International audienceThis paper presents a walermarking/fingerprinting system for relational databases. It features a built-in declarative language to specify usability constraints that watermarked data sets must comply with. For a subset of these constraints, namely, weight-independent constraints, we propose a novel watermarking strategy that consists of translating them into an integer linear program. We show two watermarking strategies: an exhaustive one based on integer linear programming constraint solving and a scalable pairing heuristic. Fingerprinting applications, for which several distinct watermarks need to be computed, benefit from the reduced computation time of our method that precomputes the watermarks only once. Moreover, we show that our method enables practical collusion-secure fingerprinting since the precomputed watermarks are based on binary alterations located at exactly the same positions. The paper includes an in-depth analysis of false-hit and false-miss occurrence probabilities for the detection algorithm. Experiments performed on our open source software WATERMILL assess the watermark robustness against common attacks and show that our method outperforms the existing ones concerning the watermark embedding speed

Biochip Array Technology (BAT) is a new technique used for screening purposes in clinical and forensic toxicology. The purpose of this article is to compare it with the standard ELISA with spectrophotometric detection (SD) in regard of its sensibility and specificity. Material and methods. Fifty five samples were analyzed on both BAT and ELISA SD; the results were confirmed using either GC-MS (for opiates, benzoilecgonine and cannabinoids) or HPLC (for barbiturates and benzodiazepines). Results. For opiates BAT technique had a sensibility of 100% and a specificity of 97.72%. Sensibility for ELISA SD technique was 92.3% and specificity 97.72%. For benzoilecgonine the sensibility and specificity for BAT was 100% whilst for ELISA SD the sensibility was 100% and specificity was 93.10%. For cannabinoids the sensibility for BAT was 90%, and specificity was 97.7% whilst for the ELISA SD technique the sensibility was 100% and the specificity was 91.11%. For barbiturates the sensibility and specificity was 100% for both methods. For benzodiazepines the sensibility for BAT was 100% and the specificity was 95.65% whilst for ELISA SD the sensibility was 100% and the specificity was 93.47%. Conclusions. The results obtained on BAT are comparable with those from ELISA-SD and have a high sensitivity and specificity compared to the used confirmatory methods. The results do not have however an increased statistical significance due to a very small number of positive results, caused by an abruptly decreasing number of positive cases in the last year, mainly due to increased used of "legal highs".

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Purpose Social network platforms are considered today as a major communication mean. Their success leads to an unprecedented growth of user-generated content; therefore, finding interesting content for a given user has become a major issue. Recommender systems allow these platforms to personalize individual experience and increase user engagement by filtering messages according to user interest and/or neighborhood. Recent research results show, however, that this content personalization might increase the echo chamber effect and create filter bubbles that restrain the diversity of opinions regarding the recommended content. Design/methodology/approach The purpose of this paper is to present a thorough study of communities on a large Twitter data set that quantifies the effect of recommender systems on users’ behavior by creating filter bubbles. The authors further propose their community-aware model (CAM) that counters the impact of different recommender systems on information consumption. Findings The authors propose their CAM that counters the impact of different recommender systems on information consumption. The study results show that filter bubbles effects concern up to 10% of users and the proposed model based on the similarities between communities enhance recommendations. Originality/value The authors proposed the CAM approach, which relies on similarities between communities to re-rank lists of recommendations to weaken the filter bubble effect for these users.