What is demo slam

Google said users can demo any technology they wish except ads , but that users need to own their own content. The list of "don'ts," however, is much more extensive, and includes bans on including public figures and personally identifying information. Google's terms and conditions include specific restrictions for each product.

There are no prizes, just promotion; users will be able to vote for a single video per week, with the contenders moving forward into a final round at some point. Some of the technologies themselves lend themselves more easily to quality creative interpretation than others; for example, an image search demonstration of Google Goggles used four volunteers to "dress up" as Mount Rushmore to see if Google could be fooled.

But another, involving stealing the Google logo , is far less interesting. A former NBA player has issued an apology after his daughter was seen at a youth basketball game in Orange County throwing a vicious sucker punch that left another girl with a concussion. A Chinese professor visiting Los Angeles early this month fought off an attacker using martial arts in an incident that has gone viral across Chinese media.

Pigai came to Los Angeles on Oct. Kesha ditched her clothes on Thursday as she communed with nature while on vacation in Hawaii. For a hack that seems relatively simple, this little eyeliner trick has gone viral. The rapper also pledged to offer full refunds to everyone who attended the concert. Find out how many millions he's earning now.

While there are no sure bets in the stock market, companies that have a track record for paying and growing their dividends offer one of the best ways to generate passive income. With that in mind, we asked some of our contributors which blue chip dividend stocks they saw as particularly strong buys now. Lewis Hamilton conceded Thursday that he may have to take a new engine and, with it, a five-place grid penalty at the Brazilian Grand Prix — a setback that might wreck his diminishing title hopes.

These new interactions allow traffic controllers to engage users, and in particular to query them for information rather than passively collecting it. Querying articipants presents the challenge of which users to probe for updates about a specific situation. In order to maximise the probability of a user responding and the accuracy of the information, we propose a strategy which takes into account the engagement levels of the user, the mobility profile and the reputation of the user.

We provide an analysis of a real-world user corpus of Twitter users contributing updates to LiveDrive, a Dublin based traffic radio station. Although data quality has been recognized as an important factor in the broad information systems research, it has received little attention in recommender systems. Data quality matters are typically addressed in recommenders by ad-hoc cleansing methods, which prune noisy or unreliable records from the data.

However, the setting of the cleansing parameters is often done arbitrarily, without thorough consideration of the data characteristics. In this work, we turn to two central data quality problems in recommender systems: sparsity and redundancy. We devise models for setting data-dependent thresholds and sampling levels, and evaluate these using a collection of public and proprietary datasets. We observe that the models accurately predict data cleansing parameters, while having minor effect on the accuracy of the generated recommendations.

Rejection is the norm in academic publishing. One of the main reasons for rejections is that the topics of the submitted papers are not relevant to the scope of the journal, even when the papers themselves are excellent. Submission to a journal that fits well with the publication may avoid this issue.

A system that is able to suggest journals that have published similar articles to the submitted papers may help authors choose where to submit. The Elsevier journal finder, a freely available online service, is one of the most comprehensive journal recommender systems, covering all scientific domains and more than 2, per-reviewed Elsevier journals. The system uses natural language processing for feature generation, and Okapi BM25 matching for the recommendation algorithm.

The procedure is to paste text, such as an abstract, and get a list of recommend journals and relevant metadata. To date, the evaluation of tag recommender algorithms has mostly been conducted in limited ways, including p -core pruned datasets, a small set of compared algorithms and solely based on recommender accuracy. In this study, we use an open-source evaluation framework to compare a rich set of state-of-the-art algorithms in six unfiltered, open datasets via various metrics, measuring not only accuracy but also the diversity, novelty and computational costs of the approaches.

We therefore provide a transparent and reproducible tag recommender evaluation in real-world folksonomies. Our results suggest that the efficacy of an algorithm highly depends on the given needs and thus, they should be of interest to both researchers and developers in the field of tag-based recommender systems. In this paper, we present work-in-progress of a recently started project that aims at studying the effect of time in recommender systems in the context of social tagging.

Despite the existence of previous work in this area, no research has yet made an extensive evaluation and comparison of time-aware recommendation methods. We studied an alternative choice-based interface for preference elicitation during the cold start phase and compared it directly with a standard rating-based interface.

In this alternative interface users started from a diverse set covering all movies and iteratively narrowed down through a matrix factorization latent feature space to smaller sets of items based on their choices. The results show that compared to a rating-based interface, the choice-based interface requires less effort and results in more satisfying recommendations, showing that it might be a promising candidate for alleviating the cold start problem of new users.

Matrix factorization is widely used in Recommender Systems. Although existing popular incremental matrix factorization methods are effectively in reducing time complexity, they simply assume that the similarity between items or users is invariant. For instance, they keep the item feature matrix unchanged and just update the user matrix without re-training the entire model. However, with the new users growing continuously, the fitting error would be accumulated since the extra distribution information of items has not been utilized.

In this paper, we present an alternative and reasonable approach, with a relaxed assumption that the similarity between items users is relatively stable after updating. Besides, our method maintains the feature dimension in a smaller size through taking advantage of matrix sketching. Experimental results show that our proposal outperforms the existing incremental matrix factorization methods.

For recommender systems, time is often an important source of information but it is also a complex dimension to apprehend. We propose here to learn item and user representations such that any timely ordered sequence of items selected by a user will be represented as a trajectory of the user in a representation space.

This allows us to rank new items for this user. We then enrich the item and user representations in order to perform rating prediction using a classical matrix factorization scheme. We demonstrate the interest of our approach regarding both item ranking and rating prediction on a series of classical benchmarks.

Conversational recommender systems help users navigate through the product space by exploiting feedback. In conversational systems based on preference-based feedback, the user selects the most preferred item from a list of recommended products.

Several existing recommender systems accomplish this by assuming the features to be independent. These days, it seems like everybody wants to be famous, or at least relatively Internet-famous.

The publicity stunt of a competition, for which it appears the only reward will be lots of Internet fame, asked users to make a video showcasing a creative way to use a Google program such as Google Voice Search, Google Translate, Google Video Chat or Google Goggles, among others. All submitted videos were then entered in a face off for the most votes.

A group of guys go through a lot of chicken wire and spraying face paint to create a replica of Mount Rushmore that Google Goggles recognizes.