Soccer aficionados will never forget the headbutt by French soccer great Zinadine Zidane during the 2006 World Cup final. Caught on video camera, Zidane's attack on Italian player Marco Materazzi after a verbal exchange got him a red ticket. He left the field, making it easier for Italy to become world champions. The world found out later about Materazzi's abusive words of Zidane's female relatives. "If we had good lip-reading technology Zidane's reaction could have been explained or they would've both gotten sent out," says Helen Bear, a computer scientist at the University of East Anglia in Norwich, UK. "Maybe the match outcome would be different."

Empowering the data-driven organization is a core element of our strategy at Hewlett Packard Enterprise. This can sound like just another fancy marketing campaign – unless you were in a seat at the Seize the Data Analytics World Tour event in Palo Alto. DreamWorks Animation's Jeff Wike presenting at the Silicon Valley event As I sat smiling through the Kung Fu Panda 3 trailer and watched Jeff Wike, Head of Technology for Film and TV Production at DreamWorks Animation, take the stage, I expected to hear how analytics helped DreamWorks Animation analyze how many people watched the film and how they chose to do so - in the theater, on demand, on what device. That information is foundational to any organization in the media industries these days. I didn't expect to hear that the HPE Vertica Advanced Analytics database improved the ability of artists to iterate design and even render panda fur by minimizing compute resources, or how the studio was able to quickly redesign the characters' facial movements when the movie was translated to Mandarin, also due to the power of analytics.

We're witnessing an explosion of applications that no longer have a graphical user interface (GUI). They've actually been around for a while, but they've only recently started spreading into the mainstream. They are called bots, virtual assistants, invisible apps. They can run on Slack, WeChat, Facebook Messenger, plain SMS, or Amazon Echo. They can be entirely driven by artificial intelligence, or there can be a human behind the curtain.

New lip-reading technology could help solve crimes by deciphering what people caught on CCTV are saying, researchers have claimed. The visual speech recognition technology developed by the University of East Anglia in Norwich can be used to determine what people are saying in situations where audio is not good enough to hear - such as on security camera footage. Helen Bear, from the university's school of computing science, said the technology could be applied to a wide range of situations from criminal investigations to entertainment. She added: "Lip-reading has been used to pinpoint words footballers have shouted in heated moments on the pitch, but is likely to be of most practical use in situations where there are high levels of noise, such as in cars or aircraft cockpits. "Crucially, whilst there are still improvements to be made, such a system could be adapted for use for a range of purposes - for example, for people with hearing or speech impairments."

New lip-reading technology developed at the University of East Anglia (UEA) could help in solving crimes and provide communication assistance for people with hearing and speech impairments. The visual speech recognition technology, created by Dr Helen L. Bear and Prof Richard Harvey of UEA's School of Computing Sciences, can be applied "any place where the audio isn't good enough to determine what people are saying," Dr Bear said. Dr Bear, whose findings will be presented at the International Conference on Acoustics, Speech and Signal Processing (ICASSP) in Shanghai on March 25, said unique problems with determining speech arise when sound isn't available - such as on CCTV footage - or if the audio is inadequate and there aren't clues to give the context of a conversation. The sounds '/p/,' '/b/,' and '/m/' all look similar on the lips, but now the machine lip-reading classification technology can differentiate between the sounds for a more accurate translation. Dr Bear said: "We are still learning the science of visual speech and what it is people need to know to create a fool-proof recognition model for lip-reading, but this classification system improves upon previous lip-reading methods by using a novel training method for the classifiers. "Potentially, a robust lip-reading system could be applied in a number of situations, from criminal investigations to entertainment.

New lip-reading technology could help solve crimes by deciphering what people caught on CCTV are saying, researchers have claimed. The visual speech recognition technology developed by the University of East Anglia in Norwich can be used to determine what people are saying in situations where audio is not good enough to hear - such as on security camera footage. Helen Bear, from the university's school of computing science, said the technology could be applied to a wide range of situations from criminal investigations to entertainment. She added: "Lip-reading has been used to pinpoint words footballers have shouted in heated moments on the pitch, but is likely to be of most practical use in situations where there are high levels of noise, such as in cars or aircraft cockpits. "Crucially, whilst there are still improvements to be made, such a system could be adapted for use for a range of purposes - for example, for people with hearing or speech impairments."

This article discusses the quadratization of Markov Logic Networks, which enables efficient approximate MAP computation by means of maximum flows. The procedure relies on a pseudo-Boolean representation of the model, and allows handling models of any order. The employed pseudo-Boolean representation can be used to identify problems that are guaranteed to be solvable in low polynomial-time. Results on common benchmark problems show that the proposed approach finds optimal assignments for most variables in excellent computational time and approximate solutions that match the quality of ILP-based solvers.

This thesis focuses on gaining linguistic insights into textual discussions on a word level. It was of special interest to distinguish messages that constructively contribute to a discussion from those that are detrimental to them. Thereby, we wanted to determine whether "I"- and "You"-messages are indicators for either of the two discussion styles. These messages are nowadays often used in guidelines for successful communication. Although their effects have been successfully evaluated multiple times, a large-scale analysis has never been conducted. Thus, we used Wikipedia Articles for Deletion (short: AfD) discussions together with the records of blocked users and developed a fully automated creation of an annotated data set. In this data set, messages were labelled either constructive or disruptive. We applied binary classifiers to the data to determine characteristic words for both discussion styles. Thereby, we also investigated whether function words like pronouns and conjunctions play an important role in distinguishing the two. We found that "You"-messages were a strong indicator for disruptive messages which matches their attributed effects on communication. However, we found "I"-messages to be indicative for disruptive messages as well which is contrary to their attributed effects. The importance of function words could neither be confirmed nor refuted. Other characteristic words for either communication style were not found. Yet, the results suggest that a different model might represent disruptive and constructive messages in textual discussions better.

We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.

Regularized least-squares approaches have been successfully applied to linear system identification. Recent approaches use quadratic penalty terms on the unknown impulse response defined by stable spline kernels, which control model space complexity by leveraging regularity and bounded-input bounded-output stability. This paper extends linear system identification to a wide class of nonsmooth stable spline estimators, where regularization functionals and data misfits can be selected from a rich set of piecewise linear quadratic penalties. This class encompasses the 1-norm, huber, and vapnik, in addition to the least-squares penalty, and the approach allows linear inequality constraints on the unknown impulse response. We develop a customized interior point solver for the entire class of proposed formulations. By representing penalties through their conjugates, we allow a simple interface that enables the user to specify any piecewise linear quadratic penalty for misfit and regularizer, together with inequality constraints on the response. The solver is locally quadratically convergent, with O(n2(m+n)) arithmetic operations per iteration, for n impulse response coefficients and m output measurements. In the system identification context, where n << m, IPsolve is competitive with available alternatives, illustrated by a comparison with TFOCS and libSVM. The modeling framework is illustrated with a range of numerical experiments, featuring robust formulations for contaminated data, relaxation systems, and nonnegativity and unimodality constraints on the impulse response. Incorporating constraints yields significant improvements in system identification. The solver used to obtain the results is distributed via an open source code repository.