It’s all about information? The Following Behaviour of Professors and PhD Students on Twitter

In this paper we investigate the role of the academic status in the following behaviour of computer scientists on Twitter. Based on a uses and gratifications perspective, we focus on the activity of a Twitter account and the reciprocity of following relationships. We propose that the account activity addresses the users' information motive only, whereas the user's academic status relates to both the information motive and community development (as in peer networking or career planning). Variables were extracted from Twitter user data. We applied a biographical approach to correctly identify the academic status (professor versus PhD student). We calculated a $2\times 2$ MANOVA on the influence of the activity of the account and the academic status (on different groups of followers) to differentiate the influence of the information motive versus the motive for community development. Results suggest that for computer scientists Twitter is mainly an information network. However, we found significant effects in the sense of career planning, that is, the accounts of professors had even in the case of low activity a relatively high number of researcher followers -- both PhD followers as well as professor followers. Additionally, there was also some weak evidence for community development gratifications in the sense of peer-networking of professors. Overall, we conclude that the academic use of Twitter is not only about information, but also about career planning and networking

Highlighting New Possibilities: A Comparison of SLOT and CT in the Analysis of 3D Printed Optical Elements

Novel printing techniques for optical elements produce characteristics in these optical elements which can sometimes not be resolved with classic testing or imaging methods. As highly individualized components often have a significant cost associated with them, destructive methods are unwanted. For this work, Scanning Laser Optical Tomography (SLOT) is considered to overcome these challenges and provide knowledge and data which would otherwise not be accessible. SLOT is directly compared to µCT which is often the go-to imaging modality for small, printed optics. Advantages such as the possibility to measure fluorescence with volumetric resolution are presented in this work in the application of a functionalized silicone waveguide. Interactions between the excitation light and the sample are also measured in SLOT in form of absorption. The importance of absorption is highlighted with the example of a 3D printed glass block which has light refractive layers that are invisible in µCT measurements. SLOT should be considered as a quality control tool by scientists and manufactures of printed optical elements as it has many advantages over µCT

Unsupervised machine learning on encrypted data

In the context of Fully Homomorphic Encryption, which allows computations on encrypted data, Machine Learning has been one of the most popular applications in the recent past. All of these works, however, have focused on supervised learning, where there is a labeled training set that is used to configure the model. In this work, we take the first step into the realm of unsupervised learning, which is an important area in Machine Learning and has many real-world applications, by addressing the clustering problem. To this end, we show how to implement the K-Means-Algorithm. This algorithm poses several challenges in the FHE context, including a division, which we tackle by using a natural encoding that allows division and may be of independent interest. While this theoretically solves the problem, performance in practice is not optimal, so we then propose some changes to the clustering algorithm to make it executable under more conventional encodings. We show that our new algorithm achieves a clustering accuracy comparable to the original K-Means-Algorithm, but has less than $5\%$ of its runtime

What Do Computer Scientists Tweet? Analyzing the Link-Sharing Practice on Twitter

Twitter communication has permeated every sphere of society. To highlight and share small pieces of information with possibly vast audiences or small circles of the interested has some value in almost any aspect of social life. But what is the value exactly for a scientific field? We perform a comprehensive study of computer scientists using Twitter and their tweeting behavior concerning the sharing of web links. Discerning the domains, hosts and individual web pages being tweeted and the differences between computer scientists and a Twitter sample enables us to look in depth at the Twitter-based information sharing practices of a scientific community. Additionally, we aim at providing a deeper understanding of the role and impact of altmetrics in computer science and give a glance at the publications mentioned on Twitter that are most relevant for the computer science community. Our results show a link sharing culture that concentrates more heavily on public and professional quality information than the Twitter sample does. The results also show a broad variety in linked sources and especially in linked publications with some publications clearly related to community-specific interests of computer scientists, while others with a strong relation to attention mechanisms in social media. This refers to the observation that Twitter is a hybrid form of social media between an information service and a social network service. Overall the computer scientists’ style of usage seems to be more on the information-oriented side and to some degree also on professional usage. Therefore, altmetrics are of considerable use in analyzing computer science

Radioactive Phosphorylation of Alcohols to Monitor Biocatalytic Diels-Alder Reactions

Nature has efficiently adopted phosphorylation for numerous biological key processes, spanning from cell signaling to energy storage and transmission. For the bioorganic chemist the number of possible ways to attach a single phosphate for radioactive labeling is surprisingly small. Here we describe a very simple and fast one-pot synthesis to phosphorylate an alcohol with phosphoric acid using trichloroacetonitrile as activating agent. Using this procedure, we efficiently attached the radioactive phosphorus isotope 32P to an anthracene diene, which is a substrate for the Diels-Alderase ribozyme—an RNA sequence that catalyzes the eponymous reaction. We used the 32P-substrate for the measurement of RNA-catalyzed reaction kinetics of several dye-labeled ribozyme variants for which precise optical activity determination (UV/vis, fluorescence) failed due to interference of the attached dyes. The reaction kinetics were analyzed by thin-layer chromatographic separation of the 32P-labeled reaction components and densitometric analysis of the substrate and product radioactivities, thereby allowing iterative optimization of the dye positions for future single-molecule studies. The phosphorylation strategy with trichloroacetonitrile may be applicable for labeling numerous other compounds that contain alcoholic hydroxyl groups

Social navigation

In this chapter we present one of the pioneer approaches in supporting users in navigating the complex information spaces, social navigation support. Social navigation support is inspired by natural tendencies of individuals to follow traces of each other in exploring the world, especially when dealing with uncertainties. In this chapter, we cover details on various approaches in implementing social navigation support in the information space as we also connect the concept to supporting theories. The first part of this chapter reviews related theories and introduces the design space of social navigation support through a series of example applications. The second part of the chapter discusses the common challenges in design and implementation of social navigation support, demonstrates how these challenges have been addressed, and reviews more recent direction of social navigation support. Furthermore, as social navigation support has been an inspirational approach to various other social information access approaches we discuss how social navigation support can be integrated with those approaches. We conclude with a review of evaluation methods for social navigation support and remarks about its current state

Numerical Method for Comparison on Homomorphically Encrypted Numbers

We propose a new method to compare numbers which are encrypted by Homomorphic Encryption (HE). Previously, comparison and min/max functions were evaluated using Boolean functions where input numbers are encrypted bit-wisely. However, the bit-wise encryption methods require relatively expensive computation of basic arithmetic operations such as addition and multiplication. In this paper, we introduce iterative algorithms that approximately compute the min/max and comparison operations of several numbers which are encrypted word-wisely. From the concrete error analyses, we show that our min/max and comparison algorithms have $\Theta(\alpha)$ and $\Theta(\alpha\log\alpha)$ computational complexity to obtain approximate values within an error rate $2^{-\alpha}$, while the previous minimax polynomial approximation method requires the exponential complexity $\Theta(2^{\alpha/2})$ and $\Theta(\sqrt{\alpha}\cdot 2^{\alpha/2})$, respectively. We also show the (sub-)optimality of our min/max and comparison algorithms in terms of asymptotic computational complexity among polynomial evaluations to obtain approximate min/max and comparison results. Our comparison algorithm is extended to several applications such as computing the top-$k$ elements and counting numbers over the threshold in encrypted state. Our new method enables word-wise HEs to enjoy comparable performance in practice with bit-wise HEs for comparison operations while showing much better performance on polynomial operations. Computing an approximate maximum value of any two $\ell$-bit integers encrypted by HEAAN, up to error $2^{\ell-10}$, takes only $1.14$ milliseconds in amortized running time, which is comparable to the result based on bit-wise HEs

Analysis of the impact of a tag recommendation system in a real-world folksonomy

Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis.This work has been supported by BES-2010-037309 FPI from the Spanish Ministry of Science and Innovation, TIN2009-14247-C02-01 from the Spanish government (F.F.), 2009-SGR-1434 from Generalitat de Catalunya (J.S.), JAEDOC069/2010 from CSIC (J.S.), FSE2007-2013 E. U. Social funds (J.S.), and FP7-2007-2013/ERC grant agreement 267583 (CompMusic; F.F. and X.S.)