COMPANY:

developer and manufacturer of printers, printer peripherals, and related consumables

A Vietnamese student with an N3 degree from the University of Aizu, Faculty of Computer Science. The university graduated from Hanoi University of Science and Technology, the top science and engineering department in Vietnam. The research uses deep learning to evaluate the quality of screen content and VR images. He specializes in Python, Matlab, and C ++. Impressed by the diligent attitude like the Japanese. decided to work in Japan and aiming to join the company in April or October 2021.

Profile

COUNTRY / REGION
Vietnam
SEX
Male
UNIVERSITY
The University of Aizu
SPECIALIZATION
Computer Science
ACADEMIC LEVEL
Masters

MESSAGE

I have been studying abroad in Japan at The University of Aizu since 2016 and finding an opportunity to work in Japan was one of my main goals when I first decided to come here to study. At the time I was focused on doing research and thus English was my priority. During my time at Aizu University, I was mainly using English for communication, both in lectures and conversations with other students. Prior to coming to Japan, I have been learning Japanese as a hobby and got JLPT N3. However, due to a lack of practice, my Japanese speaking skill was poor and proved challenges for me to find work in Japan. I started looking for jobs in Japan on my own around May 2020, it was when I was still considering whether to start working or continue with my academic study. I was doing things very casually, thinking I still have a backup plan even if I don’t manage to find a job, things would still work out somehow and I can just go back to my study and research. Looking back, this mindset was problematic and was one of the main reasons I struggled to land an offer back then. I would continue to fail several interviews and was totally dispirited after about 3 months. At this point, I was pretty much stressed out, and with the complication of the COVID19 pandemic, I was discouraged even apply for new positions. After finally finishing my master thesis draft, I decided to fully dedicate my time to job hunting. After analyzing my previous failures, I came to the realization that my Japanese skill, especially my approach to Japanese companies’ interviews, was severely lacking. After consulting with Kusakari-sensei, a Japanese teacher at Aizu University, I was introduced to the concept of Recruiting Agents, whose main purpose is to assist newly graduated students at job hunting. One of them was ASIA to JAPAN. After passing the document screening, I was invited to a Skype interview with an ASIA to JAPAN staff. The purpose was to check my conversation skills and other information to see if I was a fit at Japanese companies. Fortunately, I managed to pass this round. I still remember the words of the staff in charge of my interview, Mr. Fukushima: “I believe together we can find a good job for you”, which has been a great source of encouragement for me at the time. The next step was to prepare for the interviews. I was assigned to a mentor at ASIA to JAPAN, Mr. Kanbara, to help with brainstorming ideas and answers for the common interview questions. This was one of the most important steps to finding work in Japan, which I had overlooked in the past. With the help of the staff from ASIA to JAPAN and the guidance of my mentor, I was selected for interviews with 5 companies and proceeded to the last round for 2 of them. In the end, I was offered a position at the R&D department of R Corp. ASIA to JAPAN also let me join a Japanese course on the business manner and speaking practice, which was really helpful and informative. I have since then moved from Aizuwakamatsu to Tsukuba, and I will be joining Riso from October 1st, 2021. I would like to thank everyone in ASIA to JAPAN for this wonderful opportunity and amazing experience.

FINAL YEAR PROJECT

画質評価は、画像処理で人気のある研究トピックです。ただし、これまでのほとんどの研究は普通の画像に焦点を当てており、全方位画像に焦点を当てた研究はごくわずかです。普通の画像の場合とは異なり、ユーザーは見る瞬間に360度の画像の一部しか観察できないため、画像の特定の領域に焦点を合わせる傾向があります。普通の画像のほとんどの既存のモデルは通常、画像のすべての領域を同等に扱うため、これにより、全方位画像の品質スコアを予測することは困難な作業になります。本論文では、深層学習に基づく全方向性画質評価モデルを提案する。このモデルは、入力画像の中央領域の特徴を学習することに焦点を当てています。モデルは最初に、入力画像からサンプリングされたパッチの品質スコアを自動的に予測します。次に、画像の品質スコアは、パッチの品質スコアをそれらの位置に基づいて加重平均することによって計算されます。実験結果は、提案されたモデルが全方位画像の品質スコアを予測するための非常に有望な精度を提供することを示しています。

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