商品描述
This book systematically explores how to build reliable, cross-platform multimodal event detection systems that operate under open-world conditions in today's complex social media environment. It delves into three core challenges: information fragmentation caused by incomplete individual posts; cross-platform heterogeneity arising from the diverse ways different platforms (e.g., Twitter, Flickr) represent events; and the challenge of open-world discovery driven by the constant emergence of new event types. This book not only reveals how these challenges interact to constrain traditional methods but also provides specialized, cutting-edge solutions for each. To address these challenges, this book proposes a progressive methodological framework consisting of three specialized models (MFEK, SSMC, and DAEO). By integrating external knowledge, employing self-supervised learning, and using uncertainty-aware new category discovery mechanisms, this framework offers a comprehensive guide from theory to practice. Furthermore, the book contributes several purpose-built datasets (SED, CSED, and MSED) designed to simulate real-world scenarios, aiming to foster reproducible research and bridge the gap between laboratory results and practical deployment. This work serves as a key reference for researchers and practitioners seeking to understand and build the next generation of intelligent event monitoring systems. This book targets researchers, graduate students in the fields of Artificial Intelligence, Natural Language Processing (NLP), Multimedia Computing, and Data Mining, who are focusing on multimodal analysis and social media analytics. Data scientists, software engineers, and industry practitioners working on crisis management systems, public opinion monitoring, misinformation detection, and real-time news aggregation services will also find this book to be a valuable resource.
商品描述(中文翻譯)
本書系統性地探討如何在當今複雜的社交媒體環境中，建立可靠的跨平台多模態事件檢測系統，這些系統能夠在開放世界條件下運作。它深入分析了三個核心挑戰：由於個別貼文不完整而造成的信息碎片化；不同平台（例如 Twitter、Flickr）以多樣化方式呈現事件所產生的跨平台異質性；以及由於不斷出現的新事件類型而帶來的開放世界發現挑戰。本書不僅揭示了這些挑戰如何相互作用以限制傳統方法，還為每個挑戰提供了專門的前沿解決方案。
為了解決這些挑戰，本書提出了一個漸進的方法論框架，包含三個專門模型（MFEK、SSMC 和 DAEO）。通過整合外部知識、採用自我監督學習以及使用不確定性感知的新類別發現機制，這個框架提供了一個從理論到實踐的全面指南。此外，本書還貢獻了幾個專門構建的數據集（SED、CSED 和 MSED），旨在模擬現實世界場景，促進可重複的研究，並縮小實驗室結果與實際部署之間的差距。本研究作為尋求理解和構建下一代智能事件監控系統的研究人員和實踐者的重要參考。
本書的目標讀者為人工智慧、自然語言處理（NLP）、多媒體計算和數據挖掘領域的研究人員及研究生，特別是專注於多模態分析和社交媒體分析的讀者。從事危機管理系統、輿情監測、錯誤信息檢測和即時新聞聚合服務的數據科學家、軟體工程師和行業實踐者也會發現本書是一本有價值的資源。
作者簡介
Zehang Lin received the Bachelor's degree from Guangdong Polytechnic Normal University (Guangdong, China), the Master's degree from Guangdong University of Technology (Guangdong, China), and the PhD degree from the Hong Kong Polytechnic University (Hong Kong SAR). He is currently a lecturer at Hanshan Normal University. His research work has been published in professional journals and conferences such as IEEE Transactions on Multimedia, ACM Multimedia (ACM MM), and Information Processing & Management (IPM). He has served as a reviewer for several top conferences and journals, including AAAI, ACM MM, ICME, ICMR, IPM, and IEEE TAI. His research interests include event detection, cross-domain retrieval, unsupervised learning, and deep learning applications. Qing Li received his BEng. degree from Hunan University (Changsha, China), MSc and PhD degrees from the University of Southern California (Los Angeles, USA), all in computer science. He is currently a Chair Professor at the Hong Kong Polytechnic University, a visiting professor of the Zhejiang University, a guest professor of the University of Science and Technology of China, and an adjunct professor of the Hunan University. His research interests include multi-modal data modeling, multimedia retrieval and management, and e-learning systems. Dr. Li has published over 500 papers in technical journals and international conferences in these areas, and is actively involved in the research community by serving as a journal reviewer, programme committee chair/co-chair, and as an organizer/co-organizer of several international conferences. Currently he serves as the Chairman of the Hong Kong Web Society, a councillor of the Database Society of Chinese Computer Federation, and a Steering Committee member of the international WISE Society. He is a Fellow of IEEE, AAIA, and IET.
作者簡介(中文翻譯)
林澤航於廣東工業大學（中國廣東）獲得學士學位，於廣東科技大學（中國廣東）獲得碩士學位，並於香港理工大學（香港特別行政區）獲得博士學位。他目前是寒山師範大學的講師。他的研究工作已發表於專業期刊和會議，如《IEEE多媒體期刊》、《ACM多媒體》（ACM MM）和《資訊處理與管理》（IPM）。他曾擔任多個頂尖會議和期刊的審稿人，包括AAAI、ACM MM、ICME、ICMR、IPM和IEEE TAI。他的研究興趣包括事件檢測、跨領域檢索、無監督學習和深度學習應用。 李青於湖南大學（中國長沙）獲得工程學士學位，並於南加州大學（美國洛杉磯）獲得碩士和博士學位，均為計算機科學。他目前是香港理工大學的講座教授，浙江大學的訪問教授，中國科學技術大學的客座教授，以及湖南大學的兼任教授。他的研究興趣包括多模態數據建模、多媒體檢索與管理，以及電子學習系統。李博士在這些領域已發表超過500篇技術期刊和國際會議論文，並積極參與研究社群，擔任期刊審稿人、程序委員會主席/聯合主席，以及多個國際會議的組織者/聯合組織者。目前，他擔任香港網絡協會的主席、中國計算機學會數據庫學會的理事，以及國際WISE協會的指導委員會成員。他是IEEE、AAIA和IET的會士。
