Binary classification rules based on covariates typically depend on simple loss functions such as zero-one misclassification. Some cases may require more complex loss functions. For example, individual-level monitoring of HIV-infected individuals on antiretroviral therapy (ART) requires periodic assessment of treatment failure, defined as having a viral load (VL) value above a certain threshold. In some resource limited settings, VL tests may be limited by cost or technology, and diagnoses are based on other clinical markers. Depending on scenario, higher premium may be placed on avoiding false-positives which brings greater cost and reduced treatment options. Here, the optimal rule is determined by minimizing a weighted misclassification loss/risk. We propose a method for finding and cross-validating optimal binary classification rules under weighted misclassification loss. We focus on rules comprising a prediction score and an associated threshold, where the score is derived using an ensemble learner. Simulations and examples show that our method, which derives the score and threshold jointly, more accurately estimates overall risk and has better operating characteristics compared with methods that derive the score first and the cutoff conditionally on the score especially for finite samples.

Thispaper studies the question on whether machines can be rational. It observes the existing reasons why humans are not rational which is due to imperfect and limited information, limited and inconsistent processing power through the brain and the inability to optimize decisions and achieve maximum utility. It studies whether these limitations of humans are transferred to the limitations of machines. The conclusion reached is that even though machines are not rational advances in technological developments make these machines more rational. It also concludes that machines can be more rational than humans. Introduction Oneof the most interesting concepts invented by humans is rationality (Anand, 1993; Marwala, 2014&2015).

Faculty of Computing, Poznan University of Technology, Poland, agnieszka.lawrynowicz@cs.put.poznan.pl Abstract Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based model of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated on editing efficiency and by users. The editing efficiency study demonstrated that it is faster than a typical ontology authoring interface, especially for medium size and large ontologies. The user evaluation demonstrated that modellers make significantly less errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that Test-Driven Development is a promising approach in an ontology development methodology. Keywords:Ontology Engineering, Test-Driven Development, OWL 1. Introduction Ontology engineering is facilitated by methods and methodologies, andtooling support for them. The methodologies are mostly information system-like, high-level directions, such as variants on waterfall and lifecycle development [1, 2], although more recently, notions of Agile development are being ported to the ontology development setting, e.g., [3, 4], including testing in some form [5, 6, 7, 8].

The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018. This report summarizes the The 31st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE-2018) was held at Concordia University in Montreal, Canada, June 25–28, 2018.  IEA/AIE 2018 continued the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas including engineering, science, industry, automation a robotics, business and finance, medicine and biomedicine, bioinformatics, cyberspace, and human-machine interactions.

Most research into wild marine mammals occurs in wealthy countries. Amazingly, in 2018, we have still have very little idea what species are present, let alone the population size / health status / behaviour, etc. in many parts of the world. A solid first step to address this problem is to conduct a rapid assessment survey to determine which species of marine mammals are present in a given area. The idea of a rapid assessment survey is fairly straightforward: you take a boat out and survey the entire coastline of a country using visual observers to record the number and species of any whales and dolphins encountered. As well as being large, surface present and often charismatic animals, and so possible to detect visually at relatively long ranges, dolphins and whales are also highly vocal, using sound to communicate and some species hunt/sense their surroundings with a sophisticated a bio-sonar.

If you really want to know how to future-proof your career, your best bet is the World Economic Forum (WEF) Future of Jobs Report 2018. The report confirmed most of the things we already knew: that automation and machine learning are set to create as many jobs as they displace, that the gig economy and flexible contract work will become standard, and that knowledge of data science is going to be a key differentiator in the job market over the next few years. With more than two years having passed since the first Future of Jobs report, there have been some new developments. With the mainstreaming of chatbots and other consumer-facing artificial intelligence (AI), there's more of an understanding of how machine learning might integrate into our society. Now that we've had some time to get to know Sophia, Alexa, Pepper and the rest, there are noticeably fewer "Are robots coming to steal your job?" clickbait articles in the media.

REFUNITE, based in California, is trialing the app in Uganda where it has launched a pilot project involving 5,000 refugees – mainly form South Sudan and Democratic Republic of Congo. It hopes to scale up to 25,000 refugees within two years.

This Hedge Fund Stocks Package is designed for investors and analysts who need predictions of the best-performing stocks according to I Know First's stock market algorithm. Package Name: Hedge Fund Stocks Recommended Positions: Long Forecast Length: 1 Year (12/06/2017 – 12/06/2018) I Know First Average: 23.31% Several predictions in this long-term 1 Year forecast saw significant returns. The algorithm had correctly predicted 6 out 10 stock movements. UAA was the top performing prediction with a return of 85.01%. With these notable trade returns, the package itself registered an average return of 23.31% compared to the S&P 500's return of 27.52% for the same period.

Methods proposed in the literature towards continual deep learning typically operate in a task-based sequential learning setup. A sequence of tasks is learned, one at a time, with all data of current task available but not of previous or future tasks. Task boundaries and identities are known at all times. This setup, however, is rarely encountered in practical applications. Therefore we investigate how to transform continual learning to an online setup. We develop a system that keeps on learning over time in a streaming fashion, with data distributions gradually changing and without the notion of separate tasks. To this end, we build on the work on Memory Aware Synapses, and show how this method can be made online by providing a protocol to decide i) when to update the importance weights, ii) which data to use to update them, and iii) how to accumulate the importance weights at each update step. Experimental results show the validity of the approach in the context of two applications: (self-)supervised learning of a face recognition model by watching soap series and learning a robot to avoid collisions.

Abstract-- In an automated search system, similarity is a key concept in solving a human task. Indeed, human process is usually a natural categorization that underlies many natural abilities such as image recovery, language comprehension, decision making, or pattern recognition. In the image search axis, there are several ways to measure the similarity between images in an image database, to a query image. Image search by content is based on the similarity of the visual characteristics of the images. The distance function used to evaluate the similarity between images depends on the criteria of the search but also on the representation of the characteristics of the image; this is the main idea of the near and fuzzy sets approaches. In this article, we introduce a new category of beta type-2 fuzzy sets for the description of image characteristics as well as the near sets approach for image recovery. Finally, we illustrate our work with examples of image recovery problems used in the real world. I. INTRODUCTION He number of daily-generated images by websites and personal archives are constantly growing. Indeed, the effective management of the rapid expansion of visual information has become a major problem and a necessity for strengthening visual search technique based on visual content [3]. This necessity is behind the emergence of new visual search techniques based on visual content. It has been widely identified that the most efficient and intuitive way to research visual information is based on the properties that are extracted from the images themselves. Researchers from different communities ("Computer Vision" [4], "Database Management", "Man-machine Interface", "Information Retrieval") were attracted by this field. Since then, the search for images by content has developed quite rapidly. The intuitive idea of "any system that analyzes or automatically organizes a set of data or knowledge must use, in one form or another, a similarity operator whose purpose is to establish similarities or the relationships that exist between the manipulated information".