A comparison of two command language user interfaces for a CNC machine

1991 ◽  
Vol 1 (4) ◽  
pp. 351-363
Author(s):  
Octavio F. Torres-Chazaro ◽  
Robert J. Beaton ◽  
Michael P. Deisenroth
Keyword(s):  
User Interfaces ◽  
Cnc Machine ◽  
Command Language ◽  
Language User

scholarly journals Comparing Deep-Learning Architectures and Traditional Machine-Learning Approaches for Satire Identification in Spanish Tweets

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2075
Author(s):  
Óscar Apolinario-Arzube ◽  
José Antonio García-Díaz ◽  
José Medina-Moreira ◽  
Harry Luna-Aveiga ◽  
Rafael Valencia-García
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
User Interfaces ◽  
State Of The Art ◽  
Learning Approaches ◽  
Word Embeddings ◽  
Linguistic Features ◽  
Intended Meaning ◽  
Language User ◽  
Learning Architectures

Automatic satire identification can help to identify texts in which the intended meaning differs from the literal meaning, improving tasks such as sentiment analysis, fake news detection or natural-language user interfaces. Typically, satire identification is performed by training a supervised classifier for finding linguistic clues that can determine whether a text is satirical or not. For this, the state-of-the-art relies on neural networks fed with word embeddings that are capable of learning interesting characteristics regarding the way humans communicate. However, as far as our knowledge goes, there are no comprehensive studies that evaluate these techniques in Spanish in the satire identification domain. Consequently, in this work we evaluate several deep-learning architectures with Spanish pre-trained word-embeddings and compare the results with strong baselines based on term-counting features. This evaluation is performed with two datasets that contain satirical and non-satirical tweets written in two Spanish variants: European Spanish and Mexican Spanish. Our experimentation revealed that term-counting features achieved similar results to deep-learning approaches based on word-embeddings, both outperforming previous results based on linguistic features. Our results suggest that term-counting features and traditional machine learning models provide competitive results regarding automatic satire identification, slightly outperforming state-of-the-art models.

Development of Spoken Language User Interfaces: A Tool Kit Approach

2006 ◽  
pp. 339-347 ◽  
Author(s):  
Hassan Alam ◽  
Ahmad Fuad ◽  
Rezaur Rahman ◽  
Timotius Tjahjadi ◽  
Hua Cheng ◽  
...  
Keyword(s):  
User Interfaces ◽  
Spoken Language ◽  
Language User

scholarly journals Design of Belarusian and Russian natural language interfaces for online help systems

Informatics ◽  
2021 ◽  
Vol 18 (4) ◽  
pp. 40-52
Author(s):  
S. A. Hetsevich ◽  
Dz. A. Dzenisyk ◽  
Yu. S. Hetsevich ◽  
L. I. Kaigorodova ◽  
K. A. Nikalaenka
Keyword(s):  
Natural Language ◽  
User Interfaces ◽  
Speech Synthesis ◽  
Intelligent System ◽  
Dialogue System ◽  
Natural Language Interfaces ◽  
Voice Input ◽  
Natural Language Interface ◽  
Question And Answer ◽  
Language User

O b j e c t i v e s. The main goal of the work is a research of the natural language user interfaces and the developmentof a prototype of such an interface. The prototype is a bilingual Russian and Belarusian question-and-answer dialogue system. The research of the natural language interfaces was conducted in terms of the use of natural language for interaction between a user and a computer system. The main problems here are the ambiguity of natural language and the difficulties in the design of natural language interfaces that meet user expectations.M e t ho d s. The main principles of modelling the natural language user interfaces are considered. As an intelligent system, it consists of a database, knowledge machine and a user interface. Speech recognition and speech synthesis components make natural language interfaces more convenient from the point of view of usability.R e s u l t s. The description of the prototype of a natural language interface for a question-and-answer intelligent system is presented. The model of the prototype includes speech-to-text and text-to-speech Belarusian and Russian subsystems, generation of responses in the form of the natural language and formal text.An additional component is natural Belarusian and Russian voice input. Some of the data, required for human voice recognition, are stored as knowledge in the knowledge base or created on the basis of existing knowledge. Another important component is Belarusian and Russian voice output. This component is the top required for making the natural language interface more user-friendly.Co n c l u s i o n. The article presents the research of natural language user interfaces, the result of which provides the development and description of the prototype of the natural language interface for the intelligent question- and-answer system.

scholarly journals Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research

2022 ◽  
Author(s):  
Ross Gruetzemacher ◽  
David Paradice
Keyword(s):  
Text Mining ◽  
Language Processing ◽  
User Interfaces ◽  
Recent Progress ◽  
Language Models ◽  
Is Research ◽  
Systems Research ◽  
Information Systems Research ◽  
Wide Range ◽  
Language User

AI is widely thought to be poised to transform business, yet current perceptions of the scope of this transformation may be myopic. Recent progress in natural language processing involving transformer language models (TLMs) offers a potential avenue for AI-driven business and societal transformation that is beyond the scope of what most currently foresee. We review this recent progress as well as recent literature utilizing text mining in top IS journals to develop an outline for how future IS research can benefit from these new techniques. Our review of existing IS literature reveals that suboptimal text mining techniques are prevalent and that the more advanced TLMs could be applied to enhance and increase IS research involving text data, and to enable new IS research topics, thus creating more value for the research community. This is possible because these techniques make it easier to develop very powerful custom systems and their performance is superior to existing methods for a wide range of tasks and applications. Further, multilingual language models make possible higher quality text analytics for research in multiple languages. We also identify new avenues for IS research, like language user interfaces, that may offer even greater potential for future IS research.

Audit Command Language (ACL) Analytics: A Practical Guide for Beginners

2020 ◽  
Author(s):  
Haslinda Hassan ◽  
Raja Haslinda Raja Mohd Ali ◽  
Nurulhuda Ghazali
Keyword(s):  
Information Technology ◽  
User Interfaces ◽  
Accounting Information ◽  
Command Language ◽  
Accounting Information Systems ◽  
Integrity Verification ◽  
Advantages And Disadvantages ◽  
It Audit ◽  
The Relationship

Tired of performing an audit manually? This module provides a useful step-by-step approach to perform an audit using ACL. Easy to understand and follow. No such module in the market so far. This module is designed to assist users on how to use ACL as a powerful tool to audit. The module is divided into 8 Chapters. Chapter 1 introduces audit and information technology (IT) audit, audit assertions, audit procedures, and the relationship between audit assertions and audit procedures. Chapter 2 explains ACL in the audit, describing in brief its advantages and disadvantages. Chapter 3 assists users with using ACL. In this chapter, users will learn how to install ACL (version 9), and get familiar with the ACL menus and user interfaces. This module uses a step-by-step approach to guide users from creating a new project from ACL to viewing and modifying the table in ACL. Chapter 4 elaborates how to use ACL commands for data integrity verification. For this purpose, users will learn how to count records, total numeric fields or expression, and check for validity errors. Chapter 5 shows users how to analyse their data using the ACL command. The analyse include statistics, stratify, classify, examine the sequence, check for gaps, check for duplicates, ageing, and summarise commands. The remaining chapters cover three main accounting information systems (AIS) cycles, namely, sales and cash receipts (Chapter 6), purchase and cash payments (Chapter 7), and human resource (Chapter 8). For each cycle, cases are given for better assimilation.

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