Fatigue is the most vital factor of road fatalities and one manifestation of fatigue during driving is drowsiness . In this paper, we propose using deep Q - learning to analyze an electroencephalogram (EEG) dataset captured during a simulated endurance drivi ng test . By measur ing the correlation between drowsiness and driving performance, t h is experiment represents an important brain - computer interface (BCI) paradigm especially from an application perspective. We adapt the terminologies in the driving test to fit the reinforcement learning framework, thus formulate the drowsiness estimation problem as an optimization of a Q - learning task . B y referring to the latest deep Q - Learning technologies and attending to the characteristics of EEG data, we tailor a deep Q - network for action proposition that can indirectly estimate drowsiness . Our results show that the trained model can trace the variations of mind state in a satisfactory way against the testing EEG data, which demonstrates the feasibility and practicab ilit y of this new computation paradigm . We also show that our method outperforms the supervised learning counterpart and is superior for real applications. To the best of our knowledge, we are the first to introduce the deep reinforcement learning method to th is BCI scenario, and our method can be potentially generalized to other BCI cases . Fatigue is regarded as the most severe factor causing road fatalities [1] . To understand the correlation between fatigue and driving performance, both from theory to practice, is of persistent interest for researchers.

Deep Neural Networks (DNNs) are finding important applications in safety-critical systems such as Autonomous Vehicles (AVs), where perceiving the environment correctly and robustly is necessary for safe operation. Raising unique challenges for assurance due to their black-box nature, DNNs pose a fundamental problem for regulatory acceptance of these types of systems. Robust training --- training to minimize excessive sensitivity to small changes in input --- has emerged as one promising technique to address this challenge. However, existing robust training tools are inconvenient to use or apply to existing codebases and models: they typically only support a small subset of model elements and require users to extensively rewrite the training code. In this paper we introduce a novel framework, PaRoT, developed on the popular TensorFlow platform, that greatly reduces the barrier to entry. Our framework enables robust training to be performed on arbitrary DNNs without any rewrites to the model. We demonstrate that our framework's performance is comparable to prior art, and exemplify its ease of use on off-the-shelf, trained models and on a real-world industrial application: training a robust traffic light detection network.

Automation services for complex business processes usually require a high level of information technology literacy. There is a strong demand for a smartly assisted process automation (IPA: intelligent process automation) service that enables even general users to easily use advanced automation. A natural language interface for such automation is expected as an elemental technology for the IPA realization. The workflow targeted by IPA is generally composed of a combination of multiple tasks. However, semantic parsing, one of the natural language processing methods, for such complex workflows has not yet been fully studied. The reasons are that (1) the formal expression and grammar of the workflow required for semantic analysis have not been sufficiently examined and (2) the dataset of the workflow formal expression with its corresponding natural language description required for learning workflow semantics did not exist. This paper defines a new grammar for complex workflows with chaining machine-executable meaning representations for semantic parsing. The representations are at a high abstraction level. Additionally, an approach to creating datasets is proposed based on this grammar.

At the Last Futurist, we enjoy looking at AI Trends and digital transformation trends. In between those two are more broad technology trends. In fact these topics make up the mission statement of this new news site. However the last decade had a lot of technology and gadgets that didn't fare so well in the real world. The decade was mobile all the way, with mass adoption taking place the way we might expect the brain-computer interface (BCI) to achieve mass adoption in a future decade years from now. In the decade ahead the move to automated stores and electric vehicles are real trends, but it's important to differentiate the hype from the reality. Autonomous vehicles, quantum computing going mainstream, better self-learning AI, hang on a second! Even mass adoption of digital currencies is coming faster. From computers to the internet and smart phones, a few generations shows a lot of progress. But technology never stands still. Advertising has scaled a world of surveillance capitalism normalization and an AI-arms race is now taking place. Most technology trends and AI listicles only touch the surface of how humans are embedding technology increasingly into their lives. However looking at it from the perspectives of many industries and across technology and innovation stacks gives a more complete picture. The real world and customer experience are the real tests for new technological innovations and pivots. It will take decades for 3D printing, quantum computing and an AGI to even become mature, but an age of biotechnology and AI in healthcare, education and finance is inevitable. From Huawei, to ByteDance (TikTok), to Didi, China will wage major battles for global market share in 5G, consumer apps, E-commerce, mobile payments and ride sharing, among others. Chinese led tech companies -- with the support of the Chinese Government and venture funds such as Softbank Vision Fund -- can mean that in the 2020s China's ecosystem fully replaces Silicon Valley as the leader of innovation. In 2019, some believe this has already occurred.

These six enterprise tech trends defined 2019As we kick off another year, ZDNet's global team of editors has put together a list of five tech trends that will have a significant impact on the enterprise in 2020. The effects could be negative or positive, but will undoubtedly be substantial. Our panel of editors includes -- TechRepublic's Bill Detwiler, Larry Dignan, Chris Duckett, and Steve Ranger. Take a look back at best of the decade: ZDNet's top enterprise CEOs of the 2010s The PC was supposed to die a decade ago. Instead, this happened A decade of malware: Top botnets of the 2010s A decade of hacking: The most notable cyber-security events of the 2010s Device of the decade: Why did it take nine years for the iPad to get its own operating system?

Existing Recommender Systems mainly focus on exploiting users' feedback, e.g., ratings, and reviews on common items to detect similar users. Thus, they might fail when there are no common items of interest among users. We call this problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI). Personality-based recommender systems have shown a great success to identify similar users based on their personality types. However, there are only a few personality-based recommender systems in the literature which either discover personality explicitly through filling a questionnaire that is a tedious task, or neglect the impact of users' personal interests and level of knowledge, as a key factor to increase recommendations' acceptance. Differently, we identifying users' personality type implicitly with no burden on users and incorporate it along with users' personal interests and their level of knowledge. Experimental results on a real-world dataset demonstrate the effectiveness of our model, especially in DSW-n-FCI situations.

The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples. Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error. For several flatness measures, this correlation has been empirically validated. However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization: if a network uses ReLU activations, the network function can be reparameterized without changing its output in such a way that flatness is changed almost arbitrarily. This paper proposes a natural modification of existing flatness measures that results in invariance to reparameterization. The proposed measures imply a robustness of the network to changes in the input and the hidden layers. Connecting this feature robustness to generalization leads to a generalized definition of the representativeness of data. With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.

The wide adoption of machine learning in the critical domains such as medical diagnosis, law, education had propelled the need for interpretable techniques due to the need for end users to understand the reasoning behind decisions due to learning systems. The computational intractability of interpretable learning led practitioners to design heuristic techniques, which fail to provide sound handles to tradeoff accuracy and interpretability. Motivated by the success of MaxSA T solvers over the past decade, recently MaxSA T -based approach, called MLIC, was proposed that seeks to reduce the problem of learning interpretable rules expressed in Conjunctive Normal Form (CNF) to a MaxSA T query. While MLIC was shown to achieve accuracy similar to that of other state of the art black-box classifiers while generating small interpretable CNF formulas, the runtime performance of MLIC is significantly lagging and renders approach unusable in practice. In this context, authors raised the question: Is it possible to achieve the best of both worlds, i.e., a sound framework for interpretable learning that can take advantage of MaxSAT solvers while scaling to real-world instances? In this paper, we take a step towards answering the above question in affirmation. We propose IMLI: an incremental approach to MaxSA T based framework that achieves scalable runtime performance via partition-based training methodology. Extensive experiments on benchmarks arising from UCI repository demonstrate that IMLI achieves up to three orders of magnitude runtime improvement without loss of accuracy and interpretability.

We present a survey on multilingual neural machine translation (MNMT), which has gained a lot of traction in the recent years. MNMT has been useful in improving translation quality as a result of translation knowledge transfer (transfer learning). MNMT is more promising and interesting than its statistical machine translation counterpart because end-to-end modeling and distributed representations open new avenues for research on machine translation. Many approaches have been proposed in order to exploit multilingual parallel corpora for improving translation quality. However, the lack of a comprehensive survey makes it difficult to determine which approaches are promising and hence deserve further exploration. In this paper, we present an in-depth survey of existing literature on MNMT. We first categorize various approaches based on their central use-case and then further categorize them based on resource scenarios, underlying modeling principles, core-issues and challenges. Wherever possible we address the strengths and weaknesses of several techniques by comparing them with each other. We also discuss the future directions that MNMT research might take. This paper is aimed towards both, beginners and experts in NMT. We hope this paper will serve as a starting point as well as a source of new ideas for researchers and engineers interested in MNMT.

In December 2018, Canada and France announced plans for a new international body to study and steer the effects of artificial intelligence on the world's people and economies. Canadian prime minister Justin Trudeau said the International Panel on Artificial Intelligence would be established by the Group of Seven leading western economies and play a role in "addressing some of the ethical concerns we will face in this area." It was to be modeled on the UN's Intergovernmental Panel on Climate Change, which helped establish consensus on the world's climate crisis and recommends possible responses. Just over a year later, the IPAI has been renamed the Global Partnership on AI, but it still hasn't quite gotten off the ground. Six of the G7 are on board--with the United States the lone holdout.