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Social Information Filtering
Social Information Filtering Hang Li Noah’s Ark Lab Huawei Technologies 1 Talk Outline Huawei Noah’s Ark Lab Our Vision and Approach Social Information Filtering Weibo Robot 2 Huawei Noah’s Ark Lab Mission: ◦ Building “Noah’s Ark” to overcome the deluge of information Research Areas: ◦ Data Mining ◦ Machine Learning ◦ Natural Language Processing ◦ Information Retrieval ◦ Social Media ◦ Mobile Intelligence ◦ Human Computer Interaction Location: Hong Kong, Shenzhen, Beijing Established: June 2012 Our Vision Vision ◦ From Big Data to Deep Knowledge Goal ◦ Building intelligent assistants on mobile devices using big data on the cloud ◦ Passing Turing tests in specific domains 4 Our Approach Big Machine Learning ◦ Acquiring knowledge by large scale machine learning ◦ Example: Google, learning concept of cat from millions of images Human Computation ◦ Humans and machines jointly accomplish tasks ◦ Example: Univ. of Washington, solving large scale gene problem by leveraging human power Lifelong Learning ◦ Continuously accumulating knowledge by machine learning ◦ Example: CMU, NELL (Never Ending Language Learning) 5 Example: Intelligent Mobile Device Video 6 Two Challenges: Information Overload and Information Shortage 7 Social Information Filtering Valuable information is shared on social media Can get information by following key people Taking social media as information filter Building information assistant for each user or each group of users based on the ‘social information filter’ 8 Weibo Robot •Intelligent Information Assistant -
Conference Digest
Conference Digest 2015 IEEE International Conference on Mechatronics and Automation IEEE ICMA 2015 Beijing, China August 2 - 5, 2015 Cosponsored by IEEE Robotics and Automation Society Beijing Institute of Technology Kagawa University, Japan Technically cosponsored by The Institute of Advanced Biomedical Engineering System, BIT Intelligent Robotics Institute, Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, BIT State Key Laboratory of Intelligent Control and Decision of Complex Systems, BIT Tianjin University of Technology Harbin Engineering University Harbin Institute of Technology State Key Laboratory of Robotics and System (HIT) The Robotics Society of Japan The Japan Society of Mechanical Engineers Japan Society for Precision Engineering The Society of Instrument and Control Engineers University of Electro-Communications University of Electronic Science and Technology of China Changchun University of Science and Technology National Natural Science Foundation of China Chinese Mechanical Engineering Society Chinese Association of Automation Life Electronics Society, Chinese Institute of Electronics IEEE ICMA 2015 PROCEEDINGS Additional copies may be ordered from: IEEE Service Center 445 Hoes Lane Piscataway, NJ 08854 U.S.A. IEEE Catalog Number: CFP15839-PRT ISBN: 978-1-4799-7097-1 IEEE Catalog Number (CD-ROM): CFP15839-CDR ISBN (CD-ROM): 978-1-4799-7096-4 Copyright and Reprint Permission: Copyright and Reprint Permission: Abstracting is permitted with credit to the source. Libraries are permitted to photocopy beyond the limit of U.S. copyright law for private use of patrons those articles in this volume that carry a code at the bottom of the first page, provided the per-copy fee indicated in the code is paid through Copyright Clearance Center, 222 Rosewood Drive, Danvers, MA 01923. -
Zhuolin Jiang
Zhuolin Jiang CONTACT 50 Moulton St, Cambridge, MA, 02138 Phone: +16178732017 INFORMATION Email: [email protected] Website: http://www.umiacs.umd.edu/~zhuolin/ RESEARCH Computer Vision: Object/Human Detection and Tracking, Object Categorization, Scene INTEREST Modeling and Recognition, Image Tagging/Annotation, Face Detection/Recognition/Verification, Action Detection/Recognition Machine Learning: Discriminative Feature Learning, Supervised Clustering, Submodular Op- timization and Matroid Theory, Sparse and Low-Rank Representation, Deep Learning, On- line/Incremental/Active Learning, Transfer Learning EMPLOYMENT Raytheon BBN Technologies, USA 04/2015 - Present Scientist II Noah’s Ark Research Lab, Huawei, Hong Kong 01/2013 - 03/2015 Researcher University of Maryland, College Park, USA 11/2011 - 12/2012 Assistant Research Scientist (a parallel rank at the Univ. of Maryland to Assistant Professor) University of Maryland, College Park, USA 06/2010 - 10/2011 PostdoctoralResearchAssociate Advisor:Prof.LarryS.Davis EDUCATION University of Maryland, College Park (UMD), USA 09/2008 - 05/2010 Exchange PhD student in Computer Science Advisor: Prof. Larry S. Davis South China University of Technology (SCUT), China 09/2004 - 06/2010 Ph.D. in Computer Science GPA: 3.51/4.0 Advisor: Prof. Shaofa Li Thesis: Research on Computer Vision-based Human Action Detection and Recognition China University of Petroleum (CUP), China 09/2000 - 06/2004 B.Eng. in Computer Science GPA: 3.53/4.0 Rank: 1/113 Advisor: Prof. Faming Gong Thesis: Research on Triangular Mesh Subdivision PUBLICATIONS (Number of Citations: 1790 based on Google Scholar) Jingjing Zheng, Zhuolin Jiang, Rama Chellappa. Submodular Attribute Selection for Visual Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (to appear), 2017. -
Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models
Cybersecurity Through Nimble Task Allocation: Semantic Workflow Reasoning for Mission-Centered Network Models Yolanda Gil Information Sciences Institute University of Southern California [email protected] http://www.isi.edu/~gil USC Information Sciences Institute Yolanda Gil 1 Knowledge Technologies at ISI: From Basic Research to Transition AFOSR DARPA DOD 2000 2005 TRELLIS: Basic JIST: Capturing decisions Transition to IC’s research on information and evidence in RDEC experimental analysis capture intelligence analysis platform NSF AFRL DOD 2002 2008 Semantic workflows for Windward: Scalable Transition to IC’s large-scale scientific data Knowledge Discovery RDEC experimental analysis through Distributed Analysis platform AFOSR W3C 2005 2010 MathTrust: Basic research Provenance Incubator on social trust of open leading to standards for information sources provenance on the web AFOSR 2011 Mission - Centered USC InformationNetwork Sciences Models Institute Yolanda Gil 2 http://www.w3.org/2005/Incubator/prov/wiki W3C Provenance Standard Need standard representation for origins of information • Trust in open systems (Web), computational science, process validation and compliance W3C Provenance Incubator launched in 2009 (Y. Gil, Chair) • Collected use cases for provenance • Developed provenance requirements for the Web • Created mappings for existing provenance vocabularies • State-of-the-art report • Provenance in Web architecture Final report in Dec 2012 proposed charter for a Working Group on provenance WG started 4/2011 to develop PROV -
AAAI-16 / IAAI-16 / EAAI-16 Program
Thirtieth AAAI Conference on Artificial Intelligence Twenty-Eighth Conference on Innovative Applications of Artificial Intelligence The Sixth Symposium on Educational Advances in AI AAAI-16 / IAAI-16 / EAAI-16 Program February 12–17, 2016 Phoenix Convention Center / Hyatt Regency Phoenix Phoenix, Arizona, USA Sponsored by the Association for the Advancement of Artificial Intelligence Cosponsored by AI Journal, the National Science Foundation, Baidu, IBM Research, Infosys, Lionbridge, Microsoft Research, Disney Research, University of Southern California/Information Sciences Institute, Yahoo Labs!, ACM/SIGAI, CRA Computing Community Consortium (CCC), and David E. Smith MORNING AFTERNOON EVENING (AFTER 5:00 PM) Friday, February 12 Tutorial Forum Tutorial Forum Student Welcome Reception Workshops Workshops AAAI/SIGAI DC AAAI/SIGAI DC Saturday, February 13 Tutorial Forum Tutorial Forum Speed Dating Workshops Workshops Opening Reception AAAI/SIGAI DC AAAI/SIGAI DC EAAI EAAI Open House Invited Talks Open House Robotics Exhibits Robotics Exhibits Sunday, February 14 AAAI / IAAI Welcome / AAAI Awards Lunch with a Fellow Presidential Address: Dietterich IAAI Invited Talk: Rao AAAI Invited Talk: Krause AAAI / IAAI Technical Program AAAI / IAAI Technical Program AI Job Fair Classic Paper/Robotics Talks What’s Hot Talks / Robotics Talks Poster / Demo Session 1 EAAI EAAI Fellows Dinner Robotics/Vendor Exhibits Robotics/Vendor Exhibits Video Competition Viewing Video Competition Viewing Monday, February 15 Women’s Mentoring Breakfast AAAI Invited Talk: -
2020 AAAI Executive Council Candidate Statements
2020 AAAI Executive Council Candidate Statements The AAAI Nominating Committee (consisting of Subbarao Kambhampati (Chair), Eugene Freuder, Claire Monteleoni, Cynthia Rudin, and Matthijs Spaan) has prepared a slate of two (2) candidates for the position of AAAI President-Elect and eight (8) candidates for Executive Councilor. The term of President-Elect is two years. The President-Elect automatically becomes President after the expiration of the current President's term. The President then holds the Past President position for the next two years. Councilors serve a three-year term of office. Please vote for no more than one (1) person for the position of President-Elect, and four (4) people for the position of Councilor. Write-in votes are allowed. Each candidate was asked "What is the most pressing issue facing AI and AAAI today?" Responses appear in the paragraphs labeled "Statement.” Please read this information before voting at the AAAI Membership URL sent to you via email. President-Elect Candidates (2) • Francesca Rossi • Qiang Yang Executive Councilor Candidates (8) • Edith Elkind • Susan L. Epstein • Christopher Geib • Laura Hiatt • Martin Michalowski • Barry O’Sullivan • Balaraman Ravindran • Kartik Talamadupula President-Elect Candidates (2) Francesca Rossi Francesca Rossi is an IBM fellow and the IBM AI Ethics Global Leader at the T.J. Watson IBM Research Lab. Before joining IBM, she was a professor of computer science at the University of Padova, Italy. Her AI research interests focus on preferences, multi-agent systems, computational social choice, constraint reasoning, fairness, and value alignment. Overall, she has published over 200 scientific articles on these topics. -
A 20-Year Community Roadmap for Artificial Intelligence Research in the US
A 20-Year Community Roadmap for Begin forwarded message: From: AAAI Press <[email protected]> Subject: Recap Date: July 8, 2019 at 3:52:40 PM PDT To: Carol McKenna Hamilton <[email protected]>Artificial Intelligence Research in the US Could you check this for me? Hi Denise, While it is fresh in my mind, I wanted to provide you with a recap on the AAAI proceedings work done. Some sections of the proceedings were better than others, which is to be expected because authors in some subject areas are less likely to use gures, the use of which is a signicant factor in error rate. The vision section was an excellent example of this variability. The number of problems in this section was quite high. Although I only called out 13 papers for additional corrections, there were many problems that I simply let go. What continues to be an issue, is why the team didn’t catch many of these errors before compilation began. For example, some of the errors involved the use of the caption package, as well as the existence of setlength commands. These are errors that should have been discovered and corrected before compilation. As I’ve mentioned before, running search routines on the source of all the papers in a given section saves a great deal of time because, in part, it insures consistency. That is something that has been decidedly lacking with regard to how these papers have been (or haven’t been) corrected. It is a cause for concern because the error rate should have been improving, not getting worse. -
4Th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Preface 4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications http://bigdata-mining.org/ Recent years have witnessed a dramatic increase in our ability to collect data from various sensors, devices, in different formats, from independent or connected applications. This data flood has outpaced our capability to process, analyze, store and understand these datasets. Consider the Internet data. The web pages indexed by Google were around one million in 1998, but quickly reached 1 billion in 2000 and have already exceeded 1 trillion in 2008. This rapid expansion is accelerated by the dramatic increase in acceptance of social networking applications, such as Facebook, Twitter, Weibo, etc., that allow users to create contents freely and amplify the already huge Web volume. Furthermore, with mobile phones becoming the sensory gateway to get real-time data on people from different aspects, the vast amount of data that mobile carrier can potentially process to improve our daily life has significantly outpaced our past CDR (call data record)-based processing for billing purposes only. It can be foreseen that Internet of things (IoT) applications will raise the scale of data to an unprecedented level. People and devices (from home coffee machines to cars, to buses, railway stations and airports) are all loosely connected. Trillions of such connected components will generate a huge data ocean, and valuable information must be discovered from the data to help improve quality of life and make our world a better place. For example, after we get up every morning, in order to optimize our commute time to work and complete the optimization before we arrive at office, the system needs to process information from traffic, weather, construction, police activities to our calendar schedules, and perform deep optimization under the tight time constraints. -
A Survey on Transfer Learning
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 22, NO. 10, OCTOBER 2010 1345 A Survey on Transfer Learning Sinno Jialin Pan and Qiang Yang, Fellow, IEEE Abstract—A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world applications, this assumption may not hold. For example, we sometimes have a classification task in one domain of interest, but we only have sufficient training data in another domain of interest, where the latter data may be in a different feature space or follow a different data distribution. In such cases, knowledge transfer, if done successfully, would greatly improve the performance of learning by avoiding much expensive data-labeling efforts. In recent years, transfer learning has emerged as a new learning framework to address this problem. This survey focuses on categorizing and reviewing the current progress on transfer learning for classification, regression, and clustering problems. In this survey, we discuss the relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift. We also explore some potential future issues in transfer learning research. Index Terms—Transfer learning, survey, machine learning, data mining. Ç 1INTRODUCTION ATA mining and machine learning technologies have The need for transfer learning may arise when the data Dalready achieved significant success in many knowl- can be easily outdated. In this case, the labeled data edge engineering areas including classification, regression, obtained in one time period may not follow the same and clustering (e.g., [1], [2]). -
Annotated Table of Contents V B I P
AI MATTERS, VOLUME 1, ISSUE 1!AUGUST 2014 AI Matters Annotated Table of Contents Welcome to AI Matters ! Kiri Wagstaff, Editor Full article: http://doi.acm.org/10.1145/2639475.2639476 A welcome from the Editor of AI Matters and an en- couragement to submit for the next issue. Artificial Intelligence: No Longer Just for O You and Me Yolanda Gil, SIGAI Chair Full article: http://doi.acm.org/10.1145/2639475.2639477 The Chair of SIGAI waxes enthusiastic about the cur- Drop-in Challenge games at RoboCup. rent state of and future prospects for AI developments V Peter Stone, Patrick MacAlpine, Katie Genter, and innovations. She also reports on high school and Sam Barrett. Full image and details: student projects featured at the 2014 Intel Science http://doi.acm.org/10.1145/2639475.2655756 and Engineering Fair. Using Agent-Based Modeling and Cul- Announcing the SIGAI Career Network I N and Conference tural Algorithms to Predict the Location Sanmay Das, Susan L. Epstein, and Yolanda of Submerged Ancient Occupational Gil Sites Full article: http://doi.acm.org/10.1145/2639475.2649581 Robert G. Reynolds, Areej Salaymeh, John O'Shea, and Ashley Lemke SIGAI has created a career networking website and Full article: http://doi.acm.org/10.1145/2639475.2639479 annual conference for the benefit of early career sci- entists. Benefits include mentoring, networking, and A collaboration between archaeologists and artificial job connections. intelligence experts has discovered ancient hunting sites submerged in over 120 feet of water in Lake Future Progress in Artificial Intelligence: Huron. This is the oldest known hunting ground in the world. -
Network on Network for Tabular Data Classification in Real-World Applications
Network On Network for Tabular Data Classification in Real-world Applications Yuanfei Luo Hao Zhou Wei-Wei Tu [email protected] [email protected] [email protected] 4Paradigm Inc., Beijing, China 4Paradigm Inc., Beijing, China 4Paradigm Inc., Beijing, China Yuqiang Chen Wenyuan Dai Qiang Yang [email protected] [email protected] [email protected] 4Paradigm Inc., Beijing, China 4Paradigm Inc., Beijing, China Hong Kong University of Science and Technology, Hong Kong ABSTRACT ACM Reference Format: Tabular data is the most common data format adopted by our cus- Yuanfei Luo, Hao Zhou, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, and Qiang tomers ranging from retail, finance to E-commerce, and tabular Yang. 2020. Network On Network for Tabular Data Classification in Real- world Applications. In Proceedings of the 43rd International ACM SIGIR data classification plays an essential role to their businesses. Inthis Conference on Research and Development in Information Retrieval (SIGIR ’20), paper, we present Network On Network (NON), a practical tabular July 25–30, 2020, Virtual Event, China. ACM, New York, NY, USA, 10 pages. data classification model based on deep neural network to provide https://doi.org/10.1145/3397271.3401437 accurate predictions. Various deep methods have been proposed and promising progress has been made. However, most of them use operations like neural network and factorization machines to fuse 1 INTRODUCTION the embeddings of different features directly, and linearly combine Tabular data is widely used in many real-world applications, such the outputs of those operations to get the final prediction. As a as online advertising [11, 33] recommendation [3], fraud detec- result, the intra-field information and the non-linear interactions tion [4, 32], medical treatment [18], which is also the scenario of between those operations (e.g. -
AAAI Members Elect New President-Elect & Executive
For press inquiries only, contact: Sara Hedberg Emergent, Inc. (425) 444-7272 [email protected] FOR IMMEDIATE RELEASE AAAI members elect new President-Elect & Executive Councilors MENLO PARK, CA. – July 13, 2005. The American Association for Artificial Intelligence (AAAI), the nation’s scientific AI society, announces the new President-Elect and Executive Councilors elected by the society’s membership. Eric Horvitz of Microsoft Research has been elected AAAI President-Elect and will become President in 2007 for a two-year term. Horvitz joins President Alan MacWorth, University of British Columbia, and Ron Brachman, immediate Past President, on the presidential team. Tom Mitchell of Carnegie Mellon University completes his six years of service to AAAI. The newly elected Councilors include: Maria Gini, University of Minnesota Kevin Knight, USC/Information Sciences Institute Peter Stone, University of Texas at Austin Sebastian Thrun, Stanford University Councilors serve a three year term, and one-third of the council rotates off each year. Other Council members include: (through 2006): Steve Chien, JPL / California Institute of Technology Yolanda Gil, USC / ISI Haym Hirsh, University of Rutgers Andrew Moore, Carnegie Mellon University (through 2007): Oren Etzioni, University of Washington Lise Getoor, University of Maryland, College Park Karen Myers, SRI International Illah Nourbakhsh, NASA Ames Research Center / Carnegie Mellon University Councilors who complete their three years of service this year include: Carla Gomes, Cornell University Michael Littman, Rutgers University Maja Mataric, University of Southern California Yoav Shoham, Stanford University (more) “We are very grateful to Tom Mitchell and to these retiring Councilors who have given – and continue to give -- so generously of their time and talents to the organization,” says Carol Hamilton, AAAI Executive Director.