scholarly journals Data science for engineering design: State of the art and future directions

2021 ◽  
Vol 129 ◽  
pp. 103447
Author(s):  
Filippo Chiarello ◽  
Paola Belingheri ◽  
Gualtiero Fantoni
Keyword(s):  
Engineering Design ◽  
Data Science ◽  
State Of The Art ◽  
Future Directions

scholarly journals Data-Driven Design-by-Analogy: State of the Art and Future Directions

2021 ◽  
pp. 1-55
Author(s):  
Shuo Jiang ◽  
Jie Hu ◽  
Kristin L. Wood ◽  
Jianxi Luo
Keyword(s):  
Design Methodology ◽  
Data Science ◽  
State Of The Art ◽  
Data Driven ◽  
Target Domain ◽  
Future Directions ◽  
Structured Analysis ◽  
Design By Analogy ◽  
Conceptual Data ◽  
Design Databases

Abstract Design-by-Analogy (DbA) is a design methodology wherein new solutions, opportunities or designs are generated in a target domain based on inspiration drawn from a source domain; it can benefit designers in mitigating design fixation and improving design ideation outcomes. Recently, the increasingly available design databases and rapidly advancing data science and artificial intelligence technologies have presented new opportunities for developing data-driven methods and tools for DbA support. In this study, we survey existing data-driven DbA studies and categorize individual studies according to the data, methods, and applications in four categories, namely, analogy encoding, retrieval, mapping, and evaluation. Based on both nuanced organic review and structured analysis, this paper elucidates the state of the art of data-driven DbA research to date and benchmarks it with the frontier of data science and AI research to identify promising research opportunities and directions for the field. Finally, we propose a future conceptual data-driven DbA system that integrates all propositions.

Effects of Psychotherapy on Brain Activation Patterns in Anxiety Disorders

2016 ◽  
Vol 224 (2) ◽  
pp. 62-70 ◽  
Author(s):  
Thomas Straube
Keyword(s):  
Anxiety Disorders ◽  
Neuronal Plasticity ◽  
Functional Neuroimaging ◽  
State Of The Art ◽  
Methodological Issues ◽  
Future Directions ◽  
Activation Patterns ◽  
New Learning ◽  
Analytical Strategies ◽  
Sample Characteristics

Abstract. Psychotherapy is an effective treatment for most mental disorders, including anxiety disorders. Successful psychotherapy implies new learning experiences and therefore neural alterations. With the increasing availability of functional neuroimaging methods, it has become possible to investigate psychotherapeutically induced neuronal plasticity across the whole brain in controlled studies. However, the detectable effects strongly depend on neuroscientific methods, experimental paradigms, analytical strategies, and sample characteristics. This article summarizes the state of the art, discusses current theoretical and methodological issues, and suggests future directions of the research on the neurobiology of psychotherapy in anxiety disorders.

Data science in economics: comprehensive review of advanced machine learning and deep learning methods

2020 ◽  
Author(s):  
Saeed Nosratabadi ◽  
Amir Mosavi ◽  
Puhong Duan ◽  
Pedram Ghamisi ◽  
Ferdinand Filip ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Science ◽  
State Of The Art ◽  
Science Methods ◽  
Learning Models ◽  
Diverse Range ◽  
Hybrid Machine ◽  
Economics Research

This paper provides a state-of-the-art investigation of advances in data science in emerging economic applications. The analysis was performed on novel data science methods in four individual classes of deep learning models, hybrid deep learning models, hybrid machine learning, and ensemble models. Application domains include a wide and diverse range of economics research from the stock market, marketing, and e-commerce to corporate banking and cryptocurrency. Prisma method, a systematic literature review methodology, was used to ensure the quality of the survey. The findings reveal that the trends follow the advancement of hybrid models, which, based on the accuracy metric, outperform other learning algorithms. It is further expected that the trends will converge toward the advancements of sophisticated hybrid deep learning models.

Targeting Cystathionine Beta-Synthase Misfolding in Homocystinuria by Small Ligands: State of the Art and Future Directions

2016 ◽  
Vol 17 (13) ◽  
pp. 1455-1470 ◽  
Author(s):  
Tomas Majtan ◽  
Angel L. Pey ◽  
June Ereño-Orbea ◽  
Luis Alfonso Martínez-Cruz ◽  
Jan P. Kraus
Keyword(s):  
State Of The Art ◽  
Future Directions ◽  
Cystathionine Beta Synthase ◽  
Small Ligands

Poly(hydroxyurethane) Adhesives and Coatings: State-of-the-Art and Future Directions

2021 ◽  
Author(s):  
Alvaro Gomez-Lopez ◽  
Satyannarayana Panchireddy ◽  
Bruno Grignard ◽  
Inigo Calvo ◽  
Christine Jerome ◽  
...  
Keyword(s):  
State Of The Art ◽  
Future Directions

Interpreting testing and assessment: A state-of-the-art review

2021 ◽  
pp. 026553222110361
Author(s):  
Chao Han
Keyword(s):  
State Of The Art ◽  
Professional Certification ◽  
Automatic Assessment ◽  
Prospective Students ◽  
Assessment Practice ◽  
High Stakes ◽  
Future Directions ◽  
Assessment Tasks ◽  
Interpreter Education ◽  
Selection Of

Over the past decade, testing and assessing spoken-language interpreting has garnered an increasing amount of attention from stakeholders in interpreter education, professional certification, and interpreting research. This is because in these fields assessment results provide a critical evidential basis for high-stakes decisions, such as the selection of prospective students, the certification of interpreters, and the confirmation/refutation of research hypotheses. However, few reviews exist providing a comprehensive mapping of relevant practice and research. The present article therefore aims to offer a state-of-the-art review, summarizing the existing literature and discovering potential lacunae. In particular, the article first provides an overview of interpreting ability/competence and relevant research, followed by main testing and assessment practice (e.g., assessment tasks, assessment criteria, scoring methods, specificities of scoring operationalization), with a focus on operational diversity and psychometric properties. Second, the review describes a limited yet steadily growing body of empirical research that examines rater-mediated interpreting assessment, and casts light on automatic assessment as an emerging research topic. Third, the review discusses epistemological, psychometric, and practical challenges facing interpreting testers. Finally, it identifies future directions that could address the challenges arising from fast-changing pedagogical, educational, and professional landscapes.

scholarly journals A Survey on Bias and Fairness in Machine Learning

2021 ◽  
Vol 54 (6) ◽  
pp. 1-35
Author(s):  
Ninareh Mehrabi ◽  
Fred Morstatter ◽  
Nripsuta Saxena ◽  
Kristina Lerman ◽  
Aram Galstyan
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Real World ◽  
State Of The Art ◽  
Future Directions ◽  
Discriminatory Behavior ◽  
Real World Applications ◽  
Near Future ◽  
Different Sources

With the widespread use of artificial intelligence (AI) systems and applications in our everyday lives, accounting for fairness has gained significant importance in designing and engineering of such systems. AI systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that these decisions do not reflect discriminatory behavior toward certain groups or populations. More recently some work has been developed in traditional machine learning and deep learning that address such challenges in different subdomains. With the commercialization of these systems, researchers are becoming more aware of the biases that these applications can contain and are attempting to address them. In this survey, we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and ways they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

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