Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

As computing becomes a mainstream discipline embedded in the school curriculum and acts as an enabler for an increasing range of academic disciplines in higher education, the literature on introductory programming is growing. Although there have been several reviews that focus on specific aspects of introductory programming, there has been no broad overview of the literature exploring recent trends across the breadth of introductory programming. This paper is the report of an ITiCSE working group that conducted a systematic review in order to gain an overview of the introductory programming literature. Partitioning the literature into papers addressing the student, teaching, the curriculum, and assessment, we explore trends, highlight advances in knowledge over the past 15 years, and indicate possible directions for future research.

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Introductory programming: a systematic literature review

Explicit formula and graphs of few special functions are derived in the paper on the basis of various definitions of various fractional derivatives and various fractional integrals. Their applications are also reviewed in the paper.

/pdf/applications-of-fractional-calculus-1c2m93jo56.pdf

Applications of Fractional Calculus

Internet Banking──崭露头角的网际银行服务

Opinion Paper: "So what if ChatGPT wrote it?" Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy

The introduction of Artificial Intelligence technology enables the integration of Chatbot systems into various aspects of education. This technology is increasingly being used for educational purposes. Chatbot technology has the potential to provide quick and personalised services to everyone in the sector, including institutional employees and students. This paper presents a systematic review of previous studies on the use of Chatbots in education. A systematic review approach was used to analyse 53 articles from recognised digital databases. The review results provide a comprehensive understanding of prior research related to the use of Chatbots in education, including information on existing studies, benefits, and challenges, as well as future research areas on the implementation of Chatbot technology in the field of education. The implications of the findings were discussed, and suggestions were made.

Chatbots applications in education: A systematic review

The Syntax-Free Approach (SFA) to teaching programming was proposed in response to the perceived challenges often faced by novice programmers while taking their first course in programming. The idea of the SFA is to provide a level of abstraction over the language syntax and teach programming to novices as algorithms instead of lines of code. In this paper, we report the development of a tool that translates novice programs into detailed textual algorithms using regular expressions. We refer to these algorithms as narrations. These narrations are syntax-free, can improve readability and aid the comprehension of programs. The technique described can also be employed for automatic generation of hints or tips for novice programmers during classroom or laboratory sessions.

Abstracting and Narrating Novice Programs Using Regular Expressions

Students often struggle to communicate with their peers or lecturers about some of the issues they face during their time at university, either it be academic or personal. One of these issues is introductory programming, in instances were they choose to memorise code in order to pass rather than understanding how the logic behind the code actually works. Programming requires an understanding of how a certain logical flows and algorithm work. In this article, we discuss the difficulties that students face in introductory programming. We have also developed an interactive AI chatbot tool that students can interact with on some of the academic issues they may face. They can ask for advice on how to understand algorithms and what measures to take in order to understand programming logic and visualise metal models of the algorithms. The chatbot can also be used for personal advice. A survey that was conducted showed that there is a need for such a tool in aiding students through their university life.

Lecturer's Apprentice: A Chatbot for Assisting Novice Programmers

One helpful resource to have when presenting delicate/sensitive information is hypothetical data, or placeholders that conceal the identity of concerned parties. This is crucial in environments such as medicine and criminology as volunteers in medical research, patients of dreaded diseases, and convicts of certain crimes often prefer to remain anonymous, even when they agree to their records being shared. Recently, research based on social media has raised similar ethical concerns about privacy and the use of real users' profiles. In this paper, we present a new application of a type of formal grammar — probabilistic/stochastic context-free grammar — in the automatic generation of social media profiles using Facebook as a test case. First, we present a grammar-based formalism for describing the rules governing the formulation of reasonable user attributes (e.g. full names, date of birth, addresses, phone numbers, etc). These grammar rules are specified with associated probabilistic weights that decides when (if at all) a rule is used or chosen. Secondly, we describe the implementation of these grammar rules. Our implementation results produced one million iterations of unique Facebook profiles within three hours of execution time — with an almost-impossible probability that a profile will reoccur. 100,000 of these synthesised profiles can be viewed at: tinyurl.com/synthesisedprofiles2017. These profiles may find applications in role-playing games in health, and social media research; and the described technique may find a wider application in generation of hypothetical profiles for data anonymisation in different domains.

Synthesis of social media profiles using a probabilistic context-free grammar

Instant messaging (IM) has gained huge popularity, not only in the social space, but in many sectors of work and everyday life. One such sector is finance, where IMs are used for bargaining, trading and many other financial information exchanges between parties. A popular IM software in finance is the Instant Bloomberg (IB). The conversations (chat histories) recorded via IB often contain crucial information about the financial positions of the market, quotes for different instruments, as well as unimportant texts. Automatic comprehension of this type of text is a natural language processing task. In this paper we employ front-end compiler analyses in the automatic comprehension and summarisation of financial chats retrieved from IB. Taking the result, we present a software tool called FINCHAN that takes lengthy chat histories and attempts to recognise the syntax up to a success rate of 98%, extracts semantic information to 61% success rate, generates concise summaries of these chats, and offers analyses of hidden patterns, text-to-speech synthesis of generated summaries, with functionalities for archiving/indexing IMs.

Abejide Ade-Ibijola

Papers

Chatbots applications in education: A systematic review

Abstracting and Narrating Novice Programs Using Regular Expressions

Lecturer's Apprentice: A Chatbot for Assisting Novice Programmers

Synthesis of social media profiles using a probabilistic context-free grammar

FINCHAN: A Grammar-based Tool for Automatic Comprehension of Financial Instant Messages