How the Intelligent Recognition Industry Service (IRIS) Center is driving technological research in Artificial Intelligence

Impact ◽  
2021 ◽  
Vol 2021 (1) ◽  
pp. 12-14
Author(s):  
Chuan-Yu Chang
Keyword(s):  
Artificial Intelligence ◽  
Academic Research ◽  
International Strategy ◽  
Sound Recognition ◽  
Infant Crying ◽  
Vision Inspection ◽  
Information Engineering ◽  
Uav Image ◽  
Iris Center ◽  
Intelligent Recognition

The Intelligent Recognition Industry Service (IRIS) Center, which is part of National Yunlin University of Science and Technology (YunTech), Taiwan, connects industry and academia in order to develop artificial intelligence (AI) solutions for pressing challenges in industrial automation, healthcare and industrial living. Professor Chuan-Yu Chang is Director of IRIS and Distinguished Professor at the Department of Computer Science and Information Engineering, YunTech. IRIS has four strategic principles: its human resource strand, international strategy, industrial strategy and technology pillar. Key foci centre on advancing international collaborations, introducing intelligent recognition technology within industry, promoting the use of AI and prompting its core technologies. Via its industrial service process, which begins with the identification of an industrial requirement and flows from diagnosis and resource matching, through to the provision of a customised service and problem resolution through the implementation and upgrade of technology, IRIS offers the expertise of its academic research staff to companies with a view to solving industry problems. IRIS is a leader in R&D in Taiwan and beyond, with particular strength in intelligent recognition technology, integrating sound recognition, medical imaging, UAV image recognition and vision inspection. The Centre seeks to advance R&D in these areas in order to improve lifestyles and workflow within Taiwan and highlight IRIS's status as a powerhouse in AI research on a global stage. An example of technology being developed at IRIS is the 'Infant Crying Translator' project, in which Chang and his team are using baby cry recognition technology and have launched the world's first baby cry recognition app to help parents understand the meaning of their baby's cries.

scholarly journals Artificial Intelligence Based Commercial Risk Management Framework for SMEs

2019 ◽  
Vol 11 (16) ◽  
pp. 4501
Author(s):  
Gerda Žigienė ◽  
Egidijus Rybakovas ◽  
Robertas Alzbutas
Keyword(s):  
Artificial Intelligence ◽  
Risk Assessment ◽  
Risk Management ◽  
Academic Research ◽  
Building Blocks ◽  
Learning Technologies ◽  
Potential Contribution ◽  
Risk Management Framework ◽  
Development Goals ◽  
Risk Assessment And Management

Risk management in commercial processes is among the most important procedures affecting the competitiveness of small and medium-sized enterprises (SMEs), their innovativeness and potential contribution to global sustainable development goals (SDGs). The ecosystem of commercial processes is the prerequisite to manage risk faced by SMEs. Commercial risk assessment and management using elements of artificial intelligence, big data, and machine learning technologies could be developed and maintained as external services for a group of SMEs allowing to share costs and benefits. This paper aims to provide a conceptual framework of commercial risk assessment and management solution based on elements of artificial intelligence. This conceptualization is done on the background of scientific literature, policy documents, and risk management standards. Main building blocks of the framework in terms of commercial risk categories, data sources and workflow phases are presented in the article. Business companies, state policy, and academic research focused recommendations on the further development of the framework and its implementation are elaborated.

scholarly journals Leveraging Urban Sounds: A Commodity Multi-Microphone Hardware Approach for Sound Recognition

Proceedings ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 55
Author(s):  
Fabrizio Tappero ◽  
Rosa Alsina-Pagès ◽  
Leticia Duboc ◽  
Francesc Alías
Keyword(s):  
Artificial Intelligence ◽  
Hardware Implementation ◽  
Noise Analysis ◽  
Fog Computing ◽  
Sound Recognition ◽  
Sound Processing ◽  
Sound Analysis ◽  
Specific Focus ◽  
Analysis System ◽  
Government Legislation

City noise and sound are measured and processed with the purpose of drawing appropriate government legislation and regulations, ultimately aimed at contributing to a healthier environment for humans. The primary use of urban noise analysis is carried out with the main purpose of reporting or denouncing, to the appropriate authorities, a misconduct or correct a misuse of council resources. We believe that urban sounds carry more information than what it is extracted to date. In this paper we present a cloud-based urban sound analysis system for the capturing, processing and trading of urban sound-based information. By leveraging modern artificial intelligence algorithms running on a FOG computing city infrastructure, we will show how the presented solution can offer a valuable solution for exploiting urban sound information. A specific focus is given to the hardware implementation of the sound sensor and its multimicrophone architecture. We discuss how the presented architecture is designed to allow the trading of sound information between independent parties, transparently, using cloud-based sound processing APIs running on an inexpensive consumer-grade microphone.

scholarly journals Comparative Analysis between International Research Hotspots and National-Level Policy Keywords on Artificial Intelligence in China from 2009 to 2018

2019 ◽  
Vol 11 (23) ◽  
pp. 6574 ◽  
Author(s):  
Gao ◽  
Huang ◽  
Zhang
Keyword(s):  
Artificial Intelligence ◽  
Comparative Analysis ◽  
International Research ◽  
Rapid Development ◽  
National Level ◽  
Academic Research ◽  
Development Stage ◽  
The Relationship ◽  
Research Hotspots ◽  
Research And Policy

In the last decade, artificial intelligence (AI) has undergone many important developments in China and has risen to the level of national strategy, which is closely related to the areas of research and policy promotion. The interactive relationship between the hotspots of China’s international AI research and its national-level policy keywords is the basis for further clarification and reference in academics and political circles. There has been very little research on the interaction between academic research and policy making. Understanding the relationship between the content of academic research and the content emphasized by actual operational policy will help scholars to better apply research to practice, and help decision-makers to manage effectively. Based on 3577 English publications about AI published by Chinese scholars in 2009–2018, and 262 Chinese national-level policy documents published during this period, this study carried out scientometric analysis and quantitative analysis of policy documents through the knowledge maps of AI international research hotspots in China and the co-occurrence maps of Chinese policy keywords, and conducted a comparative analysis that divided China’s AI development into three stages: the initial exploration stage, the steady rising stage, and the rapid development stage. The studies showed that in the initial exploration stage (2009–2012), research hotspots and policy keywords had a certain alienation relationship; in the steady rising stage (2013–2015), research hotspots focused more on cutting-edge technologies and policy keywords focused more on macro-guidance, and the relationship began to become close; and in the rapid development stage (2016–2018), the research hotspots and policy keywords became closely integrated, and they were mutually infiltrated and complementary, thus realizing organic integration and close connection. Through comparative analysis between international research hotspots and national-level policy keywords on AI in China from 2009 to 2018, the development of AI in China was revealed to some extent, along with the interaction between academics and politics in the past ten years, which is of great significance for the sustainable development and effective governance of China’s artificial intelligence.

scholarly journals In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions

2019 ◽  
Vol 33 (3) ◽  
pp. 523-539 ◽  
Author(s):  
Andrea Ferrario ◽  
Michele Loi ◽  
Eleonora Viganò
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ethical Dilemmas ◽  
Academic Research ◽  
Business Organizations ◽  
Layer Model ◽  
Incremental Model ◽  
Program Of Study ◽  
Proposed Model

Abstract Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.

scholarly journals Artificial Intelligence Tools for Enhancing Customer Experience

2019 ◽  
Vol 8 (2S3) ◽  
pp. 700-706
Keyword(s):  
Artificial Intelligence ◽  
New Technologies ◽  
Academic Research ◽  
Customer Experience ◽  
Research Area ◽  
Customer Base ◽  
Significant Research ◽  
Narrative Literature ◽  
Narrative Literature Review ◽  
The Many

Customers are the key to any business and the major challenge for any established business is retaining an existing customer and acquiring a new customer. One of the many ways to reduce the churn rate and increase customer retention is to improve the customer experience. As businesses are growing, their customer base is also increasing. Each and every customer is different and needs different kind of motivators to engage with the business and hence we need to understand each and every customer uniquely. Artificial Intelligence tools can blend the gap between the business and the client, creating enormous information that can prompt further comprehension of the client’s preferences. Understanding these artificial intelligence tools and how these tools can assist organizations with retaining clients and help them give better involvement to their clients is significant. However, in academic research this significant research area stays under-focussed. Hence this study tries to address this gap by proposing a conceptual model for understanding how the Artificial Intelligence tools are can help in enhancing customer experience. The narrative literature review approach has been adopted for conceptualization of the model. The study provides implications to practitioners for designing and developing AI tools such that they enhance customer experience, to managers for designing the information technology strategy of their companies, to academicians as it helps explore new technologies in the marketing domain and to the society as it will help improve customer experience thereby leading to customer satisfaction.

scholarly journals Preface

2021 ◽  
Vol 2093 (1) ◽  
pp. 011001
Keyword(s):  
Science And Technology ◽  
Committee Member ◽  
Academic Research ◽  
Keynote Speaker ◽  
Electronic Information ◽  
Related Area ◽  
Keynote Presentation ◽  
Information Engineering ◽  
Conference Committee ◽  
Oral Presentations

We gratefully acknowledge the presence of all participants on the 2021 International Conference on Mechanical Automation and Electronic Information Engineering (MAEIE 2021), which was successfully held in Zhuhai from 24th to 26th September, 2021. MAEIE 2021 aimed to provide a platform for experts and scholars, engineers and technicians, and R&D personnel to share scientific research results and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote cooperation in the industrialization of academic results. MAEIE 2021 focused on professional fields such as Mechanical Integration, Artificial Intelligence, Intelligent Information Processing, Information Engineering, etc. About 80 participants from academic, high-education institutes and other organizations took part in the conference. The conference model was divided into two sessions, including oral presentations and keynote speeches. In the first part, some scholars, whose submissions were selected as the excellent papers, were given 15 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. In the keynote presentation part, we invited 12 professors as our keynote speakers. The first keynote speaker, Prof. Junlong Chen, IEEE Fellow, from South China University of Technology, China. The second keynote speaker, Prof. Guoliang Chen, from Shenzhen University, China. The others keynote speaker as follow: Prof. Anhui Liang, Shandong University of Science and Technology, China; Prof. Weijia Jia, IEEE Fellow, ACM Fellow, Beijing Normal University - Hong Kong Baptist University United International College, China; Prof. Nong Xiao, from Sun Yat-sen University, China; Prof. Yutong Lu, Sun Yat-sen University, China; Prof. Young Liang, Macau University of Science and Technology, China; Prof. Jianxin Wang, IEEE Senior Member, from Central South University, China; Prof. Xiaofeng Zhu, University of Electronic Science and Technology of China; Prof. Yang Yue, Nankai University, China; Prof. Li Na, from Xi’ dian University, China; Assoc. Prof. Zhengtian Fang, from University of Macau, China. They had outstanding research in Mechanical Automation and Electronic Information Engineering and other related area. The proceedings present a selection of high-quality papers submitted to the conference by researchers from universities, research institutes, and industry. All papers were subjected to peer-review by conference committee members and international reviewers. The papers were selected based on their quality and their relevance to the conference. The proceedings present recent advances in the fields of Mechanical, Force and Tactile Sensors, Industrial Tribology, Machine Vision, Algorithm and Data Structure and others related research. I would like to express special gratitude to members of the conference committee and organizers of the conference. I would also like to thank the reviewers for their valuable time and advice which helped in improving the quality of the papers selected for presentation at the conference and for publication in the proceedings. Finally, I want to thank the authors, the members of the organizing committee, the reviewers, the chairpersons, sponsors, and all other conference participants for their support of MAEIE 2021. The Committee of MAEIE 2021 List of Committee member are available in this pdf.

scholarly journals Design and Application of Transmission Line Intelligent Monitoring System

2020 ◽  
Vol 185 ◽  
pp. 01063
Author(s):  
Zengqiang Xing ◽  
WenpengCui Cui ◽  
Rui Liu ◽  
Zhe Zheng
Keyword(s):  
Artificial Intelligence ◽  
Transmission Line ◽  
Monitoring System ◽  
Network Model ◽  
Recognition Accuracy ◽  
Design Method ◽  
Target Recognition ◽  
Image Resolution ◽  
Intelligent Monitoring ◽  
Intelligent Recognition

This paper presents a design method of intelligent monitoring system for transmission lines based on artificial intelligence technology. In this design method, a low-power artificial intelligence chip - LieYing A101 is used to design an intelligent recognition module to realize real-time target recognition on a terminal device. In order to solve the problem that the original image and the input image resolution of the intelligent recognition module do not match, this paper uses a sliding window and convolutional neural network design method, which solves the image resolution mismatch problem and improves the recognition accuracy. Finally, for the problem of excessive network model size, feature channel weight pruning and 8-bit quantization methods are used to compress the network model to less than 10M, and the recognition accuracy is not sharply reduced. After the test set test and actual scene use, the external force destruction target recognition accuracy of the transmission line channel is high; this meets the application needs of customers.

AN ARTIFICIAL INTELLIGENCE APPROACH TO COURSE TIMETABLING

2008 ◽  
Vol 17 (01) ◽  
pp. 223-240 ◽  
Author(s):  
LIEN F. LAI ◽  
CHAO-CHIN WU ◽  
NIEN-LIN HSUEH ◽  
LIANG-TSUNG HUANG ◽  
SHIOW-FEN HWANG
Keyword(s):  
Artificial Intelligence ◽  
Expert Systems ◽  
Complex Problem ◽  
Relaxation Techniques ◽  
Course Timetabling ◽  
System A ◽  
Information Engineering ◽  
Hard And Soft Constraints ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

Course Timetabling is a complex problem that cannot be dealt with by using only a few general principles. The various actors (the administrator, the chairman, the instructor and the student) have their own objectives, and these objectives usually conflict. The complexity of the relationships among time slots, classes, classrooms, and instructors makes it difficult to achieve a feasible solution. In this article, we propose an artificial intelligence approach that integrates expert systems and constraint programming to implement a course timetabling system. Expert systems are utilized to incorporate knowledge into the timetabling system and to provide a reasoning capability for knowledge deduction. Separating out the knowledge base, the facts, and the inference engine in expert systems provides greater flexibility in supporting changes. The constraint hierarchy and the constraint network are utilized to capture hard and soft constraints and to reason about constraints by using constraint satisfaction and relaxation techniques. In addition, object-oriented software engineering is applied to improve the development and maintenance of the course timetabling system. A course timetabling system in the Department of Computer Science and Information Engineering at the National Changhua University of Education (NCUE) is used as an illustrative example of the proposed approach.

Criticalities and Advantages of the Use of Artificial Intelligence in Research

2022 ◽  
pp. 161-175
Author(s):  
Jessica Camargo Molano ◽  
Jacopo Cavalaglio Camargo Molano
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Public Schools ◽  
School System ◽  
Detection System ◽  
Rapid Development ◽  
Academic Research ◽  
The Face ◽  
The Impact ◽  
Machine Learning Models

In recent years, artficial intelligence, through the rapid development of machine learning and deep learning, has started to be used in different sectors, even in academic research. The objective of this study is a reflection on the possible errors that can occur when the analysis of human behavior and the development of academic research rely on artificial intelligence. To understand what errors artificial intelligence can make more easily, three cases have been analyzed: the use of the IMPACT system for the evaluation of school system in the District of Columbia Public Schools (DCPS) in Washington, the face detection system, and the “writing” of the first scientific text by artificial intelligence. In particular, this work takes into consideration the systematic errors due to the polarization of data with which the machine learning models are trained, the absence of feedback and the problem of minorities who cannot be represented through the use of big data.

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