Research on soft-biometrics showed that privacy-sensitive information can be deduced from biometric data. Utilizing biometric templates only, information about a persons gender, age, ethnicity, sexual orientation, and health state can be deduced. For many applications, these templates are expected to be used for recognition purposes only. Thus, extracting this information raises major privacy issues. Previous work proposed two kinds of learning-based solutions for this problem. The first ones provide strong privacy-enhancements, but limited to pre-defined attributes. The second ones achieve more comprehensive but weaker privacy-improvements. In this work, we propose a Privacy-Enhancing face recognition approach based on Minimum Information Units (PE-MIU). PE-MIU, as we demonstrate in this work, is a privacy-enhancement approach for face recognition templates that achieves strong privacy-improvements and is not limited to pre-defined attributes. We exploit the structural differences between face recognition and facial attribute estimation by creating templates in a mixed representation of minimal information units. These representations contain pattern of privacy-sensitive attributes in a highly randomized form. Therefore, the estimation of these attributes becomes hard for function creep attacks. During verification, these units of a probe template are assigned to the units of a reference template by solving an optimal best-matching problem. This allows our approach to maintain a high recognition ability. The experiments are conducted on three publicly available datasets and with five state-of-the-art approaches. Moreover, we conduct the experiments simulating an attacker that knows and adapts to the systems privacy mechanism. The experiments demonstrate that PE-MIU is able to suppress privacy-sensitive information to a significantly higher degree than previous work in all investigated scenarios. At the same time, our solution is able to achieve a verification performance close to that of the unmodified recognition system. Unlike previous works, our approach offers a strong and comprehensive privacy-enhancement without the need of training.

Most genomes harbor a large number of transposons, and they play an important role in evolution and gene regulation. They are also of interest to clinicians as they are involved in several diseases, including cancer and neurodegeneration. Although several methods for transposon identification are available, they are often highly specialised towards specific tasks or classes of transposons, and they lack common standards such as a unified taxonomy scheme and output file format. We present TransposonUltimate, a powerful bundle of three modules for transposon classification, annotation, and detection of transposition events. TransposonUltimate comes as a Conda package under the GPL-3.0 licence, is well documented and it is easy to install through https://github.com/DerKevinRiehl/TransposonUltimate. We benchmark the classification module on the large TransposonDB covering 891,051 sequences to demonstrate that it outperforms the currently best existing solutions. The annotation and detection modules combine sixteen existing softwares, and we illustrate its use by annotating Caenorhabditis elegans, Rhizophagus irregularis and Oryza sativa subs. japonica genomes. Finally, we use the detection module to discover 29 554 transposition events in the genomes of 20 wild type strains of C. elegans. Databases, assemblies, annotations and further findings can be downloaded from (https://doi.org/10.5281/zenodo.5518085).

MotivationMost genomes harbor a large number of transposons, and they play an important role in evolution and gene regulation. They are also of interest to clinicians as they are involved in several diseases, including cancer and neurodegeneration. Although several methods for transposon identification are available, they are often highly specialised towards specific tasks or classes of transposons, and they lack common standards such as a unified taxonomy scheme and output file format. Moreover, many methods are difficult to install, poorly documented, and difficult to reproduce.ResultsWe present TransposonUltimate, a powerful bundle of three modules for transposon classification, annotation, and detection of transposition events. TransposonUltimate comes as a Conda package under the GPL-3.0 licence, is well documented and it is easy to install. We benchmark the classification module on the large TransposonDB covering over 891,051 sequences to demonstrate that it outperforms the currently best existing solutions. The annotation and detection modules combine sixteen existing softwares, and we illustrate its use by annotating Caenorhabditis elegans, Rhizophagus irregularis and Oryza sativa subs. japonica genomes. Finally, we use the detection module to discover 29,554 transposition events in the genomes of twenty wild type strains of Caenorhabditis elegans.AvailabilityRunning software and source code available on https://github.com/DerKevinRiehl/TransposonClassifierRFSB. Databases, assemblies, annotations and further findings can be downloaded from https://cellgeni.cog.sanger.ac.uk/browser.html?shared=transposonultimate.

The purpose of this work is to investigate whether news flows can be used to effectively capture financial success of green commercial activities conducted by listed companies. The authors employ a large, cross-sectoral, global dataset, consisting of 97,954 articles from 10 online magazines, mentioning over 344 different firms that are part of more than 286 listed companies, covering the years 2004–2017 and over 32 countries. The notifications focus on GreenTech-related activities performed by companies. The authors conduct event studies to calculate abnormal returns and text analytics to gather (hyper-) textual features. Finally, the authors analyse the relationship between returns and features using ordinary least squares (OLS) regression models. Results indicate that textual features extracted from web notifications significantly provide new market information. Thus, news flow is found to serve as a reliable measure to reflect the financial success of green activities for future research on listed companies. Features, such as multimedia elements turn out to not provide new market information, while readability and sentiment measures do. The authors extend the growing literature on GreenTech by proposing the novel combination of textual and event study analysis in order to enable research on green commercial activities conducted by listed companies.

Green innovation and technology diffusion must be financially and commercially attractive to convince corporate decision makers. This paper focuses on the factors that determine the financial outcome of successful green innovation activities conducted by large, listed companies. We employ a cross-industry dataset including more than 97,954 reports on corporate environmentalism from 286 international listed companies. Our results indicate that economic, political, cultural, firm-specific, investor-related, and governance factors significantly determine the financial performance of green innovation, measured by abnormal returns. Moreover, we can show that factors that reduce the competition in green innovation markets benefit the financial success of firms operating via them.Finally, we find an opposing influence for several factors that benefit earlier stages of innovation (e.g., research output) while harming the later stages (e.g., market introduction and financial performance). These findings imply that a spatial separation strategy for different stages of innovation supports corporate environmentalism activities. Moreover, physical property rights, the governments’ willingness to support green technologies, and economic framework conditions such as oil price, GDP, or public R&D budget need to be balanced by policymakers to address and stimulate green innovation.

This study proposes the novel concept of hierarchical confusion matrix, opening the door for popular confusion-matrix-based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. The concept is developed to a generalised form and proven its applicability to all types of hierarchical classification problems including directed acyclic graphs, multi-path labelling, and non-mandatory leaf-node prediction. Finally, measures based on the novel confusion matrix are used for three real-world hierarchical classification applications and compared to established evaluation measures. The results, the conformity with important attributes of hierarchical classification schemes and its broad applicability justify its recommendation.

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