The impact of peoples' personal dispositions and personalities on their trust of robots in an emergency scenario

Humans should be able to trust that they can safely interact with their home companion robot. However, robots can exhibit occasional mechanical, programming or functional errors. We hypothesise that the severity of the consequences and the timing of a robot's different types of erroneous behaviours during an interaction may have different impacts on users' attitudes towards a domestic robot. First, we investigated human users' perceptions of the severity of various categories of potential errors that are likely to be exhibited by a domestic robot. Second, we used an interactive storyboard to evaluate participants' degree of trust in the robot after it performed tasks either correctly, or with 'small' or 'big' errors. Finally, we analysed the correlation between participants' responses regarding their personality, predisposition to trust other humans, their perceptions of robots, and their interaction with the robot. We conclude that there is correlation between the magnitude of an error performed by a robot and the corresponding loss of trust by the human towards the robot. Moreover we observed that some traits of participants' personalities (conscientiousness and agreeableness) and their disposition of trusting other humans (benevolence) significantly increased their tendency to trust a robot more during an emergency scenario.Peer reviewe

Design and Development of Ceramic Information System Based on Object Oriented Programming

The author designed an object oriented programming based sales application using use case diagrams, activity diagrams, sequence diagrams, deployment diagrams, entity relationship diagrams, logical record structures, and user interfaces. The design of this information system is expected to produce sales application programs ranging from customer data input, input data of goods, input of sales orders, print out of travel documents, print invoices, cash receipts input, print payment receipts to print reports so that sales applications can have a positive impact on the course of business which is built, reducing duplicated functions, and errors caused by human errors. With the design of a sales application, the problems that exist in the manual system can be resolved such as the system will not receive incomplete data, the system can make automatic numbering, and minimize errors that occur due to humans (human error), reduce the amount of paper usage, report generation can done easily and quickly because the data is processed by the system.Keywords: Design, Sales, Object Oriented Programming

GPTutor: an open-source AI pair programming tool alternative to Copilot

This paper presents the latest progress of GPTutor: a ChatGPT-powered programming tool extension in Visual Studio Code. The emergence of Large Language Models (LLMs) has improved software development efficiency, but their performance can be hindered by training data limitations and prompt design issues. Existing LLM development tools often operate as black boxes, with users unable to view the prompts used and unable to improve performance by correcting prompts when errors occur. To address the aforementioned issues, GPTutor was introduced as an open-source AI pair programming tool, offering an alternative to Copilot. GPTutor empowers users to customize prompts for various programming languages and scenarios, with support for 120+ human languages and 50+ programming languages. Users can fine-tune prompts to correct the errors from LLM for precision and efficient code generation. At the end of the paper, we underscore GPTutor's potential through examples, including demonstrating its proficiency in interpreting and generating Sui-Move, a newly introduced smart contract language, using prompt engineering

Farmers' Exit Decisions and Early Retirement Programs in Finland

This paper estimates farmer decisions between three discrete occupational choices: exit and close down the farming operation (1), exit and transfer the farm to a new entrant (2), or continue farming and retain the option to exit later on (3). The farmer optimisation problem is formulated as a recursive optimal stopping problem. The unknown parameters are first estimated by a switching-type, reduced form Probit models and, then by the Simulated maximum likelihood (SML) method, controlling for serial correlation in the errors. Serial correlation in the errors is controlled for by the Geweke-Hajivassiliou-Keane (GHK) simulation technique. The results suggest that the timing and the type of farmer exit decisions respond elastically to farmer characteristics, farm characteristics, and economic environment. Early retirement programs and the level of farmer retirement benefits are predicted to play a key role in steering structural development and enhancing family farms in the Nordic agricultural sectors.exit, entry, dynamic programming, switching-type Probit, Simulated Maximum Likelihood, Labor and Human Capital,

Real-time Thermal Error Compensation Module for Intelligent Ultra Precision Turning Machine (iUPTM)

AbstractAccuracy & precision are 1he main requirements for ultra precision machine tools. Many factors affect 1he performance of 1he system 1hat in turns affect 1he product quality. Among all sources of errors, the thermo mechanical deformation errors are the main contributor for 1he overall geometrical errors. This paper mainly aims at establislunent of methodology to compensate thermal deformation errors in real-time for ultra precision machine tools. The real-time thermal error compensation module has been developed and integrated to intelligent Ultra Precision Turning machine. The module includes temperatures as inputs, neural network algorithm for computing the thermal deformations errors, ‘C’ programming for real-time calculations and integration with open architecture CNC controller. The module runs in silent mode which avoids human intervention for correction of thermal deformation errors

Automatic Music Composition using Answer Set Programming

Music composition used to be a pen and paper activity. These these days music is often composed with the aid of computer software, even to the point where the computer compose parts of the score autonomously. The composition of most styles of music is governed by rules. We show that by approaching the automation, analysis and verification of composition as a knowledge representation task and formalising these rules in a suitable logical language, powerful and expressive intelligent composition tools can be easily built. This application paper describes the use of answer set programming to construct an automated system, named ANTON, that can compose melodic, harmonic and rhythmic music, diagnose errors in human compositions and serve as a computer-aided composition tool. The combination of harmonic, rhythmic and melodic composition in a single framework makes ANTON unique in the growing area of algorithmic composition. With near real-time composition, ANTON reaches the point where it can not only be used as a component in an interactive composition tool but also has the potential for live performances and concerts or automatically generated background music in a variety of applications. With the use of a fully declarative language and an "off-the-shelf" reasoning engine, ANTON provides the human composer a tool which is significantly simpler, more compact and more versatile than other existing systems. This paper has been accepted for publication in Theory and Practice of Logic Programming (TPLP).Comment: 31 pages, 10 figures. Extended version of our ICLP2008 paper. Formatted following TPLP guideline

An empirically-based debugging system for novice programmers

The research described here concerns the design and construction of an empirically-based debugging aid for first-time computer users, integrated into the Open University's SOLO programming environment. Its basis is an account of the processes involved as human experts debug faulty code, which account was later found to be supported by empirical tests on human experts. The account implies that an understanding of the intentions of the programmer is not essential to successful debugging of a certain class of programs. That class comprises programs written in a database-dependent language by users who are initially completely computer-naive and who during their course become competent to write simple programs which embody one or more basic AI techniques such as recursive inference. The debugging system, called AURAC, incorporates an explicit model of the debugging strategies used by human experts. Its understanding, therefore, is of programming in general and of the SOLO environment in particular. We present in the process a broad taxonomy of naive users' errors, showing that they can be divided into types, each type requiring a different debugging approach and indicating a different degree of expertise on the part of the perpetrator. SOLO is a conveniently delimited though nonetheless rich problem domain. Also described is a new version of SOLO itself (MacSOLO) which incorporates a large number of traps for the simple errors which plague novices, thus enabling AURAC to concentrate on the more interesting programming mistakes. AURAC is intended to operate after the event rather than whilst a program is actually being written, and is able via analysis of programming cliches and of data flows to isolate errors in the user's code. Where AURAC cannot analyse, or where its analysis yields nothing useful, it describes the corresponding section of code instead, so that the user receives a coherent output. MacSOLO and AURAC together form a unified system, based upon the principles of Simplicity, Consistency and Transparency. We show how these principles were applied during the design and construction phases