Agonistic Interventions into Public Commemorative Art:An Innovative Form of Counter-memorial Practice?

In light of recent controversies around the removal or modification of public commemorative art, such as memorials and monuments, this paper interrogates the value of competing approaches to counter-memorial practice using the framework of agonistic memory. It argues that much counter-memorial practice today, as it relates to historical memory, is dominated by a “cosmopolitan” mode that fails to offer a convincing response to the rise of right-wing populism and its instrumentalization of conflicts over public commemorative art. The article investigates two case studies of counter-memorial interventions that focus on the memory of fascism in Europe today and seeks to identify and assess emergent agonistic practices

Discourses of immigration and integration in German newspaper comments

This chapter employs a critical, constructivist theoretical perspective to address how online commenters on articles in the liberal newspaper Die Zeit characterize immigrants, integration, and German identity. While the formerly dominant ethnonational ideology about German identity is now in the minority, there is nonetheless a strong tendency to categorize and characterize immigrant background residents according to ethnonational and religious criteria. A hierarchy of immigrants has emerged, with a discourse that positions Muslims in general, and Turks in particular, as the unintegrated Other. Because Germanness is defined in opposition to Muslim practices, integration for such residents is impossible. However, the presence of competing discourses is significant; through voices that point out discrimination and view integration as a two-way process, social change may be enacted.<br/

De-anonymization attack on geolocated data

International audienceWith the advent of GPS-equipped devices, a massive amount of location data is being collected, raising the issue of the privacy risks incurred by the individuals whose movements are recorded. In this work, we focus on a specific inference attack called the de-anonymization attack, by which an adversary tries to infer the identity of a particular individual behind a set of mobility traces. More specifically, we propose an implementation of this attack based on a mobility model called Mobility Markov Chain (MMC). A MMC is built out from the mobility traces observed during the training phase and is used to perform the attack during the testing phase. We design several distance metrics quantifying the closeness between two MMCs and combine these distances to build de-anonymizers that can re-identify users in an anonymized geolocated dataset. Experiments conducted on real datasets demonstrate that the attack is both accurate and resilient to sanitization mechanisms such as downsampling