A logic program with function symbols is called finitely ground if there is a finite propositional logic program whose stable models are exactly the same as the stable models of this program. Finite groundability is an important property for logic programs with function symbols because it makes feasible to compute such programs' stable models using traditional ASP solvers. In this paper, we introduce new decidable classes of finitely ground programs called poly-bounded and k-EXP-bounded programs, which, to the best of our knowledge, strictly contain all other decidable classes of finitely ground programs discovered so far in the literature. We also study the relevant complexity properties for these classes of programs. We prove that the membership complexities for poly-bounded and k-EXP-bounded programs are EXPTIME-complete and (k+1)-EXPTIME-complete, respectively.

The paper is a half-way between the agent technology and the mathematical reasoning to model tactical decision making tasks. These models are applied to air defense (AD) domain for command and control (C2). It also addresses the issues related to evaluation of agents. The agents are designed and implemented using the agent-programming paradigm. The agents are deployed in an air combat simulated environment for performing the tasks of C2 like electronic counter counter measures, threat assessment, and weapon allocation. The simulated AD system runs without any human intervention, and represents state-of-the-art model for C2 autonomy. The use of agents as autonomous decision making entities is particularly useful in view of futuristic network centric warfare.

Electroencephalograms (EEG) are often contaminated by artifacts which make interpreting them more challenging for clinicians. Hence, automated artifact recognition systems have the potential to aid the clinical workflow. In this abstract, we share the first results on applying various machine learning algorithms to the recently released world's largest open-source artifact recognition dataset. We envision that these results will serve as a benchmark for researchers who might work with this dataset in future. Introduction Removal of artifacts from electroencephalogram (EEG) is a necessary step in analyzing EEG signals since artifacts can lead to severe misinterpretation of these signals. However, manual removal of artifacts requires trained clinicians or neurophysiologists and is a procedure that is known to be both time and resource hungry.

Systems that can automatically analyze EEG signals can aid neurologists by reducing heavy workload and delays. However, such systems need to be first trained using a labeled dataset. While large corpuses of EEG data exist, a fraction of them are labeled. Hand-labeling data increases workload for the very neurologists we try to aid. This paper proposes a semi-supervised learning algorithm that can not only extract meaningful information from large unlabeled EEG datasets but also perform task-specific learning on labeled datasets as small as 5 examples. Introduction Brain-related disorders such as epilepsy can be diagnosed by analyzing electroencephalograms (EEGs).

It's not a perfect measure, but unit sales of industrial robots give some idea of a country's industrial might. The names of the top five buyers in 2017 – China, Japan, South Korea, the US and Germany – shouldn't be too surprising. The global average is 74 per 10,000. One factor in this is the small electronics and automotive sectors here, which are two major drivers of industrial robot investment. The high number of SME and micro-businesses in Australian manufacturing is another.

We consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, in which case the existing QA methods fail due to lack of scalability. To tackle this problem, we propose a novel end-to-end reading comprehension method, which we refer to as Episodic Memory Reader (EMR) that sequentially reads the input contexts into an external memory, while replacing memories that are less important for answering unseen questions. Specifically, we train an RL agent to replace a memory entry when the memory is full in order to maximize its QA accuracy at a future timepoint, while encoding the external memory using the transformer architecture to learn representations that considers relative importance between the memory entries. We validate our model on a real-world large-scale textual QA task (TriviaQA) and a video QA task (TVQA), on which it achieves significant improvements over rule-based memory scheduling policies or an RL-based baseline that learns the query-specific importance of each memory independently.

But with a huge volume of posts popping up on these sites each day, it's difficult for even this combination of people and machines to keep up. AI still has a long way to go before it can reliably detect hate speech or violence online. Machine learning, the AI technique tech companies depend on to find unsavory content, figures out how to spot patterns in reams of data; it can identify offensive language, videos, or pictures in specific contexts. That's because these kinds of posts follow patterns on which AI can be trained. For example, if you give a machine-learning algorithm plenty of images of guns or written religious slurs, it can learn to spot those things in other images and text.

You have probably read about robots replacing human labor as a new era of automation takes root in one industry after another. But a new report suggests humans are not the only ones who might lose their jobs. In New Zealand, farmers are using drones to herd and monitor livestock, assuming a job that highly intelligent dogs have held for more than a century. The robots have not replaced the dogs entirely, Radio New Zealand reports, but they have appropriated one of the animal's most potent tools: barking. The DJI Mavic Enterprise, a $3,500 drone favored by farmers, has a feature that lets the machine record sounds and play them over a loud speaker, giving the machine the ability to mimic its canine counterparts.

Artificial intelligence can identify people in pictures, find the next TV series you should binge watch on Netflix, and even drive a car. But on Friday, when a suspected terrorist in New Zealand streamed live video to Facebook of a mass murder, the technology was of no help. The gruesome broadcast went on for at least 17 minutes until New Zealand police reported it to the social network. Recordings of the video and related posts about it rocketed across social media while companies tried to keep up. Why can't AI, which is already used by major social networks to help moderate the status updates, photos, and videos users upload, simply be deployed in greater measures to remove such violence as swiftly as it appears? A big reason is that whether it's hateful written posts, pornography, or violent images or videos, artificial intelligence still isn't great at spotting objectional content online.

As such, even though these technologies bring huge potential and opportunities, they still need to be closely monitored. The University of New South Wales Research Ethics and Compliance Support Director Dr Ted Rohr told HITNA that issues around ethics arise when healthcare access data from medical records for research, for example. "Ethics is all about deciding whether the use of technology is appropriate and is used for public good. For example, AI has its positives, but it can be misused. So, having an ethical framework allows the proper use of medical databases for research and experiments with patients using devices," he said.