HEN shown photographic images under a free-viewing (i.e task-free) paradigm, human eyes preferentially fixate on image locations which are visually salient. Multiple studies [1]-[5] have demonstrated that this fixation mechanism is bottom-up, predominantly driven by image content and richness of detail (color, texture etc.). This explanation, while satisfactory for photographic images, seems inadequate for certain categories of images such as line drawings. In particular, one class of line drawings - hand-drawn sketches - are sparse and largely devoid of detailed content. In addition, they are typically binary images containing virtually no color-based information (see Figure 1). Even so, multiple studies have demonstrated a "fixations-intonothing" phenomenon [6]-[9], wherein the eye fixations on the same stimulus by multiple subjects fall on empty regions, yet exhibit enough regularity to make gaze-based inferences. One possible explanation is that the first eye fixation conveys all there is to know ('Gestalt') about the underlying scene semantics [10] and the regularity in rest of the fixations is a statistical anomaly. However, a more intriguing explanation is that these empty region fixations aim to implicitly verify the overall consistency of the scene content depicted in the sketch [11], [12]. Which of these explanations is correct?

Immersive audio technologies such as Dolby Atmos and DTS:X create an incredible three-dimensional sound field that simply blows away traditional multi-channel home theater. Once you hear it, you just can't go back. If you've longed for immersive audio in your home theater, but balked at the thought of installing speakers in your ceiling, MartinLogan has a fantastic solution. The company's Motion AFX, $599 per pair, are Dolby Atmos-enabled speakers deliver all the benefits of object-oriented soundtracks such as Dolby Atmos and DTS:X without the hassle of opening your ceiling. Dolby Laboratories, the company behind Atmos, knew that most consumers wouldn't be able to install in-ceiling speakers.

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python's elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, https://www.python.org/, The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.

Microservices (μServices) are a fascinating evolution of the Distributed Object Computing (DOC) paradigm. Initial design of DOC attempted to solve the problem of simplifying developing complex distributed applications by applying object-oriented design principles to disparate components operating across networked infrastructure. In this model, DOC "hid" the complexity of making this work from the developer regardless of the deployment architecture through the use of complex frameworks, such as Common Object Request Broker Architecture (CORBA) and Distributed Component Object Model (DCOM). Eventually, these approaches waned in popularity as the distribution frameworks were clumsy and the separation of responsibilities between developer and operations did not meet with the promised goals. That is, developers still needed to understand too much about how the entire application behaved in a distributed mode to troubleshoot application problems and the implementation was too developer-centric to allow operations to be able to fulfill this role.

Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their common description, e.g., in binary setting, their shared attributes. In bioinformatics, biclustering is used to find genes, which are active in a subset of situations, thus being candidates for biomarkers. However, the authors of those biclustering techniques that are popular in gene expression analysis, may overlook the existing methods. For instance, BiMax algorithm is aimed at finding biclusters, which are well-known for decades as formal concepts. Moreover, even if bioinformatics classify the biclustering methods according to reasonable domain-driven criteria, their classification taxonomies may be different from survey to survey and not full as well. So, in this paper we propose to use concept lattices as a tool for taxonomy building (in the biclustering domain) and attribute exploration as means for cross-domain taxonomy completion.

Scala is a highly scalable general purpose programming language that combines aspects of both object-oriented and functional programming. It's become increasingly important in the world of data science, rivaling more established languages like Java and Python. One of the main drivers of Scala's rise to prominence has been the explosive growth of Apache Spark (which is written in Scala), giving Scala a well-earned reputation as a powerful language for data processing, machine learning, and streaming analytics. What is Scala, and what makes it so well suited to handling big data? In this article we're going to look at what sets Scala apart as a programming language, why it's becoming increasingly important to data scientists, and what skills you should look for in a Scala developer.

The goal of this object is to minimise spending attention to irrelevant details and spent time to the problem. Possible model types are continuous, binomial and multinomial. This class and corresponding functionality is object-oriented. This enables the user to focus on the statistics only, instead of paying attention to irrelevant details (how to partition the data, how to handle missing values, etc.). The most popular model classes are already available: generalised linear models (with a stepwise or lasso feature selection), support vector machines, decision trees and neural networks.

If you've never used an object-oriented programming language before, you'll need to learn a few basic concepts before you can begin writing any code. This lesson will introduce you to objects, classes, inheritance, interfaces, and packages. Each discussion focuses on how these concepts relate to the real world, while simultaneously providing an introduction to the syntax of the Java programming language. An object is a software bundle of related state and behavior. Software objects are often used to model the real-world objects that you find in everyday life.

Where Intelligent Technology Meets the Real World Home Contents Search News Services Contact PC AI Subjects A-C CLOS Common LISP Object System - An object oriented extension to Common LISP (a dialect of LISP). LISP A programming language that manipulates lists of data which led to its heavy use in AI applications. Logic Programming A type of programming consisting of facts and relationships from which the programming language can draw a conclusion. Prolog A declarative programming language based on formal logic. Widely used in AI applications.

The Loom system implements a logic-based pattern matcher that drives a production rule facility and a pattern-directed method dispatching facility that supports the definition of object-oriented methods. The high degree of integration between Loom's declarative and procedural components permits programmers to utilize logic programming, production rule, and object-oriented programming paradigms in a single application. Loom can also be used as a deductive layer that overlays an ordinary CLOS network. In this mode, users can obtain many of the benefits of using Loom without impacting the function or performance of their CLOS-based applications. A Retrospective on Loom provides more details.