Decimal to Binary Number Conversion can be Fun

Numbering systems are of great importance in Computer Science and Engineering education. The binary numbering system can be considered as one of the most fundamental, since its understanding is essential for the understanding of other Computer Science and Engineering concepts, such as data representation, data storage, computer architecture, networking, and many more. Yet, students are having difficulties understanding it. One approach which has been shown to improve learning of different science and mathematics concepts is the use of educational games. Educational games have the potential to engage and motivate learners through fun activities. This paper presents a small exploratory survey on an electronic educational game for practicing decimal to binary number conversions

The construction of meanings for trend in active graphing

The development of increased and accessible computing power has been a major agent in the current emphasis placed upon the presentation of data in graphical form as a means of informing or persuading. However research in Science and Mathematics Education has shown that skills in the interpretation and production of graphs are relatively difficult for Secondary school pupils. Exploratory studies have suggested that the use of spreadsheets might have the potential to change fundamentally how children learn graphing skills. We describe research using a pedagogic strategy developed during this exploratory work, which we call Active Graphing, in which access to spreadsheets allows graphs to be used as analytic tools within practical experiments. Through a study of pairs of 8 and 9 year old pupils working on such tasks, we have been able to identify aspects of their interaction with the experiment itself, the data collected and the graphs, and so trace the emergence of meanings for trend. © 2000 Kluwer Academic Publishers

Enabling Interactive Analytics of Secure Data using Cloud Kotta

Research, especially in the social sciences and humanities, is increasingly reliant on the application of data science methods to analyze large amounts of (often private) data. Secure data enclaves provide a solution for managing and analyzing private data. However, such enclaves do not readily support discovery science---a form of exploratory or interactive analysis by which researchers execute a range of (sometimes large) analyses in an iterative and collaborative manner. The batch computing model offered by many data enclaves is well suited to executing large compute tasks; however it is far from ideal for day-to-day discovery science. As researchers must submit jobs to queues and wait for results, the high latencies inherent in queue-based, batch computing systems hinder interactive analysis. In this paper we describe how we have augmented the Cloud Kotta secure data enclave to support collaborative and interactive analysis of sensitive data. Our model uses Jupyter notebooks as a flexible analysis environment and Python language constructs to support the execution of arbitrary functions on private data within this secure framework.Comment: To appear in Proceedings of Workshop on Scientific Cloud Computing, Washington, DC USA, June 2017 (ScienceCloud 2017), 7 page

Can ‘Open Science’ be Protected from the Evolving Regime of IPR Protections?

Increasing access charges and transactions costs arising from monopoly rights in data and information adversely affect the conduct of science, especially exploratory research programs. The latter are widely acknowledged to be critical for the sustained growth of knowledge-driven economies, but are most efficiently pursued in the “open science” mode. In some fields, informal cooperative norms of behavior among researchers– in regard to the sharing of timely access to raw data- steams and documented database resources – are being undermined by legal institutional innovations that accommodate the further privatising of the public domain in information. A variety of corrective measures are needed to restore proper balance to the IPR.

Structuring visual exploratory analysis of skill demand

The analysis of increasingly large and diverse data for meaningful interpretation and question answering is handicapped by human cognitive limitations. Consequently, semi-automatic abstraction of complex data within structured information spaces becomes increasingly important, if its knowledge content is to support intuitive, exploratory discovery. Exploration of skill demand is an area where regularly updated, multi-dimensional data may be exploited to assess capability within the workforce to manage the demands of the modern, technology- and data-driven economy. The knowledge derived may be employed by skilled practitioners in defining career pathways, to identify where, when and how to update their skillsets in line with advancing technology and changing work demands. This same knowledge may also be used to identify the combination of skills essential in recruiting for new roles. To address the challenges inherent in exploring the complex, heterogeneous, dynamic data that feeds into such applications, we investigate the use of an ontology to guide structuring of the information space, to allow individuals and institutions to interactively explore and interpret the dynamic skill demand landscape for their specific needs. As a test case we consider the relatively new and highly dynamic field of Data Science, where insightful, exploratory data analysis and knowledge discovery are critical. We employ context-driven and task-centred scenarios to explore our research questions and guide iterative design, development and formative evaluation of our ontology-driven, visual exploratory discovery and analysis approach, to measure where it adds value to users’ analytical activity. Our findings reinforce the potential in our approach, and point us to future paths to build on

Software systems through complex networks science: Review, analysis and applications

Complex software systems are among most sophisticated human-made systems, yet only little is known about the actual structure of 'good' software. We here study different software systems developed in Java from the perspective of network science. The study reveals that network theory can provide a prominent set of techniques for the exploratory analysis of large complex software system. We further identify several applications in software engineering, and propose different network-based quality indicators that address software design, efficiency, reusability, vulnerability, controllability and other. We also highlight various interesting findings, e.g., software systems are highly vulnerable to processes like bug propagation, however, they are not easily controllable

Report on Recent Linguistic Fieldwork on Pantar Island, Eastern Indonesia

Report to the National Science FoundationThis paper describes linguistic fieldwork on the Nedebang and Western Pantar (Lamma) languages undertaken June-August, 2004 under the auspices of NSF grant #0404884 SGER: Exploratory Fieldwork with the Nedebang Language of Eastern Indonesia. As such it is not intended as a linguistic description of the language s of themselves. See my reports Preliminary Notes on the Nedebang Language and Preliminary Notes on the Western Pantar Language for more information on the languages themselves.National Science Foundation grant #040488