The Human-IoT Ecosystem

2020 ◽  
pp. 1132-1156
Author(s):  
Vaughan Michell ◽  
James Olweny
Keyword(s):  
Decision Making ◽  
Real World ◽  
Human Perception ◽  
Smart Device ◽  
Context Information ◽  
Situation Theory ◽  
Powerful Approach ◽  
Iot Devices ◽  
World Environment ◽  
Support Decision Making

IoT devices offer a cheap and powerful approach to identifying real world states and situations and acting on this real world environment to change these states and the environment. Augmenting real world things with IoT technology enables the capture of real world context to support decision making and actions in the real world via powerful smart objects in a human- IoT ecosystem. Increasingly we will have to understand the Human-IoT or smart device ecosystem interaction in order to optimise and integrate the design of human and IoT systems. This chapter explores the design and categorisation of IoT devices in terms of their functionality and capability to support context to add to human perception. It then proposes how we can model the context information of both IoT devices and humans in a way that may help progress Human-IoT Ecosystem design using situation theory.

The Human-IoT Ecosystem

2017 ◽  
pp. 223-248 ◽  
Author(s):  
Vaughan Michell ◽  
James Olweny
Keyword(s):  
Decision Making ◽  
Real World ◽  
Human Perception ◽  
Smart Device ◽  
Context Information ◽  
Situation Theory ◽  
Powerful Approach ◽  
Iot Devices ◽  
World Environment ◽  
Support Decision Making

IoT devices offer a cheap and powerful approach to identifying real world states and situations and acting on this real world environment to change these states and the environment. Augmenting real world things with IoT technology enables the capture of real world context to support decision making and actions in the real world via powerful smart objects in a human- IoT ecosystem. Increasingly we will have to understand the Human-IoT or smart device ecosystem interaction in order to optimise and integrate the design of human and IoT systems. This chapter explores the design and categorisation of IoT devices in terms of their functionality and capability to support context to add to human perception. It then proposes how we can model the context information of both IoT devices and humans in a way that may help progress Human-IoT Ecosystem design using situation theory.

scholarly journals Training Intuitive Decision Making in a Simulated Real-World Environment

2012 ◽  
Vol 55 (2) ◽  
pp. 333-345 ◽  
Author(s):  
Robert Earl Patterson ◽  
Byron J. Pierce ◽  
Alan S. Boydstun ◽  
Lisa M. Ramsey ◽  
Jodi Shannan ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Intuitive Decision Making ◽  
World Environment

scholarly journals Emotions and decisions in the real world: What can we learn from quasi-field experiments?

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243044
Author(s):  
Syon P. Bhanot ◽  
Daphne Chang ◽  
Julia Lee Cunningham ◽  
Matthew Ranson
Keyword(s):  
Decision Making ◽  
Field Experiment ◽  
Real World ◽  
Field Experiments ◽  
Negative Emotion ◽  
Sports Fans ◽  
The Social ◽  
Powerful Approach ◽  
The Impact ◽  
Biased Estimates

Researchers in the social sciences have increasingly studied how emotions influence decision-making. We argue that research on emotions arising naturally in real-world environments is critical for the generalizability of insights in this domain, and therefore to the development of this field. Given this, we argue for the increased use of the “quasi-field experiment” methodology, in which participants make decisions or complete tasks after as-if-random real-world events determine their emotional state. We begin by providing the first critical review of this emerging literature, which shows that real-world events provide emotional shocks that are at least as strong as what can ethically be induced under laboratory conditions. However, we also find that most previous quasi-field experiment studies use statistical techniques that may result in biased estimates. We propose a more statistically-robust approach, and illustrate it using an experiment on negative emotion and risk-taking, in which sports fans completed risk-elicitation tasks immediately after watching a series of NFL games. Overall, we argue that when appropriate statistical methods are used, the quasi-field experiment methodology represents a powerful approach for studying the impact of emotion on decision-making.

Data Analytics Pipeline for Prediction and Decision Making in Complex Products and Systems Development

2015 ◽  
Author(s):  
Eliab Z. Opiyo
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
System Development ◽  
Systems Development ◽  
Engineering Product ◽  
Raw Data ◽  
Complex Products ◽  
Specific Objective ◽  
Iot Devices ◽  
Support Decision Making

Facilitating data analytics for effective prediction in complex products or systems development is the focus of the research described in this paper. The specific objective was to develop strategies and a data analytics pipeline with a view to supporting exploration of the design space of complex products or systems upfront. The underlying challenges tackled included how to acquire and store raw data gathered by using both the traditional methods and advanced Internet of Things (IoT) devices, how to preprocess and transform raw data into a form suited for data analytics, and how to deal with analytics. A pipeline for data analytics to support decision making in complex products or systems development is proposed and its applicability illustrated with a practical example. The incorporation of advanced analytics techniques into the proposed pipeline allows users to acquire data and to insightfully and intelligently predict aspects such as cost and assembly time early on, and to make decisions based on data that may otherwise deemed to be inaccessible or unusable. This work contributes to the efforts directed toward applying data analytics techniques in a way that can have a profound impact on an engineering product or system development process.

scholarly journals Expected Utility Based Decision Making underZ-Information and Its Application

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Rashad R. Aliev ◽  
Derar Atallah Talal Mraiziq ◽  
Oleg H. Huseynov
Keyword(s):  
Decision Making ◽  
Expected Utility ◽  
Real World ◽  
Imperfect Information ◽  
Human Perception ◽  
Relevant Information ◽  
Point Of View ◽  
General Representation ◽  
Direct Computation ◽  
Source Of Information

Real-world decision relevant information is often partially reliable. The reasons are partial reliability of the source of information, misperceptions, psychological biases, incompetence, and so forth.Z-numbers based formalization of information (Z-information) represents a natural language (NL) based value of a variable of interest in line with the related NL based reliability. What is important is thatZ-information not only is the most general representation of real-world imperfect information but also has the highest descriptive power from human perception point of view as compared to fuzzy number. In this study, we present an approach to decision making underZ-information based on direct computation overZ-numbers. This approach utilizes expected utility paradigm and is applied to a benchmark decision problem in the field of economics.

scholarly journals Supporting Real World Decision Making in Coronary Diseases Using Machine Learning

2021 ◽  
Vol 58 ◽  
pp. 004695802199733
Author(s):  
Peter Kokol ◽  
Jan Jurman ◽  
Tajda Bogovič ◽  
Tadej Završnik ◽  
Jernej Završnik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Decision Making ◽  
Decision Trees ◽  
Real World ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Second Phase ◽  
Learning Approaches ◽  
Support Decision Making

Cardiovascular diseases are one of the leading global causes of death. Following the positive experiences with machine learning in medicine we performed a study in which we assessed how machine learning can support decision making regarding coronary artery diseases. While a plethora of studies reported high accuracy rates of machine learning algorithms (MLA) in medical applications, the majority of the studies used the cleansed medical data bases without the presence of the “real world noise.” Contrary, the aim of our study was to perform machine learning on the routinely collected Anonymous Cardiovascular Database (ACD), extracted directly from a hospital information system of the University Medical Centre Maribor). Many studies used tens of different machine learning approaches with substantially varying results regarding accuracy (ACU), hence they were not usable as a base to validate the results of our study. Thus, we decided, that our study will be performed in the 2 phases. During the first phase we trained the different MLAs on a comparable University of California Irvine UCI Heart Disease Dataset. The aim of this phase was first to define the “standard” ACU values and second to reduce the set of all MLAs to the most appropriate candidates to be used on the ACD, during the second phase. Seven MLAs were selected and the standard ACUs for the 2-class diagnosis were 0.85. Surprisingly, the same MLAs achieved the ACUs around 0.96 on the ACD. A general comparison of both databases revealed that different machine learning algorithms performance differ significantly. The accuracy on the ACD reached the highest levels using decision trees and neural networks while Liner regression and AdaBoost performed best in UCI database. This might indicate that decision trees based algorithms and neural networks are better in coping with real world not “noise free” clinical data and could successfully support decision making concerned with coronary diseasesmachine learning.

Decision-Making

2021 ◽  
pp. 104-117
Author(s):  
Itay Basevitch ◽  
Gershon Tenenbaum
Keyword(s):  
Decision Making ◽  
Coping Strategies ◽  
Real World ◽  
Self Regulation ◽  
Real Life ◽  
Neurophysiological Mechanisms ◽  
Underlying Mechanisms ◽  
And Coping ◽  
World Environment

Decision-making (DM) has been studied from two main perspectives: cognitive and ecological. Findings indicate that experts have advanced DM skills that enhance performance. The underlying mechanisms of DM skills relate to the attention and anticipation capacities to function without interruption under pressure of time and to counter various sources of stress (e.g., self-regulation and coping strategies). There are still many questions that must be addressed to fully account for the DM process and apply the findings in a real-world environment. The most urgent questions relate to the neurophysiological mechanisms underlying DM, team DM processes, training and measuring DM, making creative decisions, and comprehending the process of coaches’ DM during competitive conditions and other real-life situations.

scholarly journals Real-world patient data: Can they support decision making and patient engagement?

Injury ◽  
2021 ◽  
Author(s):  
Alexander Joeris ◽  
Tracy Y Zhu ◽  
Simon Lambert ◽  
Andrea Wood ◽  
Prakash Jayakumar
Keyword(s):  
Decision Making ◽  
Real World ◽  
Patient Engagement ◽  
Patient Data ◽  
Support Decision Making

Real-world environment affects the color appearance of virtual stimuli produced by augmented reality

2019 ◽  
Vol 2019 (1) ◽  
pp. 237-242
Author(s):  
Siyuan Chen ◽  
Minchen Wei
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Mixed Reality ◽  
Rapid Development ◽  
Light Level ◽  
Color Appearance ◽  
Chromatic Adaptation ◽  
Lighting Condition ◽  
The Real ◽  
World Environment

Color appearance models have been extensively studied for characterizing and predicting the perceived color appearance of physical color stimuli under different viewing conditions. These stimuli are either surface colors reflecting illumination or self-luminous emitting radiations. With the rapid development of augmented reality (AR) and mixed reality (MR), it is critically important to understand how the color appearance of the objects that are produced by AR and MR are perceived, especially when these objects are overlaid on the real world. In this study, nine lighting conditions, with different correlated color temperature (CCT) levels and light levels, were created in a real-world environment. Under each lighting condition, human observers adjusted the color appearance of a virtual stimulus, which was overlaid on a real-world luminous environment, until it appeared the whitest. It was found that the CCT and light level of the real-world environment significantly affected the color appearance of the white stimulus, especially when the light level was high. Moreover, a lower degree of chromatic adaptation was found for viewing the virtual stimulus that was overlaid on the real world.

Sign in / Sign up

Export Citation Format

Share Document