This paper describes the design, implementation and use of a new UML modeling tool that represents a significant advance over conventional tools. Among other things, it allows the integration of class diagrams and object diagrams as well as the execution of objects. This not only enables new software architectures characterized by the integration of software with corresponding object models, but is also ideal for use in teaching, as it provides students with a particularly stimulating learning experience. A special feature of the project is that it has emerged from a long-standing international research project, which is aimed at a comprehensive multi-level architecture. The project is therefore an example of how research can lead to valuable results that arise as a side effect of other work.

This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using Mitsuba renderer, which is an open source rendering software. The proposed plugin has been validated on measured subsurface scattering data. It's shown that the proposed plugin visualizes homogeneous and heterogeneous subsurface scattering effects, accurately, compactly and computationally efficiently.

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the stakeholders with data science and Machine Learning (ML) related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks. Therefore, they failed to address the architecture viewpoints and views responsive to the concerns of the data science community. In this paper, we address this gap by establishing the architecture frameworks adapted to meet the requirements of modern applications and organizations where ML artifacts are both prevalent and crucial. In particular, we focus on ML-enabled Cyber-Physical Systems (CPSs) and propose two sets of merit criteria for their efficient development and performance assessment, namely the criteria for evaluating and benchmarking ML-enabled CPSs, and the criteria for evaluation and benchmarking of the tools intended to support users through the modeling and development pipeline. In this study, we deploy multiple empirical and qualitative research methods based on literature review and survey instruments including expert interviews and an online questionnaire. We collect, analyze, and integrate the opinions of 77 experts from more than 25 organizations in over 10 countries to devise and validate the proposed framework.

A state machine model is a mathematical model that groups all possible system occurrences, called states. The course emphasizes project-based learning, learning by doing. The goal of this course is to introduce an event-driven programming paradigm using simple and hierarchical state machines. After going through this course, you will be trained to apply the state machine approach to solve your complex embedded systems projects. If you are a beginner in the field of embedded systems, then you can take our courses in the below-mentioned order.

In work that could someday turn cell phones into sensors capable of detecting viruses and other minuscule objects, MIT researchers have built a powerful nanoscale flashlight on a chip. Their approach to designing the tiny light beam on a chip could also be used to create a variety of other nano flashlights with different beam characteristics for different applications. Think of a wide spotlight versus a beam of light focused on a single point. For many decades, scientists have used light to identify a material by observing how that light interacts with the material. They do so by essentially shining a beam of light on the material, then analyzing that light after it passes through the material.

Check out the "Model Development, Governance, Operations" sessions at the Strata Data Conference in New York, September 23-26, 2019. Best price ends June 28. Our surveys over the past couple of years have shown growing interest in machine learning (ML) among organizations from diverse industries. A few factors are contributing to this strong interest in implementing ML in products and services. First, the machine learning community has conducted groundbreaking research in many areas of interest to companies, and much of this research has been conducted out in the open via preprints and conference presentations.

Our society went from an agrarian economy to mass-producing affordable goods using steam power, electricity and, eventually, computers and automation. We've gone from the horse and buggy to the Model T, and now we're on to self-driving cars! The smart factories of the Industry 4.0 era will be powered by the internet of things, cloud computing and cyber-physical systems (CPS) technologies. Cyber-physical systems are powered by enabling cloud technologies which allow intelligent objects and cloud-based programmatic modules to communicate and interact with each other. These new cyber-physical manufacturing facilities use robotics, sensors, big data, automation, artificial intelligence, virtual reality, augmented reality, additive manufacturing, cybersecurity systems and other cutting-edge technologies to deliver unprecedented flexibility, precision and efficiency to the manufacturing process.

Extracting three dimensional objects from a single photo is still a long way from reality given the current state of technology, since it involves numerous complex tasks: the target object must be separated from its background, and its 3D pose, shape, and structure should be recognized from its projection. These tasks are difficult, even ill-posed, since they require some degree of semantic understanding of the object. To alleviate this difficulty, complex 3D models can be partitioned into simpler parts that can be extracted from the photo. However, assembling parts into an object also requires further semantic understanding and is difficult to perform automatically. Moreover, having decomposed a 3D shape into parts, the relationships between these parts should also be understood and maintained in the final composition. In this paper, we present an interactive technique to extract 3D man-made objects from a single photograph, leveraging the strengths of both humans and computers. Human perceptual abilities are used to partition, recognize, and position shape parts, using a very simple interface based on triplets of strokes, while the computer performs tasks which are computationally intensive or require accuracy. The final object model produced by our method includes its geometry and structure, as well as some of its semantics. This allows the extracted model to be readily available for intelligent editing, which maintains the shape's semantics (see Figure 1).

Commonsense is a challenge not only for representation and reasoning but also for large scale knowledge engineering required to capture the breadth of our "everyday" world. One approach to knowledge engineering is to "outsource" the effort to the public through games that generate structured commonsense knowledge from user play. To date, such games have focused on symbolic and textual knowledge. However, an effective commonsense reasoning system will require spatial and physical reasoning capabilities. In this paper, I propose a tool for gathering commonsense information from ordinary people. It is a user-friendly 3D sculpting tool for modeling and annotating models of physical objects and spaces.