Classification of description logic (DL) ontologies is a key computational problem in modern data management applications, so considerable effort has been devoted to the development and optimisation of practical reasoning calculi. Consequence-based calculi combine ideas from hypertableau and resolution in a way that has proved very effective in practice. However, existing consequence-based calculi can handle either Horn DLs (which do not support disjunction) or DLs without number restrictions. In this paper, we overcome this important limitation and present the first consequence-based calculus for deciding concept subsumption in the DL ALCHIQ+. Our calculus runs in exponential time assuming unary coding of numbers, and on ELH ontologies it runs in polynomial time. The extension to disjunctions and number restrictions is technically involved: we capture the relevant consequences using first-order clauses, and our inference rules adapt paramodulation techniques from first-order theorem proving. By using a well-known preprocessing step, the calculus can also decide concept subsumptions in SRIQ---a rich DL that covers all features of OWL 2 DL apart from nominals and datatypes. We have implemented our calculus in a new reasoner called Sequoia. We present the architecture of our reasoner and discuss several novel and important implementation techniques such as clause indexing and redundancy elimination. Finally, we present the results of an extensive performance evaluation, which revealed Sequoia to be competitive with existing reasoners. Thus, the calculus and the techniques we present in this paper provide an important addition to the repertoire of practical implementation techniques for description logic reasoning.

The project is about predicting coronary heart disease by using three different ML algorithms. And to know which is the best approach. There are roughly two controls per case of CHD. Many of the CHD positive men have undergone blood pressure reduction treatment and other programs to reduce their risk factors after their occurrence of CHD. In some cases the measurements were made after these treatments.

Tunde Adegbola, born 1 August 1955, also known as T. A. or Uncle T, is a scientist, musician, engineer, linguist and culture activist. He is best known for his work in setting up most of the pioneering private Television and Radio stations in Nigeria. He is the founder of TIWA systems, and the Executive Director of Alt-i (African Languages Technology Initiative). Tunde completed a bachelor's degree in Electrical Engineering at the University of Lagos, and later specialized in broadcast technology. He subsequently obtained a master's degree in Computer Science from the University of Wales (Swansea).

Some jobs will be replaced by Artificial Intelligence. Like a tornado, technology doesn't leave the grounds it touches the same. Artificial Intelligence is getting so good at everything and all this puts humans on the verge of losing their jobs. Employers want the best workers, and if AI does a better job, it renders human workers useless. This is the shortest version of saying that most jobs will eventually get replaced by technology.

Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical viewpoint and present positive and negative complexity results and, furthermore, complement our theoretical findings with experiments on real-world and synthetic data.

In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with a great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. Actually, the neural plasticity rules are a kind of unsupervised learning adapted for the reservoir, i.e. the hidden layer of ESN. More specifically, an investigation of Oja's rule, BCM rule and gaussian intrinsic plasticity rule was carried out in the context of EEG-based emotion recognition. The study, also, includes a comparison of the offline and online training of the ESN. When testing on the well-known affective benchmark "DEAP dataset" which contains EEG signals from 32 subjects, we find that pretraining ESN with gaussian intrinsic plasticity enhanced the classification accuracy and outperformed the results achieved with an ESN pretrained with synaptic plasticity. Four classification problems were conducted in which the system complexity is increased and the discrimination is more challenging, i.e. inter-subject emotion discrimination. Our proposed method achieves higher performance over the state of the art methods.

The ability of computers to automatically identify individual giraffes from their distinct coat patterns provides scientists with an affordable and efficient way to track population numbers. A software program developed by the conservation technology nonprofit Wild ME automatically identifies individual animals by their unique coat patterns or other distinguishing features. The nonprofit Giraffe Conservation Foundation and San Diego Zoo researcher Jenna Stacy-Dawes used the Wildbook software to take dozens of photos of a giraffe population over two days, uploaded the images and location data to the GiraffeSpotter database, and assessed giraffe numbers across three wildlife conservancies in Northern Kenya. GiraffeSpotter will be publicly accessible by the end of the year, allowing all interested parties to upload their giraffe photos and location data to the online database. GiraffeSpotter is the latest example of how artificial intelligence is being used in service of conservation.

You've probably heard that we're in the midst of an A.I. revolution.

When U.N. member states unanimously adopted the 2030 Agenda in 2015, the narrative around global development embraced a new paradigm of sustainability and inclusion--of planetary stewardship alongside economic progress, and inclusive distribution of income. This comprehensive agenda--merging social, economic and environmental dimensions of sustainability--is not supported by current modes of data collection and data analysis, so the report of the High-Level Panel on the post-2015 development agenda called for a "data revolution" to empower people through access to information.1 Today, a central development problem is that high-quality, timely, accessible data are absent in most poor countries, where development needs are greatest. In a world of unequal distributions of income and wealth across space, age and class, gender and ethnic pay gaps, and environmental risks, data that provide only national averages conceal more than they reveal. This paper argues that spatial disaggregation and timeliness could permit a process of evidence-based policy making that monitors outcomes and adjusts actions in a feedback loop that can accelerate development through learning. Big data and artificial intelligence are key elements in such a process. Emerging technologies could lead to the next quantum leap in (i) how data is collected; (ii) how data is analyzed; and (iii) how analysis is used for policymaking and the achievement of better results. Big data platforms expand the toolkit for acquiring real-time information at a granular level, while machine learning permits pattern recognition across multiple layers of input. Together, these advances could make data more accessible, scalable, and finely tuned. In turn, the availability of real-time information can shorten the feedback loop between results monitoring, learning, and policy formulation or investment, accelerating the speed and scale at which development actors can implement change.

There have been many cases of misdiagnosis in Kenya. The Kenya Medical Practitioners and Dentists Board blames machines for these cases. However, this is misleading since machines do not interpret the results. This is the role of medical personnel. Ironically, machines powered by Artificial Intelligence (AI) are learning fast and becoming so clever that it will be easier for medics to make more accurate decisions.