Hasil untuk "math.AP"

Menampilkan 20 dari ~1121837 hasil · dari DOAJ, CrossRef

JSON API
CrossRef Open Access 2017
Back to the Future? Presenting archaeology at the Green Man Festival

Matt Law, Ffion Reynolds, Jacqui Mulville

In the summer of 2011, Cardiff Osteoarchaeology Research Group was invited to present a number of archaeological engagement activities at the Green Man music festival as part of the Einstein’s Garden science learning area. The project, called Back to the Future?: Animals and archaeology in Einstein’s Garden comprised a number of activities, designed to cater for a wide range of ages as the festival audience typically includes young people and families. Over four days more than 2000 people visited the stall. This paper will briefly outline the activities presented, and will reflect on the challenges posed by outreach at a music festival, in particular how to hook the main festival demographic, and how to evaluate success.

CrossRef Open Access 2013
Meltwater und AP beenden Schnipsel-Streit

David Pachali

Wie Reuters meldet, haben die Nachrichtenagentur AP und der Monitoring-Dienst Meltwater in den USA einen Streit über Ausschnitte aus AP-Meldungen mit einem Vergleich beendet und eine offizielle "Partnerschaft" angekündigt. AP hatte Meltwater vorgeworfen, der vor allem von Firmenkunden genutzte Dienst verletze Urheberrechte der Agentur.

CrossRef 2019
Novel User Level Data Leakage Detection Algorithm

Assistant Professor, QIS College of Engineering &, T.Lakshmi Siva Rama Krishna, Technology (Autonomous), Ongole, AP, India et al.

Data leakage detection (DLD) is the most widely used detection technique in many applications such as etc. detecting data leakage by various data sources is an important research issue. Several researchers contributed to detect the data leakage by proposing various techniques. In the existing DLD techniques the performance metrics such as accuracy and time have been neglected. In this paper, we have proposed a new DLD algorithm and named it as novel user level data leakage detection algorithm (NULDLDA). In the proposed NULDLDA we have considered the user point of view to know the leakage of data by which agent among several existing agents. We have implemented and compared the NULDLDA with existing DLD. The experimental results indicate that proposed NULDLDA improved the performance over DLD with respect to time and accuracy.

Halaman 50 dari 56092