scholarly journals Visual Sensemaking of Massive Crowdsourced Data for Design Ideation

2019 ◽  
Vol 1 (1) ◽  
pp. 409-418
Author(s):  
Yuejun He ◽  
Bradley Camburn ◽  
Jianxi Luo ◽  
Maria C. Yang ◽  
Kristin L. Wood
Keyword(s):  
Language Processing ◽  
Future Research ◽  
Natural Languages ◽  
Concept Space ◽  
Word Clouds ◽  
Crowdsourced Data ◽  
New Ideas ◽  
Massive Number ◽  
Future Research Directions ◽  
Rich Information

AbstractTextual idea data from online crowdsourcing contains rich information of the concepts that underlie the original ideas and can be recombined to generate new ideas. But representing such information in a way that can stimulate new ideas is not a trivial task, because crowdsourced data are often vast and in unstructured natural languages. This paper introduces a method that uses natural language processing to summarize a massive number of idea descriptions and represents the underlying concept space as word clouds with a core-periphery structure to inspire recombinations of such concepts into new ideas. We report the use of this method in a real public-sector-sponsored project to explore ideas for future transportation system design. Word clouds that represent the concept space underlying original crowdsourced ideas are used as ideation aids and stimulate many new ideas with varied novelty, usefulness and feasibility. The new ideas suggest that the proposed method helps expand the idea space. Our analysis of these ideas and a survey with the designers who generated them shed light on how people perceive and use the word clouds as ideation aids and suggest future research directions.

scholarly journals Mining and Representing the Concept Space of Existing Ideas for Directed Ideation

2019 ◽  
Vol 141 (12) ◽  
Author(s):  
Yuejun He ◽  
Bradley Camburn ◽  
Haowen Liu ◽  
Jianxi Luo ◽  
Maria Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Idea Generation ◽  
Future Research ◽  
Natural Languages ◽  
Concept Space ◽  
Large Numbers ◽  
Recombination Processes ◽  
New Ideas ◽  
Future Research Directions ◽  
Design Ideas

AbstractDesign innovation projects often generate large numbers of design ideas from designers, users, and, increasingly, the crowd over the Internet. Such idea data are often used for selection and implementation but, in fact, can 1also be used as sources of inspiration for further idea generation. In particular, the elementary concepts that underlie the original ideas can be recombined to generate new ideas. But it is not a trivial task to retrieve concepts from raw lists of ideas and data sources in a manner that can stimulate or generate new ideas. A significant difficulty lies in the fact that idea data are often expressed in unstructured natural languages. This paper develops a methodology that uses natural language processing to extract key words as elementary concepts embedded in massive idea descriptions and represents the elementary concept space in a core–periphery structure to direct the recombination of elementary concepts into new ideas. We apply the methodology to mine and represent the concept space underlying massive crowdsourced ideas and use it to generate new ideas for future transportation system designs in a real public sector-sponsored project via humans and automated computer programs. Our analysis of the human and computer recombination processes and outcomes sheds light on future research directions for artificial intelligence in design ideation.

Machine Learning

2012 ◽  
pp. 13-22 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

Machine Learning

2011 ◽  
pp. 2462-2468 ◽  
Author(s):  
João Gama ◽  
André C.P.L.F. de Carvalho
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Text Processing ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Background Information ◽  
Future Research ◽  
Personal View ◽  
Learning Techniques ◽  
Future Research Directions

Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.

scholarly journals Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

2020 ◽  
Author(s):  
Md Nazmus Saadat ◽  
Muhammad Shuaib
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Language Processing ◽  
Future Research ◽  
Learning In Context ◽  
Postgraduate Students ◽  
History Of ◽  
Learning Platforms ◽  
Future Research Directions ◽  
Learning Machine

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

scholarly journals Machine and web translator for English to Bangla using natural language processes

1970 ◽  
Vol 5 (1) ◽  
pp. 53-61
Author(s):  
Mohammad Hasibul Haque ◽  
Md Fokhray Hossain ◽  
ANM Fauzul Hossain
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Parse Tree ◽  
Translation System ◽  
Web Pages ◽  
Natural Languages ◽  
Pos Tagging ◽  
Web Contents ◽  
Massive Number ◽  
Translation Methods

The modern web contents are mostly written in English and developing a system with the facility of translating web pages from English to Bangla that can aid the massive number of people of Bangladesh. It is very important to introduce Natural Language Processing (NLP) and is required to developing a solution of web translator. It is a technique that deals with understanding natural languages and natural language generation. It is really a challenging job to building a Web Translator with 100% efficiency and our proposed Web Translator basically uses Machine Translator as its mother concern. This paper represents an optimal way for English to Bangla machine and the Web translation & translation methods are used by translator. Naturally there are three stages for MT but here we propose a translation system which includes 4 stages, such as, POS tagging, Generating parse tree, Transfer English parse tree to Bengali parse tree and Translate English to Bangla and apply AI. An innovation initiative has scope of being upgraded in future and hopefully this work will assist to develop more improved English to Bangla Web Translator. Keywords: Machine Translator, Web Translator, POS Tagging, Parsing, HTML Parsing, Verb Mapping DOI: 10.3329/diujst.v5i1.4382 Daffodil International University Journal of Science and Technology Vol.5(1) 2010 pp.53-61

Recent advances in methods of lexical semantic relatedness – a survey

2012 ◽  
Vol 19 (4) ◽  
pp. 411-479 ◽  
Author(s):  
ZIQI ZHANG ◽  
ANNA LISA GENTILE ◽  
FABIO CIRAVEGNA
Keyword(s):  
Language Processing ◽  
Semantic Relatedness ◽  
Background Information ◽  
Future Research ◽  
Review Of The Literature ◽  
Research Directions ◽  
Lexical Semantic ◽  
Research Activities ◽  
Domain Boundaries ◽  
Future Research Directions

AbstractMeasuring lexical semantic relatedness is an important task in Natural Language Processing (NLP). It is often a prerequisite to many complex NLP tasks. Despite an extensive amount of work dedicated to this area of research, there is a lack of an up-to-date survey in the field. This paper aims to address this issue with a study that is focused on four perspectives: (i) a comparative analysis of background information resources that are essential for measuring lexical semantic relatedness; (ii) a review of the literature with a focus on recent methods that are not covered in previous surveys; (iii) discussion of the studies in the biomedical domain where novel methods have been introduced but inadequately communicated across the domain boundaries; and (iv) an evaluation of lexical semantic relatedness methods and a discussion of useful lessons for the development and application of such methods. In addition, we discuss a number of issues in this field and suggest future research directions. It is believed that this work will be a valuable reference to researchers of lexical semantic relatedness and substantially support the research activities in this field.

scholarly journals Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

2020 ◽  
Author(s):  
Thanh Thi Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Medical Image ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Daily Lives ◽  
Related Data ◽  
Future Research Directions ◽  
The Internet Of Things

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

scholarly journals Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

2021 ◽  
Vol 9 ◽  
pp. 1061-1080
Author(s):  
Prakhar Ganesh ◽  
Yao Chen ◽  
Xin Lou ◽  
Mohammad Ali Khan ◽  
Yin Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Research Attention ◽  
Model Compression ◽  
Future Research Directions ◽  
Or Applications

Abstract Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

scholarly journals The Literature Review of Platform Economy

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Chen Xue ◽  
Wuxu Tian ◽  
Xiaotao Zhao
Keyword(s):  
Historical Context ◽  
Future Research ◽  
The Internet ◽  
Business Organizations ◽  
Global Business ◽  
New Ideas ◽  
Future Research Directions ◽  
Evaluation Mechanism ◽  
The Internet Of Things ◽  
Platform Economy

Since the 1990s, the increasing development of digital-driven technologies such as the Internet, cloud computing, big data, and the Internet of Things and the popularization of computers and mobile electronic devices have accelerated the evolution of global business organizations, thus making a new form of business organization, platform economy. As the most important form of industrial organization in the new economic era, the development of the platform has received extensive attention from the academia. Through literature analysis and inductive deduction, this paper reviews the connotation of platform economy, the historical context of development, the competition and monopoly (differentiation) of multilateral platforms, the evaluation mechanism of platform, antimonopoly governance, and research methods, and provides theoretical references and new ideas for future research directions.

Strong Artificial Intelligence and Consciousness

2020 ◽  
Vol 07 (01) ◽  
pp. 63-72 ◽  
Author(s):  
Gee Wah Ng ◽  
Wang Chi Leung
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Human Life ◽  
Autonomous Systems ◽  
Holistic Approach ◽  
Future Research ◽  
Strong Ai ◽  
Strong Artificial Intelligence ◽  
Future Research Directions

In the last 10 years, Artificial Intelligence (AI) has seen successes in fields such as natural language processing, computer vision, speech recognition, robotics and autonomous systems. However, these advances are still considered as Narrow AI, i.e. AI built for very specific or constrained applications. These applications have its usefulness in improving the quality of human life; but it is not good enough to do highly general tasks like what the human can do. The holy grail of AI research is to develop Strong AI or Artificial General Intelligence (AGI), which produces human-level intelligence, i.e. the ability to sense, understand, reason, learn and act in dynamic environments. Strong AI is more than just a composition of Narrow AI technologies. We proposed that it has to be a holistic approach towards understanding and reacting to the operating environment and decision-making process. The Strong AI must be able to demonstrate sentience, emotional intelligence, imagination, effective command of other machines or robots, and self-referring and self-reflecting qualities. This paper will give an overview of current Narrow AI capabilities, present the technical gaps, and highlight future research directions for Strong AI. Could Strong AI become conscious? We provide some discussion pointers.

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