Using raw sensor data to model and train networks for Human Activity Recognition can be used in many different applications, from fitness tracking to safety monitoring applications. These models can be easily extended to be trained with different data sources for increased accuracies or an extension of classifications for different prediction classes. This paper goes into the discussion on the available dataset provided by WISDM and the unique features of each class for the different axes. Furthermore, the design of a Long Short Term Memory (LSTM) architecture model is outlined for the application of human activity recognition. An accuracy of above 94% and a loss of less than 30% has been reached in the first 500 epochs of training.

Archiving large sets of medical or cell images in digital libraries may require ordering randomly scattered sets of image data according to specific criteria, such as the spatial extent of a specific local color or contrast content that reveals different meaningful states of a physiological structure, tissue, or cell in a certain order, indicating progression or recession of a pathology, or the progressive response of a cell structure to treatment. Here we used a Self Organized Map (SOM)-based, fully automatic and unsupervised, classification procedure described in our earlier work and applied it to sets of minimally processed grayscale and/or color processed Scanning Electron Microscopy (SEM) images of CD4+ T-lymphocytes (so-called helper cells) with varying extent of HIV virion infection. It is shown that the quantization error in the SOM output after training permits to scale the spatial magnitude and the direction of change (+ or -) in local pixel contrast or color across images of a series with a reliability that exceeds that of any human expert. The procedure is easily implemented and fast, and represents a promising step towards low-cost automatic digital image archiving with minimal intervention of a human operator.

The humanity has been facing a plethora of challenges associated with infectious diseases, which kill more than 6 million people a year. Although continuous efforts have been applied to relieve the potential damages from such misfortunate events, it is unquestionable that there are many persisting challenges yet to overcome. One related issue we particularly address here is the assessment and prediction of such epidemics. In this field of study, traditional and ad-hoc models frequently fail to provide proper predictive situation awareness (PSAW), characterized by understanding the current situations and predicting the future situations. Comprehensive PSAW for infectious disease can support decision making and help to hinder disease spread. In this paper, we develop a computing system platform focusing on collective intelligence causal modeling, in order to support PSAW in the domain of infectious disease. Analyses of global epidemics require integration of multiple different data and models, which can be originated from multiple independent researchers. These models should be integrated to accurately assess and predict the infectious disease in terms of holistic view. The system shall provide three main functions: (1) collaborative causal modeling, (2) causal model integration, and (3) causal model reasoning. These functions are supported by subject-matter expert and artificial intelligence (AI), with uncertainty treatment. Subject-matter experts, as collective intelligence, develop causal models and integrate them as one joint causal model. The integrated causal model shall be used to reason about: (1) the past, regarding how the causal factors have occurred; (2) the present, regarding how the spread is going now; and (3) the future, regarding how it will proceed. Finally, we introduce one use case of predictive situation awareness for the Ebola virus disease.

Uber has unveiled the terms of a hotly anticipated stock market float which it hopes will value the ride-hailing service at more than $91bn (£70bn). While the target is $10bn less than some bankers suggested the 10-year-old firm might be worth, the valuation is more than double the value of the 116-year-old carmaker Ford and would be the largest float by a US tech company since Facebook's in 2012. Its Wall Street debut will gauge investors' excitement about the prospects of a company that has expanded rapidly from taxi services into food delivery and is now investing billions in developing driverless cars. If it hits the mark, Uber will raise around $9bn in new funds and some early investors will make big profits. Despite the scale of ts ambition, Uber lost $1.8bn last year even while its revenues surged by more than 40% to $11.3bn.

Today, Zipline is officially opening the first of four distribution centers in Ghana, inaugurating a drone-delivery network that will eventually serve 2,000 hospitals and clinics covering 12 million people. Here's what Zipline says in a press release about the new operation: The revolutionary new service will use drones to make on-demand, emergency deliveries of 148 different vaccines, blood products, and life-saving medications. The service will operate 24 hours a day, seven days a week, from 4 distribution centers--each equipped with 30 drones--and deliver to 2,000 health facilities serving 12 million people across the country. Together, all four distribution centers will make up to 600 on-demand delivery flights a day on behalf of the Government of Ghana. Each Zipline distribution center has the capacity to make up to 500 flights per day. Zipline's contract with the government of Ghana is worth US $12.5 million, but there has been significant criticism over the deal from the minority party in the Ghanaian government (backed by the Ghana Medical Association) arguing that funding was urgently needed for basic services rather than for medical drone delivery.

We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an offline benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al.. The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.

Conditional simple temporal networks with uncertainty (CSTNUs) allow for the representation of temporal plans subject to both conditional constraints and uncertain durations. Dynamic controllability (DC) of CSTNUs ensures the existence of an execution strategy able to execute the network in real time (i.e., scheduling the time points under control) depending on how these two uncontrollable parts behave. However, CSTNUs do not deal with resources. In this paper, we define conditional simple temporal networks with uncertainty and resources (CSTNURs) by injecting resources and runtime resource constraints (RRCs) into the specification. Resources are mandatory for executing the time points and their availability is represented through temporal expressions, whereas RRCs restrict resource availability by further temporal constraints among resources. We provide a fully-automated encoding to translate any CSTNUR into an equivalent timed game automaton in polynomial time for a sound and complete DC-checking.

A reliable and accurate forecasting method for crop yields is very important for the farmer, the economy of a country, and the agricultural stakeholders. However, due to weather extremes and uncertainties as a result of increasing climate change, most crop yield forecasting models are not reliable and accurate. In this paper, a hybrid crop yield probability density forecasting method via quantile regression forest and Epanechnikov kernel function (QRF-SJ) is proposed to capture the uncertainties and extremes of weather in crop yield forecasting. By assigning probability to possible crop yield values, probability density forecast gives a complete description of the yield of crops. A case study using the annual crop yield of groundnut and millet in Ghana is presented to illustrate the efficiency and robustness of the proposed technique. The proposed model is able to capture the nonlinearity between crop yield and the weather variables via random forest. The values of prediction interval coverage probability and prediction interval normalized average width for the two crops show that the constructed prediction intervals cover the target values with perfect probability. The probability density curves show that QRF-SJ method has a very high ability to forecast quality prediction intervals with a higher coverage probability. The feature importance gave a score of the importance of each weather variable in building the quantile regression forest model. The farmer and other stakeholders are able to realize the specific weather variable that affect the yield of a selected crop through feature importance. The proposed method and its application on crop yield dataset is the first of its kind in literature.

Verv, the Google-mentored energy tech startup behind the smart energy hub and green electricity sharing platform, recently announced that it has raised over £6.5 million (€7.5 million) in its Series A round led by environmental fund Earthworm. Earthworm has invested £5 million in Verv's pioneering IoT and renewable energy trading technology that could drive down household electricity bills and carbon emissions by over 20%. Other investors in the round include European innovation engine for sustainable energy, InnoEnergy, Crowdcube and international energy and services company, Centrica. Earthworm's investment is an important backing of Verv's vision to make millions of homes more green with a global network of smart hubs that offer a real-time breakdown of key appliance use and spend, as well as enable the trading of domestic renewable energy between communities. At Earthworm we are driven by sustainability and Verv represents a brilliant example of'enabling' technology.

A pillar box red electric train connects Paris, Verona and Grenada via Budapest's Liberty Bridge and on to Heidelberg Castle in a 120-hectare fantasy business park dreamt up by the Chinese billionaire Ren Zhengfei. Ren, 74, a former Red Army engineer who founded the telecommunications company Huawei in 1987 and still owns a 1.14% stake, asked the Japanese architect Kengo Kuma to recreate some of Europe's most historic cities. He hoped to inspire an army of 25,000 research and development staff to challenge Apple, Google and Samsung. While its US competitors keep their research facilities on lockdown to prevent corporate espionage (oft allegedly by the Chinese), Huawei is inviting the world's media into its labs and factories in an attempt to dispel the US government's claims that the privately held company is an arm of the Chinese state and that its technology could be used to hack into western governments. US politicians allege that Huawei's forthcoming 5G mobile phone networks could be hacked by Chinese spies to eavesdrop on sensitive phone calls, gain access to counter-terrorist operations – and potentially even kill targets by crashing driverless cars.