The advantages offered by the presence of a schema are numerous. However, many XML documents in practice are not accompanied by a (valid) schema, making schema inference an attractive research problem. The fundamental task in XML schema learning is inferring restricted subclasses of regular expressions. Most previous work either lacks support for interleaving or only has limited support for interleaving. In this paper, we first propose a new subclass Single Occurrence Regular Expressions with Interleaving (SOIRE), which has unrestricted support for interleaving. Then, based on single occurrence automaton and maximum independent set, we propose an algorithm iSOIRE to infer SOIREs. Finally, we further conduct a series of experiments on real datasets to evaluate the effectiveness of our work, comparing with both ongoing learning algorithms in academia and industrial tools in real-world. The results reveal the practicability of SOIRE and the effectiveness of iSOIRE, showing the high preciseness and conciseness of our work.

Intelligent Personal Assistants (IPAs) are software agents that can perform tasks on behalf of individuals and assist them on many of their daily activities. IPAs capabilities are expanding rapidly due to the recent advances on areas such as natural language processing, machine learning, artificial cognition, and ubiquitous computing, which equip the agents with competences to understand what users say, collect information from everyday ubiquitous devices (e.g., smartphones, wearables, tablets, laptops, cars, household appliances, etc.), learn user preferences, deliver data-driven search results, and make decisions based on user's context. Apart from the inherent complexity of building such IPAs, developers and researchers have to address many critical architectural challenges (e.g., low-latency, scalability, concurrency, ubiquity, code mobility, interoperability, support to cognitive services and reasoning, to name a few.), thereby diverting them from their main goal: building IPAs. Thus, our contribution in this paper is twofold: 1) we propose an architecture for a platform-agnostic, high-performance, ubiquitous, and distributed middleware that alleviates the burdensome task of dealing with low-level implementation details when building IPAs by adding multiple abstraction layers that hide the underlying complexity; and 2) we present an implementation of the middleware that concretizes the aforementioned architecture and allows the development of high-level capabilities while scaling the system up to hundreds of thousands of IPAs with no extra effort. We demonstrate the powerfulness of our middleware by analyzing software metrics for complexity, effort, performance, cohesion and coupling when developing a conversational IPA.

Automated text generation has been applied broadly in many domains such as marketing and robotics, and used to create chatbots, product reviews and write poetry. The ability to synthesize text, however, presents many potential risks, while access to the technology required to build generative models is becoming increasingly easy. This work is aligned with the efforts of the United Nations and other civil society organisations to highlight potential political and societal risks arising through the malicious use of text generation software, and their potential impact on human rights. As a case study, we present the findings of an experiment to generate remarks in the style of political leaders by fine-tuning a pretrained AWD- LSTM model on a dataset of speeches made at the UN General Assembly. This work highlights the ease with which this can be accomplished, as well as the threats of combining these techniques with other technologies.

Humans are able to conceive physical reality by jointly learning different facets thereof. To every pair of notions related to a perceived reality may correspond a mutual relation, which is a notion on its own, but one-level higher. Thus, we may have a description of perceived reality on at least two levels and the translation map between them is in general, due to their different content corpus, one-to-many. Following success of the unsupervised neural machine translation models, which are essentially one-to-one mappings trained separately on monolingual corpora, we examine further capabilities of unsupervised deep learning methods used there and apply these methods to sets of notions of different level and measure. Using the graph and word embedding-like techniques, we build one-to-many map without parallel data in order to establish a unified latent mental representation of the outer world, by combining notions of different kind into a unique conceptual framework. Due to latent similarity, by aligning two embedding spaces in purely unsupervised way, one obtains a geometric relation between objects of cognition on the two levels, making it possible to express a natural knowledge using one description in the context of the other.

If you're like the majority of humans on the planet, going to the dentist isn't on the top of your list of things to do for fun. But with artificial intelligence (AI) and new tech and innovative design, it might start to be a bit more intriguing. AI might not be able to do the actual brushing and flossing for you (yet), but it will certainly change your experience the next time you're sitting in the dentist's chair. From analyzing X-rays to documenting the results of your visit, artificial intelligence will be relied upon to make your dental appointment more efficient and to enhance your care. Dentem created a platform that integrates machine learning APIs, including the ability to auto-populate tooth charting.

Gary Marcus has emerged as one of deep learning's chief skeptics. In a recent interview, and a slightly less recent medium post, he discusses his feud with deep learning pioneer Yann LeCun and some of his views on how deep learning is overhyped. I find the whole thing entertaining, but at many times LeCun and Marcus are talking past each other more than with each other. Marcus seems to me to be either unaware of or ignoring certain truths about machine learning and LeCun seems to basically agree with Marcus' ideas in a way that's unsatisfying for Marcus. The temptation for me to brush 10 years of dust off of my professor hat is too much to ignore.

Fear, uncertainty and doubt are tainting the banking industry's views of artificial intelligence. There's so much noise about AI, it's reminiscent of irrational fears about electricity or even the microwave -- it's going to take away our jobs, is more dangerous than nuclear weapons and will have a negative impact on our cities. From my perspective, it's important to be cautious when we evaluate new technologies, but I'm an optimist at heart. I believe in the power of technology to create value and transform lives. The individuals who are responsible for AI have the capacity to create guardrails and ensure that these new approaches to data science do not have a negative impact.

Artificial intelligence doesn't need any extra fuel for the myths and misconceptions that surround it. Consider the phrase "black box" – its connotations are equal parts mysterious and ominous, the stuff of "The X Files" more than the day-to-day business of IT. Yet it's true that AI systems, such as machine learning or deep learning, take inputs and then produce outputs (or make decisions) with no decipherable explanation or context. The system makes a decision or takes some action, and we don't necessarily know why or how it arrived at that outcome. The system just does it.

Surveillance video is everywhere these days, and researchers are working on making it smarter and smarter. The latest advance is in the problem of constructing -- or "hallucinating" in machine learning ML parlance -- a complete image of a person from a partial or occluded photo. Occlusion occurs when the object, or body, you want to see is partially covered by an intervening object or body. In a crowded public area, say Times Square in New York, surveillance cameras would rarely get an unobstructed view of a person of interest. What is artificial general intelligence? That's where the paper Can Adversarial Networks Hallucinate Occluded People With a Plausible Aspect? by researchers from the University of Modena comes in.

While governments around the world are wrestling with the potential for massive on-rushing technological disruption of work and the jobs market, few are extending the telescope to explore what the knock-on impacts might be for the planet. Here we explore some dimensions of the issue. Although replacing humans with robots has a dystopian flavor, what, if any positives are there from successive waves of artificial intelligence (AI) and other exponentially developing technologies displacing jobs ranging from banker to construction worker? Clearly, the number of people working and the implications for commuting, conduct of their role and their resulting income-related domestic lifestyle all have a direct bearing on their consumption of resources and emissions footprint. However, while everyone wants to know the impact of smart automation, the reality is that we are all clueless as to the outcome over the next twenty years, as this fourth industrial revolution has only just started.