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2018-07-29 11:31:53
2018-09-06
2018年7月28日，由深圳大學(xué)廣東省大數(shù)據(jù)協(xié)同創(chuàng)新中心、深圳大學(xué)大數(shù)據(jù)系統(tǒng)計算技術(shù)國家工程實驗室、CCF YOCSEF深圳等聯(lián)合舉辦了以“大數(shù)據(jù)與人工智能技術(shù)"為主題的“青年計算科技校企協(xié)同創(chuàng)新技術(shù)沙龍”。 研討會邀請了來自華南師范大學(xué)、香港城市大學(xué)、香港大學(xué)、西北工業(yè)大學(xué)/深圳大學(xué)、騰訊公司等粵港澳三地的5位學(xué)者做特邀報告。
學(xué)者網(wǎng)團隊湯庸報告題目為"社交網(wǎng)絡(luò)中的數(shù)據(jù)智能應(yīng)用"。該報告主要介紹學(xué)者社交網(wǎng)絡(luò)SCHOLAT中大數(shù)據(jù)應(yīng)用案例及其數(shù)據(jù)智能研究情況，得到學(xué)術(shù)界和工業(yè)界與會人員積極反響。
“大數(shù)據(jù)與人工智能技術(shù)"沙龍會場
研討會海報（節(jié)選）
題目：社交網(wǎng)絡(luò)中的數(shù)據(jù)智能應(yīng)用
特邀講者：湯庸，華南師范大學(xué)，計算機學(xué)院院長
學(xué)者網(wǎng)創(chuàng)始人，華南師范大學(xué)學(xué)位委員會副主席、計算機學(xué)院院長，二級教授、博士導(dǎo)師，獲武漢大學(xué)學(xué)士和碩士學(xué)位、中國科技大學(xué)博士學(xué)位。中國計算機學(xué)會首批杰出會員，青工委榮譽委員、YOCSEF廣州首屆主席，目前是協(xié)同計算專業(yè)委員會副主任，廣東省計算機學(xué)會常務(wù)副會長。享受國務(wù)院政府特殊津貼，入選首批教育部新世紀(jì)優(yōu)秀人才計劃，獲寶鋼教育獎、丁穎科技獎、南粵教壇新秀、中山大學(xué)教學(xué)名師等，主持的教學(xué)科研成果獲省部級一等獎4項、二等獎5項等。更多信息請見SCHOLAT個人主頁：www.1061937.com/ytang。
報告提要：主要以華南師范大學(xué)自主研發(fā)的面向?qū)W者的社交網(wǎng)絡(luò)——學(xué)者網(wǎng)(scholat.com)為背景，介紹了個人學(xué)術(shù)空間管理、學(xué)術(shù)團隊網(wǎng)站、課程教學(xué)網(wǎng)站及學(xué)術(shù)圈的創(chuàng)建，以及學(xué)術(shù)搜索和推薦服務(wù)等學(xué)者社交網(wǎng)絡(luò)及大數(shù)據(jù)應(yīng)用。
題目：Event Cube: a Conceptual Framework for Multi-sourced Event Management and Multi-dimensional Analysis
特邀講者：李青，香港城市大學(xué)電腦科學(xué)系，多媒體軟件工程研究中心創(chuàng)始主任
Qing Li is a Professor at the Department of Computer Science, and the Director of the Engineering Research Centre on Multimedia Software at the City University of Hong Kong, where he joined as a faculty member since Sept 1998. He received his B.Eng. from Hunan University (Changsha), and M.Sc. and Ph.D. degrees from the University of Southern California (Los Angeles), all in computer science. His research interests include multi-modal data management, conceptual data modeling, social media and Web services, and e-learning systems. He has authored/co-authored over 400 publications in these areas. He is actively involved in the research community and has served as an associate editor of a number of major technical journals including IEEE Transactions on Knowledge and Data Engineering (TKDE), ACM Transactions on Internet Technology (TOIT), Data and Knowledge Engineering (DKE), World Wide Web (WWW), and Journal of Web Engineering, in addition to being a Conference and Program Chair/Co-Chair of numerous major international conferences. He also sits in the Steering Committees of DASFAA, ER, ACM RecSys, IEEE U-MEDIA, and ICWL. Prof. Li is a Fellow of IET (UK), a senior member of IEEE (US) and a distinguished member of CCF (China).
報告提要： The publicly available data such as the massive and dynamically updated news and social media data streams (a.k.a. big data) covers the various aspects of social activities, personal views and Expressions, which points to the importance of understanding and discovering the
knowledge patterns underlying the big data, and the need of developing methodologies and techniques to discover real-world events from such big data, to manage and to analyze the discovered events in an efficient and elegant way. In this talk we introduce techniques of discovering events from the multi-modal big data and building an event cube model to support event queries and analysis, by addressing the tasks of data cleansing, data fusion, event detection and modeling. Preliminary experimental results on some of the tasks will be reported. We further explore and connect the important events discovered in a multimodal collection of inputs from various public sources, uncover their co-occurrence and track down the spatial and temporal dependency to answer the challenging questions of "how" and "why". A novel event cube (EC) model is devised to support various queries and analysis tasks of events; such events include those discovered by techniques of untargeted event detection (UED) and targeted event detection (TED) from multi-sourced data. More specifically, based on essential event elements of 5W1H, the EC model is developed to organize the discovered events from multiple dimensions, and to operate on the events at various levels of granularity, which facilitates analyzing and mining hidden/inherent relationships among the events effectively.
題目：Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks
特邀講者：Reynold Cheng，香港大學(xué)，計算機系副教授
Dr. Reynold Cheng obtained his MSc and PhD from Department of Computer Science of Purdue University in 2003 and 2005 respectively. He was an Assistant Professor in HKU since 2008-11. He was granted an Outstanding Young Researcher Award 2011-12 by HKU. He is the Chair of the Department Research Postgraduate Committee of HKU. He is an editorial board member of TKDE, DAPD and IS, and was a guest editor for TKDE, DAPD, and Geoinformatica. He is an area chair of ICDE 2017, a senior PC member for DASFAA 2015, PC
co-chair of APWeb 2015, area chair for CIKM 2014, area chair for Encyclopedia of Database Systems, program co-chair of SSTD 2013, and a workshop co-chair of ICDE 2014. He received an Outstanding Service Award in the CIKM 2009 conference.
報告提要： A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures, such as PCRW, PathSim, and HeteSim, can be used in various applications, including information retrieval, entity resolution, and product recommendation. These metrics are based on the use of meta paths, essentially a sequence of node classes and edge types between two nodes in a HIN. In this tutorial, we will give a detailed review of meta paths, as well as how they are used to define relevance. In a large and complex HIN, retrieving meta paths manually can be complex, expensive, and error-prone. Hence, we will explore systematic methods for finding meta paths. In particular, we will study a solution based on the Query-by-Example (QBE) paradigm, which allows us to discovery meta paths in an effective and efficient manner.
We further generalise the notion of a meta path to "meta structure", which is a directed acyclic graph of object types with edge types connecting them. Meta structure, which is more expressive than the meta path, can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we also study an algorithm with data structures proposed to support their evaluation. Finally, we will examine solutions for performing query recommendation based on meta paths. We will also discuss future research directions in HINs.
題目：大規(guī)模結(jié)構(gòu)化學(xué)習(xí)及其高效聚類算法
特邀講者：聶飛平，西北工業(yè)大學(xué)教授，深圳大學(xué)兼職特聘教授，青年**
聶飛平，西北工業(yè)大學(xué)教授，深圳大學(xué)兼職特聘教授，中組部青年****。2008年至2009年曾在新加坡南洋理工大學(xué)從事研究工作，之后在美國德州大學(xué)阿靈頓分校先后擔(dān)任研究助理教授，研究副教授，研究教授，2015年入選中組部青年****。主要研究興趣為模式識別與機器學(xué)習(xí)中的理論和方法設(shè)計，并將所設(shè)計的方法成功應(yīng)用于圖像分割與標(biāo)注、多媒體信息理解與檢索、生物信息學(xué)等領(lǐng)域的實際問題中。已在TPAMI、IJCV、TNNLS、ICML、NIPS、SIGKDD等國際頂尖期刊和會議上發(fā)表學(xué)術(shù)論文百余篇，其中在中國計算機學(xué)會（CCF）推薦的A類期刊和會議上發(fā)表論文100余篇。據(jù)Google Scholar統(tǒng)計，論文總引用為10000余次，H指數(shù)為52。常年應(yīng)邀擔(dān)任相關(guān)領(lǐng)域頂級期刊和會議的審稿專家或程序委員會委員，并同時應(yīng)邀擔(dān)任IEEE Transactions on Neural Networks and Learning Systems、Information Science等多個國際一流SCI期刊的編委。
報告提要：數(shù)據(jù)聚類是機器學(xué)習(xí)和數(shù)據(jù)挖掘研究中的一個基本問題。在數(shù)十年的研究中，已經(jīng)提出了很多聚類方法，而基于圖的聚類方法是其中最有效的方法之一。傳統(tǒng)的圖聚類方法需要用戶事先給定一個圖，然后采用松弛技巧將問題轉(zhuǎn)化為一個可解的問題。由于一般的圖不具有結(jié)構(gòu)性，因此得到的解是連續(xù)的，需要利用離散技術(shù)得到最終的聚類結(jié)果，從而使得聚類結(jié)果十分依賴于初始化。針對這些問題，我們提出了一種結(jié)構(gòu)化圖學(xué)習(xí)方法，通過學(xué)習(xí)一個具有結(jié)構(gòu)的圖，使得我們可以直接得到聚類結(jié)果，不再依賴于初始化。該新方法具有性能優(yōu)越，穩(wěn)定等優(yōu)點，并且其中的結(jié)構(gòu)化圖學(xué)習(xí)思想可以應(yīng)用在其他基于圖的機器學(xué)習(xí)方法中，具有很大的應(yīng)用價值和啟發(fā)性。
題目：騰訊知識圖譜的AI應(yīng)用
特邀講者：成杰峰，騰訊專家級研究員，騰訊AI安全負責(zé)人
成杰峰，香港中文大學(xué)博士，香港大學(xué)博士后。曾任中國科學(xué)院副教授，華為諾亞方舟實驗室（香港）研究員，現(xiàn)任職騰訊專家研究員、騰訊AI安全負責(zé)人。研發(fā)了一系列分析大規(guī)模巨型圖的核心技術(shù)。負責(zé)研發(fā)了華為VENUS圖計算系統(tǒng)、騰訊安全AI引擎、騰訊云星圖知識圖譜的研發(fā)。曾主持過包括國家自然科學(xué)基金項目、廣東省科技重大專項等多項國家、省重點項目；在TKDE、JVLDB、VLDB、ICDE、KDD、CIKM等國際頂刊/會上發(fā)表40余篇學(xué)術(shù)論文，累計引用千余次，產(chǎn)生了20余項國內(nèi)國際專利。
報告提要：將介紹騰訊云星圖知識圖譜在AI方面的一些應(yīng)用。
