Management Game

Digital disruption and continuous productivity improvement require more from people management, thus raising the bar for leadership competencies. International studies indicate that leadership competence gaps are large and traditional leadership training methods does not seem to solve this problem. This article's findings supports this situation. The authors will open the complexity behind organizational productivity development and present game theoretical architecture that simulates management behavior effects to human performance. New methods enable practice-based learning that enables formatting leaders' behavior so that it will create long-term success with continuous change. The authors will present gamified leadership training procedure and discuss the practical learning experiences from a management simulation game. The authors' study reveals challenges at interactive leadership skills, thus, it is argued, that there seems to be problems at the leadership mind-set. Therefore, more sophisticated learning methods and tools should be used.

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Self-leadership Training Review

10.31124/advance.7771175.v1 ◽

2019 ◽

Author(s):

Andrew Sidwell ◽

Michael Perry

Keyword(s):

Leadership Training ◽

Future Research ◽

Training Methods ◽

Current State ◽

A Current

The purpose of this article was to examine the current state of self-leadership training. The authors analyzed all published, publicly available studies (in English) pertaining to self-leadership training methods, offering a current state of self-leadership training, and implications for future research.

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MODELS OF GENERATION OF INPUT DATA OF TRAINING OF NEURAL NETWORK MODULES FOR DIAGNOSTIC OF DISEASES IN UROLOGY

RADIOELECTRONIC AND COMPUTER SYSTEMS ◽

10.32620/reks.2019.4.13 ◽

2019 ◽

pp. 116-122

Author(s):

Mykola Ivanovych Fedorenko

Keyword(s):

Neural Network ◽

Mathematical Model ◽

Input Data ◽

Probabilistic Approach ◽

Training Data ◽

Digital Data ◽

Training Methods ◽

Training Procedure ◽

Network Modules ◽

Parameter Values

The subject of the research presented in the article is neural network modules (NNMs), which are used to solve problems in the practice of diagnosing diseases in urology. This work aims to develop a mathematical model for generating a multitude of uroflowmetric parameters, in particular, graphs of uroflowrograms of the required volume, used as input data for NNM training. Objective: to develop a mathematical model for the formation of uroflowmetric parameters using a probabilistic approach based on a uniform "white noise". To develop an effective algorithm for the procedure for generating new parameter values and tools for its implementation. Methods used: NNM training methods, mathematical modeling methods, digital signal processing methods, tools for generating and processing random numerical sequences, digital data filtering methods. The following results were obtained: when creating and implementing a mathematical model for generating a large amount of training data, the requirements of randomness are taken into account when obtaining new values of uroflowmetric parameters. And at the same time, the obtained noise values are filtered to values of a given range, which are percentage-wise comparable to the amplitude value of the uroflowmetric parameter. Conclusions. The scientific novelty of the results is as follows: the NNM training method for recognizing diseases in urology has been improved by developing a mathematical model to generate uroflowmetric parameters for NNM training. The presented model allows you to create the necessary amount of data for training neural network modules in the course of experimental research on the recognition of diseases. The generation of uroflowmetric parameters is based on adding noise to the parameter values. This allows you to change the input data of the NNM training in a given range. This ensures the creation of the required input volume of the NNM training procedure. In the future, this contributes to the testing process of trained neural network modules with reliable information on the diagnosis of diseases in urology.

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A Socratic epistemology for verbal emotional intelligence

10.7287/peerj.preprints.1292 ◽

2015 ◽

Author(s):

Abe Kazemzadeh ◽

James Gibson ◽

Panayiotis Georgiou ◽

Sungbok Lee ◽

Shrikanth Narayanan

Keyword(s):

Cognitive Abilities ◽

Human Performance ◽

Amazon Mechanical Turk ◽

Linguistic Knowledge ◽

User Engagement ◽

Large Set ◽

Training Procedure ◽

Pushdown Automaton ◽

Question Asking ◽

Emotion Words

We describe and experimentally validate a question-asking framework for machine-learned linguistic knowledge about human emotions. Using the Socratic method as a theoretical inspiration, we develop an experimental method and computational model for computers to learn subjective information about emotions by playing emotion twenty questions (EMO20Q), a game of twenty questions limited to words denoting emotions. Using human-human EMO20Q data we bootstrap a sequential Bayesian model that drives a generalized pushdown automaton-based dialog agent that further learns from 300 human-computer dialogs collected on Amazon Mechanical Turk. The human-human EMO20Q dialogs show the capability of humans to use a large, rich, subjective vocabulary of emotion words. Training on successive batches of human-computer EMO20Q dialogs shows that the automated agent is able to learn from subsequent human-computer interactions. Our results show that the training procedure enables the agent to learn a large set of emotions words. The fully trained agent successfully completes EMO20Q at 67% of human performance and 30% better than the bootstrapped agent. Even when the agent fails to guess the human opponent's emotion word in the EMO20Q game, the agent's behavior of searching for knowledge makes it appear human-like, which enables the agent maintain user engagement and learn new, out-of-vocabulary words. These results lead us to conclude that the question-asking methodology and its implementation as a sequential Bayes pushdown automaton are a successful model for the cognitive abilities involved in learning, retrieving, and using emotion words by an automated agent in a dialog setting.

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Physical Training Methods For Mine Rescuers In 2015

ACTA Universitatis Cibiniensis ◽

10.1515/aucts-2015-0021 ◽

2015 ◽

Vol 66 (1) ◽

pp. 23-28

Author(s):

Cristian Costa ◽

Lucian Lupu ◽

Eduard Edelhauser

Keyword(s):

Heart Rate ◽

Physical Training ◽

Interval Training ◽

Variation Method ◽

Specific Work ◽

Training Methods ◽

Training Procedure ◽

Training Models ◽

Mine Rescue ◽

Made In

Abstract We have studied physical mine rescue training programs and health-related and rescue-related fitness tasks during a mine rescue competition, made in China and Australia and on these basis we have design our own pre physical training method. We have stored the heart rate measured in bites per minute (bpm) during the 2012 year periodical training for 21 mine rescuers. We have designed a physical training procedure based on six training models: Body Building, Method of isometric efforts, Method of Interval Training, Volume variation method, Structured method for basic grip and release and Specific work method. Then we measured again during the 2014 year periodical training, the heart rate for the same mine rescuer having the physical training procedure performed before. We have notice that the trained person have now lower bpm, during the tests that could represent better performances during the rescue actions. Our research were made in the Laboratory for Risk-Rescue Operations of the INCD INSEMEX Petroşani, Romania.

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A Socratic epistemology for verbal emotional intelligence

PeerJ Computer Science ◽

10.7717/peerj-cs.40 ◽

2016 ◽

Vol 2 ◽

pp. e40

Author(s):

Abe Kazemzadeh ◽

James Gibson ◽

Panayiotis Georgiou ◽

Sungbok Lee ◽

Shrikanth Narayanan

Keyword(s):

Cognitive Abilities ◽

Human Performance ◽

Amazon Mechanical Turk ◽

Linguistic Knowledge ◽

User Engagement ◽

Large Set ◽

Training Procedure ◽

Pushdown Automaton ◽

Question Asking ◽

Emotion Words

We describe and experimentally validate a question-asking framework for machine-learned linguistic knowledge about human emotions. Using the Socratic method as a theoretical inspiration, we develop an experimental method and computational model for computers to learn subjective information about emotions by playing emotion twenty questions (EMO20Q), a game of twenty questions limited to words denoting emotions. Using human–human EMO20Q data we bootstrap a sequential Bayesian model that drives a generalized pushdown automaton-based dialog agent that further learns from 300 human–computer dialogs collected on Amazon Mechanical Turk. The human–human EMO20Q dialogs show the capability of humans to use a large, rich, subjective vocabulary of emotion words. Training on successive batches of human–computer EMO20Q dialogs shows that the automated agent is able to learn from subsequent human–computer interactions. Our results show that the training procedure enables the agent to learn a large set of emotion words. The fully trained agent successfully completes EMO20Q at 67% of human performance and 30% better than the bootstrapped agent. Even when the agent fails to guess the human opponent’s emotion word in the EMO20Q game, the agent’s behavior of searching for knowledge makes it appear human-like, which enables the agent to maintain user engagement and learn new, out-of-vocabulary words. These results lead us to conclude that the question-asking methodology and its implementation as a sequential Bayes pushdown automaton are a successful model for the cognitive abilities involved in learning, retrieving, and using emotion words by an automated agent in a dialog setting.

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User experience and learning experience in a 4D virtual reality simulation game

International Journal of Serious Games ◽

10.17083/ijsg.v6i4.305 ◽

2019 ◽

Vol 6 (4) ◽

pp. 49-66 ◽

Cited By ~ 2

Author(s):

Maiju Salovaara-Hiltunen ◽

Katja Heikkinen ◽

Jaana-Maija Koivisto

Keyword(s):

Virtual Reality ◽

User Experience ◽

Simulation Training ◽

Educational Games ◽

Learning Experience ◽

Simulation Game ◽

Training Methods ◽

Virtual Reality Simulation ◽

Continuous Training ◽

Simulation Games

Simulation training is an effective way of teaching in healthcare, yet it requires a great deal of time and effort. Virtual gear technology brings us new promising training methods, yet there is very little research data about the user experience and learning in immersive virtual environments. This research was based on the idea that 4D virtual reality simulation games can supplement traditional simulation training and provide consistent training to a wide group of professionals. As learning should be effective, it should also be pleasant enough to motivate professionals for continuous training. Therefore, user experience was emphasized in this study and learning effectiveness was not measured. This study explored the gaming and learning experience, as well as usability, in a multi-phase scenario based on the evidence-based theory of resuscitation. The participants played the scenario and were interviewed immediately afterwards. Their experiences of the 4D virtual simulation game were explored in the context of educational games and general theories of UX. Material from 13 thematic interviews was analyzed by applying a deductive content analysis. The findings suggest that gaming and learning experiences are very individual and vivid. Immersion created by the virtual gear had an essential impact on the overall experience. In addition, authenticity, interaction and feedback were important elements of learning experience. Usability had a major role on the whole. The findings are discussed in relation to earlier studies and actual practise as well as trustworthiness and challenges of overall implications.

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The Effectiveness of Virtual Reality for Administering Spatial Navigation Training to Firefighters

Presence Teleoperators & Virtual Environments ◽

10.1162/pres.1997.6.1.73 ◽

1997 ◽

Vol 6 (1) ◽

pp. 73-86 ◽

Cited By ~ 155

Author(s):

James P. Bliss ◽

Philip D. Tidwell ◽

Michael A. Guest

Keyword(s):

Virtual Reality ◽

Test Performance ◽

Human Performance ◽

Training Methods ◽

Training Groups ◽

Science Building ◽

Training Control ◽

Experimental Training ◽

The University ◽

Administrative Science

Because fire rescue personnel often enter unfamiliar buildings to perform critical tasks like rescues, the importance of finding new and improved ways to train route navigation is becoming paramount. This research was designed to compare three methods for training firefighters to navigate a rescue route in an unfamiliar building. Thirty firefighters from the Madison County, Alabama, area were trained to navigate through the Administrative Science Building at The University of Alabama in Huntsville. The firefighters, who had not had any experience with the Administrative Science Building prior to the experiment, were randomly assigned to one of three experimental training groups: Blueprint, Virtual Reality, or No Training (Control). After training, we measured the total navigation time and number of wrong turns exhibited by firefighters in the actual building. Participants were required to rescue a mock baby (a life-sized doll) following the specific trained route. Measures of test performance were compared among groups by using analyses of variance (ANOVAs). The results indicated that firefighters trained with virtual reality or blueprints performed a quicker and more accurate rescue than those without training. Furthermore, the speed and accuracy of rescue performance did not differ significantly between virtual reality and blueprint training groups. These results indicate that virtual reality training, if constructed and implemented properly, may provide an effective alternative to current navigation training methods. The results are discussed with regard to theories of transfer of training and human performance in virtual environments.

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Self-leadership Training Review

10.31124/advance.7771175 ◽

2019 ◽

Author(s):

Andrew Sidwell ◽

Michael Perry

Keyword(s):

Leadership Training ◽

Future Research ◽

Training Methods ◽

Current State ◽

A Current

The purpose of this article was to examine the current state of self-leadership training. The authors analyzed all published, publicly available studies (in English) pertaining to self-leadership training methods, offering a current state of self-leadership training, and implications for future research.

Download Full-text