Considering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer. Introduction This research study is set in an African context, where the main problem is an economic, social development and the means to achieve it. Indeed, after the failure of several development models in the recent decades, theoretical research seems to be turning to the development knowledgebased approaches (UNESCO, 2014). The place of knowledge, science and technology in the current dynamics of growth gives rise to intensify the reflection within the economic field.

The duck is famous for two things: really liking bread (even though they're not supposed to be eating it), and wielding insanely complicated reproductive bits. More specifically, male ducks have corkscrew-shaped penises, while females' reproductive tracts corkscrew in the opposite direction. But you know who tends to get short shrift for their own bizarrely complicated reproductive system? That would be the rhino. "It's composed of a number of interlocking ridges or rings that makes it look like a number of S's connected to each other for 8 to 12 inches," says Barbara Durrant, director of reproductive sciences at the San Diego Zoo.

Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on. Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital. Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques. Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system. For the purpose of developing the prototype expert system SWI-prolog editor also used. The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer.

Neural approaches to Natural Language Generation (NLG) have been promising for goal-oriented dialogue. One of the challenges of productionizing these approaches, however, is the ability to control response quality, and ensure that generated responses are acceptable. We propose the use of a generate, filter, and rank framework, in which candidate responses are first filtered to eliminate unacceptable responses, and then ranked to select the best response. While acceptability includes grammatical correctness and semantic correctness, we focus only on grammaticality classification in this paper, and show that existing datasets for grammatical error correction don't correctly capture the distribution of errors that data-driven generators are likely to make. We release a grammatical classification and semantic correctness classification dataset for the weather domain that consists of responses generated by 3 data-driven NLG systems. We then explore two supervised learning approaches (CNNs and GBDTs) for classifying grammaticality. Our experiments show that grammaticality classification is very sensitive to the distribution of errors in the data, and that these distributions vary significantly with both the source of the response as well as the domain. We show that it's possible to achieve high precision with reasonable recall on our dataset.

Young isolated neutron stars (INS) most commonly manifest themselves as rotationally powered pulsars (RPPs) which involve conventional radio pulsars as well as gamma-ray pulsars (GRPs) and rotating radio transients (RRATs). Some other young INS families manifest themselves as anomalous X-ray pulsars (AXPs) and soft gamma-ray repeaters (SGRs) which are commonly accepted as magnetars, i.e.\ magnetically powered neutron stars with decaying super-strong fields. Yet some other young INS are identified as central compact objects (CCOs) and X-ray dim isolated neutron stars (XDINs) which are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analyzing the distribution of these pulsar families in period $P$ and period derivative $\dot{P}$ parameter space. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature and proper motion of all discovered components. We verify that DPGMM is robust and provides hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.

Finding the source of a disturbance or fault in complex systems such as industrial chemical processing plants can be a difficult task and consume a significant number of engineering hours. In many cases, a systematic elimination procedure is considered to be the only feasible approach but can cause undesired process upsets. Practitioners desire robust alternative approaches. This paper presents an unsupervised, data-driven method for ranking process elements according to the magnitude and novelty of their influence. Partial bivariate transfer entropy estimation is used to infer a weighted directed graph of process elements. Eigenvector centrality is applied to rank network nodes according to their overall effect. As the ranking of process elements rely on emerging properties that depend on the aggregate of many connections, the results are robust to errors in the estimation of individual edge properties and the inclusion of indirect connections that do not represent the true causal structure of the process. A monitoring chart of continuously calculated process element importance scores over multiple overlapping time regions can assist with incipient fault detection. Ranking results combined with visual inspection of information transfer networks is also useful for root cause analysis of known faults and disturbances. A software implementation of the proposed method is available.

WASHINGTON - Embattled U.S. aviation giant Boeing on Thursday insisted on the "fundamental safety" of its 737 Max aircraft but pledged to take all necessary steps to ensure the jets' airworthiness. The statements came hours after Ethiopian officials said pilots of a doomed plane that crashed last month, leaving 157 people dead, had followed the company's recommendations. The preliminary findings released Thursday by transportation authorities in Addis Ababa put the American aircraft giant under even greater pressure to restore public trust amid mounting signs the company's onboard anti-stall systems were at fault in crashes involving its formerly top-selling 737 Max aircraft -- incidents that left nearly 350 people dead in less than five months. "We remain confident in the fundamental safety of the 737 Max," CEO Dennis Muilenburg said in a statement, adding that impending software fixes would make the aircraft "among the safest airplanes ever to fly." Muilenburg also acknowledged, however, that an "erroneous activation" of Boeing's Maneuvering Characteristics Augmentation System had occurred. The system is designed to prevent stalls but may have forced the Ethiopian and Indonesian jets into the ground.

Is all of machine learning supervised to some degree? The field of machine learning has traditionally been categorized pedagogically into $supervised~vs~unsupervised~learning$; where supervised learning has typically referred to learning from labeled data, while unsupervised learning has typically referred to learning from unlabeled data. In this paper, we assert that all machine learning is in fact supervised to some degree, and that the scope of supervision is necessarily commensurate to the scope of learning potential. In particular, we argue that clustering algorithms such as k-means, and dimensionality reduction algorithms such as principal component analysis, variational autoencoders, and deep belief networks are each internally supervised by the data itself to learn their respective representations of its features. Furthermore, these algorithms are not capable of external inference until their respective outputs (clusters, principal components, or representation codes) have been identified and externally labeled in effect. As such, they do not suffice as examples of unsupervised learning. We propose that the categorization `supervised vs unsupervised learning' be dispensed with, and instead, learning algorithms be categorized as either $internally~or~externally~supervised$ (or both). We believe this change in perspective will yield new fundamental insights into the structure and character of data and of learning algorithms.

The idea behind the guillotine is this: If you're going to execute someone, you may as well do it efficiently and humanely, at least by 18th-century standards. Decapitating the condemned with an ax or sword may take a few swings--unacceptable for carrying out justice in a "civilized" society. The guillotine, on the other hand, is downright surgical, a perversely methodical way to end a life. Now mosquitoes are getting the same treatment in the pursuit of a vaccine for malaria, a disease that killed 440,000 people in 2016. To produce a vaccine for mass deployment, biotech firm Sanaria has to decapitate and dissect out the salivary glands, which hold the malaria-causing parasite, for each individual mosquito--by hand.

Google's taking a more'natural' approach to it's April Fool's Day this year, adding a plant-to-human interface called Google Tulips and a'Snakes' game for Maps in the latest installment of phony and fun products. According to the company, Google Tulips, available only on April 1, allows users to communicate with one of world's most beloved flowers -- the tulip -- to ascertain just what exactly is on their minds. 'Decoding the language of plants and flowers has been a decades-long challenge. But that changes today,' reads the company's statement on its farcical new service. In a video for the April Fool's gag, plants are shown beckoning for more water, sunlight, and even providing a source of conversation for an elderly woman.