Precaching of Incomplete Content and Its On Demand Completion

A mechanism to separate a content item provided by a content item service into a first portion that is to be pre-cached by a device of a user and a second portion that is to be initially withheld from the device of the user. The first portion of the content item may correspond to a majority of the content item and may be transmitted to the device. Subsequently, the user may request playback of the content item. In response to the request, the device of the user may retrieve the second portion of the content item that was previously withheld from the user. The second portion may then be inserted into the first portion of the content item so that the complete content item may be played

Describing and Understanding Neighborhood Characteristics through Online Social Media

Geotagged data can be used to describe regions in the world and discover local themes. However, not all data produced within a region is necessarily specifically descriptive of that area. To surface the content that is characteristic for a region, we present the geographical hierarchy model (GHM), a probabilistic model based on the assumption that data observed in a region is a random mixture of content that pertains to different levels of a hierarchy. We apply the GHM to a dataset of 8 million Flickr photos in order to discriminate between content (i.e., tags) that specifically characterizes a region (e.g., neighborhood) and content that characterizes surrounding areas or more general themes. Knowledge of the discriminative and non-discriminative terms used throughout the hierarchy enables us to quantify the uniqueness of a given region and to compare similar but distant regions. Our evaluation demonstrates that our model improves upon traditional Naive Bayes classification by 47% and hierarchical TF-IDF by 27%. We further highlight the differences and commonalities with human reasoning about what is locally characteristic for a neighborhood, distilled from ten interviews and a survey that covered themes such as time, events, and prior regional knowledgeComment: Accepted in WWW 2015, 2015, Florence, Ital

The lifecycle of geotagged data

The world is a big place. At any given instant something is happening somewhere, but even when nothing in particular is going on people still find ways to generate data, such as posting on s

New trends and ideas in visual concept detection

The MIR Flickr collection consists of 25000 high-quality photographic images of thousands of Flickr users, made available under the Creative Commons license. The database includes all the original user tags and EXIF metadata. Additionally, detailed and accurate annotations are provided for topics corresponding to the most prominent visual concepts in the user tag data. The rich metadata allow for a wide variety of image retrieval benchmarking scenarios. In this paper, we provide an overview of the various strategies that were devised for automatic visual concept detection using the MIR Flickr collection. In particular we discuss results from various experiments in combining social data and low-level content-based descriptors to improve the accuracy of visual concept classifiers. Additionally, we present retrieval result

Knee disorders in primary care: design and patient selection of the HONEUR knee cohort.

BACKGROUND: Knee complaints are a frequent reason for consultation in general practice. These patients constitute a specific population compared to secondary care patients. However, information to base treatment decisions on is generally derived from specialistic settings. Our cohort study is aimed at collecting knowledge about prognosis and prognostic factors of knee complaints presented in a primary care setting. This paper describes the methods used for data collection, and discusses potential selectiveness of patient recruitment. METHODS: This is a descriptive prospective cohort study with one-year follow-up. 40 Dutch GPs recruited consecutive patients with incident knee complaints aged 12 years and above from October 2001 to October 2003. Patients were assessed with questionnaires and standardised physical examinations. Additional measurements of subgroups included MRI for recent knee traumas and device assessed function measurements for non-traumatic patients. After the inclusion period we retrospectively searched the computerized medical files of participating GPs to obtain a sample to determine possible selective recruitment. We assessed differences in proportions of gender, traumatic onset of injury and age groups between participants and non-participants using Odds Ratios (OR) and 95% confidence intervals. RESULTS: We recruited 1068 patients. In a sample of 310 patients visiting the GP, we detected some selective recruitment, indicating an underrepresentation of patients aged 12 to 35 years (OR 1.70; 1.15-2.77), especially among men (OR 2.16; 1.12-4.18). The underrepresentation of patients with traumatic onset of injury was not statistically significant. CONCLUSION: This cohort is unique in its size, setting, and its range of both age and type of knee complaints. We believe the detected selective recruitment is unlikely to introduce significant bias, as the cohort will be divided into subgroups according to age group or traumatic onset of injury for future analyses. However, the underrepresentation of men in the age group of 12 to 35 years of age warrants caution. Based on the available data, we believe our cohort is an acceptable representation of patients with new knee complaints consulting the GP, and we expect no problems with extrapolation of the results to the general Dutch population

Relevance Feedback in Content-Based Image Retrieval: Promising Directions

In this paper we describe the current state of the art in relevance feedback as seen from a content-based image retrieval perspective. Recent technological advancements have spurred new research directions and have contributed new insights in the problem of visual information retrieval. We survey the main scientific problems and approaches in the last 4 years and give some recommendations for the future.

The lifecycle of geotagged data

The world is a big place. At any given instant something is happening somewhere, but even when nothing in particular is going on people still find ways to generate data, such as posting on s