Artificial intelligence, alongside proper training and education, can manage even the worst of security breaches into a positive outcome for airports and their users, says Kristina Dores, Chief, Aerodromes & Ground Aids at Namibia Civil Aviation Authority, and Brad Hayes, CTO at Circadence Corporation. However, the key question is when (not if) will organisations take the steps to prepare for the coming wave of digitisation? Highly-interconnected and increasingly-digitised systems are a necessary part of modern airport infrastructure. Furthermore, vulnerabilities at these interfaces – through personnel and digital systems alike – lead to an increased threat of intrusion and potentially catastrophic disruption. This problem is not one that we can simply train and hire our way out of as these systems and their attack surfaces do not scale linearly in complexity.

In this paper we investigate the Timed Alternating-Time Temporal Logic (TATL), a discrete-time extension of ATL. In particular, we propose, systematize, and further study semantic variants of TATL, based on different notions of a strategy. The notions are derived from different assumptions about the agents’ memory and observational capabilities, and range from timed perfect recall to untimed memoryless plans. We also introduce a new semantics based on counting the number of visits to locations during the play. We show that all the semantics, except for the untimed memoryless one, are equivalent when punctuality constraints are not allowed in the formulae. In fact, abilities in all those notions of a strategy collapse to the “counting” semantics with only two actions allowed per location. On the other hand, this simple pattern does not extend to the full TATL. As a consequence, we establish a hierarchy of TATL semantics, based on the expressivity of the underlying strategies, and we show when some of the semantics coincide. In particular, we prove that more compact representations are possible for a reasonable subset of TATL specifications, which should improve the efficiency of model checking and strategy synthesis.

As intelligent systems are increasingly capable of performing their tasks without the n eed for continuous human input, direction, or supervision, new human - machine interaction concepts are needed. A promising approac h to this end is human - agent teaming, which envisions a novel interaction form where humans and machines behave as equal team partners . This paper presents an overview of the current state of the art in human - agent teaming, including the analysis of human - agent teams on five dimensions; a framework describing important teaming functionalities; a technical architecture, called SAIL, supporting social human - agent teaming through the modular implementation of the human - agent teaming functionalities; a technica l implementation of the architecture; and a proof - of - concept prototype created with the framework and architecture. We conclude this paper with a reflection on where we stand and a glance into the future showing the way forward .

MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech Emna Rejaibi a,b,c, Ali Komaty d, Fabrice Meriaudeau e, Said Agrebi c, Alice Othmani a a Universit e Paris-Est, LISSI, UPEC, 94400 Vitry sur Seine, France b INSAT Institut National des Sciences Appliqu ees et de T echnologie, Centre Urbain Nord BP 676-1080, Tunis, Tunisie c Y obitrust, T echnopark El Gazala B11 Route de Raoued Km 3.5, 2088 Ariana, Tunisie d University of Sciences and Arts in Lebanon, Ghobeiry, Liban e Universit e de Bourgogne Franche Comt e, ImvIA EA7535/ IFTIM Abstract Major depression, also known as clinical depression, is a constant sense of despair and hopelessness. It is a major mental disorder that can a ff ect people of any age including children and that a ff ect negatively person's personal life, work life, social life and health conditions. Globally, over 300 million people of all ages are estimated to su ff er from clinical depression. A deep recurrent neural network-based framework is presented in this paper to detect depression and to predict its severity level from speech. Low-level and high-level audio features are extracted from audio recordings to predict the 24 scores of the Patient Health Questionnaire (a depression assessment test) and the binary class of depression diagnosis. To overcome the problem of the small size of Speech Depression Recognition (SDR) datasets, data augmentation techniques are used to expand the labeled training set and also transfer learning is performed where the proposed model is trained on a related task and reused as starting point for the proposed model on SDR task. The proposed framework is evaluated on the DAIC-WOZ corpus of the A VEC2017 challenge and promising results are obtained. An overall accuracy of 76.27% with a root mean square error of 0.4 is achieved in assessing depression, while a root mean square error of 0.168 is achieved in predicting the depression severity levels. Introduction Depression is a mental disorder caused by several factors: psychological, social or even physical factors. Psychological factors are related to permanent stress and the inability to successfully cope with di fficult situations. Social factors concern relationship struggles with family or friends and physical factors cover head injuries. Depression describes a loss of interest in every exciting and joyful aspect of everyday life. Mood disorders and mood swings are temporary mental states taking an essential part of daily events, whereas, depression is more permanent and can lead to suicide at its extreme severity levels.

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Choosing appropriate step sizes is critical for reducing the computational cost of training large-scale neural network models. Mini-batch sub-sampling (MBSS) is often employed for computational tractability. However, MBSS introduces a sampling error, that can manifest as a bias or variance in a line search. This is because MBSS can be performed statically, where the mini-batch is updated only when the search direction changes, or dynamically, where the mini-batch is updated every-time the function is evaluated. Static MBSS results in a smooth loss function along a search direction, reflecting low variance but large bias in the estimated "true" (or full batch) minimum. Conversely, dynamic MBSS results in a point-wise discontinuous function, with computable gradients using backpropagation, along a search direction, reflecting high variance but lower bias in the estimated "true" (or full batch) minimum. In this study, quadratic line search approximations are considered to study the quality of function and derivative information to construct approximations for dynamic MBSS loss functions. An empirical study is conducted where function and derivative information are enforced in various ways for the quadratic approximations. The results for various neural network problems show that being selective on what information is enforced helps to reduce the variance of predicted step sizes.

The ability of the computer program to imitate the human intelligence needed for the task is termed as artificial intelligence (AI). Integration of the artificial intelligence in education sector creates revolution through its result driven approach. The applications in solving the issues such as language processing, reasoning, planning, and cognitive modeling increases the demand for the AI in the education sector. In another learning approach, AI can help organize and synthesize content to support content delivery. The Research Insights has added a new report to its source.

Next week at AI Research Week, hosted by the MIT-IBM Watson AI Lab in Cambridge, MA, we will publish the first major release of the Adversarial Robustness 360 Toolbox (ART). Initially released in April 2018, ART is an open-source library for adversarial machine learning that provides researchers and developers with state-of-the-art tools to defend and verify AI models against adversarial attacks. ART v1.0 marks a milestone in AI security, introducing new features that extend ART to conventional machine learning models and a variety of data types beyond images: The number of reports on real-world exploitations using adversarial attacks against AI is growing, as in the case of anti-virus software, highlighting the importance of understanding, improving and monitoring the adversarial robustness of AI models. ART provides a comprehensive and growing set of tools to systematically assess and improve the robustness of AI models against adversarial attacks, including evasion and poisoning. In evasion attacks, the adversary crafts small changes to the original input to an AI model in order to influence its behaviour.

"Part of what we want is to educate the writing community in a really open way. So we have set this up to find and incubate new writing talent and get them up to speed with what we do." To qualify for The Writers' Room, a track record of screen credits was preferred but not essential.

The Assistant Secretary General and Director of the Regional Bureau for Africa of the United Nations Development Programme (UNDP), Ms. Ahunna Eziakonwa, called on African countries to take advantage of the opportunities offered by digital technologies such as artificial intelligence (AI), blockchains and machine learning, and deploy these in various sectors for the achievement of the 17 Sustainable Development Goals (SDGs). She made the call during a panel session at a side event at the 7th Tokyo International Conference on African Development (TICAD7) in Yokohama, Japan. The event, titled "From Idea to Action: Harnessing the Potential of Science, Technology and Innovation (STI) in Africa's Development", was organized by the Japan International Cooperation Agency (JICA) and the World Bank. Ms. Eziakonwa noted that, Africa needs to harness the potential of STI for development by prioritizing policies and making investments to increase access to state-of-the-art technologies such as e-governance, finance and digital literacy and skills – at secondary and TVET (Technical and Vocational Education and Training) level. She called for the adoption of innovative financing schemes that combine both public and private sector resources and technical expertise for the achievement of the three dimensions of sustainable development: economic, social and environment.