A Novel Approach for Collaborative Interaction With Mixed Reality in Value Engineering

2011 ◽  
Author(s):  
Robert E. Wendrich
Keyword(s):  
Decision Making ◽  
User Interfaces ◽  
Real World ◽  
Mixed Reality ◽  
Focal Point ◽  
Problem Definition ◽  
Value Engineering ◽  
Data Sets ◽  
Design Environment ◽  
Trade Offs

Design and engineering in real-world projects is often influenced by reduction of the problem definition, trade-offs during decision-making, possible loss of information and monetary issues like budget constraints or value-for-money problems. In many engineering projects various stakeholders take part in the project process on various levels of communication, engineering and decision-making. During project meetings and VE sessions between the different stakeholder’s, information and data is gathered and put down analogue and/or digitally, consequently stored in reports, minutes and other modes of representation. Results and conclusions derived from these interactions are often influenced by the user’s field of experience and expertise. Personal stakes, idiosyncrasy, expectations, preferences and interpretations of the various project parts could have implications, interfere or procrastinate non-functionality and possible rupture in the collaborative setting and process leading to diminished prospective project targets, requirements and solutions. We present a hybrid tool as a Virtual Assistant (VA) during a collaborative Value Engineering (VE) session in a real-world design and engineering case. The tool supports interaction and decision-making in conjunction with a physical workbench as focal point (-s), user-interfaces that intuit the user during processing. The hybrid environment allows the users to interact un-tethered with real-world materials, images, drawings, objects and drawing instruments. In course of the processing captures are made of the various topics or issues at stake and logged as iterative instances in a database. Real-time visualization on a monitor of the captured instances are shown and progressively listed in the on-screen user interface. During or after the session the stakeholders can go through the iterative time-listing and synthesize the instances according to i.e. topic, dominance, choice or to the degree of priority. After structuring and sorting the data sets the information can be exported to a data or video file. All stakeholders receive or have access to the data files and can track-back the complete process progression. The system and information generated affords reflection, knowledge sharing and cooperation. Redistribution of data sets to other stakeholders, management or third parties becomes more efficient and congruous. Our approach we took during this experiment was to [re]search the communication, interaction and decision-making progressions of the various stakeholders during the VE-session. We observed the behavioral aspects during the various stages of user interaction, following the decision making process and the use of the tool during the course of the session. We captured the complete session on video for analysis and evaluation of the VE process within a hybrid design environment.

VP33 Decision Making Clinical Scenarios: A Support Method For Health Technology Assessment

2017 ◽  
Vol 33 (S1) ◽  
pp. 163-164
Author(s):  
Luiz Santoro Neto
Keyword(s):  
Decision Making ◽  
Health Technology Assessment ◽  
Technology Assessment ◽  
Real World ◽  
Health Technology ◽  
Public Healthcare ◽  
Value Judgments ◽  
New Drug ◽  
Trade Offs ◽  
Clinical Scenarios

INTRODUCTION:The method appraises the stakeholders value judgments in the Health Technology Assessment (HTA) process, through a new model of research that addresses clinical scenarios to simulate real world HTA dilemmas and support decision making. The scenarios are based on criteria, such as clinical and epidemiological elements, and also, economic, social and ethical factors. The stakeholders decisions can induce strategic impacts in different HTA fields. We agreed to call this model Decision Making Clinical Scenarios (DMCS).METHODS:The model of research is based on a cross exploratory research, through a DMCS questionnaire applied to stakeholder respondents. The first survey was composed of four scenarios. The scenarios introduce value judgments, preferences and structuring choices, under specific circumstances. The scenarios are based on trade-offs involving HTA, such as budget impact, sources of funding, patients eligibility, technology characteristics and disease epidemiology. The stakeholders points of view are analyzed, through groups that represent payers, suppliers, developers, researchers, prescribers, regulators, government, patients and society.RESULTS:The scenarios have been shown to be understandable for all stakeholders groups. When testing the model with hypothetical dilemmas through clinical scenarios, the results are strongly influenced by each presented trade-off. We can observe specific trends and motivations when analyzing the stakeholders groups separately. The results are always evaluated and validated through statistical analysis. A total of 193 stakeholders answered the survey. The majority were male (n = 104; 53.9 percent) and aged between 31 and 40 years (n = 71; 36.8 percent). In scenario 1, almost half of respondents (n = 95; 49.2 percent opted not to incorporate the new drug and in scenario 2, an even higher proportion chose not to incorporate the new drug (n = 112; 58.0 percent). In scenario 3, most have responded to not incorporate the new treatment for any age group (n = 81; 42.0 percent). In scenario 4, 65 percent of respondents opted for the preferential allocation for prevention, rather than treatment (n = 125; 64.8 percent). Overall results showed a conservative trend, considering the presented criteria and trade-offs.CONCLUSIONS:We concluded that most stakeholders are not guided only by the clinical benefit of a decision. They valorize the importance of funding mechanisms and budget control, and consider economic, social, ethical, clinical and epidemiological aspects. This study model seems to be useful to evaluate the trends of decision makers conduct. We understand that the use of clinical scenarios brings the discussion into the enviroment and dynamics of the HTA process, where outcome impacts can be analyzed properly. This model can be explored in further research, using flexible criteria for each desired scenario, through real world situations. This model can be used to evaluate impacts in strategic subjects, as budget allocation, public healthcare policies, and patient-shared decision making.

scholarly journals Penalty-Enhanced Utility-Based Multi-Criteria Recommendations

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 551
Author(s):  
Yong Zheng
Keyword(s):  
Decision Making ◽  
Recommender Systems ◽  
Real World ◽  
User Preferences ◽  
Experimental Results ◽  
Data Sets ◽  
Real World Data ◽  
Promising Solution ◽  
Multiple Domains

Recommender systems have been successfully applied to assist decision making in multiple domains and applications. Multi-criteria recommender systems try to take the user preferences on multiple criteria into consideration, in order to further improve the quality of the recommendations. Most recently, the utility-based multi-criteria recommendation approach has been proposed as an effective and promising solution. However, the issue of over-/under-expectations was ignored in the approach, which may bring risks to the recommendation model. In this paper, we propose a penalty-enhanced model to alleviate this issue. Our experimental results based on multiple real-world data sets can demonstrate the effectiveness of the proposed solutions. In addition, the outcomes of the proposed solution can also help explain the characteristics of the applications by observing the treatment on the issue of over-/under-expectations.

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.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Renewable energy selection based on a new entropy and dissimilarity measure on an interval-valued neutrosophic set

2021 ◽  
pp. 1-18
Author(s):  
ShuoYan Chou ◽  
Truong ThiThuy Duong ◽  
Nguyen Xuan Thao
Keyword(s):  
Decision Making ◽  
Renewable Energy ◽  
Membership Function ◽  
Fossil Fuels ◽  
Multiple Criteria Decision Making ◽  
Clean Energy ◽  
Dissimilarity Measure ◽  
Neutrosophic Set ◽  
Trade Offs ◽  
And Performance

Energy plays a central part in economic development, yet alongside fossil fuels bring vast environmental impact. In recent years, renewable energy has gradually become a viable source for clean energy to alleviate and decouple with a negative connotation. Different types of renewable energy are not without trade-offs beyond costs and performance. Multiple-criteria decision-making (MCDM) has become one of the most prominent tools in making decisions with multiple conflicting criteria existing in many complex real-world problems. Information obtained for decision making may be ambiguous or uncertain. Neutrosophic is an extension of fuzzy set types with three membership functions: truth membership function, falsity membership function and indeterminacy membership function. It is a useful tool when dealing with uncertainty issues. Entropy measures the uncertainty of information under neutrosophic circumstances which can be used to identify the weights of criteria in MCDM model. Meanwhile, the dissimilarity measure is useful in dealing with the ranking of alternatives in term of distance. This article proposes to build a new entropy and dissimilarity measure as well as to construct a novel MCDM model based on them to improve the inclusiveness of the perspectives for decision making. In this paper, we also give out a case study of using this model through the process of a renewable energy selection scenario in Taiwan performed and assessed.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Hfinger: Malware HTTP Request Fingerprinting

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 507
Author(s):  
Piotr Białczak ◽  
Wojciech Mazurczyk
Keyword(s):  
Real World ◽  
Network Traffic ◽  
Experimental Evaluation ◽  
Data Sets ◽  
Real World Data ◽  
Malicious Software ◽  
Default Mode ◽  
World Data ◽  
Effectiveness Analysis ◽  
Http Protocol

Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence
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