This experience report is part of an ongoing NSF-funded grant project involving an alliance of six California State University campuses, aimed at promoting Latinx student retention through community engagement in early computer science courses. The project focuses on integrating socially responsible computing (SRC) into the curriculum to transform computing culture and invite marginalized students to participate. At our campus, we integrated SRC concepts into the CS2 course on Data Structures and Algorithms. Initially, SRC concepts were introduced into assignments and projects, which showed promising results but highlighted challenges: the assignments and projects were instructor-created, leading to a gap between students and the concepts. Students passively received topics without proactive participation, resulting in a lack of perceived real-world impact.

Website visitors are performing increasingly complex computational work on the websites' behalf, such as validating forms, rendering animations, and producing data visualizations. In this article, we explore the possibility of increasing the work offloaded to web visitors' browsers. The idle computing cycles of web visitors can be turned into a large-scale distributed data processing engine, which we term gray computing. Past research has looked primarily at either volunteer computing with specialized clients or browser-based volunteer computing where the visitors keep their browsers open to a single web page for a long period of time. This article provides a comprehensive analysis of the architecture, performance, security, cost effectiveness, user experience, and other issues of gray computing distributed data processing engines with heterogeneous computing power, non-uniform page view times, and high computing pool volatility. Several real-world applications are examined and gray computing is shown to be cost effective for a number of complex tasks ranging from computer vision to bioinformatics to cryptology.

Websites routinely distribute small amounts of work to visitors' browsers in order to validate forms, render animations, and perform other computations. This paper examines the feasibility, cost effectiveness, and approaches for increasing the workloads offloaded to web visitors' browsers in order to turn them into a large-scale distributed data processing engine, which we term gray computing. Past research has looked primarily at either non-browser based volunteer computing or browser-based volunteer computing where the visitors keep their browsers open to a single web page for a long period of time. This paper provides a deep analysis of the architectural, cost effectiveness, user experience, performance, security, and other issues of gray computing distributed data processing engines with high heterogeneity, non-uniform page view times, and high computing pool volatility.

This paper describes the state of the art in software frameworks for executing Software Defined Radio (SDR) components. These frameworks are catalyzing drastic changes in signal processing by enabling software engineers and signal processing engineers to work in tandem on core challenges, such as effectively processing large amounts of data in real-time on limited hardware resources. In addition to a historical perspective of this area, we showcase the REDHAWK framework as an example of a modern SDR framework which provides many facilities for distributed SDR deployment.

ABSTRACT Domain‐Specific Modeling Languages (DSMLs) are playing an increasingly significant role in software development. By raising the level of abstraction using notations that are representative of a specific domain, DSMLs allow the core essence of a problem to be separated from irrelevant accidental complexities, which are typically found at the implementation level in source code. In addition to modeling the functional aspects of a system, a number of nonfunctional properties (e.g., quality of service constraints and timing requirements) also need to be integrated into models in order to reach a complete specification of a system. This is particularly true for domains that have distributed real time and embedded needs. Given a base model with functional components, maintaining the nonfunctional properties that crosscut the base model has become an essential modeling task when using DSMLs. The task of maintaining nonfunctional properties in DSMLs is traditionally supported by manual model editing or by using model transformation languages. However, these approaches are challenging to use for those unfamiliar with the specific details of a modeling transformation language and the underlying metamodel of the domain, which presents a7 steep learning curve for many users. This paper presents a demonstration‐based approach to automate the maintenance of nonfunctional properties in DSMLs. Instead of writing model transformation rules explicitly, users demonstrate how to apply the nonfunctional properties by directly editing the concrete model instances and simulating a single case of the maintenance process. By recording a user's operations, an inference engine analyzes the user's intention and generates generic model transformation patterns automatically, which can be refined by users and then reused to automate the same evolution and maintenance task in other models. Using this approach, users are able to automate the maintenance tasks without learning a complex model transformation language. In addition, because the demonstration is performed on model instances, users are isolated from the underlying abstract metamodel definitions. Our demonstration‐based approach has been applied to several scenarios, such as auto scaling and model layout. The specific contribution in this paper is the application of the demonstration‐based approach to capture crosscutting concerns representative of aspects at the modeling level. Several examples are presented across multiple modeling languages to demonstrate the benefits of our approach. Copyright © 2013 John Wiley & Sons, Ltd.

Aspect-oriented modeling (AOM) is a technique to separate concerns that crosscut the modularity boundaries of a modeling hierarchy. AOM is traditionally supported by manual editing or writing model transformation rules that refine a base model. However, traditional model weaving approaches present challenges to those who are unfamiliar with a model transformation language or metamodel definitions. This poster describes an approach to weave aspect models by recording and analyzing demonstrated operations by end-users.

Model evolution is an essential activity in software system modeling, which is traditionally supported by manual editing or writing model transformation rules. However, the current state of practice for model evolution presents challenges to those who are unfamiliar with model transformation languages or metamodel definitions. This demonstration presents a demonstration-based approach that assists end-users through automation of model evolution tasks (e.g., refactoring, model scaling, and aspect weaving).

Model transformation is a core technology in Domain-Specific Modeling (DSM). While a number of model transformation languages and tools have been developed to support model transformation activities, the layout of visual models in the transformation process is not often considered. In many cases, after a transformation is performed the layout of the resulting model must be manually rearranged, which can be time consuming. The automatic layout arrangement features provided by some modeling tools usually do not take a user's preferences or the semantics of the model into consideration, and therefore could potentially alter the desired layout in an undesired manner. This paper describes a new approach to enable users to specify the model layout in a model transformation. We applied the Model Transformation By Demonstration (MTBD) approach and extended it to let users specify the layout information using the concept of "What You See Is What You Get" (WYSIWYG), so that the complex layout specification can be simplified.