Australia is deploying a fleet of uncrewed robot boats to patrol its waters and monitor weather and wildlife. They will also flag boats potentially transporting asylum seekers, a plan that has concerned human rights groups. The 5-metre-long vessels, known as Bluebottles after an Australian jellyfish, look like miniature sailing yachts. They use a combination of wind, wave and solar power to maintain a steady 5-knot speed in all conditions. Sydney-based Ocius Technology delivered the prototype in 2017 and Australia's Ministry of Defence has now awarded an AU$5.5 million (£3m) …

As artificial intelligence (AI) and its associated activities of machine learning (ML) and deep learning (DL) become embedded in the economic and social fabric of developed economies, maintaining the security of these systems and the data they use is paramount. The global cyber security market was estimated by IDC to be worth $107 billion in 2019, growing to $151 billion by 2023. Most of this will be spent on services based around software and hardware designed to protect systems against intrusions by hackers seeking to steal data or compromise networks. However, an area of concern often missed is the integrity and reliability of the data which is being used for the training datasets relied on by ML algorithms. Data poisoning could become a significant attack vector used by hackers to undermine AI systems and the organisations building businesses and processes around them.

Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3rd July 2020). Topics included industry take up, human factors, explainabiloity (explainability, justifyability, exploitability) and GI benchmarks. We also contrast various recent online approaches (e.g. SBST 2020) to holding virtual computer science conferences and workshops via the WWW on the Internet without face-2-face interaction. Finally we speculate on how the Coronavirus Covid-19 Pandemic will affect research next year and into the future.

In many applications, one may benefit from the collaborative collection of data for sensing a physical phenomenon, which is known as social sensing. We show how to make social sensing (1) predictable, in the sense of guaranteeing that the number of queries per participant will be independent of the initial state, in expectation, even when the population of participants varies over time, and (2) fair, in the sense of guaranteeing that the number of queries per participant will be equalised among the participants, in expectation, even when the population of participants varies over time. In a use case, we consider a large, high-density network of participating parked vehicles. When awoken by an administrative centre, this network proceeds to search for moving, missing entities of interest using RFID-based techniques. We regulate the number and geographical distribution of the parked vehicles that are "Switched On" and thus actively searching for the moving entity of interest. In doing so, we seek to conserve vehicular energy consumption while, at the same time, maintaining good geographical coverage of the city such that the moving entity of interest is likely to be located within an acceptable time frame. Which vehicle participants are "Switched On" at any point in time is determined periodically through the use of stochastic techniques. This is illustrated on the example of a missing Alzheimer's patient in Melbourne, Australia.

The pervasiveness of deep neural networks (DNNs) in technology, matched with the ubiquity of cloud-based training and transfer learning, is giving rise to a new frontier for cybersecurity whereby `structural malware' is manifest as compromised weights and activation pathways for unsecure DNNs. In particular, DNNs can be designed to have backdoors in which an adversary can easily and reliably fool a classifier by adding to any image a pattern of pixels called a trigger. Since DNNs are black-box algorithms, it is generally difficult to detect a backdoor or any other type of structural malware. To efficiently provide a reliable signal for the absence/presence of backdoors, we propose a rapid feature-generation step in which we study how DNNs respond to noise-infused images with varying noise intensity. This results in titration curves, which are a type of `fingerprinting' for DNNs. We find that DNNs with backdoors are more sensitive to input noise and respond in a characteristic way that reveals the backdoor and where it leads (i.e,. its target). Our empirical results demonstrate that we can accurately detect a backdoor with high confidence orders-of-magnitude faster than existing approaches (i.e., seconds versus hours). Our method also yields a titration-score that can automate the detection of compromised DNNs, whereas existing backdoor-detection strategies are not automated.

In this paper, we propose a novel multi-variate algorithm using a triple-regression methodology to predict the airborne-pollen allergy season that can be customized for each patient in the long term. To improve the prediction accuracy, we first perform a pre-processing to integrate the historical data of pollen concentration and various inferential signals from other covariates such as the meteorological data. We then propose a novel algorithm which encompasses three-stage regressions: in Stage 1, a regression model to predict the start/end date of a airborne-pollen allergy season is trained from a feature matrix extracted from 12 time series of the covariates with a rolling window; in Stage 2, a regression model to predict the corresponding uncertainty is trained based on the feature matrix and the prediction result from Stage 1; in Stage 3, a weighted linear regression model is built upon prediction results from Stage 1 and 2. It is observed and proved that Stage 3 contributes to the improved forecasting accuracy and the reduced uncertainty of the multi-variate triple-regression algorithm. Based on different allergy sensitivity level, the triggering concentration of the pollen - the definition of the allergy season can be customized individually. In our backtesting, a mean absolute error (MAE) of 4.7 days was achieved using the algorithm. We conclude that this algorithm could be applicable in both generic and long-term forecasting problems.

The F-measure is widely used to assess the performance of classification algorithms. However, some researchers find it lacking in intuitive interpretation, questioning the appropriateness of combining two aspects of performance as conceptually distinct as precision and recall, and also questioning whether the harmonic mean is the best way to combine them. To ease this concern, we describe a simple transformation of the F-measure, which we call F* (F-star), which has an immediate practical interpretation.

As covid-19 disrupted the world in March, online retail giant Amazon struggled to respond to the sudden shift caused by the pandemic. Household items like bottled water and toilet paper, which never ran out of stock, suddenly became in short supply. One- and two-day deliveries were delayed for several days. Though Amazon CEO Jeff Bezos would go on to make $24 billion during the pandemic, initially, the company struggled with adjusting its logistics, transportation, supply chain, purchasing, and third-party seller processes to prioritize stocking and delivering higher-priority items. Under normal circumstances, Amazon's complicated logistics are mostly handled by artificial intelligence algorithms.

For a few seconds, it seems real. Then, on the horizon, the landscape gives way to rugged coastline, and, as the plane flies closer, we glimpse the rippling waves glinting in the evening sun. In real life, I have not seen the ocean for five months and, although I'm just sitting in my kitchen watching a virtual presentation of a video game, I feel a surge of emotion. When the latest instalment in Microsoft's decades-old Flight Simulator series was first shown at the E3 video game event last year, it drew gasps from the audience. Using two petabytes of geographic data culled from Bing Maps, together with cutting-edge, machine learning algorithms running on the company's Azure cloud computing network, the game presents a near-photorealistic depiction of the entire planet.

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.