Spotted: Eddy Travels is an AI-enabled personal travel assistant that operates within popular chat applications, such as WhatsApp, Facebook Messenger, Viber, Slack and Telegram. Based on the user's chat conversations, Eddy uses a language processing system that makes tailored travel recommendations. This enables to unify all booking needs to one place, from flights to hotels. Eddy even recommends personalized activities. For example, if your friend mentions taking a trip to Tanzania, Eddy could recommend a safe area to stay in, accommodation, the best travel insurance, tours, etc.

Spotted: Eddy Travels is an AI-enabled personal travel assistant that operates within popular chat applications, such as WhatsApp, Facebook Messenger, Viber, Slack and Telegram. Based on the user's chat conversations, Eddy uses a language processing system that makes tailored travel recommendations. This enables to unify all booking needs to one place, from flights to hotels. Eddy even recommends personalized activities. For example, if your friend mentions taking a trip to Tanzania, Eddy could recommend a safe area to stay in, accommodation, the best travel insurance, tours, etc.

Artificial intelligence (AI) technologies are forecast to add US$15 trillion to the global economy by 2030. According to the findings of our Index and as might be expected, the governments of countries in the Global North are better placed to take advantage of these gains than those in the Global South. There is a risk, therefore, that countries in the Global South could be left behind by the so-called fourth industrial revolution. Not only will they not reap the potential benefits of AI, but there is also the danger that unequal implementation widens global inequalities. AI has the power to transform the way that governments around the world deliver public services. In turn, this could greatly improve citizens' experiences of government. Governments are already implementing AI in their operations and service delivery, to improve efficiency, save time and money, and deliver better quality public services. In 2017, Oxford Insights created the world's first Government AI Readiness Index, to answer the question: how well placed are national governments to take advantage of the benefits of AI in their operations and delivery of public services? The results sought to capture the current capacity of governments to exploit the innovative potential of AI. The 2019 Government AI Readiness Index, produced with the support of the International Development Research Centre (IDRC), sees a development of our methodology, and an expansion of scope to cover all UN countries (from our previous group of OECD members). It scores the governments of 194 countries and territories according to their preparedness to use AI in the delivery of public services. The overall score is comprised of 11 input metrics, grouped under four high-level clusters: governance; infrastructure and data; skills and education; and government and public services. The data is derived from a variety of resources, ranging from our own desk research into AI strategies, to databases such as the number of registered AI startups on Crunchbase, to indices such as the UN eGovernment Development Index. We divided the countries by region, principally following UN groupings, with the chief exception of the Western European and Others Group, which we separated to allow more in-depth analysis of higher scoring governments.

CANBERRA – An Australian government minister on Wednesday expressed concern for three Australians arrested in Iran on suspicion of spying and separated their plight from a tense standoff in the Middle East over the weekend attack on Saudi Arabian oil facilities. Trade Minister Simon Birmingham was responding after Iran on Tuesday acknowledged for the first time that it is holding three Australian citizens, including two British dual nationals, on suspicion of espionage. "The government continues to seek information and clarity around these matters," Birmingham told Australian Broadcasting Corp. "We are concerned for the welfare of these individuals and work to make sure their treatment is as fair as possible." Iran confirmed the arrests of Melbourne University Middle East expert Kylie Moore-Gilbert in October and travel blogging couple Mark Firkin and Jolie King in July as fallout continues from Saturday's fiery missile and drone attack on the heart of Saudi Arabia's oil industry. Secretary of State Mike Pompeo was headed to Jiddah in Saudi Arabia on Tuesday to discuss possible responses to what U.S. officials believe was an attack coming from Iranian soil.

The potential use of drones to cripple as much as half of Saudi national oil production this week highlights a growing threat in modern-day conflict. The attack has shown that Saudi Arabia -- the world's third largest defence spender -- is incapable of defending arguably its most protected non-military installation in Abqaiq. It is estimated to have halted around 5 per cent of international crude output, has shocked markets and spiked prices globally. Only a decade ago, such an attack by a low-cost, remote weapon systems was largely unthinkable. And players on the world stage have seized on the shift, with groups such as Islamic State and Mexican drug cartels creating their own improvised explosive vehicles from rudimentary hobby kits purchased online and in stores.

Poachers killed almost a third of the African elephant population between 2007 and 2014, a recent census found. Researchers hope artificial intelligence can help stop poachers and other threats, too.

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.