Graduate School of Information Science, NAIST
Summer Seminar
Aug. 06-08, 2018
Summer Seminar 2018 will be held on the Graduate School of Information Science, NAIST, on August 06-08. You can experience the cutting-edge research on Information Science during this Summer Seminar.
We are looking forward to your application!
| Date |
Aug. 6 - 7, 2018, or Aug. 6 - 8, 2018 (Depend on the topics) |
| Venue |
Division of Information Science, Graduate School of Science and Technology, NAIST (Takayama 8916-5, Ikoma, Nara 630-0192, Japan) |
| Intended audience |
Education higher than undergraduate including non-academic applicants who are planning to become IS students.
If you are not a candidate, please apply the Lab tour |
| Capacity |
Depends on each theme (please check the table below).
If the number of applicants exceeds the capacity, a selection process will be done by the corresponding laboratory.
|
| Admission fee |
FREE! However, we don't cover any travel or accommodation costs. We also don't issue any documents for applying visa etc. |
| Application form |
|
| Contact us |
Summer Seminar Committee
ss1808[at]is.naist.jp |
- 2018/7/12 The assignment is determined.
Confirm the assignment
- 2018/7/10 Application site is closed
- 2018/6/1 Website is open and the list of themes is uploaded
- Aug. 6 Mon.
09:20 Registration (IS L2 lecture room)
09:30 - 10:00 Introduction (IS L2 lecture room)
10:00 - 17:00 Seminar (Lab.)
- Aug. 7 Tue.
09:20 - 16:45 Seminar (Lab.)
16:50 - 17:20 Ceremony (IS L2 lecture room)
- Aug. 8 Wed.
09:20 - 17:00 Seminar (Lab.)
- 1. Introduction and Programming of Non-Neumann Computers
| Laboratory: |
Computing Architecture |
| Summary: |
According to the end of miniaturization of CMOS semiconductors, it is said that the performance improvement of conventional Von Neumann type computers (CPU and GPU) will stop. You will explore programming and execution of non-Von Neumann type computers such as FPGA computing, aproximate analog computers and systolic array type computers which are promissing architecture for future performance improvement of computers. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
4 |
| Qualification: |
Programming experience |
- 2. Algorithm design toward fault-tolerant systems
| Laboratory: |
Dependable System |
| Summary: |
Most existing systems, such as the Internet, clouds, and IoTs, are distributed systems, which consist of many computers that can communicate with each other. In such systems, it is inevitable that some computers will become faulty. In this seminar, we will design fault-tolerant algorithms, which can work correctly in distributed systems with some faults. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
None. |
- 3. Algorithm design toward molecular robots
| Laboratory: |
Dependable System |
| Summary: |
Recently DNA-based sensors, actuators, and logic gates have been developed, and now molecular robots, which are composed of these materials, are also under development. Since molecular robots are very weak compared to common robots, we need to design very simple algorithms to control molecular robots. In this seminar, we will design simple algorithms to achieve some tasks such as group partition. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
None. |
- 4. Why are FPGAs' performance different? But they are the same.
| Laboratory: |
Dependable System Laboratory |
| Summary: |
FPGA is an integrated circuit designed to be configured by a designer. Recently, FPGAs have been widely utilized for big data analytics, financial transaction, machine learning accelerator, and so on. In this seminar, we will provide two FPGAs whose performances are different. With designing ring oscillators on these FPGAs, we have the applicants consider the causes of the difference of their performance from measurement and circuit simulation. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
2 |
| Qualification: |
Nothing |
- 6. Construction of a Parallel Distributed Learning System
| Laboratory: |
Mobile Computing Laboratory |
| Summary: |
Mobile agents are computer software to execute processes on computers through a network. In addition, recently machine learning has been attracted public attention. In this seminar, the participants will construct a mobile agents-based parallel distributed learning system using a development platform, program library, and several devices such as the Raspberry Pi. The participants will confirm that mobile agents learn, run, and move through a network. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
3 |
| Qualification: |
None |
- 7. Simulation: Theory and Practice
| Laboratory: |
Mobile Computing Laboratory |
| Summary: |
The goal of this project is to gain the ability of developing customized simulators. The project involves learning simulation theory (elements of probability, generating random variables and discrete event simulation approach, etc.) and simulation programming practice. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Experience in C/C++/Java programming |
- 8. Development of Programming Assistant Bots
| Laboratory: |
Software Engineering Laboraroty |
| Summary: |
Software Engineering Laboratory develops a bot system to assist programming activities. It understands programming constructs under development and automatically detects misspelled statements and duplicated code fragments, and recommends useful components, for instance. You study programming analysis techniques and also the design and implementation of the bot system in this seminar. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
4 |
| Qualification: |
Programming experience |
- 9. Software Data Mining and Analysis using the Jupyter Notebook
| Laboratory: |
Software Engineering Laboratory |
| Summary: |
We will work on data analysis, visualization, and machine learning using the Jupyter Notebook. The techniques will include analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing such as Spark. The programming language Python and its libraries used for data manipulation, and finally machine learning techniques for learning patterns in the data. Let's mine real software development data to find useful knowledge! Our datasets will include large-scale (over thousand) analysis of library components from over 12 different programming languages (including JavaScript, R, Ruby, Python etc.) |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
5 |
| Qualification: |
Programming experience |
- 10. Blockchain Oriented Software Development
| Laboratory: |
Software Engineering Laboratory |
| Summary: |
An introductory seminar for software development based on blockchain technologies. Participants will try developing and deploying several smart contracts on Ethereum Testnet. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Programming experience |
- 11. Real-time anomaly detection from software profilers using deep learning
| Laboratory: |
Software Engineering Laboratory |
| Summary: |
Software profilers allow developers to quickly search and identify bottlenecks and leaks that consume much execution time in running software. Using a newly developed software profiler developed in Software Engineering Laboratory, we try detecting performance bugs automatically using deep learning frameworks. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
2 |
| Qualification: |
Programming experience of GAN and deep learning |
- 13. Virtual network programming for Cloud computing
| Laboratory: |
Software Design and Analysis |
| Summary: |
In this seminar, we will learn about virtual network programming, which is an emerging network technology for Cloud infrastructures. Cloud computing has been brought on by server virtualization, and for the next step, network virtualization has been also focused on. SDN (Software Defined Networking) technology, introducing programmability into network infrastructures, has therefore gathered a lot of attention. We will learn how to program virtual networks using OpenFlow, a typical implementation of SDN. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Nothing. But, programming experience in Ruby is preferred. |
- 14. Malware analysis practice ~building a testbed, traffic measurement, analysis, forensics, and visualization~
| Laboratory: |
Laboratory for Cyber Resilience |
| Summary: |
In this class, you can have some experiences related to malware analysis. For example, building an analysis environment (SDN-based closed network, honeypot, and so on), measurement and analysis of malware traffic, behavioral analysis of malwares, forensics, malware visualization, and so on. You can choose topics and difficulties from for beginner to professional.
|
| Date: |
2 days (8/6 - 7) |
| Capacity: |
5 |
| Qualification: |
you should have basic knowledges about malware and command line skills |
- 18. Introduction to machine learning and its application to EEG signals
| Laboratory: |
Augmented Human Communication Laboratory |
| Summary: |
We present a introduction to EEG data recording and EEG signal processing. Additionally, we learn machine learning techniques to detect human psychological states from EEG. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
2 |
| Qualification: |
Beginners are welcome. |
- 19. Machine Translation using Neural Networks
| Laboratory: |
Augmented Human Communication Laboratory |
| Summary: |
Participants will learn basics of machine translation techniques using neural networks and conduct some quick experiments to see its ability. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
2 |
| Qualification: |
Experience on programming in Python and Unix command line UI |
- 20. Dialogue policy learning based on Q-learning
| Laboratory: |
Augmented Human Communication Laboratory |
| Summary: |
Learn Q-learning, a learning algorithm of reinforcement learning, and apply the algorithm for the learning of decision making policy of dialogue systems. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Basic programming skills using Python |
- 21. Understanding Human Speech with Automatic Speech Recognition System based on Deep Learning
| Laboratory: |
Augmented Human Communication Laboratory |
| Summary: |
Let's develop a machine that can automatically recognize natural spoken language. We'll start from learning the basic technology (e.g., "What is human speech from machines point of view?", "What challenges in developing speech recognition?", etc). Finally, we'll work together on developing speech recognition system that understand human speech based on deep learning. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
2 |
| Qualification: |
Experience on programming in Python and Unix command line UI |
- 23. Learning wireless communications via software defined radio
| Laboratory: |
Network systems lab. |
| Summary: |
The purpose of this seminar is constructing wireless communication system on software defined radio. Attendee can learn the key technologies in communication engineering, such as digital modulation and demodulation scheme, OFDM (orthogonal frequency division multiplex),
multipath fading, and bit error analysis. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
2 |
| Qualification: |
Basic programming skills |
- 24. Experiencing the world of vision augmentation using a video see-through HMD
| Laboratory: |
Cybernetics and Reality Engineering |
| Summary: |
In recent years, a research field called human augmentation that empowers human abilities by science and technology has been actively studied. As an introduction to it, the participants of this seminar will develop a vision augmentation system that modulates the appearance of the surroundings in a variety of manners, by editing live video of a stereo camera and observing it through a head mounted display. How convenient or fun the system becomes will depend on your imagination. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
2 |
| Qualification: |
Experience in C or C++ is preferred |
- 25. Implementing Telexistence system
| Laboratory: |
Cybernetics and Reality Engineering |
| Summary: |
Work or showing some presence conveyed from a remote site is called Telexistence. As one kind of a boot camp, the seminar participants are going to add some functions and extra role to a movable robot equipped with a camera. A telexistance system you made may become for remote repair/fix or telecommunication robot. That depends on work you conducted. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
2 |
| Qualification: |
Experience in C or C++ is preferred |
- 26. Deep Learning Meets Augmented Reality
| Laboratory: |
Interactive Media Design Lab |
| Summary: |
Would you like to learn how to use machine learning and neural networks? Are you interested in Augmented Reality (AR) and User Interfaces? Then this 2-day seminar is for you! During this seminar you will learn how to build simple neural nets with Python, and AR applications with Unity. You get to experience AR presented on the Microsoft Hololens, and see how you can use neural nets to create more intuitive applications. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
6 |
| Qualification: |
Helpful (but not necessary) Skills:
- Programming experience in Python
- Programming experience in Unity |
- 27. Surface normal estimation from light shading
| Laboratory: |
Optical Media Interface |
| Summary: |
When the position of light source changes, the shading, the intensity in the image, also varies depending on the shape of the object. This implies that the 3D shape of the object can be observed by multipe light source directions. In this seminar, you can learn how to reconstruct the 3D shape from multipe shading images, which is called photometric stereo, and you will implement the algorithm. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Experience in either C, C++, or Python is a plus. |
- 28. Visualizing invisible using hyperspectral imaging
| Laboratory: |
Optical Medial Interface |
| Summary: |
Hyperspectral images have rich information about the target obejct. The reflection intensity of colored light reflects the structure of material that cannot be seen by a human eye. In this seminar, you can learn how to visualize invisible information from hyperspectral images by recording finely separated colored lights. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Experience in either C, C++, or Python is a plus. |
- 32. IoT application development based on Raspberry Pi
| Laboratory: |
Large-Scale Systems Management Laboratory |
| Summary: |
The Internet of Things (IoT), where a variety of things (e.g., people, sensors, mobile phones) can interact with one another through an Internet-like infrastructure, serves a fundamental architecture for a wide range of important applications such as e-health, smart home and environmental monitoring. In this project, we will use Raspberry Pi and some sensors like temperature and humidity sensors to develop a simple IoT application. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Programming experience in C or Python |
- 33. Decoding of "codes" coded in the brain
| Laboratory: |
Mathematical Informatics Lab. and Software Engineering Lab. |
| Summary: |
The purpose of this seminar is to learn principle of brain activity measurement and machine learning techniques.
The participants will work on analyzing brain activity data to decode the information of "codes" that subjects read during fMRI measurement. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
6 |
| Qualification: |
None. |
- 34. Understanding and Generation of Medical Images Using Deep Learning
| Laboratory: |
Imaging-based Computational Biomedicine |
| Summary: |
This seminar provides an opportunity to work with medical images and deep learning algorithms for image recognition and generation such as Convolutional Neural Network (CNN) or Generative Adversarial Network (GAN). Specifically, the potential projects include detection of anatomical landmarks in CT and MR images or generation of CT image from MR image. The attendant will cultivate practical programming skill and problem-solving skill through implementation of those algorithms. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
3 |
| Qualification: |
Programming experience is a plus |
- 35. Analysis of Musculoskeletal Structure using MR and Ultrasound Imaging
| Laboratory: |
Imaging-based Computational Biomedicine |
| Summary: |
In this seminar, the attendee will operate two medical image acquisition systems, 1) an MRI that is capable of acquiring images in the standing position, and 2) an ultrasound imaging system with three-dimensional reconstruction capability, to analyze musculoskeletal anatomy and function. First, the attendee will learn the basic imaging principles and the operation procedure of those imaging devices, then actually perform the acquisition, obtain images, and apply several analysis tools. |
| Date: |
3 days (8/6 - 8) |
| Capacity: |
3 |
| Qualification: |
Programming experience is a plus |
- 36. Data Mining Techniques
| Laboratory: |
Computatuinal Systems Biology |
| Summary: |
In the present era, almost every branch of science and technology has become data intensive requiring algorithms for data analysis and techniques for data visualizations. Under this program we will introduce clustering algorithms applicable to multivariate data such as principal component analysis (PCA) and hierarchical clustering, techniques of how to convert multivariate data into networks, network visualization and network clustering. So get modern skills for handling Big Data. |
| Date: |
2 days (8/6 - 7) |
| Capacity: |
3 |
| Qualification: |
Elementary knowledge of computer programming |
| Slide: |
slideshare |
- 37. Analysis of biomedical data using deep learning
| Laboratory: |
Computational Systems Biology |
| Summary: |
Recently, development of digitalized medical imaging technology and wearable devises for healthcare sensing allows us to obtain the huge amount of biomedical data. And it becomes more and more important to apply computational methods for statistical data analysis in order to improve the quality and efficiency of diagnosis. In this seminar, you can learn the fundamental knowledge of deep learning methods and experience the approach to use deep learning to pathological images and time series signals of biomedical devices.
|
| Date: |
3 days (8/6 - 8) |
| Capacity: |
3 |
| Qualification: |
with basic programming knowledges and skills |
- 38. Vision and AI for robotics applications
| Laboratory: |
Robotics Laboratory |
| Summary: |
In this seminar, we use robot vision and machine learning techniques to recognize objects from images. Such technology is necessary for a robot to work in daily-life environments.
|
| Date: |
2 days (8/6 - 7) |
| Capacity: |
4 |
| Qualification: |
Programming experience. |
Lab tour
Please visit Lab Tour.
Please visit Access Map.
Please visit FAQ(in Japanese).
Handling of the Seminar when public transport services are suspended, etc.
1. Handling of the Seminar when public transport services are suspended
The Seminar will be cancelled when the services of the Kintetsu lines (Keihanna, Nara, and Kyoto) and/or Nara Kotsu Bus lines (routes serving Gakken Kita-Ikoma Sta., Gakuenmae Sta., and Takanohara Sta.) are suspended due to a major disaster, accident, etc. The table below shows the handling of the seminar when public transport services are restored.
2. Handling of the seminar when a weather warning is issued
The Seminar will be cancelled when an Emergency Warning and a storm(or snowstorm) warning is issued in Ikoma City, Nara City and the area including those cities. The table below shows the handling of the seminar when the warning is cancelled.
| Status at 7:00 a.m./10:00 a.m. | Handling of the seminar |
| Public transport services are restored and the warning is cancelled at or before 7:00 a.m. | The Seminar are held for the whole day |
| Public transport services are restored and the warning is cancelled at or before 10:00 a.m. | The Seminar are held in the afternoon |
| Public transport services remain suspended and the warning remains in effect after 10:00 a.m. | The Seminar are cancelled for the whole day |
Note: Information on the TV, Internet, etc. is used to check if public transport services are suspended/restored or a warning is issued/cancelled.
What is an Emergency Warning?
The issuance of an Emergency Warning for an area indicates a level of exceptional risk of a magnitude observed
only once every few decades. Residents should pay attention to their surroundings and relevant information such as
municipal evacuation advisories and orders, and should take all steps necessary to protect life.
