Private politics daily: What makes firms the target of internet/media criticism? An empirical investigation of firm, industry, and institutional factors

Private politics refers to situations in which activists or NGOs try to push firms to conform to social standards (regarding, for instance, human rights and environmental protection) without public policy intervention. The existing literature on private politics has focused on large campaigns such as consumer boycotts, and looked at the impact of those boycotts on firms' financial performance and on the likelihood that firms comply with activist demands. Even though these large campaigns are important, focusing on them leads to neglecting the fact that a large portion of the time and resources that activists consecrate to private politics is used to monitor firms and criticize them through Internet posting and media statements, rather than to launch high profile campaigns. Little is known, however, about what drives these activists when they criticize companies, why they target certain companies and not others, and whether this criticism should be considered as a primary step in the production of full-fledged campaigns. In this paper, we fill this gap by exploring a unique international database of CSR-based criticisms against Fortune 500 companies for the 2006-2009 period. This database allows us to look at the impact of a broad range of factors including industry differences, country/institutional differences and firm-specific dimensions, on the likelihood that a certain firm will be targeted by activist critique. Results indicate that criticism is driven by strategic intents. Similar to previous literature, large and visible firms in certain industries are more targeted than others. In addition, these firms also tend to come from countries with strong institutions and high standards of living

Mr. DLib: Recommendations-as-a-Service (RaaS) for Academia

Only few digital libraries and reference managers offer recommender systems, although such systems could assist users facing information overload. In this paper, we introduce Mr. DLib's recommendations-as-a-service, which allows third parties to easily integrate a recommender system into their products. We explain the recommender approaches implemented in Mr. DLib (content-based filtering among others), and present details on 57 million recommendations, which Mr. DLib delivered to its partner GESIS Sowiport. Finally, we outline our plans for future development, including integration into JabRef, establishing a living lab, and providing personalized recommendations.Comment: Accepted for publication at the JCDL conference 201

High-quality draft genome sequence of Bifidobacterium longum E18, isolated from a healthy adult

Bifidobacteria are important gastrointestinal commensals of a number of animals, including humans, and various beneficial effects on host health have been attributed to them. Here, we announce the noncontiguous finished genome sequence of Bifidobacterium longum E18, isolated from a healthy adult, which reveals traits involved in its interaction with the host

On the utility of bytewise approximate matching in computer science with a special focus on digital forensics investigations

Handling hundreds of thousands of files is a major challenge in today’s digital forensics. In order to cope with this information overload, investigators often apply hash functions for automated input identification. Besides identifying exact duplicates, which is mostly solved running cryptographic hash functions, it is also necessary to cope with similar inputs (e.g., different versions of files), embedded objects (e.g., a JPG within a office document), and fragments (e.g., network packets). Thus, the essential idea is to complement the use of cryptographic hash functions, to detect data objects with bytewise identical representation, with the capability to find objects with bytewise similar representations. Unlike cryptographic hash functions, which have a wide range of applications and have been studied as well as tested for a long time, approximate matching algorithms are still in their early development stages. More precisely, currently the community is missing a definition, an evaluation methodology and (additional) fields of application. Therefore, this thesis aims at establishing approximate matching in computer sciences with a special focus on digital forensic investigations. One of our firsts step was to develop a generic definition for approximate matching, in collaboration with the National Institute of Standards and Technology (NIST) which is applicable to the different levels approximate matching, e.g., bytewise and semantic. A subsequent detailed analysis of both existing approaches uncovers different strengths and weaknesses, therefore we present improvements. To extend the range of algorithms, this work introduces three of our new algorithms, that are based on well-known techniques of computer sciences. A core contribution of this thesis is the open source evaluation framework called FRASH which assesses tools on different criteria. Besides traditional properties (borrowed from hash functions) like generation efficiency and space efficiency (compression), we conceive methods to determine precision and recall rates based on synthetic as well as real world data. Since digital investigations are often time critical, we improve the performance of automated file identification by a mechanism we call prefetching. Compared to a straight forward analysis, the performance increases by almost 40% without additional hardware. In this context we also discuss the impact of different hashing/approximate matching algorithms for digital investigations and conclude that it is absolutely reasonable to apply crypto hashing as well as bytewise/semantic approximate matching algorithms in a prosecution. To extend the fields of application, this thesis demonstrates the capabilities of applying approximate matching on network traffic analysis and biometric template protection. Our research shows that approximate matching is perfectly suited for data leakage prevention and can also be applied for biometric template protection, biometric data compression and efficient biometric identification

Cortical processing of somatosensory information in people with blindness

This dissertation focused on the investigation of somatosensory cortical processing in individuals with blindness. We compared event-related potentials (ERP) measured through electroencephalography (EEG) in individuals with and without blindness to gain insights into the neurophysiological correlates of behavioural group differences in a tactile task. Our primary interest lay in exploring the processes involved in the anticipation and maintenance of tactile information in modality-specific and supramodal brain regions. Two research articles have been published within the framework of this cumulative dissertation. The first article examines somatosensory memory processes focusing on contralateral delay activity (CDA), a slow cortical negativity developing during short term tactile memory maintenance. The second article investigates contingent negative variation (CNV) as the primary outcome measure, an ERP component that is considered a reflection of preparatory and anticipatory processes, which are also associated with internal model formation. The aims of this thesis were to gain a deeper understanding of somatosensory cortical processing in general and to explore the changes in its functioning following vision loss. Additionally, this investigation into post-perceptual cortical processes holds broader significance by providing insights into the intricate mechanisms of cortical sensory processing and neural plasticity