IPR2021-01458
`U.S. Patent 8,489,599
`
`UNITED STATES PATENT AND TRADEMARK OFFICE
`______________________
`
`BEFORE THE PATENT TRIAL AND APPEAL BOARD
`______________________
`TWITTER, INC.,
`Petitioner
`
`v.
`
`PALO ALTO RESEARCH CENTER INC.,
`Patent Owner
`______________________
`Case IPR2021-01458
`Patent 8,489,599
`______________________
`
`DECLARATION OF MICHAEL F. MILEA
`
`
`
`
`Twitter Exhibit 1015
`Page 00001
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`

`

`IPR2021-01458
`U.S. Patent 8,489,599
`
`I, Michael F. Milea, make the following Declaration pursuant to 28 U.S.C. § 1746:
`
`1.
`
`I am an associate at the law firm of Paul, Weiss, Rifkind, Wharton &
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`Garrison LLP.
`
`2.
`
`I provide this Declaration in connection with the above-identified Petition
`
`for Inter Partes Review proceeding that is being requested at the U.S. Patent and
`
`Trademark Office under 35 U.S.C. §§ 311-319, 37 C.F.R. § 42. Unless otherwise
`
`stated, the facts stated in this Declaration are based on my personal knowledge.
`
`3.
`
`Attached hereto as Appendix A is a true and correct copy of an excerpted
`
`article titled “CASTmiddleware: Security Middleware of Context-Awareness
`
`Simulation Toolkit for Ubiquitous Computer Research Environment” and other
`
`excerpted portions of Volume 344 of Lecture Notes in Control and Information
`
`Sciences, entitled Intelligent Control and Automation, International Conference on
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`Intelligent Computing, ICI 2006 Kunming China, August 16–19, 2006. I
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`understand that the Research Department of my law firm (at my instruction)
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`requested a hard copy of the book from Texas A&M University, which had the
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`book catalogued in WorldCat. The book was shipped to me via FedEx on August
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`27, 2021, and I received it on August 28, 2021. I imaged excerpts of the book and
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`an appendix label on the first page has been added but no alterations have been
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`made.
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`Page 00002
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`

`

`IPR2021-01458
`U.S. Patent 8,489,599
`I have compared the copy of “CASTmiddleware: Security Middleware of
`
`4.
`
`Context-Awareness Simulation Toolkit for Ubiquitous Computer Research
`
`Environment” that is included in Appendix A to this declaration to the copy of that
`
`same article that is provided as EX1006 in the above-identified Petition for Inter
`
`Partes Review proceeding and have confirmed that those copies are identical.
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`5.
`
`EX1005 in the above-identified Petition for Inter Partes Review proceeding
`
`is a true and correct copy of Yau, et al., “A Context-aware and Adaptive Learning
`
`Schedule framework for supporting learners’ daily routines.” I understand that the
`
`Research Department of my law firm (at my instruction) downloaded EX1005
`
`from IEEE Xplore (https://ieeexplore.ieee.org/document/4196333). An exhibit
`
`label on the first page has been added and page numbers on all pages have been
`
`added to the bottom of this document but no alterations have been made.
`
`6.
`
`I have been warned that willful false statements and the like are punishable
`
`by fine or imprisonment, or both. I make this declaration of my own personal
`
`knowledge, and all statements are true. If called to testify as to the truth of the
`
`matters stated herein, I could and would testify competently.
`
`7.
`
`I declare under penalty of perjury that the foregoing is true and correct.
`
`Executed on this 31st day of August, 2021 at New York, NY.
`
` __________________
`
` Michael F. Milea
`
`2
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`Page 00003
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`APPENDIX A
`APPENDIX A
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`Page 00004
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`Page 00004
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`ee
`_S__—= LECTURE NOTES IN CONTROL
`ree) brpeh ae.4wees SCIENCES
`
`) Springer Page 00005
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`Intelligent Control
`and Automation
`
`International Conference
`on Intelligent Computing, ICIC 2006
`Kunming, China, August 2006
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`Page 00005
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`

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`Lecture Notes
`in Control and Information Sciences 344
`
`Editors: M. Thoma, M. Morari
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`Page 00006
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`

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`De-Shuan~ ~uang, Kang Li,
`George Wilham Irwin (Eds.)
`
`Intelligent Control
`and Automation
`International Conf ere nee on
`Intelligent Computing, ICIC 2006
`Kunming, China, August 16-19, 2006
`
`~ Springer
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`Page 00007
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`

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`s,,rtc.•s Advisory Uourd
`1, t\ll~ihVl'I , p Mi:minp_, P. Kokotovk.
`A I\. K\11 , ha11i.k1. 11. K WHKl'lllttak ,
`A R,,nl/ l't', .I N T1,1tsiklis
`
`}:ditors
`De-Shuang l luang
`lnstitntc of lntdligcnt Machines
`('hrnc~t· Academy of Sciences
`lkfr1, Anhui. China
`E·m,ul: dshu:mg(n'liim.ac.cn
`
`Kang Li
`Queen' University
`Belfast, UK
`E-mail: K.Li@qub.ac.uk
`
`George William Irwin
`Queen's University
`Bclfasl, UK
`E- mail: G.Irwin@qub.ac.uk
`
`Library of Congress Control Number: 2006930913
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`ISSN print edition: 0170-8643
`ISSN electronic edition: 1610-741 I
`ISBN-IO 3-540-37255-5 Springer Berlin Heidelberg New York
`ISBN-13 978-3-540-37255-4 Springer Berlin Heidelberg New York
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`This work is subject to copyright. All rights are reserved, whether the whole or part of the materi~ is
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`Preface
`
`The International Conference on Intelligent Computing (ICIC) was formed to provide
`an annual forum dedicated to the emerging and challenging topics in artificial
`intelligence, machine learning, bioinformatics, and computational biology, etc. It aims
`to bring together researchers and practitioners from both academia and industry to
`share ideas, problems and solutions related to the multifaceted aspects of intelligent
`computing.
`!CIC 2006 held in Kunming, Yunnan, China, August 16-19, 2006, was the second
`International Conference on Intelligent Computing, built upon the success of ICIC
`2005 held in Hefei, China, 2005.
`This year, the conference concentrated mainly on the theories and methodologies
`as well as the emerging applications of intelligent computing. It intended to unify the
`contemporary intelligent computing techniques within an integral framework that
`highlights the trends in advanced computational intelligence and bridges theoretical
`research with applications. In particular, bio-inspired computing emerged as having a
`key role in pursuing for novel technology in recent years. The resulting techniques
`vitalize life science engineering and daily life applications. In light of this trend, the
`theme for this conference was "Emerging Intelligent Computing Technology and
`Applications". Papers related to this theme were especially solicited, including
`theories, methodologies, and applications in science and technology.
`ICIC 2006 received over 3000 submissions from 36 countries and regions. All
`papers went through a rigorous peer review procedure and each paper received at least
`three review reports. Based on the review reports, the Program Committee finally
`selected 703 high-quality papers for presentation at ICIC 2006. These papers cover 29
`topics and 16 special sessions, and are included in five volumes of proceedings
`published by Springer, including one volume of Lecture Notes in Computer Science
`(LNCS), one volume of Lecture Notes in Artificial Intelligence (LNAI), one volume
`of Lecture Notes in Bioinformatics (LNBI), and two volumes of Lecture Notes in
`Control and Information Sciences (LNCIS).
`This volume of Lecture Notes in Control and Information Sciences (LNCIS)
`includes 142 papers covering 4 relevant topics and 1 special session topics.
`The organizers of ICIC 2006, including Yunan University, the Institute of
`Intelligent Machines of the Chinese Academy of Science, and Queen's University
`Belfast, have made enormous effort to ensure the success of ICIC 2006. We hereby
`would like to thank the members of the ICIC 2006 Advisory Committee for their
`guidance and advice, the members of the Program Committee and the referees for
`their collective effort in reviewing and soliciting the papers, and the members of the
`Publication Committee for their significant editorial work. We would like to thank
`Alfred Hofmann, executive editor from Springer, for his frank and helpful advice and
`guidance throughout and for his support in publishing the proceedings in the Lecture
`Notes series. In particular, we would like to thank all the authors for contributing their
`
`Page 00009
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`pc1pcrs. \Virhout rite high-quality submissions from the authors, the sue
`.
`.
`.
`.
`· cess
`f
`conlcrcnl't.' would not have been possible. Finally, we arc espec1al1y &rat fu O the
`IEEE Computational lntdligcncc Society, The International Neural Netwo\ J to the
`r S0ciety
`and the.' National Sl'icncc Foundation of China for the their sponsorship.
`
`June 2006
`
`De-Shuan Ii
`Institute of Intelligent Ja ~ng
`chines
`Chinese Academy of Sci·
`ences f'L ·
`• '--lllJla
`KMgL·
`.
`,
`Q
`1
`ueen s Unrversity B Ji'.
`e 1ast, lJI(
`George William 1 .
`Queen's University BeJi'.
`l"WJn
`1ast, UK
`
`Page 00010
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`

`

`Table of Contents
`
`Blind Source Separation
`
`A Unified Framework of Morphological Associative Memories
`Naiqin Feng, Yuhui Qiu, Fang Wang, Yuqiang Sun.............. . ..
`
`1
`
`A New Speech Denoising Method Based on WPD-ICA Feature
`Extraction
`Qinghua Huang, Jie Yang, Yue Zhou . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`An Efficient Algorithm for Blind Separation of Binary Symmetrical
`Signals
`Wenbo Xia, Beihai Tan. . ............................ . ........ ..
`
`A New Blind Source Separation Algorithm Based on Second-Order
`Statistics for TITO
`ZhenLi Wang, Xiong Wei Zhang, Tie Yong Cao . . . . . . . . . . . . . . . . . . . .
`
`A New Step-Adaptive Natural Gradient Algorithm for Blind Source
`Separation
`Huan Tao, Jian-yun Zhang, Lin Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`An Efficient Blind SIMO Channel Identification Algorithm Via
`Eigenvalue Decomposition
`Min Shi, Qingming Yi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`An Improved Independent Component Analysis Algorithm and Its
`Application in Preprocessing of Bearing Sounds
`Guangrui Wen, Liangsheng Qu, Xining Zhang.....................
`
`Array Signal MP Decomposition and Its Preliminary Applications
`to DOA Estimation
`Jianying Wang, Lei Chen, Zhongke Yin . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`Mixture Matrix Identification of Underdetermined Blind Source
`Separation Based on Plane Clustering Algorithm
`Beihai Tan, Yuli Fu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`Non-linear Blind Source Separation Using Constrained Genetic
`Algorithm
`Zuyuan Yang, Yongle Wan................... .... ......... . . . ...
`
`12
`
`21
`
`29
`
`35
`
`41
`
`48
`
`54
`
`60
`
`66
`
`Page 00011
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`

`

`Sensor
`
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`etwork
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`Efficient Con
`an Zeng . .
`in Sensor
`He, )'uanyu
`Encrfl)'
`di' for Coverage
`ra12.11a11g
`eds and ~ 1 Haibin Yu . .. .
`.
`The Critical Spe Wei Liang,
`r Networks
`Wireless Senso
`....... .
`Chuonzhi Zang,
`l Schema for
`. . . . . .
`A Distributed QoS ~~~~r·o· ........... .
`Jin Wu ....... .
`
`.....
`. . .. · · ·
`Networks
`
`Sensor
`
`Networks
`..
`. . . .
`
`83
`
`95
`
`106
`
`118
`
`124
`
`130
`
`138
`
`for Context
`. g System
`Data Processm
`k
`I Situ Sensor
`G Lee Mi Par '
`A Framework of n-
`·
`
`Dong VU Ok Kim .... . ... . .
`'
`..........
`Yang Koo Lee,
`Awareness .
`Young J,n Juntak Cheol Kim, Kyung
`.
`d Detect10n
`Keun Ho Ryu,
`. al Model for Energy-
`Efficient Coverage an
`A Mathematic
`.. ......
`.
`in Wireless Sensor Networ~s D i Zhi Wang, Youxian Sun . ....
`Xiaodong Wang, Huaping a'
`
`.
`
`A Method of Controlling Packet Transm1ss
`. ion Rate with Fuzzy Logic
`G . Ti Park
`.... .. ...... .
`for Ad Hoc Networks
`.... .
`Kyung-Bae Chang, Tae-Hwan Son, wi- ae
`
`A Novel Algorithm for Doppler Frequency Rate Estimation
`of Spaceborne Synthetic Aperture Radar
`.
`Shiqi Huang, Daizhi Liu, Liang Chen, Yunfeng Liu . .. · · · · · · · · · · · ·
`
`. . 144
`
`A Novel Genetic Algorithm to Optimize QoS Multicast Routing
`Guangbin Bao, Zhanting Yuan, Qiuyu Zhang, X uhui Chen . . . . · · · · · ·
`
`~ Probe far the Performance of Low-.1\ate Wireless Personal Area
`
`Networks
`
`150
`
`Shuqin llen, Khin Mi Mi Aung, Jong Sou Park . . . . . . . . . . . . . . . . . . . . 158
`AG-Ne, An Automatic Gene
`11
`.
`t'
`ra '00 •chmque of Network Components
`fo,. Dynamic NetworJ M
`anagenient
`Eun fl ,
`.
`t
`te Kim, Myung Jin Lee, Keu,,. Ho fl,
`yu ................... .
`
`. . 165
`
`Page 00012
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`Table of Contents
`
`XV
`
`Clustering Algorithm in Wireless Sensor Ndworks Using Transmit
`Power Control and Soft Computing
`Kyung-Bae Chang, Young-Bae Kong, Owi-Tae Park . . . . . . . . . . . . . . . . 171
`
`Discriminating Fire Detection Via Support Vector Machines
`Heshou Wang, Shuibo Zheng, Chi Chen, Wenbin Yang, Lei Wu,
`Xin Cheng, Minrui Fei, Chuanping Hu . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
`
`Dynamic Deployment Optimization in Wireless Sensor Networks
`Xue }Vang, Sheng Wang, Junjie Ma. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
`
`Energy-Efficient Aggregation Control for Mobile Sensor Networks
`Liang Yuan, Weidong Chen, Yugeng Xi . . . . . . . . . . . . . . . . . . . . . . . . . . 188
`
`Intelligent l\IAC Protocol for Efficient Support of Multiple SOPs
`in UWB-Based Sensor Networks
`Peng Gong, Peng Xue, Duk K yung Kim . . . . . . . . . . . . . . . . . . . . . . . . . . 194
`
`Topology Control in Wireless Sensor Networks with Interference
`Consideration
`Yanxiang He, Yuanyuan Zeng . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202
`
`Intelligent Control and Automation
`
`Adaptive Depth Control for Autonomous Underwater Vehicles Based
`on Feedforward Neural Networks
`Yang Shi, Weiqi Qian, Weisheng Yan, Jun Li . . . . . . . . . . . . . . . . . . . . . 207
`
`Adaptive Fuzzy Sliding-Mode Control for Non-minimum Phase
`Overload System of Missile
`Yongping Bao, Wenchao Du, Daquan Tang, Xiuzhen Yang,
`Jinyong Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
`
`An Improved Genetic & Ant Colony Optimization Algorithm and Its
`Applications
`Tiaoping Fu, Yushu Liu, Jiguo Zeng, Jianhua Chen . . . . . . . . . . . . . . . . 229
`
`Application of Adaptive Disturbance Canceling to Attitude Control
`of Flexible Satellite
`Ya-qiu Liu, Jun Cao, Wen-long Song............................. 240
`
`Application of Resource Allocating Network and Particle Swarm
`Optimization to ALS
`Jih-Gau Juang, Bo-Shian Lin, Feng-Chu Lin . . . . . . . . . . . . . . . . . . . . . . 252
`
`Page 00013
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`

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`Sclf-Growing Fuzzy ;'four; . Hsu I-Fang Chung ..................... .
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`264
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`Mu.llfaf a Tombul, Ersin Ogul · · · · · · · · · · · · · · · · · · · · · · · · · · · ·
`
`A Novel Multi-agent Based Complex Process Control System and Its
`Application
`Yi-Nan Guo, Jian Cheng, Dun-wei Gong, Jian-hua Zhang ...... · · · ·
`
`Neural Network Based Soft Switching Control of a Single Phase AC
`Voltage Restorer
`Kayhan Gulez, Tank Veli Mumcu, Ibrahim Aliskan .. .............. .
`
`N<-'Ural Network Training Using PSQ Algorithm in ATM Traffic Control
`Yuan-wci Jmg, Tao Ren, Yu-cheng Zhou . ........................ .
`
`Parameter Identification of Dynarn· . l S
`ica YHtcrn,; DE.Uic<l on Improved
`Particle ~wa.rrn Optimization
`Meiymg Ye
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`Page 00014
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`

`

`Table of Contents XVII
`
`Stability Analysis of Network Data Flow Control for Dynamic Link
`Capacity Case
`Yuequan Yang, Yaqin Li, Min Tan, Jianqiang Yi, John T. Wen,
`X uewu Guo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395
`
`Matrix 1'1easure Stability Criteria for a Class of Switched Linear
`Systems
`Hongbo Zou, Hongye Su, Jian Chu............................... 407
`
`Study on FOG-SINS/ GPS Integrated Attitude Determination System
`Using Adaptive Kalman Filter
`Xiyuan Chen.................................................. 417
`
`Supervisory Control for Rotary Kiln Temperature Based
`on Reinforcement Learning
`Xiaojie Zhou, Heng Yue, Tianyou Chai, Binhao Fang . . . . . . . . . . . . . . 428
`
`A Data Reorganization Algorithm to Improve Transmission Efficiency
`in CAN Networks
`Jung-Ki Choi, Sungyun Jung, Kwang-Ryul Baek ... ....... . ... .... . 438
`
`A Neural Network Approach to QoS Management in Networked
`Control Systems over Ethernet
`Wenhong Zhao, Feng Xia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444
`
`A Novel Micro-positioning Controller for Piezoelectric Actuators
`Van-Tsai Liu, Chun-Liang Lin, Hsiang-Chan Huang, Zi-Jie Jian . . . . 450
`
`A Study on the Robust Control of an Inverted Pendulum Using
`Discrete Sliding-Mode Control
`J. Y. Yang, H.J. Lee, J.H. Hwang, N.K. Lee, H. W. Lee, G.A. Lee,
`S.M. Bae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 456
`
`A VRC Algorithm Based on Fuzzy Immune PIO-Smith
`Ling He, Yu-cheng Zhou, Yuan-wei Jing, Hai-yu Zhu........ . ...... 463
`
`Absolute Stability of State Feedback Time-Delay Systems
`Hanlin He, Zhongsheng Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469
`
`Adaptive Fuzzy Control of Nonlinear Systems Based on Terminal
`Sliding Mode
`Shuanghe Yu ........... , . ....... . ..... ... . . . .. .... , . , .. , , · . ·.. 474
`
`Simulation of Air Fuel Ratio Control Using Radius Basis Function
`Neural Network
`Zhixiang Hou ....... . . . .................. . ....... . ... .... ..... . 480
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`Page 00015
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`·
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`the Atmospheric
`
`'7h
`Pei-Tao Zhao, Jia Su, Xin Fang,
`.
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`Yue-Feng Zhao, Yin-Chao LJ, ang,
`Jun Xie, K ai-f eng Qv · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · 514
`Design of a Robust Output Feedback Controller for Robot Manipulators
`Using Visual Feedback
`
`Min Seok Jie, Chin Su Kim, Kang Woong Lee ................ ·····
`
`520
`
`Fuzzy Sliding Mode Controller with RBF Neural Network for Robotic
`Manipulator lrajectory lracking
`
`Ayca Gokhan Ak, Calip Cansever....... .. ........ . . . . . . . . . . . . . . . 527
`
`Hybrid Fuzzy Neural Network Control for Complex Industrial Process
`Qingyu Yang, Lincang Ju, Bibo Ge, Ren Shi, Yuanli Cai ........... · 53
`3
`f I
`Intelligent Vehicle Control by Optima] Se!
`t·
`M. Junaid Khan Danya y.
`J
`ec Ion o mage Data
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`lluJ .. B-i Expert System for Seiectin
`Guozkong Zhang, Linchen Sh
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`546
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`

`Table of Contents
`
`XIX
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`\;cural ~et work-Based an Adapt ivc Discre1 <!-Tirrw Global Sliding ~lode
`Control Scheme
`Zhr,nyon ivong. Ju,gfJnr1q Zhong. Zh1mr1 Chen, Yrm zhao He . . . . . . . . . 565
`
`Real Cod('d Genetic .\lgori Ihm for Opt.imizing Fuzzy Logi, Control of
`Grccnhous · ::\Iicroclimat P.
`Fang Xu . Jiaoliao Ch en. L ibin Zhong, Ilongwu, Zhan . . . . . . . . . . . . . . . 571
`
`Re.search and Implementation on t he Mobile Intelligent Controller for
`Home Automation Service
`J onghwa Choi, Dongkyoo Shin , Dongil Shin . . . . . . . . . . . . . . . . . . . . . . . 578
`
`Sampled-D ata Syst ems with Quantization and Slowly Varying Inputs
`Ge Guo. Huipu X u, Yuan Tian .... . . . . . . .. . . . ... . ...... .... . .... 584
`
`Set-Stabiliza tion with Occasional Feedback
`Ge Guo, Jigong Li . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 590
`
`Spat ial Reasoning for Collision Detection and Hardware Implementation
`Chirag N epal. Seung Woo Nam, Dohyung K im , Kyungsook Han.. . ... 596
`
`St ability Criteria for Switched Linear Systems
`Hongbo Zou. Hongye Su, Jian Chu... . .... . . . . . ... . ... .. . . ...... . 602
`
`Suppressing of Chaotic State Based on Delay Feedback
`lVenli Zhao, Linze Wang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 608
`
`Vector Controlled Pi\ISM Drive Based on Adaptive Neuro-fuzzy Speed
`Controller
`X ianqing Cao, Liping Fan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 616
`
`D ata Fusion, Knowledge Discovery and Data Mining
`
`.. Intelligent Yardstick", an Approach of Ranking to Filter Non-promising
`Attributes from Schema in Data Mining Process
`Afoham mad AI. Hassan ......... . .... .. .. . . ... . ......... . ... . .. .
`
`623
`
`A Local Computing-Based Hierarchical Clustering Algorithm Building
`.
`.
`Density Trees
`lVei-di Dai, .lie-Liu, Da-yi Zhao, Zhen-hua Liu, Jun-xian Zhang,
`P i-Lian He .. .. . . · . · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · ·
`
`633
`
`Support Vector Clustering and Type-Entropy Based .Joint
`De-interleaving/recognition System of Radar Pulse Sequence
`Qiang Guo. Zherig Li, Xingzltou Zhang . , ... · · · · · · · · · · · · · · · · · · · · · ·
`
`642
`
`Page 00017
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`. . . . . . . . . . . . . . 654
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`~arning to Semantically Classify Email Messages
`Eric J,ang .. · · · · · · · · · · · · · · · · · ·
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`Strategies with a Grid Search
`Ruiming Liu, Erqi Liu, Jie Yang, Ming Li, Fanglin Wang . . . . . . . . . . . 712
`
`Prediction of Sinter Burn-Through Point Based on Support Vector
`Machines
`Xiaofeng Wu, Minrui Fei, Heshou Wang, Shuibo Zheng. . . . . . . . . . . . . 722
`
`Under-Sampling Approaches for Improving Prediction of the Minority
`Class in an Imbalanced Dataset
`·
`Show-Jane Yen, Yue-Shi Lee
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`u ti-focus Image Fu ,
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`Table of Contents
`
`XXI
`
`\ '\ t'W IHS \Y r lmnge Pu~ilm 1\ll'thod Based 011 \\'eighted Regional
`
`J,
`
`lru. Bmgkuu ) rn, .};1111 Liu . .Jin wen Tian . . . . . . . . . . . . . . . . . . . . . 765
`
`\
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`• '" ,1 \l~ 1 rithm of i\lining l\lnltidimensional Association Rules
`ll°<P \ rn \ t. RuJmg \rang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 771
`
`.\ ~ '" ,1 Discrdizer for Knowledge Discovery Based on Multiknowledge
`.\ pproadw'
`trngX i<lng n·u. Girijesh Prasad. TAI Af cGinnity, David Bell,
`qrnaChun Zhong. Jiwcn Guan. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 778
`
`A ~ oYel ~ etu·al etwork for Sensor Fusion Applied to Wood Growth
`Ring 1Ioisture 1\Ieasurement
`J!ingbao Li. Sh iqiang Zh eng, Jun Hua . . . . . . . . . . . . . . . . . . . . . . . . . . . . 784
`
`A XoYel Reduct ion Algorithm Based Decomposition and Merging
`,.,trategy
`Feng Hu. Xinghua Fan, Simon.X Yang, Chaohai Tao . .............. 790
`
`A Pattern Distance-Based Evolutionary Approach to Time Series
`Segmentation
`Jingwen } u, Jian Yin. Duanning Zhou, Jun Zhang. . . . . . . . . . . . . . . . . 797
`
`A Quality Prediction :Method of Injection Molding Processes Using
`Sub-stage PCA-81
`X iaoPing Guo, Fu.Li Wang, MingXing Jia . . . . . . . . . . . . . . . . . . . . . . . . 803
`
`A Robust Algorithm for Watermark Numeric Relational Databases
`Xinchun Cui, Xiaolin Qin, Gang Sheng, Jiping Zheng . . . . . . . . . . . . . . 810
`
`A Study on the RAP Approach and Its Application
`Jian Cao, Gengui Zhou, Feng Tang . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
`
`An Analytical Model for Web Prefetching
`Lei Shi, Lin Wei, Zhimin Gu, Yingjie Han, Yun Shi . . . . . . . . . . . . . . .
`
`16
`
`22
`
`An Optimal Interval for Computing Course and Ship Speed in Marine
`Gravity Survey Based on Approximate Reasoning
`Lihua Zhang, Chong Fang, Xiaosan Ge, Yilong Li . . . . . . . . . . . . . . . . .
`
`2
`
`Application of Aswciation Rules in Education
`Sylvia Encheva, Sharil Tomin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 834
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`Page 00019
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`Achir\'cmrnt and Learning 11cthods During Vacation
`Hw-SuA· Kim. Yang-Sae Moon, Jinho Kim, Woong-Kee Loh . . . . . . . . . 857
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`Databa."c and Comparative Identification of Prophages
`K. \'. Sr1v~dhya, Geeta V Rao, Raghavenderan L, Preeti Mehta,
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