Newsletter No. 390

No. 390, 4.1.2012 5 陳麗雲教授於英國劍橋大學取得工程學學士、碩士及博士學位，曾獲菲臘親王獎學金及國王學 院獎學金，她曾於劍橋大學資訊工程學系語言、視像及機械人小組工作，該組其時負責人為已 故的Prof. Frank Fallside，他帶領陳教授了解神經網絡這門當時許多人仍抱懷疑態度的研究範 疇。陳教授現為中大計算機科學與工程學系副教授，兼任工程學院副院長（教育）。 陳教授的研究興趣為神經網絡 模型和數據挖掘技巧，應用於 時序分析、圖像分析及辨認、金 融工程及生物資訊，包括組合 管理、危機分析、基因表達數據 分析等。陳教授研究成果纍纍， 曾發表多篇研究報告，又研發嶄 新的應用程式，但她仍然努力不 懈，致力研究人類及電腦如何從 資訊發現知識。陳教授曾參與 編輯多本著作，並於學術期刊及 會議發表逾百篇論文。 Prof. Chan Lai-wan received her degrees of BA, MA and PhD in Engineering from the Cambridge University in England. She was a recipient of the Prince Philip Scholarship and the King’s College Studentship. She worked in the Speech, Vision and Robotics Group in Information Engineering led by the late Prof. Frank Fallside, who introduced neural networks to her when others were skeptical of this approach. She is now a professor in the Department of Computer Science and Engineering and is concurrently serving as the associate dean (education) in the Faculty of Engineering of CUHK. Her research interest is in the modeling of neural networks and the techniques in data mining. Applications are in time series analysis, image analysis and recognition, financial engineering and bioinformatics, including portfolio management, risk analysis, and gene expression data analysis etc. Over the years, although many new research results have been published and new applications have been made, she is still working towards the goal of finding how human and computers discover knowledge from data. She has co-edited a number of books and has published well over 100 referenced journal and conference papers. www.iso.cuhk.edu.hk/english/features/style-speaks/index.html It is undeniable that computers’ learning is bounded by the materials that have been fed into them. Professor Chan explains, for example, when you teach a computer to identify the letter A, it would distinguish it from other letters fed into it by sorting out the distinctive features of the image of the letter A. However, with the knowledge acquired, computers may sometimes give you unexpected answers. Two different networks may dish out different information even though they have acquired the same knowledge and have been fed the same information for processing. For example, a network may tell you that the number after 1, 2, 4 in an arithmetic progression is 8, while another one may tell you it is 7. Both answers are correct. It is because the way a computer thinks of an answer is determined by its learning process. When there is more than one answer, it will give you the one that it has first learned. Just like their human counterparts, neural networks can be divided into fast learners and slow learners. The major cause is that different networks have different setups, which would affect the learning process, including its accuracy and learning speed when faced with different problems. ‘It’s like certain people are good at numbers, while some are good at literature. In computing terms, they’re the results of different setups.’ Besides, it may take 10 years, 20 years or more for a person to solve a very complex problem. This also holds true for computers. But if you break the problem down into smaller parts and tackle them one by one, it will be easier to solve. In the past, computers need human experts to break down problems for them. Recently, a computational model that can automatically break problems down has been created. ‘This is the new direction in the development of neural networks and is a breakthrough in artificial intelligence research.’ Professor Chan’s another research focus is data mining. She explains that the interest in data mining began in the early 1990s. With the advance of information technology, computers are now more powerful in storing and sifting through large amounts of data. Data mining refers to the technique of digging out hidden messages or useful intelligence from an enormous amount of data. Data mining and neural networks are closely linked. The former is arguably the result of the latter. Neural networks would be very useful tools for data mining if they have learned to identify a particular feature from a large amount of data and recognize a particular pattern. Professor Chan focuses on data mining in financial applications, trying to locate the relationship between or the common factors of different shares or different types of shares. She uses algorithms to single out certain independent components from data. Each of these components is completely uncorrelated to others. And they are indicators of the rise and fall of share prices. It is useful for risk management. ‘In the past, it was believed that each component was technically uncorrelated to each other. But we found that it is better to extract and process independent components than to process uncorrelated components,’ said Professor Chan. The Internet is so big and all encompassing today. Wherever you go on the Internet, you leave traces behind, such as your e-mail messages, online transaction records, web browsing history, social networking website information. All of them are mineable data that are useful for marketing, or product and service design. Without our notice, we leave lasting digital footprints on the net. In that case, do we have any privacy at all? ‘Data mining does not target specific individuals, but a group of people. We’re trying to have a better understanding of people from their data,’ explains Professor Chan. ‘Data mining has a wide range of potential applications. For instance, now there is controversy over voter registration. By using the technique of data mining, we can find out under what circumstances or with what elements, foul play would be possible. This will enable us to take better precautions against it.’ Forms of Address For secretaries and speech-writers, Debrett’s Correct Form is a valuable resource. It may prove to be a beam of hope and inspiration that descends on a much scratched scalp. Despite its heavy tilt towards the British way, the book does provide clear references on formal and informal addresses. Its section on ‘Academics’ details how Chancellors, Vice-Chancellors, College Heads and Professors should be addressed formally and socially at the beginning of letters, on envelopes and in speech. In the section ‘Medicine’, the book reiterates the British insistence on two distinct forms of address for the broad disciplines of medicine (including general practice) and surgery. A physician with a medical degree is addressed ‘Doctor’ in speech, whereas a surgeon (including gynaecologist and dental surgeon) is addressed as ‘Mr/Miss/Mrs’. Before a formal speech is given, a number of personalities are mentioned in the preamble. Who should be included, and in what order? The advice of Debrett’s Correct Form is to keep the list short, though ‘subject to avoiding any omission which would cause justifiable offence’, and begin with the host (if the speech is not delivered by the host him/herself), referred to by his office. For example: Mr. Chairman Mr. Vice-Chancellor Mr. Master (that is, if spoken at a College dinner) Editor 陳麗雲教授與她的學生 Prof. Chan Lai-wan (centre) and her students In Plain View