Intelligent services provide the power of AI to developers via simple RESTful API endpoints, abstracting away many complexities of machine learning. However, most of these intelligent services-such as computer vision-continually learn with time. When the internals within the abstracted 'black box' become hidden and evolve, pitfalls emerge in the robustness of applications that depend on these evolving services. Without adapting the way developers plan and construct projects reliant on intelligent services, significant gaps and risks result in both project planning and development. Therefore, how can software engineers best mitigate software evolution risk moving forward, thereby ensuring that their own applications maintain quality? Our proposal is an architectural tactic designed to improve intelligent service-dependent software robustness. The tactic involves creating an application-specific benchmark dataset baselined against an intelligent service, enabling evolutionary behaviour changes to be mitigated. A technical evaluation of our implementation of this architecture demonstrates how the tactic can identify 1,054 cases of substantial confidence evolution and 2,461 cases of substantial changes to response label sets using a dataset consisting of 331 images that evolve when sent to a service.

The paper presents the IWCS 2019 shared task on semantic parsing where the goal is to produce Discourse Representation Structures (DRSs) for English sentences. DRSs originate from Discourse Representation Theory and represent scoped meaning representations that capture the semantics of negation, modals, quantification, and presupposition triggers. Additionally, concepts and event-participants in DRSs are described with WordNet synsets and the thematic roles from VerbNet. To measure similarity between two DRSs, they are represented in a clausal form, i.e. as a set of tuples. Participant systems were expected to produce DRSs in this clausal form. Taking into account the rich lexical information, explicit scope marking, a high number of shared variables among clauses, and highly-constrained format of valid DRSs, all these makes the DRS parsing a challenging NLP task. The results of the shared task displayed improvements over the existing state-of-the-art parser.

Orthogonal Monte Carlo (OMC) is a very effective sampling algorithm imposing structural geometric conditions (orthogonality) on samples for variance reduction. Due to its simplicity and superior performance as compared to its Quasi Monte Carlo counterparts, OMC is used in a wide spectrum of challenging machine learning applications ranging from scalable kernel methods to predictive recurrent neural networks, generative models and reinforcement learning. However theoretical understanding of the method remains very limited. In this paper we shed new light on the theoretical principles behind OMC, applying theory of negatively dependent random variables to obtain several new concentration results. We also propose a novel extensions of the method leveraging number theory techniques and particle algorithms, called Near-Orthogonal Monte Carlo (NOMC). We show that NOMC is the first algorithm consistently outperforming OMC in applications ranging from kernel methods to approximating distances in probabilistic metric spaces.

Coronavirus disease (COVID-19) spread forecasting is an important task to track the growth of the pandemic. Existing predictions are merely based on qualitative analyses and mathematical modeling. The use of available big data with machine learning is still limited in COVID-19 growth prediction even though the availability of data is abundance. To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time. The structure of an LSTM layer is searched heuristically until the best validation score is achieved. First, we trained training data containing confirmed cases from around the globe. We achieved favorable performance compared with that of the recurrent neural network (RNN) method with a comparable low validation error. The evaluation is conducted based on graph visualization and root mean squared error (RMSE). We found that it is not easy to achieve the same quantity of confirmed cases over time. However, LSTM provide a similar pattern between the actual cases and prediction. In the future, our proposed prediction can be used for anticipating forthcoming pandemics. The code is provided here: https://github.com/cbasemaster/lstmcorona

In recent years, researchers have become increasingly interested in outlying aspect mining. Outlying aspect mining is the task of finding a set of feature(s), where a given data object is different from the rest of the data objects. Remarkably few studies have been designed to address the problem of outlying aspect mining; therefore, little is known about outlying aspect mining approaches and their strengths and weaknesses among researchers. In this work, we have grouped existing outlying aspect mining approaches in three different categories. For each category, we have provided existing work that falls in that category and then provided their strengths and weaknesses in those categories. We also offer time complexity comparison of the current techniques since it is a crucial issue in the real-world scenario. The motive behind this paper is to give a better understanding of the existing outlying aspect mining techniques and how these techniques have been developed.

It feels like drones were built for this moment. The coronavirus pandemic has forced everyone to spend the majority of their time indoors and, where possible, maintain a healthy distance from anyone that doesn't live in the same building. Companies have introduced numerous measures to minimize the threat and spread of infection. Countless stores have acrylic screens, for instance, and many delivery drivers leave orders at your doorstep. But a robot -- or specifically, a drone -- offers a potentially safer and quicker method of exchanging goods and services. It's no wonder, then, that so many commercial UAV (unmanned aerial vehicle) operators are flourishing at the moment. In a time of crisis, they're keen to step forward and showcase the impact that drone deliveries can have on society.

Dialogue systems dealing with multi-domain tasks are highly required. How to record the state remains a key problem in a task-oriented dialogue system. Normally we use human-defined features as dialogue states and apply a state tracker to extract these features. However, the performance of such a system is limited by the error propagation of a state tracker. In this paper, we propose a dialogue generation model that needs no external state trackers and still benefits from human-labeled semantic data. By using a teacher-student framework, several teacher models are firstly trained in their individual domains, learn dialogue policies from labeled states. And then the learned knowledge and experience are merged and transferred to a universal student model, which takes raw utterance as its input. Experiments show that the dialogue system trained under our framework outperforms the one uses a belief tracker.

Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction. Our approach employs optimal transport (OT) to find a minimal cost alignment between the inputs. However, directly applying OT often produces dense and therefore uninterpretable alignments. To overcome this limitation, we introduce novel constrained variants of the OT problem that result in highly sparse alignments with controllable sparsity. Our model is end-to-end differentiable using the Sinkhorn algorithm for OT and can be trained without any alignment annotations. We evaluate our model on the StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models.

The U.S. Air Force is finally pushing into the world of robot combat drones, vowing to fly the first of its "Skyborg" drones by 2023. The service envisions Skyborg as a merging of artificial intelligence with jet-powered drones. The result will be drones capable of flying alongside fighter jets, carrying out dangerous missions. Skyborg drones will be much cheaper than piloted aircraft, allowing the Air Force to grow its fleet at a lower cost. The Air Force, according to Defense News, will award a total of $400 million to one or more companies to develop different types of Skyborg drones.

An artificial intelligence agent named Tala may open the door on a new way of gathering feedback from New Zealand's Samoan community. The Talanoa Project is a pilot project that uses IBM's artificial intelligence virtual agent solution, Watson, to interact in real time in Samoan for public consultation and community engagement. Developed and designed by Beca, business director Matthew Ensor said it was about consulting with'the silent majority' in the public on projects and community facilities. "We don't hear so much from the people where language is a barrier, where culturally there's no tradition of responding to public consultation. "We then created a conversational agent, it's like a chat-bot and what it does is it mimics the kind of conversation that you would have with a consultation expert," Mr Ensor said. "It will ask open questions about your thoughts on different things and really lets the person lead the conversation rather than a survey form where the questions are completely scripted." Steve O'Donnell from IBM New Zealand's Managing Partner for Global Business Services said this was the first time IBM Watson Assistant had been used for public consultation in New Zealand in a language other than English. "What we are seeing now is AI being able to scale down, and drive value in many industries," he said. "IBM Watson has already transformed the world of customer service, due largely to its ability to understand human sentiment and interact naturally with people and Tala is a promising first step towards that." The Talanoa Project, part funded by Callaghan Innovation, tested Tala among a few dozen Samoan speakers, asking them for their thoughts on their local community facilities. The focus group of Samoans ranged from 19-years of age to 77 being the oldest and included Samoan elders, law students, psychologists and sociologists. "It was overwhelmingly positive the response we got back from the Samoan community," Mr Ensor said. "We had a few people share that it was great to hear technology using their native language.