Determining the programming language of a source code file has been considered in the research community; it has been shown that Machine Learning (ML) and Natural Language Processing (NLP) algorithms can be effective in identifying the programming language of source code files. However, determining the programming language of a code snippet or a few lines of source code is still a challenging task. Online forums such as Stack Overflow and code repositories such as GitHub contain a large number of code snippets. In this paper, we describe Source Code Classification (SCC), a classifier that can identify the programming language of code snippets written in 21 different programming languages. A Multinomial Naive Bayes (MNB) classifier is employed which is trained using Stack Overflow posts. It is shown to achieve an accuracy of 75% which is higher than that with Programming Languages Identification (PLI a proprietary online classifier of snippets) whose accuracy is only 55.5%. The average score for precision, recall and the F1 score with the proposed tool are 0.76, 0.75 and 0.75, respectively. In addition, it can distinguish between code snippets from a family of programming languages such as C, C++ and C#, and can also identify the programming language version such as C# 3.0, C# 4.0 and C# 5.0.

Recent progress in logic programming (e.g., the development of the Answer Set Programming paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online answer set programming environment with a self contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.

Automatic spelling and grammatical correction systems are one of the most widely used tools within natural language applications. In this thesis, we assume the task of error correction as a type of monolingual machine translation where the source sentence is potentially erroneous and the target sentence should be the corrected form of the input. Our main focus in this project is building neural network models for the task of error correction. In particular, we investigate sequence-to-sequence and attention-based models which have recently shown a higher performance than the state-of-the-art of many language processing problems. We demonstrate that neural machine translation models can be successfully applied to the task of error correction. While the experiments of this research are performed on an Arabic corpus, our methods in this thesis can be easily applied to any language.

Automatically predicting the level of non-native English speakers given their written essays is an interesting machine learning problem. In this work I present the system "balikasg" that achieved the state-of-the-art performance in the CAp 2018 data science challenge among 14 systems. I detail the feature extraction, feature engineering and model selection steps and I evaluate how these decisions impact the system's performance. The paper concludes with remarks for future work.

Scalable Deep Learning services are contingent on several constraints. Depending on your target application, you may require low latency, enhanced security or long-term cost effectiveness. Hosting your Deep Learning model on the cloud may not be the best solution in such cases. Deep Learning on the edge alleviates the above issues, and provides other benefits. Edge here refers to the computation that is performed locally on the consumer's products.

The terms'data science' and'analytics' have had a great surge in usage for a long time now. Data has become the driving force of all established companies these days. In this ever-evolving sector of technology, success and failure stories are found daily. But only a few of them are as inspiring as the story of Ankush Bhandari, a tiffin service provider who became a data scientist. Ankush Bhandari completed his Master in Economics at Fergusson College, Pune, and started his entrepreneurial venture – Kaivalya Foods – as a tiffin service provider.

THE TVET EXPERT OF THE WEEK, p18 Madeleine Anne Decker is information and knowledge management specialist for the Canadian Vocational Association/Association canadienne de la formation professionnelle. The Canadian Vocational Association (CVA) was created in 1960 to promote and foster education and training which leads to occupational competence. The CVA is also a world leader in DACUM training, development and research, and Canada's premier voice and expert in competency-based learning and training. Madeleine joined the CVA team in 2011 to increase the visibility by publishing a monthly newsletter and creating two TVET online databases. She added to these tasks the role of community manager by feeding the Association's Twitter and LinkedIn accounts.

By 2022, artificial intelligence and related technologies will change the jobs people do and the people who do the jobs. Anyone who believes the Fourth Industrial Revolution powered by artificial intelligence (AI) is still off in some distant science fiction landscape needs to take a gander at Future of Jobs Report 2018 by the World Economic Forum, based in Switzerland. The report based on a survey of executives at more than 300 global companies finds that the workplace is expected to change dramatically between 2018 and 2022 by four technologies: "ubiquitous high-speed mobile internet; artificial intelligence; widespread adoption of big data analytics; and cloud technology." In five years the report predicts business will be expanding their current adoption of IoT. "Machine learning and virtual reality are poised to likewise receive considerable business investment," the report states.

and Poster program is to provide including their capabilities and workings.

Background: Science is experiencing a reproducibility crisis. Artificial intelligence research is not an exception. Objective: To give practical and pragmatic recommendations for how to document AI research so that the results are reproducible. Method: Our analysis of the literature shows that AI publications fall short of providing enough documentation to facilitate reproducibility. Our suggested best practices are based on a framework for reproducibility and recommendations given for other disciplines. Results: We have made an author checklist based on our investigation and provided examples for how every item in the checklist can be documented. Conclusion: We encourage reviewers to use the suggested best practices and author checklist when reviewing submissions for AAAI publications and future AAAI conferences.