Ship Type Recognition Based on Ship Navigating Trajectory and Convolutional Neural Network

With the aim to solve the problem of missing or tampering of ship type information in AIS information, in this paper, a novel ship type recognition scheme based on ship navigating trajectory and convolutional neural network (CNN) is proposed. Firstly, according to speed and acceleration of the ship, three ship navigating situations, i.e., static, normal navigation and maneuvering, are integrated into the process of trajectory images generation in the form of pixels. Then, three kinds of modular network structures with different depths are trained and optimized to determine the appropriate convolutional neural network structure. In the validation phase of the model, a large amount of verified data with a time span of one month was used, covering a variety of water conditions including open water, ports, rivers and lakes. Following this approach, a kind of CNN scheme which can be directly used to identify ship types in a wide range of waters is proposed. This scheme can be used to judge the ship type when the static information is completely missing and to test the data when the ship type information is partially missing.

Language-Independent Type Inference of the Instances from Multilingual Wikipedia

Extracting knowledge from Wikipedia has attracted much attention in recent ten years. One of the most valuable kinds of knowledge is type information, which refers to the axioms stating that an instance is of a certain type. Current approaches for inferring the types of instances from Wikipedia mainly rely on some language-specific rules. Since these rules cannot catch the semantic associations between instances and classes (i.e. candidate types), it may lead to mistakes and omissions in the process of type inference. The authors propose a new approach leveraging attributes to perform language-independent type inference of the instances from Wikipedia. The proposed approach is applied to the whole English and Chinese Wikipedia, which results in the first version of MulType (Multilingual Type Information), a knowledge base describing the types of instances from multilingual Wikipedia. Experimental results show that not only the proposed approach outperforms the state-of-the-art comparison methods, but also MulType contains lots of new and high-quality type information.

Evaluating a theory-based hypothesis against its complement using an AIC-type information criterion with an application to facial burn injury

An information criterion (IC) like the Akaike IC (AIC), can be used to select the best hypothesis from a set of competing theory-based hypotheses. An IC developed to evaluate theory-based order-restricted hypotheses is the GORIC. Like for any IC, the values themselves are not interpretable but only comparable. To improve the interpretation regarding the strength, GORIC weights and related evidence ratios can be computed. However, if the unconstrained hypothesis (the default) is used as competing hypothesis, the evidence ratio is not affected by sample-size nor effect-size in case the hypothesis of interest is (also) in agreement with the data. In practice, this means that in such a case strong support for the order-restricted hypothesis is not reflected by a high evidence ratio. Therefore, we introduce the evaluation of an order-restricted hypothesis against its complement using the GORIC (weights). We show how to compute the GORIC value for the complement, which cannot be achieved by current methods. In a small simulation study, we show that the evidence ratio for the order-restricted hypothesis versus the complement increases for larger samples and/or effect-sizes, while the evidence ratio for the order-restricted hypothesis versus the unconstrained hypothesis remains bounded. An empirical example about facial burn injury illustrates our method and shows that using the complement as competing hypothesis results in much more support for the hypothesis of interest than using the unconstrained hypothesis as competing hypothesis.

Evaluation of Inequality Constrained Hypotheses Using a Generalization of the AIC

In the social and behavioral sciences, it is often not interesting to evaluate the null hypothesis by means of a p-value. Researchers are often more interested in quantifying the evidence in the data (as opposed to using p-values) with respect to their own expectationsrepresented by equality and/or inequality constrained hypotheses (as opposed to the null hypothesis). This article proposes an Akaike-type information criterion (AIC; Akaike, 1973, 1974) called the generalized order-restricted information criterion approximation (GORICA) that evaluates (in)equality constrained hypotheses under a very broad range of statistical models. The results of five simulation studies provide empirical evidence showing that the performance of the GORICA on selecting the best hypothesis out of a set of (in)equality constrained hypotheses is convincing. To illustrate the use of the GORICA, the expectations of researchers are investigated in a logistic regression, multilevel regression, and structural equation model.

Stress analysis of finite orthotropic composite containing an elliptical hole

Although the mechanical integrity of a member can be highly influenced by associated stresses, determining the latter can be very challenging for finite orthotropic composites containing cutouts. This is particularly so if the external loading is not well known, a common situation in practical situations. Acknowledging the above, a finite elliptically-perforated orthotropic tensile laminate is stress analyzed by combining measured displacement data with relevant analytical and numerical tools. Knowledge of the external loading is unnecessary. Results are verified independently and the concepts are applicable to other situations. The developed technology can provide important design-type information for orthotropic composites. In particular, the ability to apply analyses for perforated composite structures which assume infinite geometry to finite geometries is demonstrated.

A Synopsis of Croton (Euphorbiaceae) in Michoacán, Mexico

A taxonomic and nomenclatural review of the genus Croton (Euphorbiaceae) in the Mexican state of Michoacán is presented. Six sections and 20 species are here reported. The greatest diversity is in the Balsas Depression province, where at least 12 species occur. There is a strong tendency to thrive in tropical deciduous forest. An identification key is provided, and for each species, the following information is included: protologue citation, type information, habit, habitat, and elevation within the state, regional and global distribution, and phenology. Relevant synonyms are listed, as too are herbarium specimens. Lectotypes are designated for Croton draco, C. niveus, and C. calvescens. One species, Croton rojasii, is described as new and illustrated with photos. It is known only from tropical deciduous forest in the Zicuirán-Infiernillo Biosphere Reserve, at elevations from 400 to 700 m. It belongs to sect. Geiseleria and is remarkable because of the strongly unequal pistillate sepals, the larger of which becoming accrescent and exceeding the fruit.

OwnKit: Ownership Inference for Java

<p>Object ownership allows us to statically control run-time aliasing in order to provide a strong notion of object encapsulation. Unfortunately in order to use ownership, code must first be annotated with extra type information. This imposes a heavy burden on the programmer, and has contributed to the slow adoption of ownership. Ownership inference is the process of reconstructing ownership type information based on the existing ownership patterns in code. This thesis presents OwnKit—an automatic ownership inference tool for Java. OwnKit conducts inference in a modular way: by only considering a single class at the time. The modularity makes our algorithm highly scalable in both time and memory usage.</p>

OwnKit: Ownership Inference for Java

<p>Object ownership allows us to statically control run-time aliasing in order to provide a strong notion of object encapsulation. Unfortunately in order to use ownership, code must first be annotated with extra type information. This imposes a heavy burden on the programmer, and has contributed to the slow adoption of ownership. Ownership inference is the process of reconstructing ownership type information based on the existing ownership patterns in code. This thesis presents OwnKit—an automatic ownership inference tool for Java. OwnKit conducts inference in a modular way: by only considering a single class at the time. The modularity makes our algorithm highly scalable in both time and memory usage.</p>

Generic Ownership: a Practical Approach to Ownership and Confinement in Object-Oriented Programming Languages

<p>Modern object-oriented programming languages support many techniques that simplify the work of a programmer. Among them is generic types: the ability to create generic descriptions of algorithms and object structures that will be automatically specialised by supplying the type information when they are used. At the same time, object-oriented technologies still suffer from aliasing: the case of many objects in a program's memory referring to the same object via different references. Ownership types enforce encapsulation in object-oriented programs by ensuring that objects cannot be referred to from the outside of the object(s) that own them. Existing ownership programming languages either do not support generic types or attempt to add them on top of ownership restrictions. The goal of this work is to bring object ownership into mainstream object-oriented programming languages. This thesis presents Generic Ownership which provides perobject ownership on top of a generic imperative language. Surprisingly, the resulting system not only provides ownership guarantees comparable to the established systems, but also requires few additional language mechanisms to achieve them due to full reuse of generic types. In this thesis I formalise the core of Generic Ownership, highlighting that the restriction of this calls, owner preservation over subtyping, and appropriate owner nesting are the only necessary requirements for ownership. I describe two formalisms: (1) a simple formalism, capturing confinement in a functional setting, and (2) a complete formalism, providing a way for Generic Ownership to support both deep and shallow variations of ownership types. I support the formal work by describing how the Ownership Generic Java (OGJ) language is implemented as a minimal extension to Java 5. OGJ is the first publicly available language implementation that supports ownership, confinement, and generic types at the same time. I demonstrate OGJ in practice: show how to use OGJ to write programs and provide insights into the implementations of Generic Ownership.</p>