scholarly journals When faced with increasing complexity: The effectiveness of AI assistance for drone design

2021 ◽  
pp. 1-38
Author(s):  
Binyang Song ◽  
Nicolas F Soria Zurita ◽  
Hannah Nolte ◽  
Harshika Singh ◽  
Jonathan Cagan ◽  
...  
Keyword(s):  
Design Process ◽  
Quantitative Methods ◽  
Search Strategy ◽  
Design Research ◽  
Markov Models ◽  
Hidden Markov ◽  
Parameter Design ◽  
Research Platform ◽  
Design Effectiveness ◽  
The Individual

Abstract As Artificial Intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants' design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study is analyzed to assess the design process and effectiveness using quantitative methods, such as Hidden Markov Models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but exhibits a reduced advantage in addressing highly complex objectives. During the design process, the individual designers working with AI assistance employ a relatively explorative search strategy, while the individual designers working without AI assistance devote more effort to parameter design.

Addressing Challenges to Problem Complexity: Effectiveness of AI Assistance During the Design Process

2021 ◽  
Author(s):  
Binyang Song ◽  
Nicolás F. Soria Zurita ◽  
Hannah Nolte ◽  
Harshika Singh ◽  
Jonathan Cagan ◽  
...  
Keyword(s):  
Design Process ◽  
Quantitative Methods ◽  
Search Strategy ◽  
Design Research ◽  
Markov Models ◽  
Parameter Design ◽  
Problem Complexity ◽  
Research Platform ◽  
Design Effectiveness ◽  
The Individual

Abstract As Artificial Intelligence (AI) assistance tools become more ubiquitous in engineering design, it becomes increasingly necessary to understand the influence of AI assistance on the design process and design effectiveness. Previous work has shown the advantages of incorporating AI design agents to assist human designers. However, the influence of AI assistance on the behavior of designers during the design process is still unknown. This study examines the differences in participants’ design process and effectiveness with and without AI assistance during a complex drone design task using the HyForm design research platform. Data collected from this study is analyzed to assess the design process and effectiveness using quantitative methods, such as Hidden Markov Models and network analysis. The results indicate that AI assistance is most beneficial when addressing moderately complex objectives but exhibits a reduced advantage in addressing highly complex objectives. During the design process, the individual designers working with AI assistance employ a relatively explorative search strategy, while the individual designers working without AI assistance devote more effort to parameter design.

Using hidden Markov models to find discrete targets in continuous sociophonetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Daniel Duncan
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Abstract Representation ◽  
Language Variation And Change ◽  
Novel Approach ◽  
Single Target ◽  
The Individual ◽  
Individual Speaker

Abstract Advances in sociophonetic research resulted in features once sorted into discrete bins now being measured continuously. This has implied a shift in what sociolinguists view as the abstract representation of the sociolinguistic variable. When measured discretely, variation is variation in selection: one variant is selected for production, and factors influencing language variation and change are influencing the frequency at which variants are selected. Measured continuously, variation is variation in execution: speakers have a single target for production, which they approximate with varying success. This paper suggests that both approaches can and should be considered in sociophonetic analysis. To that end, I offer the use of hidden Markov models (HMMs) as a novel approach to find speakers’ multiple targets within continuous data. Using the lot vowel among whites in Greater St. Louis as a case study, I compare 2-state and 1-state HMMs constructed at the individual speaker level. Ten of fifty-two speakers’ production is shown to involve the regular use of distinct fronted and backed variants of the vowel. This finding illustrates HMMs’ capacity to allow us to consider variation as both variant selection and execution, making them a useful tool in the analysis of sociophonetic data.

scholarly journals MuBeFE: Multimodal Behavioural Features Extraction Method

2021 ◽  
Vol 27 (3) ◽  
pp. 254-284
Author(s):  
Alessia D'Andrea ◽  
Maria Chiara Caschera ◽  
Fernando Ferri ◽  
Patrizia Grifoni
Keyword(s):  
Extraction Method ◽  
Markov Models ◽  
Hidden Markov ◽  
Evaluation Process ◽  
Communication Style ◽  
Communicative Intention ◽  
The Social ◽  
Social Style ◽  
The Individual ◽  
The Voice

The paper aims to provide a method to analyse and observe the characteristics that distinguish the individual communication style such as the voice intonation, the size and slant used in handwriting and the trait, pressure and dimension used for sketching. These features are referred to as Communication Extensional Features. Observing from the Communication Extensional Features, the user’s behavioural features, such as the communicative intention, the social style and personality traits can be extracted. These behavioural features are referred to as Communication Intentional Features. For the extraction of Communication Intentional Features, a method based on Hidden Markov Models is provided in the paper. The Communication Intentional Features have been extracted at the modal and multimodal level; this represents an important novelty provided by the paper. The accuracy of the method was tested both at modal and multimodal levels. The evaluation process results indicate an accuracy of 93.3% for the Modal layer (handwriting layer) and 95.3% for the Multimodal layer.

scholarly journals Regime-Switching Factor Investing with Hidden Markov Models

2020 ◽  
Vol 13 (12) ◽  
pp. 311
Author(s):  
Matthew Wang ◽  
Yi-Hong Lin ◽  
Ilya Mikhelson
Keyword(s):  
Regime Switching ◽  
Historical Data ◽  
Markov Models ◽  
Factor Model ◽  
Hidden Markov ◽  
Investment Strategy ◽  
Factor Models ◽  
Individual Factor ◽  
Market Regimes ◽  
The Individual

This study uses the hidden Markov model (HMM) to identify different market regimes in the US stock market and proposes an investment strategy that switches factor investment models depending on the current detected regime. We first backtested an array of different factor models over a roughly 10.5 year period from January 2007 to September 2017, then we trained the HMM on S&P 500 ETF historical data to identify market regimes of that period. By analyzing the relationship between factor model returns and different market regimes, we are able to establish the basis of our regime-switching investing model. We then back-tested our model on out-of-sample historical data from September 2017 to April 2020 and found that it both delivers higher absolute returns and performs better than each of the individual factor models according to traditional portfolio benchmarking metrics.

Roles of externalisation activities in the design process

2016 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Maral Babapour Chafi
Keyword(s):  
Design Process ◽  
Design Research ◽  
Research Literature ◽  
Physical Models ◽  
Future Research ◽  
Knowledge Gaps ◽  
Design Processes ◽  
Digital Modelling

Designers engage in various activities, dealing with different materials and media to externalise and represent their form ideas. This paper presents a review of design research literature regarding externalisation activities in design process: sketching, building physical models and digital modelling. The aim has been to review research on the roles of media and representations in design processes, and highlight knowledge gaps and questions for future research.

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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