Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
1,229
result(s) for
"Visual programming (Computer science)"
Sort by:
The Pascal Visual Object Classes Challenge: A Retrospective
The
Pascal
Visual Object Classes (VOC) challenge consists of two components: (i) a publicly available
dataset
of images together with ground truth annotation and standardised evaluation software; and (ii) an annual
competition
and workshop. There are five challenges: classification, detection, segmentation, action classification, and person layout. In this paper we provide a review of the challenge from 2008–2012. The paper is intended for two audiences:
algorithm designers
, researchers who want to see what the state of the art is, as measured by performance on the VOC datasets, along with the limitations and weak points of the current generation of algorithms; and,
challenge designers
, who want to see what we as organisers have learnt from the process and our recommendations for the organisation of future challenges. To analyse the performance of submitted algorithms on the VOC datasets we introduce a number of novel evaluation methods: a bootstrapping method for determining whether differences in the performance of two algorithms are significant or not; a normalised average precision so that performance can be compared across classes with different proportions of positive instances; a clustering method for visualising the performance across multiple algorithms so that the hard and easy images can be identified; and the use of a joint classifier over the submitted algorithms in order to measure their complementarity and combined performance. We also analyse the community’s progress through time using the methods of Hoiem et al. (Proceedings of European Conference on Computer Vision,
2012
) to identify the types of occurring errors. We conclude the paper with an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges.
Journal Article
Microsoft Visual C♯ step by step
\"Expand your expertise--and teach yourself the fundamentals of programming with the latest version of Visual C♯ with Visual Studio 2017. If you are an experienced software developer, you'll get all the guidance, exercises, and code you need to start building responsive, scalable, cloud-connected applications that can run almost anywhere.\"--Back cover.
Book
AIBugHunter: A Practical tool for predicting, classifying and repairing software vulnerabilities
Many Machine Learning(ML)-based approaches have been proposed to automatically detect, localize, and repair software vulnerabilities. While ML-based methods are more effective than program analysis-based vulnerability analysis tools, few have been integrated into modern Integrated Development Environments (IDEs), hindering practical adoption. To bridge this critical gap, we propose in this article
AIBugHunter
, a novel Machine Learning-based software vulnerability analysis tool for C/C++ languages that is integrated into the Visual Studio Code (VS Code) IDE.
AIBugHunter
helps software developers to achieve real-time vulnerability detection, explanation, and repairs during programming. In particular,
AIBugHunter
scans through developers’ source code to (1) locate vulnerabilities, (2) identify vulnerability types, (3) estimate vulnerability severity, and (4) suggest vulnerability repairs. We integrate our previous works (i.e., LineVul and VulRepair) to achieve vulnerability localization and repairs. In this article, we propose a novel multi-objective optimization (MOO)-based vulnerability classification approach and a transformer-based estimation approach to help
AIBugHunter
accurately identify vulnerability types and estimate severity. Our empirical experiments on a large dataset consisting of 188K+ C/C++ functions confirm that our proposed approaches are more accurate than other state-of-the-art baseline methods for vulnerability classification and estimation. Furthermore, we conduct qualitative evaluations including a survey study and a user study to obtain software practitioners’ perceptions of our
AIBugHunter
tool and assess the impact that
AIBugHunter
may have on developers’ productivity in security aspects. Our survey study shows that our
AIBugHunter
is perceived as useful where 90% of the participants consider adopting our
AIBugHunter
during their software development. Last but not least, our user study shows that our
AIBugHunter
can enhance developers’ productivity in combating cybersecurity issues during software development.
AIBugHunter
is now publicly available in the Visual Studio Code marketplace.
Journal Article
Techniques and Challenges of Image Segmentation: A Review
Image segmentation, which has become a research hotspot in the field of image processing and computer vision, refers to the process of dividing an image into meaningful and non-overlapping regions, and it is an essential step in natural scene understanding. Despite decades of effort and many achievements, there are still challenges in feature extraction and model design. In this paper, we review the advancement in image segmentation methods systematically. According to the segmentation principles and image data characteristics, three important stages of image segmentation are mainly reviewed, which are classic segmentation, collaborative segmentation, and semantic segmentation based on deep learning. We elaborate on the main algorithms and key techniques in each stage, compare, and summarize the advantages and defects of different segmentation models, and discuss their applicability. Finally, we analyze the main challenges and development trends of image segmentation techniques.
Journal Article
Practical Microsoft Visual Studio 2015
Learn the details of the most highly recommended practices of software development using the latest version of Visual Studio 2015. Recommended practices are grouped by development phase and explained in far more detail than the typical tips and tricks compilations. This book also contains detailed coverage of recognized patterns and practices used to create software in a timely manner with expected quality in the context of using specific Visual Studio 2015 features. Creating software is part defined process and part empirical process. While there is no single \"best\" process to employ in all development scenarios, MVP author Peter Ritchie helps readers navigate the complexity of development options and decide which techniques and Visual Studio 2015 features to use based on the needs of their particular project. Readers will learn practices such as those related to working in teams, design and architecture, refactoring, source code control workflows, unit testing, performance testing, coding practices, use of common patterns, code analysis, IDE extensions, and more. What You Will Learn Use patterns and practices within Visual Studio Implement practices of software creation Work in teams Develop workflows for software projects Who This Book Is For Beginning and intermediate software developers and architects. -- Provided by publisher.
Book
The Impact of SRA-Programming on Computational Thinking in a Visual Oriented Programming Environment
Visual programming environments are popular instruments in teaching Computational Thinking (CT) in schools today. Applying Sense-Reason-Act (SRA) programming can influence the development of computational thinking when forcing pupils to anticipate the unforeseen in their computer programs. SRA-programming originates from the programming of tangible robots, but can also be of equal value in visual programming with on-screen output. The underlying rationale is that programming in a visual programming environment using SRA leads to more understanding of the computational concepts addressed, resulting in a higher level of computational skill compared to visual programming without the application of SRA. Furthermore, it has been hypothesised that if pupils in a visual programming environment can anticipate unforeseen events and solve programming tasks by applying SRA, they will be better able to solve complex computational thinking tasks. To establish if characteristic differences in the development of computational thinking can be measured when SRA-programming is applied in a visual programming environment with an on-screen output, we assessed the applicability of SRA-programming with visual output as the main component of the execution of developed code. This research uses a pre-test post-test design that reveals significant differences in the development of computational thinking in two treatment conditions. To assess CT, the Computational Thinking Test (CTt) was used. Results show that when using SRA-programming in a visual programming environment it leads to an increased understanding of complex computational concepts, which results in a significant increase in the development of computational thinking.
Journal Article
The Systems Biology Graphical Notation
A group of scientists in the systems biology community propose visual conventions for drawing biological diagrams.
Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.
Journal Article
The Pascal Visual Object Classes (VOC) Challenge
The
Pascal
Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as
the
benchmark for object detection.
This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
Journal Article