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Contains fulltext : 181051.pdf (Publisher’s version ) (Open Access) Social Media for Emergency Relief and preparedness (SMERP2017), 09 april 2017

After the collapse of the imperial power of the Guptas, Northern India was split up into a number of independent states. They were governed by numerous ruling dynasties till they were conquered by the Moslems In the latter part of the thirteenth century A.D. The history of India of this period, as a matter of fact, is the narrative of the rise and fall of these ruling houses. The Paramâras were one of those dynasties, who ruled in Malwa, Arthuna and Chandravati. Upendra, the founder of the family, was a member of the Rastrakuta race. He, with the help of the Râstrakuta Govinda III, established his authority in Malwa, about 810 A.D., by the total expulaion of the Gurjara-Pratihâras. Upendra's son, Dambarasimha, was the founder of the Arthuna branch. The fifth king, siyaka-Harsa, exalted the family to the imperial rank by defeating the Râstrakutas of the Deccan. Since then the family, following the custom of the age, assumed the mythical name Paramâra. Siyaka's son, Munja, was the founder of the Chandravati, Jalor and Bhinmal branches. Munja's nephew, Bhoja, was well known for his military success and literary achievements. The Paramâras lost their political power in the early years of the fourteenth century A.D. During their magnificent rule, Malwa rose to the acme of its glory, being enriched by literatures and adorned by beautiful temples and lakes.

Dublin City University participated with a consortium of colleagues from NUI Galway and Universitat Politecnica de Catalunya in two tasks in TRECVid 2016, Instance Search (INS) and Video to Text (VTT). For the INS task we developed a framework consisting of face detection and representation and place detection and representation, with a user annotation of top-ranked videos. For the VTT task we ran 1,000 concept detectors from the VGG-16 deep CNN on 10 keyframes per video and submitted 4 runs for caption re-ranking, based on BM25, Fusion, word2vec and a fusion of baseline BM25 and word2vec. With the same pre-processing for caption generation we used an open source image-to-caption CNN-RNN toolkit NeuralTalk2 to generate a caption for each keyframe and combine them.

For the participation of Dublin City University (DCU) in the INEX-2012 tweet contextualization task, we investigated sentence retrieval methodologies. The task requires providing the context to an ad-hoc real-life tweet. This context is to be constructed from Wikipedia articles. Our approach involves indexing the passages in Wikipedia articles as separate retrievable units, extracting sentences from the top ranked passages, computing the sentence selection score for each such sentence with respect to the query, and then returning the top most similar ones. The simple sentence selection strategy performed quite well in the task. Our best run has ranked rst from the readability perspective and ranked eighth as ordered by informativeness out of 33 ocial runs.