Mathematics and Computer Science :Proceedings of Annual Workshop on Mathematics and Computer Science, March 25, 2014, JOSAI UNIVERSITY

In the research area of physical random number generation, a kind of “post-process” function to improve the randomness of the generated bit sequence has been studied. There a two-dimensional integer sequence indexed by the input and the output lengths of the post-process functions is associated to the evaluation of optimal quality of such functions. In this short note, we briefly survey the previous work on the study of this integer sequence, and propose some research topics for future work.Mathematics and Computer Science : Proceedings of Annual Workshop on Mathematics and Computer Science, held at Josai University on March 25 in 2014 / edited by Masatoshi IIDA, Manabu INUMA, Kiyoko NISHIZAW

A true random number generator based on gait data for the Internet of You

The Internet of Things (IoT) is more and more a reality, and every day the number of connected objects increases. The growth is practically exponential -there are currently about 8 billion and expected to reach 21 billion in 2025. The applications of these devices are very diverse and range from home automation, through traffic monitoring or pollution, to sensors to monitor our health or improve our performance. While the potential of their applications seems to be unlimited, the cyber-security of these devices and their communications is critical for a flourishing deployment. Random Number Generators (RNGs) are essential to many security tasks such as seeds for key-generation or nonces used in authentication protocols. Till now, True Random Number Generators (TRNGs) are mainly based on physical phenomena, but there is a new trend that uses signals from our body (e.g., electrocardiograms) as an entropy source. Inspired by the last wave, we propose a new TRNG based on gait data (six 3-axis gyroscopes and accelerometers sensors over the subjects). We test both the quality of the entropic source (NIST SP800-90B) and the quality of the random bits generated (ENT, DIEHARDER and NIST 800-22). From this in-depth analysis, we can conclude that: 1) the gait data is a good source of entropy for random bit generation; 2) our proposed TRNG outputs bits that behave like a random variable. All this confirms the feasibility and the excellent properties of the proposed generator.This work was supported in part by the Spanish Ministry of Economy and Competitiveness under Contract ESP2015-68245-C4-1-P, in part by the Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation under Grant P2019-CARDIOSEC, and in part bythe Comunidad de Madrid, Spain, under Project CYNAMON (P2018/TCS-4566), co-financed by the European Structural Funds (ESF andFEDER

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Bias in judgement: Comparing individuals and groups

The relative susceptibility of individuals and groups to systematic judgmental biases is considered. An overview of the relevant empirical literature reveals no clear or general pattern. However, a theoretical analysis employing J. H. Davis's (1973) social decision scheme (SDS) model reveals that the relative magnitude of individual and group bias depends upon several factors, including group size, initial individual judgment, the magnitude of bias among individuals, the type of bias, and most of all, the group-judgment process. It is concluded that there can be no simple answer to the question, "Which are more biased, individuals or groups?," but the SDS model offers a framework for specifying some of the conditions under which individuals are both more and less biased than groups