Because of the structural parallelisms between music and computing, it has long been suggested that coding music could be a good way for young children to engage in and learn about computer science (CS). Despite these...
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ISBN:
(纸本)9798400701313
Because of the structural parallelisms between music and computing, it has long been suggested that coding music could be a good way for young children to engage in and learn about computer science (CS). Despite these suggestions, coding music has not reached a wider audience of young children, and the approach's potential to engage them has not been thoroughly demonstrated. To facilitate the adoption of coding music activities, we created M-flow, a flow-based programming platform that allows young children to code music intuitively from the outset. Then, we developed a standards-aligned curriculum that teachers applied in their fourth-grade classrooms. Surveys indicate that children were greatly engaged, the experience successfully exposed them to and increased their self-efficacy toward programming. Our results indicate that with the appropriate coding platform, coding music can be a powerful way to engage children in CS.
Participatory sensing (PS) platforms enable stakeholders to collect, analyse and visualise data for a particular interest. Despite high societal demand, developing a new PS platform remains a labour-intensive, nonreus...
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ISBN:
(纸本)9781450360708
Participatory sensing (PS) platforms enable stakeholders to collect, analyse and visualise data for a particular interest. Despite high societal demand, developing a new PS platform remains a labour-intensive, nonreusable process that requires high technical expertise. We present DISCOPAR, a visual reactive flow-based domain-specific language geared towards the construction of reusable citizen observatories. With DISCOPAR, users interact with visual components to implement the various elements of a PS platforms without having to worry about its underlying technological complexities. We validate our approach through experiments using real-world empirical usability studies of ICT-agnostic users. The results show that DISCOPAR allows users with limited technological knowledge to create their own PS platform.
This paper introduces a computing framework that combines flow-based programming (FBP) and Large Language Models (LLMs) to enable Just-In-Time programming (JITP). JITP empowers users, regardless of their programming e...
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ISBN:
(纸本)9798350308600
This paper introduces a computing framework that combines flow-based programming (FBP) and Large Language Models (LLMs) to enable Just-In-Time programming (JITP). JITP empowers users, regardless of their programming expertise, to actively participate in the development and automation process by leveraging their task-time algorithmic insights. By seamlessly integrating LLMs into the FBP workflow, the framework allows users to request and generate code in real-time, enabling dynamic code execution within a flow-based program. The paper explores the motivations, principles, and benefits of JITP, showcasing its potential in automating tasks, orchestrating data workflows, and accelerating software development. Through a fully implemented JITP framework using the Composable platform, we explore several examples and use cases to illustrate the benefits of the framework in data engineering, data science and software development. The results demonstrate how the fusion of FBP and LLMs creates a powerful and user-centric computing paradigm.
Power-efficiency has been a key issue for today's application and system design, ranging from embedded systems to data centers. While application-specific designs and optimizations may improve the power efficiency...
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Power-efficiency has been a key issue for today's application and system design, ranging from embedded systems to data centers. While application-specific designs and optimizations may improve the power efficiency, it requires significant efforts to co-design the hardware and software, which are difficult to re-use. On the hardware front, the trend of heterogeneous computing enables custom designs for specific applications by integrating different types of processors and reconfigurable hardware to handle computeintensive tasks. However, what is still missing is an elegant application framework, i.e., a programming environment and a runtime system, to develop portable applications which can offload tasks or be reconfigured dynamically to run on a variety of systems efficiently. Our ongoing work, MobileFBP, provides an application framework which aims to support heterogeneous and reconfigurable systems. Using the framework, the developers build portable applications with a dataflowprogramming paradigm, and the MobileFBP runtime system dynamically schedules the task components to run on available computing resources locally or remotely based on the application profiles. We hope that this ability produces high-level portable applications and reduces the efforts and skills needed for the developers to optimize their applications on a range of systems. This paper describes this work and presents our preliminary results. (C) 2013 Elsevier B.V. All rights reserved.
作者：
Mahapatra, TanmayaTech Univ Munich
Inst Med Informat Stat & Epidemiol IMedIS Sch Med Ismaninger Str 22 D-81675 Munich Germany Tech Univ Munich
Software & Syst Engn Res Grp Boltzmannstr 03 D-85748 Garching Germany
The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application ...
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The growing number of Internet of Things (IoT) devices provide a massive pool of sensing data. However, turning data into actionable insights is not a trivial task, especially in the context of IoT, where application development itself is complex. The process entails working with heterogeneous devices via various communication protocols to co-ordinate and fetch datasets, followed by a series of data transformations. Graphical mashup tools, based on the principles of flow-based programming paradigm, operating at a higher-level of abstraction are in widespread use to support rapid prototyping of IoT applications. Nevertheless, the current state-of-the-art mashup tools suffer from several architectural limitations which prevent composing in-flow data analytics pipelines. In response to this, the paper contributes by (i) designing novel flow-based programming concepts based on the actor model to support data analytics pipelines in mashup tools, prototyping the ideas in a new mashup tool called aFlux and providing a detailed comparison with the existing state-of-the-art and (ii) enabling easy prototyping of streaming applications in mashup tools by abstracting the behavioural configurations of stream processing via graphical flows and validating the ease as well as the effectiveness of composing stream processing pipelines from an end-user perspective in a traffic simulation scenario.
Purpose - The purpose of this paper is to develop the model of telemetry data processing flow (TDPF) for TDPF development and the TDPF run-time infrastructure to improve the spacecraft health monitoring capability. De...
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Purpose - The purpose of this paper is to develop the model of telemetry data processing flow (TDPF) for TDPF development and the TDPF run-time infrastructure to improve the spacecraft health monitoring capability. Design/methodology/approach - This research tries to develop the TDPF by flow-based programming (FBP) method and the component-based telemetry data processing software. Findings - The result from the case study is positive, thus reflecting the appropriateness of the suggested method. Practical implications - Application of the proposed TDPF model and the component-based telemetry data processing software may result in improved development efficiency and less development costs. Originality/value - This paper provides an effective way to develop TDPF without recompiling the software. It greatly facilitates the TDPF development that hopefully will save the TDPF development cost.
The thermal shift assay (TSA)-also known as differential scanning fluorimetry (DSF), thermofluor, and T-m shift-is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD...
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The thermal shift assay (TSA)-also known as differential scanning fluorimetry (DSF), thermofluor, and T-m shift-is one of the most popular biophysical screening techniques used in fragment-based ligand discovery (FBLD) to detect protein-ligand interactions. By comparing the thermal stability of a target protein in the presence and absence of a ligand, potential binders can be identified. The technique is easy to set up, has low protein consumption, and can be run on most real-time polymerase chain reaction (PCR) instruments. While data analysis is straightforward in principle, it becomes cumbersome and time-consuming when the screens involve multiple 96- or 384-well plates. There are several approaches that aim to streamline this process, but most involve proprietary software, programming knowledge, or are designed for specific instrument output files. We therefore developed an analysis workflow implemented in the Konstanz Information Miner (KNIME), a free and open-source data analytics platform, which greatly streamlined our data processing timeline for 384-well plates. The implementation is code-free and freely available to the community for improvement and customization to accommodate a wide range of instrument input files and workflows.
Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to d...
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ISBN:
(纸本)9798350301137
Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software design approaches make it difficult to track and discover such relationships at system scale, which leads to growing intellectual debt. In this paper we consider an alternative approach to software design, flow-based programming (FBP), and draw the attention of the community to the connection between dataflow graphs produced by FBP and structural causal models. With expository examples we show how this connection can be leveraged to improve day-to-day tasks in software projects, including fault localisation, business analysis and experimentation.
In this paper, we introduced a Hybrid Data-flow Visual Programing Language (HDVPL), which is an extended C/C++ language with a visual frontend and a dataflow runtime library. Although, most of the popular dataflow vis...
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ISBN:
(纸本)9781450394451
In this paper, we introduced a Hybrid Data-flow Visual Programing Language (HDVPL), which is an extended C/C++ language with a visual frontend and a dataflow runtime library. Although, most of the popular dataflow visual programming languages are designed for specialized purposes, HDVPL is for general-purpose programming. Unlike the others, the dataflow node behavior of HDVPL can be customized by programmer. Our intuitive visual interface can easily build a general-purpose dataflow program. It provides a visual editor to create nodes and connect them to form a DAG of dataflow task. This makes the beginner of computer programming capable of building parallel programs easily. With subgraph feature, complex hierarchical graphs can be built with container node. After the whole program is accomplished, the HDVPL can translate it into text-based source code and compile it into object file, which will be linked with HDVPL dataflow runtime library. To visualize dataflow programs in runtime, we integrated our dataflow library with frontend visual editor. The visual frontend will show the detailed information about the running program in console window.
Due to the significant growth of the demand for data-intensive computing, in addition to the emergence of new parallel and distributed computing technologies, scientists and domain experts are leveraging languages spe...
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ISBN:
(纸本)9789897582837
Due to the significant growth of the demand for data-intensive computing, in addition to the emergence of new parallel and distributed computing technologies, scientists and domain experts are leveraging languages specialized for their problem domain, i.e., domain-specific languages, to help them describe their problems and solutions, instead of using general purpose programming languages. The goal of these languages is to improve the productivity and efficiency of the development and simulation of concurrent scientific models and systems. Moreover, they help to expose parallelism and to specify the concurrency within a component or across different independent components. In this paper, we introduce the concept of domain-specific flow-based languages which allows domain experts to use flow-based languages adapted to a particular problem domain. flow-based programming is used to support concurrency, while the domain-specific part of these languages is used to define atomic processes and domain-specific validation rules for composite processes. We propose a modeling language that can be used to develop such domain-specific languages. Since this language allows one to define other languages, we often refer to it as a meta-modeling language.
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