不過, 手上還累積了幾件事要馬上做:
1. 94 國科會結案報告。(差不多快完成了)
2. 一篇論文審查! (已經過了兩個禮拜, 編輯在催)
3. 出國参加會議的報告書。
對了, 有一個好消息是: 等了兩個多星期的明信片今天寄到了。
Google AdSense 寄來我的 PIN (Personal Identification Number) 了 !


英文能力是從生活中累積而來的, 而不是從課堂中獲得的!
剛剛在 Yahoo奇摩新聞 看到一則 路透社( Reuters ) 有關雪豹 ( snow leopard ) 的報導: 喀什米爾瀕臨絕種的雪豹將受到印度政府的特別保護, 這段新聞讓我想起以前在念書時, 搜尋台灣雲豹相關報導的那段日子。在我印象中, 台灣雲豹在野外被發現的紀錄已經是幾十年前的事了, 因此, 應該已經算野外絕跡!

昨天上午與學生 meeting, 由於小和的實驗上沒有具體進度, 所以我們就針對微軟所發表的一篇論文討論。這篇論文的首位作者是 Kumar Chellapilla, 任職於 Microsoft Research, 研究主題之一就是 Human Interaction Proof, 至今一共發表了四篇論文, 昨天我們討論的論文是:Abstract摘要的第一句話說明HIPs 變得越來越常見的原因。
Human interaction proofs (HIPs) have become common place on the internet due to their effectiveness in deterring automated abuse of online services intended for humans. However, there is a co-evolutionary arms race in progress and these proofs are becoming more difficult for genuine users while attackers are getting better at breaking existing HIPs. We studied various popular HIPs on the internet to understand their strength and human friendliness. To determine HIP strength, we adopted a direct approach of building computer attacks using image processing and machine learning techniques. To understand human-friendliness, a sequence of users studies were conducted to investigate HIP character recognition by humans under a variety of visual distortions and clutter commonly employed in reading-based HIPs. We found that many of the online HIPs are pure recognition tasks that can be easily broken using machine learning. The stronger HIPs tend to pose a combination of segmentation and recognition challenges. Further, the HIP user studies show that given correct segmentation, computers are much better at HIP character recognition than humans.































