NortonLifeLock (Ireland)

BackgroundThe inclusion of skateboarding in the Olympics suggests that athletes and coaches are seeking ways to enhance their chances of succeeding on the world stage. Understanding what constitutes performance, and what physical, neuromuscular, and biomechanical capacities underlie it, is likely critical to success.ObjectiveThe aim was to overview the current literature and identify knowledge gaps related to competitive skateboarding performance and associated physical, technical, and tactical demands of Olympic skateboarding disciplines.MethodsA systematic scoping review was performed considering the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (Extension for Scoping Reviews) guidelines. Data sources were MEDLINE (Ovid), Scopus, SPORTDiscus, and PubMed. We included all peer-reviewed literature after 1970 describing the physiological, neuromuscular, biomechanical, and/or tactical aspects of skateboarding.ResultsNineteen original articles explored the physiological (n = 9), biomechanical (n = 8), and technical (n = 10) demands of skateboarding. No research explored the tactical demands of competition. Moreover, although competitive males (n = 2 studies) and females (n = 1 study) were recruited as participants, no research directly related skateboarding demands to performance success in competitive environments.ConclusionsUltimately, what constitutes and distinguishes competitive skateboarding is unexplored. There is some evidence indicating aspects of the sport require flexibility and elevated and fast force output of the lower limbs, which may be valuable when attempting to maximise ollie height. Nonetheless, a lack of ecological validity, such as using static ollie tests as opposed to rolling, restricted our ability to provide practical recommendations, and inconsistency of terminology complicated delineating discipline-specific outcomes. Future researchers should first look to objectively identify what skaters do in competition before assessing what qualities enable their performance.

Tremendous efforts have been dedicated to improving the effectiveness of multi-label learning with incomplete label assignments. Most of the current techniques assume that the input features of data instances are complete. Nevertheless, the co-occurrence of highly incomplete features and weak label assignments is a challenging and widely perceived issue in real-world multi-label learning applications due to a number of practical reasons including incomplete data collection, moderate labels from annotators, etc. Existing multi-label learning algorithms are not directly applicable when the observed features are highly incomplete. In this work, we attack this problem by proposing a weakly supervised multi-label learning approach, based on the idea of collaborative embedding. This approach provides a flexible framework to conduct efficient multi-label classification at both transductive and inductive mode by coupling the process of reconstructing missing features and weak label assignments in a joint optimisation framework. It is designed to collaboratively recover feature and label information, and extract the predictive association between the feature profile and the multi-label tag of the same data instance. Substantial experiments on public benchmark datasets and real security event data validate that our proposed method can provide distinctively more accurate transductive and inductive classification than other state-of-the-art algorithms.

In this paper we present our new design of NNCTPH, a scalable algorithm to build an approximate k-NN graph from large text datasets. The algorithm uses a modified version of Context Triggered Piecewise Hashing to bin the input data into buckets, and uses NN-Descent, a versatile graph-building algorithm, inside each bucket. We use datasets consisting of the subject of spam emails to experimentally test the influence of the different parameters of the algorithm on the number of computed similarities, on processing time, and on the quality of the final graph. We also compare the algorithm with a sequential and a MapReduce implementation of NN-Descent. For our datasets, the algorithm proved to be up to ten times faster than NN-Descent, for the same quality of produced graph. Moreover, the speedup increased with the size of the dataset, making NNCTPH a sensible choice for very large text datasets.

Spam has been infesting our emails and Web experience for decades; distributing phishing scams, adult/dating scams, rogue security software, ransomware, money laundering and banking scams… the list goes on. Fortunately, in the last few years, user awareness has increased and email spam filters have become more effective, catching over 99% of spam. The downside is that spammers are constantly changing their techniques as well as looking for new target platforms and means of delivery, and as the world is going mobile so too are the spammers. Indeed, mobile messaging spam has become a real problem and is steadily increasing year-over-year. We have been analyzing SMS spam data from a large US carrier for over six months, and we have observed all these threats, and more, indiscriminately targeting large numbers of subscribers. In this paper, we touch on such questions as what is driving SMS spam, how do the spammers operate, what are their activity patterns and how have they evolved over time. We also discuss what types of challenges SMS spam has created in terms of filtering, as well as security.