LONDON – Guinness World Records celebrates its annual records day on Thursday, honoring a long list of people who have done highly improbable things better than anyone else. The World Records Day often includes a Rubik's cube solved in unlikely circumstances against the clock, and this year was no exception. China's Que Jianyu recorded the fastest time to crack the puzzle upside down: 15.84 seconds. The Harlem Globetrotters, the U.S. stunt basketball team, generally fields a number of record-setters, or at least attempts. This year, one of them was Torch George, who managed the most under-the-leg tumbles -- 32 -- and at 5 feet 3 inches (160 cm) may also set a record as the most diminutive Globetrotter.

A Chinese surveillance company, Watrix, has developed a new system for "gait recognition" that can identify people up to 165 feet away based on how they walk. This means that if a person is wearing a mask or is at an awkward angle, the software can use existing footage to detect them. CEO of Watrix, Huang Yongzhen, told the Associated Press in an interview that the software can't be fooled by limping or other out-of-the-ordinary stances because it analyzes a person's entire body. Watrix's gait recognition technology is fed a video clip of the person walking, cuts a silhouette and creates a model of the way a person walks. While Watrix claims its technology has a 94 percent accuracy rate, analysis is not done live and in real-time.

To read the full report click here for the digital edition. FinTech has revolutionized the way that banks and insurance companies function. Rather than prioritizing themselves and their services as in the past, banks must emphasize client needs in today's new technological era. This focus on personalized financial services manifests itself in FinTech--a financial infrastructure for consumer enablement. As FinTech applies data and technology to financial services in an effort to address industry challenges, artificial intelligence is essential to FinTech's existence and usage.

The announcement was one of several at the Fujitsu Forum in Munich to feature AI developments. Fujitsu Intelligence Technology brings together the company's AI work in Japan and around the world to run it from Vancouver, Canada. The area has many research institutions such as the University of Toronto engaged in AI and quantum computing research, as well as startup technology companies. The government there is pushing the country towards AI. "In Vancouver and across British Columbia, Fujitsu will have the opportunity to collaborate with our state-of-the-art universities and research facilities to discover new ways that artificial intelligence can help solve local and global challenges," said John Horgan, Premier of the Province of British Columbia.

Awareness of the possible impacts associated with artificial intelligence has risen in proportion to progress in the field. While there are tremendous benefits to society, many argue that there are just as many, if not more, concerns related to advanced forms of artificial intelligence. Accordingly, research into methods to develop artificial intelligence safely is increasingly important. In this paper, we provide an overview of one such safety paradigm: containment with a critical lens aimed toward generative adversarial networks and potentially malicious artificial intelligence. Additionally, we illuminate the potential for a developmental blindspot in the stovepiping of containment mechanisms.

The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications. While practitioners celebrate this as an economical way to get more synthetic data that can be used to train downstream classifiers, it is not clear that they recognize the inherent pitfalls of this technique. In this paper, we aim to exhort practitioners against deriving any false sense of security against data biases based on data augmentation. To drive this point home, we show that starting with a dataset consisting of head-shots of engineering researchers, GAN-based augmentation "imagines" synthetic engineers, most of whom have masculine features and white skin color (inferred from a human subject study conducted on Amazon Mechanical Turk). This demonstrates how biases inherent in the training data are reinforced, and sometimes even amplified, by GAN-based data augmentation; it should serve as a cautionary tale for the lay practitioners.

The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework, and which can be successfully interfaced with approaches to instantiated argumentation. The theory is developed in three steps. First, the paper introduces a graded generalization of the two key notions underpinning Dung's semantics: self-defense and conflict-freeness. This leads to a natural generalization of Dung's semantics, whereby standard extensions are weakened or strengthened depending on the level of self-defense and conflict-freeness they meet. The paper investigates the fixpoint theory of these semantics, establishing existence results for them. Second, the paper shows how graded semantics readily provide an approach to argument rankings, offering a novel contribution to the recently growing research programme on ranking-based semantics. Third, this novel approach to argument ranking is applied and studied in the context of instantiated argumentation frameworks, and in so doing is shown to account for a simple form of accrual of arguments within the Dung paradigm. Finally, the theory is compared in detail with existing approaches.

This paper presents a discovery that the length of the entities follows a family of scale-free power law distributions. The concept of entity here broadly includes the named entity, entity mention, time expression, and domain-specific entity that are well investigated in natural language processing and related areas. The power law distributions in entity length have well-defined means and finite variances and possess the scale-free property. We explain the phenomenon of power laws in entity length by the principle of least effort in communication and the preferential mechanism.

Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study.

Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact that the LSTMs can help to capture long-term dependencies, its ability to pay different degree of attention on sub-window feature within multiple time-steps is insufficient. To address this issue, an evolutionary attention-based LSTM training with competitive random search is proposed for multivariate time series prediction. By transferring shared parameters, an evolutionary attention learning approach is introduced to the LSTMs model. Thus, like that for biological evolution, the pattern for importance-based attention sampling can be confirmed during temporal relationship mining. To refrain from being trapped into partial optimization like traditional gradient-based methods, an evolutionary computation inspired competitive random search method is proposed, which can well configure the parameters in the attention layer. Experimental results have illustrated that the proposed model can achieve competetive prediction performance compared with other baseline methods.