How to Identify Scientifc Revolutions?

Conceptualizing scientific revolutions by means of explicating their causes, their underlying structure and implications has been an important part of Kuhn's philosophy of science and belongs to its legacy. In this paper we show that such “explanatory concepts” of revolutions should be distinguished from a concept based on the identification criteria of scientific revolutions. The aim of this paper is to offer such a concept, and to show that it can be fruitfully used for a further elaboration of the explanatory conceptions of revolutions. On the one hand, our concept can be used to test the preciseness and accuracy of these conceptions, by examining to what extent their criteria fit revolutions as they are defined by our concept. On the other hand, our concept can serve as the basis on which these conceptions can be further specified. We will present four different explanatory concepts of revolutions – Kuhn's, Thagard's, Chen's and Barker's, and Laudan's – and point to the ways in which each of them can be further specified in view of our concept

“I Identify with Her,” “I Identify with Him”: Unpacking the Dynamics of Personal Identification in Organizations

Despite recognizing the importance of personal identification in organizations, researchers have rarely explored its dynamics. We define personal identification as perceived oneness with another individual, where one defines oneself in terms of the other. While many scholars have found that personal identification is associated with helpful effects, others have found it harmful. To resolve this contradiction, we distinguish between three paths to personal identification—threat-focused, opportunity-focused, and closeness-focused paths—and articulate a model that includes each. We examine the contextual features, how individuals’ identities are constructed, and the likely outcomes that follow in the three paths. We conclude with a discussion of how the threat-, opportunity-, and closeness-focused personal identification processes potentially blend, as well as implications for future research and practice

How to identify a Strange Star

Contrary to young neutron stars, young strange stars are not subject to the r-mode instability which slows rapidly rotating, hot neutron stars to rotation periods near 10 ms via gravitational wave emission. Young millisecond pulsars are therefore likely to be strange stars rather than neutron stars, or at least to contain significant quantities of quark matter in the interior.Comment: 4 pages, 1 figur

Can a Turing Player identify itself?

We show that the problem of whether two Turing Machines are functionally equivalent is undecidable and explain why this is significant for the theory of repeated play and evolution

Learning to Identify Ambiguous and Misleading News Headlines

Accuracy is one of the basic principles of journalism. However, it is increasingly hard to manage due to the diversity of news media. Some editors of online news tend to use catchy headlines which trick readers into clicking. These headlines are either ambiguous or misleading, degrading the reading experience of the audience. Thus, identifying inaccurate news headlines is a task worth studying. Previous work names these headlines "clickbaits" and mainly focus on the features extracted from the headlines, which limits the performance since the consistency between headlines and news bodies is underappreciated. In this paper, we clearly redefine the problem and identify ambiguous and misleading headlines separately. We utilize class sequential rules to exploit structure information when detecting ambiguous headlines. For the identification of misleading headlines, we extract features based on the congruence between headlines and bodies. To make use of the large unlabeled data set, we apply a co-training method and gain an increase in performance. The experiment results show the effectiveness of our methods. Then we use our classifiers to detect inaccurate headlines crawled from different sources and conduct a data analysis.Comment: Accepted by IJCAI 201

Using Qualitative Hypotheses to Identify Inaccurate Data

Identifying inaccurate data has long been regarded as a significant and difficult problem in AI. In this paper, we present a new method for identifying inaccurate data on the basis of qualitative correlations among related data. First, we introduce the definitions of related data and qualitative correlations among related data. Then we put forward a new concept called support coefficient function (SCF). SCF can be used to extract, represent, and calculate qualitative correlations among related data within a dataset. We propose an approach to determining dynamic shift intervals of inaccurate data, and an approach to calculating possibility of identifying inaccurate data, respectively. Both of the approaches are based on SCF. Finally we present an algorithm for identifying inaccurate data by using qualitative correlations among related data as confirmatory or disconfirmatory evidence. We have developed a practical system for interpreting infrared spectra by applying the method, and have fully tested the system against several hundred real spectra. The experimental results show that the method is significantly better than the conventional methods used in many similar systems.Comment: See http://www.jair.org/ for any accompanying file

A Bayesian Approach to Identify Bitcoin Users

Bitcoin is a digital currency and electronic payment system operating over a peer-to-peer network on the Internet. One of its most important properties is the high level of anonymity it provides for its users. The users are identified by their Bitcoin addresses, which are random strings in the public records of transactions, the blockchain. When a user initiates a Bitcoin-transaction, his Bitcoin client program relays messages to other clients through the Bitcoin network. Monitoring the propagation of these messages and analyzing them carefully reveal hidden relations. In this paper, we develop a mathematical model using a probabilistic approach to link Bitcoin addresses and transactions to the originator IP address. To utilize our model, we carried out experiments by installing more than a hundred modified Bitcoin clients distributed in the network to observe as many messages as possible. During a two month observation period we were able to identify several thousand Bitcoin clients and bind their transactions to geographical locations

Investigation to identify paint coatings resistive to microorganism growth

All selected coatings contain nutrients that support microbial growth and survival. Incorporation of microbiocidal agents into coatings more susceptible to attack is recommended for improved inhibition of microorganism growth and for increased protection against deterioration of coatings by microorganisms