of available datasets has grown, so too have the ethical questions raised by big data research. These questions become increasingly urgent as data and research agendas move well beyond those typical of the computational and natural sciences, to more directly address sensitive aspects of human behavior, interaction, and health. The tools of big data research are increasingly woven into our daily lives, including mining digital medical records for scientific and economic insights, mapping relationships via social media, capturing individuals’ speech and action via sensors, tracking movement across space, shaping police and security policy via “predictive policing,” and much more

Modelling Immunological Memory

Accurate immunological models offer the possibility of performing highthroughput experiments in silico that can predict, or at least suggest, in vivo phenomena. In this chapter, we compare various models of immunological memory. We first validate an experimental immunological simulator, developed by the authors, by simulating several theories of immunological memory with known results. We then use the same system to evaluate the predicted effects of a theory of immunological memory. The resulting model has not been explored before in artificial immune systems research, and we compare the simulated in silico output with in vivo measurements. Although the theory appears valid, we suggest that there are a common set of reasons why immunological memory models are a useful support tool; not conclusive in themselves

Email at Work

Presents findings from a survey conducted in April and May 2002, to examine the role of email in mainstream work situations. Documents how workers utilize, value, and are affected by email, and looks at the future of email use in the workplace

Language acquisition and machine learning

In this paper, we review recent progress in the field of machine learning and examine its implications for computational models of language acquisition. As a framework for understanding this research, we propose four component tasks involved in learning from experience - aggregation, clustering, characterization, and storage. We then consider four common problems studied by machine learning researchers - learning from examples, heuristics learning, conceptual clustering, and learning macro-operators - describing each in terms of our framework. After this, we turn to the problem of grammar acquisition, relating this problem to other learning tasks and reviewing four AI systems that have addressed the problem. Finally, we note some limitations of the earlier work and propose an alternative approach to modeling the mechanisms underlying language acquisition

A method to support SMEs to optimize their manufacturing operations

In the last decades the gap between enterprise systems, like Enterprise Resource Planning (ERP), and process control systems has been filled with the development of software systems, commonly referred to as Manufacturing Operations Management (MOM). The ISA-95 standard provides a detailed functional description of this intermediate layer in the CIM pyramid. This standard supports manufacturing companies, system integrators and software vendors by using the same terminology in their communication for integrating their enterprise and control systems. Most of the time, these software systems address bigger companies which are convinced of the strategic advantages for their MOM projects: reduction of risks, costs and errors. This paper introduces an analysis and justification method that reduces the barriers to adoption of MOM systems for small and medium enterprises (SMEs). By applying the method an SME gets an idea of the possible improvements for the materials and information flow required for the production of goods or services