5,291 research outputs found
A New Adaptive Namespace Administration For Ultra Big Storage Systems
We propose a close real-time and cost-effective semantic questions based approach, called FAST. The thought behind FAST is to investigate and abuse the semantic connection inside and among datasets by means of relationship mindful hashing and reasonable level organized tending to essentially lessen the preparing dormancy, while bringing about acceptably little loss of information look exactness. The close continuous property of FASTenables quick ID of associated records and the critical narrowing of the extent of information to be prepared. FASTsupports a few sorts of information examination, which can be actualized in existing accessible stockpiling frameworks. We lead a true utilize case in which kids detailed missing in a to a great degree swarmed condition (e.g., an exceedingly famous beautiful spot on a pinnacle visitor day) are distinguished in an opportune mold by examining 60 million pictures utilizing FAST
Spartan Daily, March 9, 2001
Volume 116, Issue 31https://scholarworks.sjsu.edu/spartandaily/9667/thumbnail.jp
A New Semantic Correlation Among Data Sets to Reduce Processing Latency
We propose close continuous and savvy semantic inquiries based methodology, called FAST. The thought behind FAST is to investigate and abuse the semantic connection inside and among datasets by means of relationship correlation-aware hashing and sensible level organized tending to altogether decrease the preparing idleness, while causing acceptably little loss of data look exactness. The near-real-time property of FAST enables quick distinguishing proof of related documents and the huge narrowing of the extent of data to be handled. FAST supports a few kinds of data investigation, which can be executed in existing accessible storage frameworks. We direct a true use case in which youngsters revealed missing in a to a great degree swarmed condition (e.g., an exceedingly famous grand spot on a pinnacle vacationer day) are recognized in an opportune design by investigating 60 million pictures using FAST
An Adaptive Namespace Management for Ultra Large Storage Systems
Existing distributed storage frameworks for the most part neglect to offer a sufficient ability for the semantic questions. Since the genuine esteem or worth of information intensely relies upon how proficiently semantic pursuit can be done on the information in (near- ) real-time, vast parts of information wind up with their qualities being lost or essentially diminished because of the information staleness. With a specific end goal to completely assess the framework execution, we actualize all segments and functionalities of FAST in a model framework. The model framework is utilized to assess a utilization instance of close constant information examination of computerized pictures. We gather a major and genuine picture set that comprises of more than 60 million pictures (more than 200 TB storage limit) taken of a best traveler spot amid an occasion. Utilizing this genuine picture dataset as a contextual investigation, we assess the execution of FAST of finding missing youngsters from the picture dataset and contrast it and the cutting edge plans. The contextual investigation assessment exhibits the proficiency and adequacy of FAST in the execution changes and vitality reserve funds
Strengthening Democracy, Increasing Opportunities: Impacts of Advocacy, Organizing, and Civic Engagement in Los Angeles
Analyzes the policy impacts and monetary benefits fifteen Los Angeles County community organizations achieved for marginalized groups with foundation support in 2004-08. Presents effective strategies used and recommends greater roles for local funders
The role of context in image annotation and recommendation
With the rise of smart phones, lifelogging devices (e.g. Google Glass) and popularity of image sharing websites (e.g. Flickr), users are capturing and sharing every aspect of their life online producing a wealth of visual content. Of these uploaded images, the majority are poorly annotated or exist in complete semantic isolation making the process of building retrieval systems difficult as one must firstly understand the meaning of an image in order to retrieve it. To alleviate this problem, many image sharing websites offer manual annotation tools which allow the user to âtagâ their photos, however, these techniques are laborious and as a result have been poorly adopted; SigurbjoÌrnsson and van Zwol (2008) showed that 64% of images uploaded to Flickr are annotated with < 4 tags. Due to this, an entire body of research has focused on the automatic annotation of images (Hanbury, 2008; Smeulders et al., 2000; Zhang et al., 2012a) where one attempts to bridge the semantic gap between an imageâs appearance and meaning e.g. the objects present. Despite two decades of research the semantic gap still largely exists and as a result automatic annotation models often offer unsatisfactory performance for industrial implementation. Further, these techniques can only annotate what they see, thus ignoring the âbigger pictureâ surrounding an image (e.g. its location, the event, the people present etc). Much work has therefore focused on building photo tag recommendation (PTR) methods which aid the user in the annotation process by suggesting tags related to those already present. These works have mainly focused on computing relationships between tags based on historical images e.g. that NY and timessquare co-exist in many images and are therefore highly correlated. However, tags are inherently noisy, sparse and ill-defined often resulting in poor PTR accuracy e.g. does NY refer to New York or New Year? This thesis proposes the exploitation of an imageâs context which, unlike textual evidences, is always present, in order to alleviate this ambiguity in the tag recommendation process. Specifically we exploit the âwhat, who, where, when and howâ of the image capture process in order to complement textual evidences in various photo tag recommendation and retrieval scenarios.
In part II, we combine text, content-based (e.g. # of faces present) and contextual (e.g. day-of-the-week taken) signals for tag recommendation purposes, achieving up to a 75% improvement to precision@5 in comparison to a text-only TF-IDF baseline. We then consider external knowledge sources (i.e. Wikipedia & Twitter) as an alternative to (slower moving) Flickr in order to build recommendation models on, showing that similar accuracy could be achieved on these faster moving, yet entirely textual, datasets. In part II, we also highlight the merits of diversifying tag recommendation lists before discussing at length various problems with existing automatic image annotation and photo tag recommendation evaluation collections.
In part III, we propose three new image retrieval scenarios, namely âvisual event summarisationâ, âimage popularity predictionâ and âlifelog summarisationâ. In the first scenario, we attempt to produce a rank of relevant and diverse images for various news events by (i) removing irrelevant images such memes and visual duplicates (ii) before semantically clustering images based on the tweets in which they were originally posted. Using this approach, we were able to achieve over 50% precision for images in the top 5 ranks. In the second retrieval scenario, we show that by combining contextual and content-based features from images, we are able to predict if it will become âpopularâ (or not) with 74% accuracy, using an SVM classifier. Finally, in chapter 9 we employ blur detection and perceptual-hash clustering in order to remove noisy images from lifelogs, before combining visual and geo-temporal signals in order to capture a userâs âkey momentsâ within their day. We believe that the results of this thesis show an important step towards building effective image retrieval models when there lacks sufficient textual content (i.e. a cold start)
Current, November 30, 1978
https://irl.umsl.edu/current1970s/1256/thumbnail.jp
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