Main Project Office
Professor
Tsai Tsung-Han
PI of MOE AI competition and labeled data acquisition project
Department of Computer Science and Information Engineering
at National Central University
Director of Taiwanese Association for Artificial Intelligence
Associate Professor
Li Hung-i
co - PI of MOE AI competition and labeled data acquisition project
Department of Electrical Engineering at National Taiwan University
Assistant Professor
Wu Shih-Hung
co -PI of MOE AI competition and labeled data acquisition project
Department of Computer Science and Information Engineering
at Chaoyang University of Technology
Project Assistant: Miss Weng
Phone : (03)422-7151 #35323 / 0903616392
E-mail : moe.ai.ncu@gmail.com / yuhanweng @ncu.edu.tw
Detection of tumor airway spread in pathological slice images of lung adenocarcinoma
AI CUP 2023
AI CUP 2022
肺腺癌病理切片影像之腫瘤氣道擴散偵測
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
The application of artificial intelligence to analyze digital pathological images is an emerging field with great potential in recent years. By analyzing tumor pathological images, a wealth of tumor-related information can be obtained for clinical application. "Spread through air spaces (STAS)" is a newly discovered pathological feature of lung adenocarcinoma in recent years, which refers to the spread of tumor cells from the edge of the tumor along the alveolar cavity to the adjacent normal lung tissue. Tumor airway spread has a significant impact on the risk of recurrence in lung adenocarcinoma patients after surgery, and is currently a routine item in pathological examination of lung adenocarcinoma. For H&E-stained digital pathological full-section images of lung adenocarcinoma, this competition provides STAS annotation information at the region of interest (ROI) outside the tumor with a frame and an irregular shape at the pixel level, and then holds two competitions: ( 1) Use object detection method to find STAS; (2) Use image segmentation method to cut STAS outline.
This competition provides a wealth of awards, cloud computing resources, software tools and training courses to help participants with strong strength and tools to achieve good results during the competition, such as the opportunity to use the largest AI high-speed GPU computing cloud resources in Taiwan - TWCC , to assist cross-node high-performance computing to quickly execute large-scale applications, shorten computing time through AIHPC high-performance acceleration, and optimize models to produce the most accurate competition results.
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
Field crop survey
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
The investigation of the current situation of agricultural land crops can use personnel with cameras to take photos and record them on the spot. However, the agricultural land area is extensive, and the hills in our country are divided and distributed in fragments. time. AI technology has made great progress in image recognition work in recent years, and it is suitable for importing into the work procedures of farmland crop status investigation, and speeds up the acquisition of relevant information by agricultural administrative units.
Although there are complete AI data sets for people's livelihood/industry/medical care, there is a relative lack of agricultural data. Therefore, a large amount of professional manpower will be required to collect and analyze agricultural data for future AI smart agriculture needs; through this competition, It will help students understand the application requirements of agricultural data sets and agricultural industry image recognition, and cultivate students' experience and technical ability in applying AI for image recognition in the agricultural field.
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
Automatic Positioning and Application of Rice UAV Full Color Image Plant Position
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
Taiwan has a long history of orchid cultivation, a wide variety, and its output and quality are recognized internationally. Taiwan has the world's leading research and development of orchid breeding, and has the most varieties of Phalaenopsis in the world. 90% of Phalaenopsis is exported, making it a domestic exquisite The amount of agriculture is the first. However, due to the advancement of agricultural biotechnology, the propagation of a large number of tissue seedlings has affected the research and development of new varieties. In addition, other countries are actively investing in breeding production. Most breeding manufacturers have their own varieties that focus on cultivating. Because the types of orchid varieties are relatively similar, often Professionals are needed to make the distinction. At present, there is no identification software and technology for Phalaenopsis species in the world. This competition intends to invite experts and masters with AI, machine learning, and deep learning expertise through holding orchid species identification and classification competitions. The high-resolution image recognition model for orchid items can not only enhance the competitiveness of the industry, but also allow the public to have a better understanding of orchids, which are high-economic crops, and further increase the sales and output value of Phalaenopsis.
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
Intelligent counting of UAV flying vehicles (crops, vehicles and crowd counting)
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
In recent years, the impact brought by the rapid development of science and technology has gradually penetrated into the human living environment. Unmanned aerial vehicles provide a wider field of view and higher mobility and flexibility. So far, they have been applied in many different industrial fields, such as geographic information collection, traffic monitoring, item delivery, communication network relay stations and other different types. This project is expected to be co-operated by Professor Xie Junwei from the School of Smart Science and Green Energy, National Yangming Chiao Tung University, Professor Zhuang Renhui from the School of Information, and Assistant Professor Zhang Mingqing from the State University of New York at Albany, focusing on AI and image recognition and the application of unmanned aerial vehicles. Participants start from the problems that may be encountered in daily life, use the principles of deep learning as the basis, use the core knowledge of artificial intelligence to develop applications in depth, and then apply technologies related to unmanned aerial vehicles to the actual environment, combining talents from different fields and giving full play to their creativity , put the professional knowledge you usually learn into practice, and improve your personal and
The practical ability and competitiveness of the team.
The theme of this competition is AI and Image Recognition-Smart Counting of UAV Flight Vehicles (Crops, Vehicles and Crowd Counting). UAVs have high mobility and remote control functions, and can quickly and easily reach areas that are not easily accessible With a high-resolution camera, it looks like an eagle’s eye. It can look down on the ground from the air, and record all changes on the ground in detail in the image without omission. This kind of high mobility and wide application makes all countries in the world regard drones as their next priority. An integral part of the next generation of Industry 4.0 revolution. At present, there is no such aerial image analysis competition in China. This project will be based on aerial images of drones, and use deep learning principles and other related training modules to carry out high-altitude crops, vehicles, and crowd counting technology and identification. Through this competition, we will use the method of learning by doing to master key aerial photography analysis technologies, cultivate forward-looking AI R&D talents, and guide high-level R&D talents to contribute and serve to various social classes, bringing new impetus to the country's high-tech industry.
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
Interpretive Information Labeling Project for Natural Language Understanding
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
The explainability of machine learning models is a highly anticipated project in the implementation and application of artificial intelligence technology. If the model can not only provide accurate predictions, but also provide evidence to support its predicted behavior, humans will have the opportunity to discover errors in model judgments in advance, greatly improve human trust in the model, and make artificial intelligence technology more applicable to key decision-making fields area.
The current natural language processing model has achieved high performance for this type of task, which shows that the recent deep learning technology has been able to master part of the language reasoning ability. However, when the model provides accurate inference (i.e., three-category task), how to obtain the result of the inference, and the explanatory elements in it are still not fully studied.
This project aims at natural language reasoning. It is hoped that the model can predict logical relationships such as implication, contradiction, and irrelevance between sentences, and at the same time find key fragments in sentences as supporting information for the prediction results. On the one hand, such information can provide opportunities for researchers to better understand the behavior inside the model, and promote the research of natural language processing. At the same time, when the artificial intelligence system is applied in the future, it can provide the judgment basis of the model for humans to evaluate the reliability of the judgment of the model.
AI CUP 2021
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
Recently, with the rise of artificial intelligence, the introduction of precise technology into the concept of smart value-added technology in agriculture will not only help increase productivity, but also improve population shortages and income from agricultural production. It is expected that high automation and standardization will be achieved through AI technology, which improve output value and effectively carry out industrial transformation.
This goal hopes to introduce concepts based on automated and accurate AI image recognition technology. In the future, we will use Taiwan's local database to build an automatic mango sieve fruit system. In the future, we hope to cooperate with government units such as the Agricultural Committee to complete the mango intelligence resume. This provides a simple and clear mango purchase mechanism for the general public in Taiwan, while at the same time pulling the local Aiwen mango brand refinement.
Through the competition, you can quickly grow this related technology and train related intelligent agricultural talents. By grasping the three elements of technology cultivation, technology traceability and brand value, in order to improve the quality of local mangoes and establish the brand value of Taiwan mangoes. it will not only help Taiwan's overall outward expansion of competitiveness, but also have the opportunity to establish with other countries. The connection has raised the popularity of Taiwan's high-economic vegetable and fruit brands and even the internationalization of planting indicators. At the same time, in response to this theme that is closely related to life, Taiwan's AI talents have also been cultivated, which has spread to different agricultural crops and laid the foundation for smart agriculture.
Traditional Chinese Scene Character Recognition Competition
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
AI CUP 2021
Automatic Positioning and Application of Rice UAV Full Color Image Plant Position
Professor
Liu Li-Yu
principal investigator
Department of agronomy
at
National Taiwan University
With the rapid development of software and hardware, the application of artificial intelligence has been successfully applied in a wide range of fields. However, the application of artificial intelligence in agriculture is quite limited. In addition to the long growth cycle of crops and the difficulty of accumulating large amounts of data, the data labeling highly relies on agricultural experts, which makes it seriously insufficient for training data. Aerial telemetry images and Internet text are a large amount of information that is relatively easy to obtain.
In response to the development of "new agriculture" from the government, this project first predicts the use of high-resolution drone full-color images for plant location labeling, and the release of plant epidemics from the open platform of Council of Agriculture Executive Yuan R.O.C. In the future, based on the labeling results, artificial intelligence can be used to build rice field plant identification and text identification analysis modules to enhance the soft power of Taiwan’s future agricultural development.
The results of the labeling can be applied to the application of drones to spray plant protection agents in small quantities in the future to achieve the dual purpose of saving costs and maintaining environmental sustainability. It is also in line with the recent goals of the application of unmanned plant protection machines actively promoted by the Council of Agriculture Executive Yuan R.O.C.
Associate Professor
Ku Lun-Wei
co-principal investigator
Department of Engineering Science and Ocean Engineering
at
National Taiwan University
AI CUP 2020 / 2021
Decision Analysis of Medical Information and Construction System of Dialogue Corpus
Professor
Kao Hung - Yu
principal investigator
Department of Computer Science and Information Engineering
at National Cheng Kung University
According to the provisions of the Health Insurance Portability and Accountability Act, in the clinical medical text records, the content of the patient's private information must be erased or modified. In the outpatient medical-patient dialogue data, there are many private content of the people seeking medical treatment. Such a large amount of data requires an automated way to identify these private content, which facilitates the work of medical staff and accelerates the establishment of medical big data.
This competition provides outpatient dialogues and consultation dialogues of related interviews collected from the outpatient clinics of Chengda Hospital. The private content and types of the dialogues are manually marked. And the data is divided into training set, construction set (development set) and test set.
The main goal of this competition is to identify and extract content containing private information from the dialogue between doctors and the public, and to classify the privacy type of the content.
Professor
Ko Nai-Ying
co-principal investigator
Department of Nursing
at National Cheng Kung University
AI CUP 2020
Associate Professor
Li Chi-Chun
principal investigator
Department of Electrical Engineering
at National Tsing Hua University
Recently, with the rise of artificial intelligence, the introduction of precise technology into the concept of smart value-added technology in agriculture will not only help increase productivity, but also improve population shortages and income from agricultural production. It is expected that high automation and standardization will be achieved through AI technology, which improve output value and effectively carry out industrial transformation.
This goal hopes to introduce concepts based on automated and accurate AI image recognition technology. In the future, we will use Taiwan's local database to build an automatic mango sieve fruit system. In the future, we hope to cooperate with government units such as the Agricultural Committee to complete the mango intelligence resume. This provides a simple and clear mango purchase mechanism for the general public in Taiwan, while at the same time pulling the local Aiwen mango brand refinement.
Through the competition, you can quickly grow this related technology and train related intelligent agricultural talents. By grasping the three elements of technology cultivation, technology traceability and brand value, in order to improve the quality of local mangoes and establish the brand value of Taiwan mangoes. it will not only help Taiwan's overall outward expansion of competitiveness, but also have the opportunity to establish with other countries. The connection has raised the popularity of Taiwan's high-economic vegetable and fruit brands and even the internationalization of planting indicators. At the same time, in response to this theme that is closely related to life, Taiwan's AI talents have also been cultivated, which has spread to different agricultural crops and laid the foundation for smart agriculture.
Assistant professor
Hsu Chih-Chung
co-principal investigator
Department of Information management
at National Pingtung University of Science and Technology
AI CUP 2020
Singing and Chord Recognition Competition
Professor
Chang Chih-Hsing
principal investigator
Department of Computer Science and Information Engineering
at National Taiwan University
With the development of the times, the way of listening to music has shifted from the previous CD to various online music platforms.
The 2016 IFPI report pointed out that the output value of digital music has officially exceeded the output value of physical music, and the output value of physical music is decreasing year after year, showing that the trend is on the digital music side.
The development of digital music has driven many related AI intelligent applications, including original song identification, humming song selection, and song classification. Among them, Line Music, KKBOX, and Spotify have successively established machine learning or artificial intelligence departments, which analyze the user's music preferences through song content and user's listening habits. The various AI services above are provided to allow users to easily listen to their favorite music, thereby generating a space of added value.
Through the development of digital music, various music-related services such as song recommendation and humming search have also become more and more. Domestic companies such as KKBOX and foreign countries such as Spotify have also set up their own machine learning departments to do research and expansion of automatic song recommendation.
Such services can be divided into two parts in the study of machine learning. One is to make recommendations based on user behavior, and the other is to make recommendations based on the essence of the song. In the essence of the song, many basic feature method studies are necessary. The basic elements of the song's main melody, chords, song structure, genre and tempo, constituting how a song should be classified and recommended. So the talents who familiar with these basic elements of music analysis and machine learning methods in the current music industry are urgently needed to be cultivated.
Competition on Machine Reading of
Artificial Intelligence Papers
AI CUP 2019
Distinguished Professor
Chen Ming-Hsien
principal investigator
Department of Electrical Engineering at National Taiwan University
In this competition, we will let students try to solve a problem that has long been a headache for computer science researchers with the technique of semantic analysis: "How to design a system that can automatically read the abstract of the paper, mark and unify the invention, use or application To compare algorithmic systems. "
It will be an unavoidable attempt to let the machine automatically sort out these algorithms that are constantly being developed. Even if the manpower is affordable, letting the machine automatically integrate related algorithms will allow researchers to free up time to do more meaningful things.
Professor
Lin Shou-Te
co-principal investigator
Department of Computer Science and Information Engineering
at National Taiwan University
Associate Research Fellow
Yeh Mi-Yen
co-principal investigator
Institute of Information Science, Academia Sinica
AI CUP 2019
Competition on News Stance Retrieval
Associate Professor
Cheng Pu-Jen
principal investigator
Department of Computer Science and Information Engineering
at National Taiwan University
News on controversial issues has always been the focus of viewers' attention and discussion. News media often need to report different positions. If a large number of news documents can be quickly searched for news with a specific position on various controversial issues, It will not only help people understand the different perceptions and different values of these issues from different positions, but also be of great reference value to the decision-making process.
This competition encourages students to use creativity and technology to solve the challenges of data retrieval and opinion exploration applications. It also looks forward to training and cultivating talents in artificial intelligence fields such as information retrieval, natural language processing and machine learning in China through the competition.
Associate Research Fellow
Ku Lun-Wei
co-principal investigator
Institute of Information Science, Academia Sinica
AI CUP 2018 / 2019
Competition on Biomedical Paper Analysis
Distinguished Research Fellow
Hsu Wen-Lien
principal investigator
Institute of Information Science, Academia Sinica
This competition will use natural language processing technology as the core, and the open competition corpus will enable students to apply artificial intelligence technology to basic clinical medical history data analysis to advanced bioinformatics document corpus.
Students participating in this competition will have a practical understanding of the processes and techniques of analyzing biomedical data and reflect the translation of medical thinking, so that students can understand the big thinking of connecting basic medicine, bioinformatics research, and clinical treatment with natural language processing technology.