Mosquitoes are the only known vector of malaria, which leads to hundreds of thousands of deaths each year. Understanding the number and location of potential mosquito vectors is of paramount importance to aid the reduction of malaria transmission cases. In recent years, deep learning has become widely used for bioacoustic classification tasks. In order to enable further research applications in this field, we release a new dataset of mosquito audio recordings. With over a thousand contributors, we obtained 195,434 labels of two second duration, of which approximately 10 percent signify mosquito events. We present an example use of the dataset, in which we train a convolutional neural network on log-Mel features, showcasing the information content of the labels. We hope this will become a vital resource for those researching all aspects of malaria, and add to the existing audio datasets for bioacoustic detection and signal processing.

Algorithmic fairness in decision-making has been studied extensively in static settings where one-shot decisions are made on tasks such as classification. However, in practice most decision-making processes are of a sequential nature, where decisions made in the past may have an impact on future data. This is particularly the case when decisions affect the individuals or users generating the data used for future decisions. In this survey, we review existing literature on the fairness of data-driven sequential decision-making. We will focus on two types of sequential decisions: (1) past decisions have no impact on the underlying user population and thus no impact on future data; (2) past decisions have an impact on the underlying user population and therefore the future data, which can then impact future decisions. In each case the impact of various fairness interventions on the underlying population is examined.

This paper introduces epistemic graphs as a generalization of the epistemic approach to probabilistic argumentation. In these graphs, an argument can be believed or disbelieved up to a given degree, thus providing a more fine--grained alternative to the standard Dung's approaches when it comes to determining the status of a given argument. Furthermore, the flexibility of the epistemic approach allows us to both model the rationale behind the existing semantics as well as completely deviate from them when required. Epistemic graphs can model both attack and support as well as relations that are neither support nor attack. The way other arguments influence a given argument is expressed by the epistemic constraints that can restrict the belief we have in an argument with a varying degree of specificity. The fact that we can specify the rules under which arguments should be evaluated and we can include constraints between unrelated arguments permits the framework to be more context--sensitive. It also allows for better modelling of imperfect agents, which can be important in multi--agent applications.

Have you seen Barack Obama call Donald Trump a "complete dipshit", or Mark Zuckerberg brag about having "total control of billions of people's stolen data", or witnessed Jon Snow's moving apology for the dismal ending to Game of Thrones? Answer yes and you've seen a deepfake. The 21st century's answer to Photoshopping, deepfakes use a form of artificial intelligence called deep learning to make images of fake events, hence the name deepfake. Want to put new words in a politician's mouth, star in your favourite movie, or dance like a pro? Then it's time to make a deepfake.

DUBAI, UNITED ARAB EMIRATES – A flurry of diplomatic visits and meetings crisscrossing the Persian Gulf have driven urgent efforts in recent days to defuse the possibility of all-out war after the U.S. killed Iran's top military commander. Global leaders and top diplomats are repeating the mantra of "de-escalation" and "dialog," yet none has publicly laid out a path to achieving either. The United States and Iran have said they do not want war, but fears have grown that the crisis could spin out of Tehran's or Washington's control. Tensions have careened from one crisis to another since President Donald Trump withdrew the U.S. from Iran's nuclear deal with world powers. The U.S. drone strike that killed Revolutionary Guard Gen. Qassem Soleimani and a senior Iraqi militia leader in Baghdad on Jan. 3 was seen as a major provocation.

Artificial intelligence doesn't need'agile' governance, it needs firm regulation

Chinese search engine giant Baidu is looking to work with Indian institutes as the company looks for local expertise in AI. As India is the second-largest home in terms of the internet user base, with nearly 12 percent in the world, Baidu Co-Founder, CEO and Chairman Robin Li is seeing the country as an opportunity for areas such as artificial intelligence. On his first-ever visit to India, Robin talked here at IIT Madras Tech Fest, Shaastra 2020, titled Innovation in the Age of Artificial Intelligence (AI). He said the company is looking to work with Indian institutions in the future to make a better world through innovation. India is one of the fastest-growing smartphone markets in the world, and a very large developing country, right next to China.

A N -way array or N -th order tensor T is a multidimensional array in the product R I 1 ... I N of the vector spaces R I i for i 1, 2,...,N . A vector x R I 1 is a first-order tensor, and a matrix M R I 1 I 2 is a second-order tensor. The goal of NTF is to approximate a tensor T by a structured tensor X . Using the squared Frobenius norm as a distance metric, defined as nullXnull 2 F null j 1,j 2,...j NX 2 j 1j 2...j N, NTF is the following optimization problem: min a (i) p 0, 1 i N, 1 p r null null null null null nullT r null p 1 N null i 1a (i) p null null null null null null 2 F, (1) This work was supported by the Fonds de la Recherche Scientifique - FNRS and the Fonds Wetenschappelijk Onderzoek - Vlanderen (FWO) under EOS Project no O005318F-RG47, and by the European Research Council (ERC starting grant no 679515).

Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security consisting in the detection of hostile action based on the unusual nature of events observed on the Information System.In our previous work (presented at C\&ESAR 2018 and FIC 2019), we have associated deep neural networks auto-encoders for anomaly detection and graph-based events correlation to address major limitations in UEBA systems. This resulted in reduced false positive and false negative rates, improved alert explainability, while maintaining real-time performances and scalability. However, we did not address the natural evolution of behaviours through time, also known as concept drift. To maintain effective detection capabilities, an anomaly-based detection system must be continually trained, which opens a door to an adversary that can conduct the so-called "frog-boiling" attack by progressively distilling unnoticed attack traces inside the behavioural models until the complete attack is considered normal. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise. We also present preliminary work on adversarial AI conducting deception attack, which, in term, will be used to help assess and improve the defense system. These defensive and offensive AI implement joint, continual and active learning, in a step that is necessary in assessing, validating and certifying AI-based defensive solutions.

A merger between humans and machines is coming, and it's not what you may have thought. Something mysterious flickered into reality when our ancestors first learned to extract knowledge from their heads and embed it in tools. Now, millions of years later, our tools are fusing with us and, in so doing, bringing about something that is part biological and part technological. We are incubating this new intelligence in our organizations, but it is also true that it represents an extension of ourselves. Humanity is like a seed in an enigmatic womb made up of artificial intelligence and automation.