ゲーム攻略・無料ウィキレンタルのアットウィキ (@WIKI)

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html title dinamic filter /title body body bgcolor="rosybrown" /etc/sysconfig/iptables-config br IPTABLES_MODULES="ip_conntrack_ftp" br pre NEWがないと一切の通信がダメ。ポートが開かない。NEWだけは特別なのだアプリI/OSADAPROTSPDPCONN.STAT FTPOUT自分FTP鯖TCPー２１NEW（一番最初のステータスですよ）、ESTABLISHED　コマンドチャンネル？ FTPINFTP鯖自分TCP２１ーESTABLISHED（全部ACKがついてますから？）　コマンドチャンネル？ポート番号２１ってFTPの制御用のポートだよね何番のポートと何番のポートで通信しますよでRELATED（関係のある）通信になる ↓データチャンネル FTPOUT自分FTP鯖TCPーーRELATED、ESTABLISHED FTPINFTP鯖自分TCPーーESTABLISHED   font size=5 color=darkblue b iptables追加ftpパッシブモードクライアントルール /b /font #dinamicfilter ftp comand-ch -A OUTPUT -p tcp --dport 21 -m state --state NEW,ESTABLISHED -j ACCEPT -A INPUT -p tcp --sport 21 -m state --state ESTABLISHED -j ACCEPT #dinamicfilter ftp date-ch -A OUTPUT -p tcp -m state --state RELATED,ESTABLISHED -m helper --helper ftp -j ACCEPT -A INPUT -p tcp -m state --state ESTABLISHED -m helper --helper ftp -j ACCEPT   -m state 接続状態の指定接続状態にはNEW、INVALID、ESTABLISHED、RELATEDが指定できる   -m helper unknown   -A 指定したチェインにルールを追加する -F -Aとは逆でチェインの内容を削除する   font size=5 color=darkblue b 検証 /b /font [root@neteng18 ~]# [root@neteng18 ~]# ftp 192.168.128.1 Connected to 192.168.128.1. 220 (vsFTPd 2.0.5) 530 Please login with USER and PASS. 530 Please login with USER and PASS. KERBEROS_V4 rejected as an authentication type Name (192.168.128.1 root) neteng18 331 Please specify the password. Password 230 Login successful. Remote system type is UNIX. Using binary mode to transfer files. ftp ascii 200 Switching to ASCII mode. ftp get welcome local welcome remote welcome 227 Entering Passive Mode (192,168,128,1,115,140) 150 Opening BINARY mode data connection for welcome (24 bytes). WARNING! 2 bare linefeeds received in ASCII mode File may not have transferred correctly. 226 File send OK. 24 bytes received in 0.00013 seconds (1.9e+02 Kbytes/s) ftp quit 221 Goodbye. [root@neteng18 ~]# cat /proc/net/ip_conntrack tcp 6 112 TIME_WAIT src=192.168.128.212 dst=192.168.128.1 sport=45107 dport=29580 packets=4 bytes=216 src=192.168.128.1 dst=192.168.128.212 sport=29580 dport=45107 packets=4 bytes=240 [ASSURED] mark=0 secmark=0 use=1 tcp 6 117 TIME_WAIT src=192.168.128.212 dst=192.168.128.1 sport=54614 dport=21 packets=21 bytes=1201 src=192.168.128.1 dst=192.168.128.212 sport=21 dport=54614 packets=15 bytes=1139 [ASSURED] mark=0 secmark=0 use=2 [root@neteng18 ~]# cat welcome Welcome to ftp server!   b font size=4 color=darkblue ノーマルモード /font /b FTP上でpassiveと打つとON/OFFを切り替えることができるパッシブモードの設定だとコマンドをやっても通信ができないのですようー私案ー合意はもうできている上で要はノーマルということはFTP鯖からの２０番ポートからのTCPデータを受信できればよいだからアプリI/OSADAProtSPDPState FTPINFTP鯖自分TCP２０ーRELATED,ESTABLISHED FTPOUT自分FTP鯖TCPー２０ESTABLISHED FTPクライアントの設定ノーマルデータCHを送受信するにはこれだけで良い #dinamicfilter ftp date-ch -A INPUT -p tcp --sport 20 -m state --state RELATED,ESTABLISHED -m helper --helper ftp -j ACCEPT -A OUTPUT -p tcp --dport 20 -m state --state ESTABLISHED -m helper --helper ftp -j ACCEPT font size=5 color=darkblue b 検証 /b /font [root@neteng18 ~]# ftp 192.168.128.1 Connected to 192.168.128.1. 220 (vsFTPd 2.0.5) 530 Please login with USER and PASS. 530 Please login with USER and PASS. KERBEROS_V4 rejected as an authentication type Name (192.168.128.1 root) neteng18 331 Please specify the password. Password 530 Login incorrect. Login failed. ftp quit 221 Goodbye. [root@neteng18 ~]# ftp 192.168.128.1 Connected to 192.168.128.1. 220 (vsFTPd 2.0.5) 530 Please login with USER and PASS. 530 Please login with USER and PASS. KERBEROS_V4 rejected as an authentication type Name (192.168.128.1 root) neteng18 331 Please specify the password. Password 230 Login successful. Remote system type is UNIX. Using binary mode to transfer files. ftp passive Passive mode off. ftp ls 200 PORT command successful. Consider using PASV. 150 Here comes the directory listing. -rw-r--r-- 1 518 500 24 Feb 25 02 34 welcome 226 Directory send OK. ftp get welcome local welcome remote welcome 200 PORT command successful. Consider using PASV. 150 Opening BINARY mode data connection for welcome (24 bytes). 226 File send OK. 24 bytes received in 8.6e-05 seconds (2.7e+02 Kbytes/s) ftp quit 221 Goodbye. [root@neteng18 ~]# cat /proc/net/ip_conntrack tcp 6 96 TIME_WAIT src=192.168.128.1 dst=192.168.128.212 sport=20 dport=35862 packets=5 bytes=333 src=192.168.128.212 dst=192.168.128.1 sport=35862 dport=20 packets=3 bytes=164 [ASSURED] mark=0 secmark=0 use=1 tcp 6 79 TIME_WAIT src=192.168.128.212 dst=192.168.128.1 sport=35714 dport=21 packets=16 bytes=911 src=192.168.128.1 dst=192.168.128.212 sport=21 dport=35714 packets=13 bytes=888 [ASSURED] mark=0 secmark=0 use=1 tcp 6 112 TIME_WAIT src=192.168.128.212 dst=192.168.128.1 sport=35715 dport=21 packets=23 bytes=1364 src=192.168.128.1 dst=192.168.128.212 sport=21 dport=35715 packets=18 bytes=1410 [ASSURED] mark=0 secmark=0 use=3 tcp 6 107 TIME_WAIT src=192.168.128.1 dst=192.168.128.212 sport=20 dport=60047 packets=5 bytes=292 src=192.168.128.212 dst=192.168.128.1 sport=60047 dport=20 packets=3 bytes=164 [ASSURED] mark=0 secmark=0 use=1 [root@neteng18 ~]# a href="index.html" 戻る /a /body /html  

marketresearch @ ウィキ - CRM Analytics Market

https://w.atwiki.jp/marketresearch/pages/29.html

CRM Analytics Market Report Overview According to the latest QMI Market research study, the Predicate CRM Analytics Market size proportion in terms of sales become worth USD and CAGR developing at a compound approximately throughout the forecast duration 2023 to 2032. additionally provide their clients functions like document-breaking client care, traceability, real-time records, and on-time shipping statistics way to CRM Analytics Market answers. Furthermore, era allows deliver chain companions to understand the correct location of their merchandise as well as authenticity, product safety, satisfactory, and reliability. It is projected that the growing call for CRM Analytics Market The worldwide representative checking arrangement CRM Analytics market is vigorously divided because of the presence of countless organizations working on the lookout. Moreover, the solid presence of market players with restricted geographic concentration and striking client based inside the nearby market has additionally supported the general seriousness existing on the lookout. By and by, a critical number of CRM Analytics market players working in the worldwide worker observing arrangement various membership based representative checking answers for various industry verticals Click To Access Sample Copy of This Report https //www.quincemarketinsights.com/request-sample-63286?utm_source=offpage/pranali CRM Analytics Market Dynamics The CRM Analytics Marker better exchange straightforwardness presented by is probably going to help the worldwide CRM Analytics market development. Minimal expense, secure, and expedient installment handling administrations are made conceivable by the CRM Analytics Market Size as a result of the utilization of encoded conveyed record innovation. This makes it conceivable to confirm exchanges progressively without utilizing go-betweens like clearinghouses and banks. The huge ascent of computerized installments in the retail business has expanded interest for dispersed record innovation. Straightforwardness, security, detectability, and productivity will all work on because of the store network s use of innovation. The arrangement brings providers and purchasers groups together on a similar stage to effectively and safely shares the information. CRM Analytics Market Competitive Landscape The report likewise gives an inside and out investigation of the market s principal rivals, as well as data on their intensity. The examination likewise distinguishes and investigations significant business systems utilized by these fundamental CRM Analytics market players, like A portion of the primary contenders ruling the worldwide CRM Analytics market incorporate Key Players Salesforce Inc., IBM, SAP AG, SAS Institute Inc., Oracle, Teradata, Accenture, Angoss Software, Microsoft. CRM Analytics Market Segmentation Analysis The global CRM Analytics market is segmented based on product By type, application, end-user, and region. The Insight of type, the market has been segmented consortium The developing utilization of innovation that empowers information to be uninhibitedly traded between firms, the production network the executives fragment presently overwhelms the overall CRM Analytics market. This pattern is Segmentation By Type (Sales Analytics, Customer Analytics, Contact Center Analytics, Marketing Analytics, Web Social Media Analytics), By Deployment Model (Cloud, On-Premises), By End-User (Large Enterprises, Small And Medium Businesses), By Vertical (Banking, Financial Services And Insurance (BFSI), Telecommunications And IT, Retail Wholesales, Energy And Utilities, Manufacturing, Healthcare And Life Science, Transportation And Logistics, Media And Entertainment, Hospitality) supposed to fuel classification development. To more readily comprehend end clients, production network the board frameworks CRM Analytics Market Regional Analysis The Geographical Analysis 2021 largest global CRM Analytics market share. To keep its place in the global market, the region has made a large investment in CRM Analytics Market. Trending Innovation technologies like brilliant installments, agreements, and others are currently generally utilized because of innovation arrangement. Tech goliaths Europe, Asia Pacific, North America, Africa, South America, and Middle East, and it is expected to register strong growth during the forecast period. The market in Asia-Pacific is expected to register the highest CAGR during the forecast period. North America (United States, Canada and Mexico) Asia-Pacific (China, Japan, Korea, India, Southeast Asia and Australia) South America (Brazil, Argentina) Europe (Germany, France, United Kingdom, Russia and Italy) Middle East Africa (UAE, Egypt, Saudi Arabia, and South Africa) Drivers In this Exploration, the CRM Analytics Market report gives a full illumination of the main thrusts of the CRM Analytics market. It features the vitally main thrusts of the market, The main considerations driving the development of the CRM Analytics market are the rising Important on merging advanced and actual universes Restraints Expanding Spot concentrate It covers different ventures that are creating in similar field, distinguishes the fundamental areas of utilization and figures out which of them will assume a significant part. The report additionally inspects a portion of the new innovations and improvements introduced by makers that are supposed to become remarkable motors for the worldwide CRM Analytics market. Make an Enquiry for purchasing this Report @ https //www.quincemarketinsights.com/enquiry-before-buying/enquiry-before-buying-63286?utm_source=offpage/pranali Years considered for this report Historical year – 2019-2020 Base year – 2021 Estimated Year -2022 Forecast period – 2023 to 2032 FAQ What is the market size and growth rate forecast for CRM Analytics? What are the Important driving factors propelling the CRM Analytics Market forward? What are the Key leading companies in the CRM Analytics Market Industry? What segments does the CRM Analytics covers? Who are the top manufacturers in the CRM Analytics market? What are the major market opportunities, challenges, and threats faced by the CRM Analytics market? About Us QMI has the most comprehensive collection of market research products and services available on the web. We deliver reports from virtually all major publications and refresh our list regularly to provide you with immediate online access to the world’s most extensive and up-to-date archive of professional insights into global markets, companies, goods, and patterns. Contact us Quince Market Insights Phone +1 208 405 2835 Email sales@quincemarketinsights.com Website https //www.quincemarketinsights.com/

無料で使える便利ツール - csv html変換

https://w.atwiki.jp/freetool/pages/35.html

csvファイルをhtmlに変換テンプレートファイルにCSVファイルの各項目を順番に置換するソフト。 CSVループコンバータホームページ作成ツールCSVslicer csvファイルをhtmlに一括変換(出力)・html自動生成ツール(自動作成) RSS から CSVに RSS to CSV Converter RSS to CSV Converter CSV形式からMovableType形式に変換 csv2mt.php CSV to MT

wicky @ ウィキ - HTML

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HTML

自分用覚え書き @Wiki - HTML5

https://w.atwiki.jp/oboegaki1002/pages/32.html

HTML5で使い方が変更されたタグのまとめ ●a要素リンク設定をするaタグですがアンカーリンクの使い方と別ウインドウにリンクを設定するtarget属性の使い方が一部変更になっています。またリンクの設定する範囲がいままではdivタグやpタグなどのブロックレベル要素や複数のタグを囲ったりすることができませんでしたが、HTML5からはできるようになりました。アンカーリンクとしての使い方が変わりました xhtml1.0でアンカーリンクを設定する場合は移動したい場所にa要素のname属性とid属性をセットし、別のa要素のhref属性でそれを参照していました。 HTML5ではページのアンカーを表すためだけにa要素を使うことはできなくなり、name属性も廃止になりました。 HTML5でアンカーを表す場合は、要素に関わらすid属性を指定することでアンカーを機能させることができるようになります。 HTML5ではアンカー設定の際に＜a id= top ＞と記述しなくても可能となります target属性が非推奨ではなくなりましたリンクを別ウインドウで表示させる際によく使用していたtarget=_blankですが、実際は今でも使われておりましたが、strictでは非推奨となっており、非推奨タグを使いたくないという場合には別ウインドウで表示させたいときはJavascriptで別ウインドウでリンクを開く対応などをしておりましたが、HTML5ではtarget属性が非推奨ではなくなりました。別ウインドウでリンクを開く際のtarget= _blank が非推奨ではなくなりました div要素や複数のタグをまとめてa要素で囲むことができますこれがHTML5で便利になり、使い方の幅が広くなった変更です。 HTML5からはブロック要素・インライン要素の分類はなくなりますので、a要素でdiv要素やp要素、複数の要素を囲むことが出来ます。ただし、a要素はトランスペアレントとして規定されているので、その親要素が許すコンテンツ・モデルを囲むことができるという点に注意する必要があります。また、親要素に関わらずa要素にはbutton要素やiframe要素などのインタラクティブ・コンテンツを入れることが出来ないので注意してください。ただし、これは今までもあまり使われていなかったのであまり気にする必要はないでしょう。 ●address要素 address要素は記述位置によって意味が変わるようになったようですが、こちらはあまり気にしなくていいでしょう。記事を表すarticle要素内で使用すれば記事著者への連絡先になり、直近の祖先がbody要素の場合ならWebサイト管理者への連絡先を意味することになります。これまでコピーライトなどもaddressタグを使用していた場合には、そちらは使用できなくなりましたので注意が必要です。コピーライト表記などには使われなくなりましたので注意しましょう ●b要素 b要素は他と区別したいテキスト部分に使います b要素はもともと太文字を表示する見た目だけに特化した要素でしたが、HTML5からは文書内のキーワードや記事リードなどの強調や重要性を持たないが他と区別したいテキストを表す場合に使用するようになります。以前は＜b＞タグは太字でしたが＜strong＞タグに切り替わって使われなくなり、重要性の持たない部分を太字にするには＜span＞タグにclassを設定して太字などにしていましたが、HTML5では＜b＞タグを使っても良いでしょう。 b要素を実際に使う場合は『strong要素・em要素・mark要素・cite要素』などの使用用途が定義されている要素に適さない場合に使用することになります。つまり、他の文章と区別したい場合はまずふさわしい要素を探し、どの要素にも当てはまらない場合の最終的な手段としてb要素を選択するというのが良いと思います。 ●cite要素情報の引用元タイトルやテーマ・作品を表す要素 cite要素はもともとあまり使われることが少なかったタグですが、情報の引用元タイトルやテーマ・作品を表す要素となります。これまでcite要素は情報元の書籍や論文などの著者名を制作者の解釈によって使用することがありましたが、HTML5から人名などをマークアップすることはできなくなりましたので注意が必要です。 ●dl要素 dl要素は旧来のような定義リストという意味が無くなり、記述リストとして利用するようになります。定義リストにする場合はdt要素に定義語を表すdfn要素を用いる必要があります。 dt要素とdd要素は必ずしも1対1である必要はありません。 1対複数もしくはその逆、複数対複数でも構いません。ただしdt要素とdd要素は必ず関連性があるようにする必要があります。 ●hr要素途中で話題を変える時の目印となる区切り線 hr要素は旧来では罫線を表す要素でしたが、HTML5からは意味的な段落を表すようになります。ただし、セクションの区切りに使用することはできませんので注意が必要です。見た目の線をCSSでdisplay noneとして隠すのなら使わない方がよいのかもしれません。 ●i要素斜体で表示される文字列 i要素はイタリック体を表すだけの見た目だけに特化した要素でしたが、HTML5からは技術用語などの専門用語・他言語の慣用句、または思考・船舶の名前など、他の文章と区別しているテキスト範囲に使います。 ●s要素テキストに引く打ち消し線（取り消し線）旧来は文章テキストの打ち消し線を引く要素でしたが、HTML5ではもう正確ではなくなった内容や関連性がなくなった内容を表す要素になります。 ●small要素免責条項や警告といった細目を表す要素に変更 small要素は小さい文字を表す要素でしたが、HTML5では『免責条項・著作権表記・警告』などの細目テキストを表すようになります。また、文章での注釈や補足としても使用することもできます。最もメジャーな使用例はページフッターの著作権表記のマークアップです。 ●strong要素重要性を伝えるテキストの範囲を表す要素 strong要素は以前のような強調の意味が無くなり重要性という意味が加われました。強調を表したいテキストにはem要素を使うようにします。また、入れ子にすることで重要度を高めることができます。 ●u要素重要性を伝えるテキストの範囲を表す要素下線を表していたu要素ですが、HTML5から中国語での固有名詞を明示するためのラベル付けや、単語のスペルミスに対してラベル付けする場合に利用されるようになりました。また、ハイパーリンクのテキスト(下線付きのテキスト)と見間違えることがあるので紛らわしい箇所での使用には注意する必要があります。（参考：HTML5で使い方が変更されたタグをまとめよう http //www.mdn.co.jp/di/webcreators/）

aurora72source2 @ ウィキ - 002.html

https://w.atwiki.jp/aurora72source2/pages/38.html

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お役立ち情報スケジュール https //confsearch.ethz.ch/?query=STOC+FOCS+SODA+CCC+ICALP+ITCS+LICS+IPCO+ISSAC+SoCG+PODS+COLT+EC+ESA+STACS+APPROX+RANDOM+MFCS+SWAT+WADS+ISAAC+FUN http //www.conferencelist.info/upcoming.html http //community.dur.ac.uk/tom.friedetzky/conf.html http //www.lix.polytechnique.fr/~hermann/conf.html http //csconf.net/deadlines 国際会議・雑誌 MSAR field ratings (2014) http //www.conferenceranks.com/visualization/msar2014.html Google scholar Top publications https //scholar.google.com/citations?view_op=top_venues vq=eng_theoreticalcomputerscience Ranking of CS Departments based on the Number of Papers in Theoretical Computer Science https //projects.csail.mit.edu/dnd/ranking/ Computer Science Conference Rankings https //webdocs.cs.ualberta.ca/~zaiane/htmldocs/ConfRanking.html Acceptance ratio of some Theoretical Computer Science Conferences https //www.lamsade.dauphine.fr/~sikora/ratio/confs.php ML-DM-AI Papers by Researchers in Japan https //knuu.github.io/pages/ml-dm-ai_jp_papers.html Conference Ranks http //www.conferenceranks.com/ Acceptance rates for the top-tier AI-related conferences https //github.com/lixin4ever/Conference-Acceptance-Rate https //perso.crans.org/genest/conf.html https //www.aminer.org/ranks/conf Computer Science Conference Rankings https //dsl.cds.iisc.ac.in/publications/CS_ConfRank.htm Journals (etc.) in Discrete Mathematics and related fields http //www.math.iit.edu/~kaul/Journals.html List of TCS conferences and workshops https //cstheory.stackexchange.com/questions/7900/list-of-tcs-conferences-and-workshops GII-GRIN-SCIE (GGS) Conference Rating http //www.consorzio-cini.it/gii-grin-scie-rating.html CORE Computer Science Journal Rankings http //cic.tju.edu.cn/faculty/zhileiliu/doc/COREComputerScienceJournalRankings.html Computer Science Conference Rank https //www.camille-kurtz.com/index_fichiers/html/CSRank.html CORE Conference Portal http //portal.core.edu.au/conf-ranks/ CORE Journal Portal http //portal.core.edu.au/jnl-ranks/ 中国计算机学会推荐 https //www.ccf.org.cn/Academic_Evaluation/By_category/ 清华大学计算机学科群推荐学术会议和期刊列表 https //numbda.cs.tsinghua.edu.cn/~yuwj/TH-CPL.pdf 清华大学交叉信息研究院重要国际学术会议及核心期刊 https //iiis.tsinghua.edu.cn/uploadfile/cs_conference_list.pdf 頂尖國際會議表列 https //www.csie.ncu.edu.tw/file/98ef5b203937077d24098c335abcf0ca 计算机学术期刊排名 https //sites.google.com/site/luzhaoshomepage/Home/journal-list/ji-suan-ji-xue-shu-qi-kan-pai-ming-computer-science-journal-rankings まとめサイト Best Paper Awards in Computer Science (since 1996) http //jeffhuang.com/best_paper_awards.html データベース勉強会Wiki http //www.kde.cs.tsukuba.ac.jp/dbreading/ Statistics of acceptance rate for the main AI conferences https //github.com/lixin4ever/Conference-Acceptance-Rate Hot Topics on Big Data Algorithms, Analytics and Applications https //www.cse.ust.hk/~leichen/courses/comp6311D/ http //akoide.hatenablog.com/ http //www.orgnet.com/hijackers.html http //11011110.livejournal.com/260838.html http //www.ipsj.or.jp/journal/info/75NC.html 専門知識の仕入れ方 by 吉田さん http //research.preferred.jp/2011/09/how-to-learn/ 岩間研の輪講 http //www.lab2.kuis.kyoto-u.ac.jp/fswikiout/wiki.cgi?action=LIST Laplacian Linear Equations, Graph Sparsification, Local Clustering, Low-Stretch Trees, etc. https //sites.google.com/a/yale.edu/laplacian/ Combinatorial Reconfiguration Wiki http //reconf.wikidot.com/ Connected Papers https //www.connectedpapers.com/ 英語論文の査読表現集 https //staff.aist.go.jp/a.ohta/japanese/study/Review_ex_top.htm Computational Intractability A Guide to Algorithmic Lower Bounds https //hardness.mit.edu/ What Books Should Everyone Read? https //cstheory.stackexchange.com/questions/3253/what-books-should-everyone-read Mathematical Writing by. Donald E. Knuth, Tracy Larrabee, and Paul M. Roberts https //jmlr.csail.mit.edu/reviewing-papers/knuth_mathematical_writing.pdf 講義 PCP and hardness of approximation 解説とか On Dinur s Proof of the PCP Theorem https //www.ams.org/journals/bull/2007-44-01/S0273-0979-06-01143-8/S0273-0979-06-01143-8.pdf クラスNPの新しい特徴づけ https //ipsj.ixsq.nii.ac.jp/ej/index.php?action=pages_view_main active_action=repository_action_common_download item_id=4159 item_no=1 attribute_id=1 file_no=1 page_id=13 block_id=8 https //cstheory.stackexchange.com/questions/45/what-are-good-references-to-understanding-the-proof-of-the-pcp-theorem https //www.cs.umd.edu/~gasarch/TOPICS/pcp/pcp.html https //sites.google.com/view/pcpfest/program Approximability of Optimization Problems (1999?, Madhu Sudan) http //people.csail.mit.edu/madhu/FT99/course.html ( low-degree test ) 😋CSE 532 Computational Complexity Essentials (2004, Paul Beame) https //courses.cs.washington.edu/courses/cse532/04sp/ ( low-degree test ) 😋CSE 533 The PCP Theorem and Hardness of Approximation (2005, Venkatesan Guruswami Ryan O Donnell) https //courses.cs.washington.edu/courses/cse533/05au/ (Dinur s proof) CS 294 PCP and Hardness of Approximation (2006, Luca Trevisan) https //cs.stanford.edu/people/trevisan/pcp/ (講義録少) 😋Course 236603 Probabilistically Checkable Proofs (2007, Eli Ben-Sasson) https //eli.net.technion.ac.il/files/2013/03/notes_2007_Fall.pdf (PCPP; robust PCP) CS359 Hardness of Approximation (Tim Roughgarden, 2007) https //timroughgarden.org/w07b/w07b.html (講義録少) 😋15-854(B) Advanced Approximation Algorithms (2008, Anupam Gupta Ryan O Donnell) https //www.cs.cmu.edu/~anupamg/adv-approx/ 😋6.895 Probabilistically Checkable Proofs and Hardness of Approximation (2010, Dana Moshkovitz) https //www.cs.utexas.edu/~danama/courses/pcp-mit/index.html ( low-degree test ) Prahladh Harsha CMSC 39600 PCPs, codes and inapproximability (2007, Prahladh Harsha) https //www.tifr.res.in/~prahladh/teaching/07autumn/ (講義録少) 😋Limits of approximation algorithms PCPs and Unique Games (2009―10, Prahladh Harsha) https //www.tifr.res.in/~prahladh/teaching/2009-10/limits/ ( low-degree test ) PCPs and Limits of approximation algorithms (2014―15, Prahladh Harsha) https //www.tifr.res.in/~prahladh/teaching/2014-15/limits/ (講義録少) Approximation Algorithms and Hardness of Approximation (2013, Ola Svensson Alantha Newman) https //theory.epfl.ch/osven/courses/Approx13/ (Dinur s proof) 😋CS294 Probabilistically Checkable and Interactive Proof Systems (2019, Alessandro Chiesa) http //people.eecs.berkeley.edu/~alexch/classes/CS294-S2019.html ( 講義動画神 , low-degree test) 15-859T A Theorist s Toolkit (2013, Ryan O Donnell) http //www.cs.cmu.edu/~odonnell/toolkit13/ Algorithmic Lower Bounds Fun with Hardness Proofs (2014/2019, Erik Demaine) https //ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-890-algorithmic-lower-bounds-fun-with-hardness-proofs-fall-2014/index.htm http //courses.csail.mit.edu/6.892/spring19/lectures/ CS395T Pseudorandomness (2017, David Zuckerman) https //www.cs.utexas.edu/~diz/395T/17/ Pseudorandomness (Salil Vadhan, monograph) https //people.seas.harvard.edu/~salil/pseudorandomness/ Expander graphs Expander Graphs and their applications (2020, Irit Dinur) https //www.wisdom.weizmann.ac.il/~dinuri/courses/20-expanders/index.htm Expander Graphs in Computer Science (2010, He Sun) https //resources.mpi-inf.mpg.de/departments/d1/teaching/ws10/EG/WS10.html Course 67659 Expander graphs and their applications (2002, Nati Linial Avi Wigderson) https //www.boazbarak.org/expandercourse/ Counting and Sampling Markov Chain Monte Carlo Methods (2006, Eric Vigoda) https //www.cc.gatech.edu/~vigoda/MCMC_Course/ CSE 599 Counting and Sampling (2017, Shayan Oveis Gharan) https //homes.cs.washington.edu/~shayan/courses/sampling/ CS 294 Markov Chain Monte Carlo Foundations Applications, (Alistair Sinclair) https //people.eecs.berkeley.edu/~sinclair/cs294/f09.html CS294-180 Partition Functions Algorithms Complexity (2020, Alistair Sinclair) https //people.eecs.berkeley.edu/~sinclair/cs294/f20.html CSE 599 Polynomial Paradigm in Algorithm Design (2020, Shayan Oveis Gharan) https //homes.cs.washington.edu/~shayan/courses/polynomials/ Math 270 The Geometry of Polynomials in Algorithms, Combinatorics, and Probability (2015, Nikhil Srivastava) https //math.berkeley.edu/~nikhil/courses/270/ Bridging Continuous and Discrete Optimization (2017) https //simons.berkeley.edu/programs/optimization2017 Geometry of Polynomials https //simons.berkeley.edu/programs/geometry2019 Counting and Sampling (2020, EPFL) https //www.epfl.ch/schools/ic/tcs/counting-and-sampling-2020/ Markov Chains and Counting (Alan Frieze, book) https //www.math.cmu.edu/~af1p/Teaching/MCC17/MC.html Others Parameterized Complexity (2019, Saket Saurabh) https //sites.google.com/view/sakethome/teaching/parameterized-complexity Proofs, beliefs, and algorithms through the lens of sum-of-squares https //www.sumofsquares.org/public/index.html Stat260/CompSci294 Topics in Spectral Graph Methods (Michael Mahoney) https //www.stat.berkeley.edu/~mmahoney/s15-stat260-cs294/ Topics in Theoretical Computer Science An Algorithmist s Toolkit (Jonathan Kelner) https //ocw.mit.edu/courses/mathematics/18-409-topics-in-theoretical-computer-science-an-algorithmists-toolkit-fall-2009/ 6.889 Algorithms for Planar Graphs and Beyond (Fall 2011) http //courses.csail.mit.edu/6.889/fall11/lectures/ 15-855 Graduate Computational Complexity Theory (2017, Ryan O Donnell) http //www.cs.cmu.edu/~odonnell/complexity17/ その他 Journals with quick reviewing - Theoretical Computer Science Stack Exchange https //cstheory.stackexchange.com/questions/8335/journals-with-quick-reviewing Backlog of MathematicsResearch Journals https //www.ams.org/journals/notices/201810/rnoti-p1289.pdf Online TCS Seminars https //cstheory.stackexchange.com/questions/46930/online-tcs-seminars Algorithms Randomization Computation https //sites.google.com/di.uniroma1.it/arc/home Felix Reidl https //tcs.rwth-aachen.de/~reidl/ https //rjlipton.wordpress.com/2014/12/21/modulating-the-permanent/ https //barthesi.gricad-pages.univ-grenoble-alpes.fr/personal-website/dpps/2018-26-11-dpps_intro/ Thirty-Three Miniatures Mathematical and Algorithmic Applications of Linear Algebra https //kam.mff.cuni.cz/~matousek/stml-53-matousek-1.pdf Research in Progress https //researchinprogress.tumblr.com/ 情報拡散投票者モデル A model for spatial conflict Biometrika 1973 Ergodic theorems for weakly interacting infinite systems and the voter model Annals of Probability 1975. Influence Maximization 関連バイラルマーケティング Mining the Network Value of Customers Mining Knowledge-Sharing Sites for Viral Marketing 元ネタ Maximizing the Spread of Influence through a Social Network 理論的結果 On the Approximability of Influence in Social Networks 影響最大化／影響力推定の爆速アルゴリズムシミュレーション CELF++ Optimizing the Greedy Algorithm for Influence Maximization in Social ... WWW 2011 Efficient Influence Maximization in Social Networks KDD 2009 StaticGreedy Solving the Scalability-Accuracy Dilemma in Influence Maximization CIKM 2013 UBLF An Upper Bound Based Approach to Discover Influential Nodes in Social ... ICDM 2013 An Upper Bound based Greedy Algorithm for Mining Top-k Influential Nodes in ... WWW 2014 Extracting Influential Nodes for Information Diffusion on a Social Network AAAI 2007 IMGPU GPU-Accelerated Influence Maximization in Large-Scale Social Networks TPDS 2014 Influence Maximization in Big Networks An Incremental Algorithm for ... IJCAI 2015 Influence at Scale Distributed Computation of Complex Contagion in Networks KDD 2015 Outward Influence and Cascade Size Estimation in Billion-scale Networks SIGMETRICS 2017 RIS Maximizing Social Influence in Nearly Optimal Time SODA 2014 Influence Maximization Near-Optimal Time Complexity Meets Practical Efficiency SIGMOD 2014 Social Influence Spectrum with Guarantees Computing More in Less Time CSoNet 2015 Influence Maximization in Near-Linear Time A Martingale Approach SIGMOD 2015 Cost-aware Targeted Viral Marketing in Billion-scale Networks INFOCOM 2016 Stop-and-Stare Optimal Sampling Algorithms for Viral Marketing in ... SIGMOD 2016 Revisiting the Stop-and-Stare Algorithms for Influence Maximization PVLDB 2017 Why approximate when you can get the exact? Optimal Targeted Viral Marketing ... INFOCOM 2017 Importance Sketching of Influence Dynamics in Billion-scale Networks ICDM 2017 ヒューリスティクス Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale ... KDD 2010 Scalable Influence Maximization in Social Networks under the Linear ... ICDM 2010 IRIE Scalable and Robust Influence Maximization in Social Networks ICDM 2012 Simulated Annealing Based Influence Maximization in Social Networks AAAI 2011 On Approximation of Real-World Influence Spread PKDD 2012 Scalable and Parallelizable Processing of Influence Maximization for ... ICDE 2013 Simpath An Efficient Algorithm for Influence Maximization under the Linear ... ICDM 2011 Probabilistic Solutions of Influence Propagation on Networks CIKM 2013 Community-based Greedy Algorithm for Mining Top-K Influential Nodes in ... KDD 2010 Efficient algorithms for influence maximization in social networks KAIS 2012 CINEMA Conformity-Aware Greedy Algorithm for Influence Maximization in ... EDBT 2013 A Novel and Model Independent Approach for Efficient Influence Maximization ... WISE 2013 Influence Spread in Large-Scale Social Networks - A Belief Propagation Approach ECML PKDD 2012 IMRank Influence Maximization via Finding Self-Consistent Ranking SIGIR 2014 ASIM A Scalable Algorithm for Influence Maximization under the Independent ... WWW 2015 Holistic Influence Maximization Combining Scalability and Efficiency with ... SIGMOD 2016 影響拡散高速計算 Efficient influence spread estimation for influence maximization under the ... Exact Computation of Influence Spread by Binary Decision Diagrams WWW 2017 Computing and maximizing influence in linear threshold and triggering models NIPS 2016 その他 Influence Maximization in Undirected Networks SODA 2014 Debunking the Myths of Influence Maximization An In-Depth Benchmarking Study SIGMOD 2017 謎 Maximizing the Spread of Cascades Using Network Design UAI 2010 The complexity of influence maximization problem in the deterministic linear ... JCO 2012 目的関数が違う Personalized Influence Maximization on Social Networks Stability of Influence Maximization Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a ... On minimizing budget and time in influence propagation over social networks Minimizing Seed Set for Viral Marketing Online Influence Maximization Minimum-Cost Information Dissemination in Social Networks Robust Influence Maximization (He-Kempe) Robust Influence Maximization (Chen+) Robust Influence Maximization (Lowalekar+) Spheres of Influence for More Effective Viral Marketing 変種設定インターネット広告 Real-time Targeted Influence Maximization for Online Advertisements VLDB 2015 Viral Marketing Meets Social Advertising Ad Allocation with Minimum Regret VLDB 2015 Revenue Maximization in Incentivized Social Advertising VLDB 2017 疎化・粗大化 Sparsification of Influence Networks Fast Influence-based Coarsening for Large Networks 予測 Prediction of Information Diffusion Probabilities for Independent Cascade Model Learning Continuous-Time Information Diffusion Model for Social Behavioral ... Learning Influence Probabilities In Social Networks Learning Stochastic Models of Information Flow Predicting Information Diffusion on Social Networks with Partial Knowledge Latent Feature Independent Cascade Model for Social Propagation Learning Diffusion Probability based on Node Attributes in Social Networks Topic-aware Social Influence Propagation Models Uncovering the Temporal Dynamics of Diffusion Networks モデリング時間 A Data-Based Approach to Social Influence Maximization Time-Critical Influence Maximization in Social Networks with Time-Delayed ... Time Constrained Influence Maximization in Social Networks Uncovering the Temporal Dynamics of Diffusion Networks On Influential Node Discovery in Dynamic Social Networks Influence Maximization with Novelty Decay in Social Networks トピック・カテゴリ Topic-aware Social Influence Propagation Models Diversified Social Influence Maximization モデルは同じ，目的関数が違うトピック・カテゴリのアルゴリズム Online Topic-aware Influence Maximization Queries EDBT 2014 Real-time Topic-aware Influence Maximization Using Preprocessing CSoNet 2015 Online Topic-Aware Influence Maximization VLDB 2015 負/競合 Competitive Influence Maximization in Social Networks WINE 2007 Word of Mouth Rumor Dissemination in Social Networks SIROCCO 2008 Threshold Models for Competitive Influence in Social Networks WINE 2010 Influence Maximization in Social Networks When Negative Opinions May Emerge ... Influence Blocking Maximization in Social Networks under the Competitive ... Maximizing Influence in a Competitive Social Network A Follower s Perspective ICEC 2007 New Models for Competitive Contagion Opinion maximization in social networks 意見 Maximizing Influence in an Ising Network A Mean-Field Optimal Solution Isingモデル投票者モデルオリジナル Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model A Model for Spatial Conflict A Note on Maximizing the Spread of Influence in Social Networks WINE 2007 Influence Diffusion Dynamics and Influence Maximization in Social Networks ... WSDM 2013 Maximizing the Long-term Integral Influence in Social Networks Under the ... WWW 2014 適応的二段階アプローチ Scalable Methods for Adaptively Seeding a Social Network WWW 2015 その他 How to Influence People with Partial Incentives Mining Social Networks Using Heat Diffusion Processes for Marketing ... Influence Maximization with Viral Product Design Profit Maximization over Social Networks On Budgeted Influence Maximization in Social Networks In Search of Influential Event Organizers in Online Social Networks Linear Computation for Independent Social Influence Efficient Location-Aware Influence Maximization Dynamic Influence Maximization Under Increasing Returns to Scale Online Influence Maximization Real-time Targeted Influence Maximization for Online Advertisements VLDB 2015 連続時間独立カスケード(CT-IC)モデル Uncovering the Temporal Dynamics of Diffusion Networks ICML 2011 Influence Maximization in Continuous Time Diffusion Networks ICML 2012 Scalable Influence Estimation in Continuous-Time Diffusion Networks NIPS 2013 Tight Bounds for Influence in Diffusion Networks and Application to Bond ... NIPS 2014 Anytime Influence Bounds and the Explosive Behavior of Continuous-Time ... NIPS 2015 汚染最小化 Minimizing the Spread of Contamination by Blocking Links in a Network Blocking Links to Minimize Contamination Spread in a Social Network Negative Influence Minimizing by Blocking Nodes in Social Networks Finding Spread Blockers in Dynamic Networks 動的アルゴリズム Influence Maximization in Dynamic Social Networks Maximizing the Extent of Spread in a Dynamic Network On Influential Nodes Tracking in Dynamic Social Networks Real-Time Influence Maximization on Dynamic Social Streams PVLDB 2017 斉藤和巳さん一派 Tractable Models for Information Diffusion in Social Networks PKDD 2006 Extracting Influential Nodes for Information Diffusion on a Social Network AAAI 2007 Minimizing the Spread of Contamination by Blocking Links in a Network AAAI 2008 Prediction of Information Diffusion Probabilities for Independent Cascade Model KES 2008 Learning Continuous-Time Information Diffusion Model for Social Behavioral ... ACML 2009 Selecting Information Diffusion Models over Social Networks for Behavioral ... ECML PKDD 2010 (ACML 09と同じ？) Blocking Links to Minimize Contamination Spread in a Social Network TKDD 2009 Finding Influential Nodes in a Social Network from Information Diffusion Data SBP 2009 Learning information diffusion model in a social network for predicting influence of nodes Intell. Data Anal. 2011 Learning Diffusion Probability based on Node Attributes in Social Networks ISMIS 2011 Uncertain Graphs On a Routing Problem Within Probabilistic Graphs ... INFOCOM 2007 The Most Reliable Subgraph Problem PKDD 2007 Frequent Subgraph Pattern Mining on Uncertain Graph Data CIKM 2009 Fast Discovery of Reliable Subnetworks ASONAM 2010 k-Nearest Neighbors in Uncertain Graphs VLDB 2010 Finding Top-k Maximal Cliques in an Uncertain Graph ICDE 2010 Fast Discovery of Reliable k-terminal Subgraphs PAKDD 2010 Discovering Frequent Subgraphs over Uncertain Graph Databases under ... KDD 2010 BMC An Efficient Method to Evaluate Probabilistic Reachability Queries DASFAA 2011 Efficient Discovery of Frequent Subgraph Patterns in Uncertain Graph Databases EDBT 2011 Discovering Highly Reliable Subgraphs in Uncertain Graphs KDD 2011 Distance Constraint Reachability Computation in Uncertain Graphs VLDB 2011 Efficient Subgraph Search over Large Uncertain Graphs VLDB 2011 Reliable Clustering on Uncertain Graphs ICDM 2012 Polynomial-Time Algorithm for Finding Densest Subgraphs in Uncertain Graphs MLG 2013 Clustering Large Probabilistic Graphs TKDE 2013 The Pursuit of a Good Possible World Extracting Representative Instances of ... SIGMOD 2014 Efficient and Accurate Query Evaluation on Uncertain Graphs via Recursive ... ICDE 2014 Fast Reliability Search in Uncertain Graphs EDBT 2014 Top-k Reliable Edge Colors in Uncertain Graphs CIKM 2015 Top-k Reliability Search on Uncertain Graphs ICDM 2015 Assessing Attack Vulnerability in Networks with Uncertainty INFOCOM 2015 Triangle-Based Representative Possible Worlds of Uncertain Graphs DASFAA 2016 Truss Decomposition of Probabilistic Graphs Semantics and Algorithms SIGMOD 2016 ネットワーク信頼性 A practical bounding algorithm for computing two-terminal reliability based ... Comput. Math. Appl. 2011 OR系 Minimum-Risk Maximum Clique Problem k-means Streaming k-means approximation StreamKM++ A Clustering Algorithm for Data Streams k-means++ The Advantages of Careful Seeding Streaming k-means on Well-Clusterable Data A Local Search Approximation Algorithm for k-Means Clustering Fast and Accurate k-means For Large Datasets Hartigan s Method k-means Clustering without Voronoi Hartigan s K-Means Versus Lloyd s K-Means - Is It Time for a Change? Using the Triangle Inequality to Accelerate k-Means Making k-means even faster Accelerated k-means with adaptive distance bounds PageRank 高速計算 Extrapolation Methods for Accelerating PageRank Computations FAST-PPR Scaling Personalized PageRank Estimation for Large Graphs 動的更新 Link Evolution Analysis and Algorithms Fast Incremental and Personalized PageRank PageRank on an Evolving Graph Efficient PageRank Tracking in Evolving Networks 私，前原貴憲，河原林健一バックボタン The Effect of the Back Button in a Random Walk Application for PageRank BackRank an Alternative for PageRank? Spectral Clustering A Random Walks View of Spectral Segmentation Kernel k-means, Spectral Clustering and Normalized Cuts http //ranger.uta.edu/~chqding/Spectral/ https //arxiv.org/abs/0711.0189 A Tutorial on Spectral Clustering. Ulrike von Luxburg Laplacian https //sites.google.com/a/yale.edu/laplacian/ 理論計算機科学 + ... ACM Symposium on Theory of Computing STOC 2013 Fast Approximation Algorithms for the Diameter and Radius of Sparse Graphs STOC 2014 The matching polytope has exponential extension complexity Approximation Algorithms for Regret-Bounded Vehicle Routing and Applications ... Approximate Distance Oracle with Constant Query Time Zig-zag Sort A Simple Deterministic Data-Oblivious Sorting Algorithm ... Minimum Bisection is Fixed Parameter Tractable IEEE Symposium on Foundations of Computer Science FOCS 2013 https //sites.google.com/site/tcsreading/home/focs2013 The Price of Stability for Undirected Broadcast Network Design with Fair ... Learning Sums of Independent Integer Random Variables OSNAP Faster numerical linear algebra algorithms via sparser subspace ... Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for ... Algebraic Algorithms for b-Matching, Shortest Undirected Paths, and f-Factors Improved approximation for 3-dimensional matching via bounded pathwidth ... Independent Set, Induced Matching, and Pricing Connections and Tight ... Approximating Minimum-Cost k-Node Connected Subgraphs via Independence-Free ... Online Node-weighted Steiner Forest and Extensions via Disk Paintings An LMP O(log n)-Approximation Algorithm for Node Weighted Prize Collecting ... Approximating Bin Packing within O(log OPT*loglog OPT) bins Strong Backdoors to Bounded Treewidth SAT ACM-SIAM Symposium on Discrete Algorithms SODA 2008 On the Approximability of Influence in Social Networks SODA 2014 Maximizing Social Influence in Nearly Optimal Time Influence Maximization in Undirected Networks International Symposium on Algorithms and Computation ACM Conference on Innovations in Theoretical Computer Science アルゴリズム + ... Workshop on Algorithm Engineering and Experiments ALENEX 2016 Computing Top-k Closeness Centrality Faster in Unweighted Graphs International Symposium on Experimental Algorithms SEA 2015 Is Nearly-linear the Same in Theory and Practice? A Case Study with a ... Workshop on Algorithms and Models for the Web Graph WAW 2012 Dynamic PageRank using Evolving Teleportation SIGMETRICS 2017 Outward Influence and Cascade Size Estimation in Billion-scale Networks ジャーナル版はProceedings of the ACM on Measurement and Analysis of Computing Systems (POMACS) データマイニング + ... ACM SIGKDD Conference on Knowledge Discovery and Data Mining KDD 2001 Mining the Network Value of Customers Co-clustering documents and words using Bipartite Spectral Graph Partitioning KDD 2002 Mining Knowledge-Sharing Sites for Viral Marketing KDD 2007 ✔Cost-effective Outbreak Detection in Networks KDD 2008 ✔Influence and Correlation in Social Networks KDD 2009 Efficient Influence Maximization in Social Networks ✔On Compressing Social Networks KDD 2010 Inferring Networks of Diffusion and Influence Scalable Influence Maximization for Prevalent Viral Marketing in Large-Scale ... Community-based Greedy Algorithm for Mining Top-K Influential Nodes in ... Discovering Frequent Subgraphs over Uncertain Graph Databases under ... Semi-Supervised Feature Selection for Graph Classification KDD 2011 Discovering Highly Reliable Subgraphs in Uncertain Graphs Sparsification of Influence Networks KDD 2012 Streaming Graph Partitioning for Large Distributed Graphs PageRank on an Evolving Graph Information Diffusion and External Influence in Networks Vertex Neighborhoods, Low Conductance Cuts, and Good Seeds for Local ... Information Propagation Game a Tool to Acquire Human Playing Data for ... Chromatic Correlation Clustering Efficient Personalized PageRank with Accuracy Assurance KDD 2013 Denser than the Densest Subgraph Extracting Optimal Quasi-Cliques with ... Redundancy-Aware Maximal Cliques Trial and Error in Influential Social Networks Workshop on Mining and Learning with Graphs (MLG) Polynomial-Time Algorithm for Finding Densest Subgraphs in Uncertain Graphs KDD 2014 Stability of Influence Maximization Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a ... Heat Kernel Based Community Detection Balanced Graph Edge Partition Correlation Clustering in MapReduce Streaming Submodular Maximization Massive Data Summarization on the Fly Fast Influence-based Coarsening for Large Networks FAST-PPR Scaling Personalized PageRank Estimation for Large Graphs KDD 2015 Influence at Scale Distributed Computation of Complex Contagion in Networks Efficient Algorithms for Public-Private Social Networks Reciprocity in Social Networks with Capacity Constraints Online Influence Maximization Locally Densest Subgraph Discovery ✔Scalable Large Near-Clique Detection in Large-Scale Networks via Sampling Non-exhaustive, Overlapping Clustering via Low-Rank Semidefinite Programming KDD 2016 ✔Robust Influence Maximization (He-Kempe) Robust Influence Maximization (Chen+) FRAUDAR Bounding Graph Fraud in the Face of Camouflage KDD 2018 Approximating the Spectrum of a Graph IEEE International Conference on Data Mining ICDM 2006 Fast Random Walk with Restart and Its Applications ICDM 2010 Scalable Influence Maximization in Social Networks under the Linear ... Modeling Information Diffusion in Implicit Networks ICDM 2011 Simpath An Efficient Algorithm for Influence Maximization under the Linear ... On the Hardness of Graph Anonymization Overlapping correlation clustering Minimizing Seed Set for Viral Marketing ICDM 2012 Reliable Clustering on Uncertain Graphs IRIE Scalable and Robust Influence Maximization in Social Networks Predicting Directed Links using Nondiagonal Matrix Decompositions Inferring the Underlying Structure of Information Cascades Topic-aware Social Influence Propagation Models Time Constrained Influence Maximization in Social Networks Profit Maximization over Social Networks ICDM 2013 Influence Maximization in Dynamic Social Networks UBLF An Upper Bound Based Approach to Discover Influential Nodes in Social ... Influence-based Network-oblivious Community Detection Linear Computation for Independent Social Influence ICDM 2014 Quick Detection of High-degree Entities in Large Directed Networks ICDM 2015 Top-k Reliability Search on Uncertain Graphs ✔Catching the head, tail, and everything in between a streaming algorithm ... ICDM 2017 Importance Sketching of Influence Dynamics in Billion-scale Networks European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases PKDD 2006 Tractable Models for Information Diffusion in Social Networks PKDD 2007 The Most Reliable Subgraph Problem PKDD 2012 On Approximation of Real-World Influence Spread ECML PKDD 2010 Selecting Information Diffusion Models over Social Networks for Behavioral ... ECML PKDD 2012 Influence Spread in Large-Scale Social Networks - A Belief Propagation Approach ECML PKDD 2016 Temporal PageRank SIAM International Conference on Data Mining SDM 2010 Fast Single-Pair SimRank Computation SDM 2011 Influence Maximization in Social Networks When Negative Opinions May Emerge ... Maximising the Quality of Influence SDM 2012 On Influential Node Discovery in Dynamic Social Networks Influence Blocking Maximization in Social Networks under the Competitive ... ✔Fast Robustness Estimation in Large Social Graphs Communities and Anomaly ... SDM 2013 Triadic Measures on Graphs The Power of Wedge Sampling k-means-- A unified approach to clustering and outlier detection Opinion maximization in social networks SDM 2014 Influence Maximization with Viral Product Design Future Influence Ranking of Scientific Literature VoG Summarizing and Understanding Large Graphs Make It or Break It Manipulating Robustness in Large Networks Accelerating Graph Adjacency Matrix Multiplications with Adjacency Forest SDM 2015 Selecting Shortcuts for a Smaller World Where Graph Topology Matters The Robust Subgraph Problem On Influential Nodes Tracking in Dynamic Social Networks ✔Non-exhaustive, Overlapping k-means SDM 2017 A Dual-tree Algorithm for Fast k-means Clustering with Large k Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD 2010 Fast Discovery of Reliable k-terminal Subgraphs ソーシャルネットワーク + ... IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2009 Spectral Counting of Triangles in Power-Law Networks via Element-Wise ... Reducing Social Network Dimensions Using Matrix Factorization Methods Dynamic and Static Influence Models on Starbucks Networks ASONAM 2010 Fast Discovery of Reliable Subnetworks ASONAM 2011 Dynamic Social Influence Analysis through Time-dependent Factor Graphs ASONAM 2012 Influence of the Dynamic Social Network Timeframe Type and Size on the Group ... Diffusion Centrality in Social Networks Visual Analysis of Dynamic Networks using Change Centrality ASONAM 2014 Diversified Social Influence Maximization ASONAM 2015 Structure-Preserving Sparsification of Social Networks ACM Conference on Online Social Networks COSN 2013 Scalable Similarity Estimation in Social Networks Closeness, Node Labels, ... Counting Triangles in Large Graphs using Randomized Matrix Trace Estimation International Conference on Computational Social Networks CSoNet 2015 Real-time Topic-aware Influence Maximization Using Preprocessing Social Influence Spectrum with Guarantees Computing More in Less Time SNA-KDD (International Workshop on Social Network Mining and Analysis) Finding Spread Blockers in Dynamic Networks データベース + ... ACM SIGMOD International Conference on Management of Data SIGMOD 2011 On k-skip Shortest Paths Local Graph Sparsification for Scalable Clustering SIGMOD 2013 Massive Graph Triangulation Efficiently Computing k-Edge Connected Components via Graph Decomposition I/O Efficient Computing SCCs in Massive Graphs TurboISO Towards Ultrafast and Robust Subgraph Isomorphism Search in Large ... TF-Label a Topological-Folding Labeling Scheme for Reachability Querying in ... Online Search of Overlapping Communities Efficient Ad-hoc Search for Personalized PageRank SIGMOD 2014 In Search of Influential Event Organizers in Online Social Networks Efficient Location-Aware Influence Maximization Querying K-Truss Community in Large and Dynamic Graph The Pursuit of a Good Possible World Extracting Representative Instances of ... Influence Maximization Near-Optimal Time Complexity Meets Practical Efficiency SIGMOD 2015 COMMIT A Scalable Approach to Mining Communication Motifs from Dynamic Networks Minimum Spanning Trees in Temporal Graphs Influence Maximization in Near-Linear Time A Martingale Approach SIGMOD 2016 Spheres of Influence for More Effective Viral Marketing ✔Speedup Graph Processing by Graph Ordering Distributed Set Reachability ✔Truss Decomposition of Probabilistic Graphs Semantics and Algorithms Holistic Influence Maximization Combining Scalability and Efficiency with ... Stop-and-Stare Optimal Sampling Algorithms for Viral Marketing in ... TIM+やIMMより高性能（と謳う）影響最大化アルゴリズム SIGMOD 2017 Debunking the Myths of Influence Maximization An In-Depth Benchmarking Study Computing A Near-Maximum Independent Set in Linear Time by Reducing-Peeling DAG Reduction Fast Answering Reachability Queries Scaling Locally Linear Embedding Dynamic Density Based Clustering IEEE International Conference on Data Engineering ICDE 2010 Finding Top-k Maximal Cliques in an Uncertain Graph ICDE 2011 Outlier Detection in Graph Streams ICDE 2012 Learning Stochastic Models of Information Flow Extracting Analyzing and Visualizing Triangle K-Core Motifs within Networks ICDE 2013 Scalable and Parallelizable Processing of Influence Maximization for ... Scalable Maximum Clique Computation Using MapReduce Faster Random Walks By Rewiring Online Social Networks On-The-Fly Sampling Node Pairs Over Large Graphs ICDE 2014 How to Partition a Billion-Node Graph Random-walk Domination in Large Graphs Evaluating Multi-Way Joins over Discounted Hitting Time Efficient and Accurate Query Evaluation on Uncertain Graphs via Recursive ... International Conference on Very Large Data Bases VLDB 2010 Shortest Path Computation on Air Indexes Fast Incremental and Personalized PageRank k-Nearest Neighbors in Uncertain Graphs VLDB 2011 On Triangulation-based Dense Neighborhood Graph Discovery Distance Constraint Reachability Computation in Uncertain Graphs Efficient Subgraph Search over Large Uncertain Graphs VLDB 2012 Keyword-aware Optimal Route Search gSketch On Query Estimation in Graph Streams A Data-Based Approach to Social Influence Maximization Scalable K-Means++ Fast and Exact Top-k Search for Random Walk with Restart VLDB 2013 iRoad A Framework For Scalable Predictive Query Processing On Road Networks Top-K Nearest Keyword Search on Large Graphs Memory Efficient Minimum Substring Partitioning Piggybacking on Social Networks Streaming Algorithms for k-core Decomposition VLDB 2014 More is Simpler Effectively and Efficiently Assessing Node Pair ... On k-Path Covers and their Applications Crowdsourcing Algorithms for Entity Resolution VLDB 2015 Viral Marketing Meets Social Advertising Ad Allocation with Minimum Regret Online Topic-Aware Influence Maximization Real-time Targeted Influence Maximization for Online Advertisements VLDB 2016 Fast Algorithm for the Lasso based L1-Graph Construction Online Entity Resolution Using an Oracle VLDB 2017 Revenue Maximization in Incentivized Social Advertising Real-Time Influence Maximization on Dynamic Social Streams Revisiting the Stop-and-Stare Algorithms for Influence Maximization ACM International Conference on Information and Knowledge Management CIKM 2008 Mining Social Networks Using Heat Diffusion Processes for Marketing ... The query-flow graph model and applications CIKM 2009 Frequent Subgraph Pattern Mining on Uncertain Graph Data CIKM 2011 Suggesting Ghost Edges for a Smaller World CIKM 2012 Delineating Social Network Data Anonymization via Random Edge Perturbation ✔Gelling, and Melting, Large Graphs by Edge Manipulation CIKM 2013 StaticGreedy Solving the Scalability-Accuracy Dilemma in Influence Maximization Personalized Influence Maximization on Social Networks Probabilistic Solutions of Influence Propagation on Networks Efficiently Anonymizing Social Networks with Reachability Preservation Overlapping Community Detection Using Seed Set Expansion CIKM 2014 Pushing the Envelope in Graph Compression CIKM 2015 Top-k Reliable Edge Colors in Uncertain Graphs International Conference on Extending Database Technology EDBT 2011 Efficient Discovery of Frequent Subgraph Patterns in Uncertain Graph Databases EDBT 2013 CINEMA Conformity-Aware Greedy Algorithm for Influence Maximization in ... EDBT 2014 Online Topic-aware Influence Maximization Queries Privacy Preserving Estimation of Social Influence ✔Fast Reliability Search in Uncertain Graphs EDBT 2015 Identifying Converging Pairs of Nodes on a Budget International Conference on Database Systems for Advanced Applications DASFAA 2011 BMC An Efficient Method to Evaluate Probabilistic Reachability Queries DASFAA 2016 Triangle-Based Representative Possible Worlds of Uncertain Graphs ウェブ + ... International World Wide Web Conference WWW 2003 Extrapolation Methods for Accelerating PageRank Computations WWW 2004 The Effect of the Back Button in a Random Walk Application for PageRank RandomSurfer with Back Step Propagation of Trust and Distrust WWW 2005 BackRank an Alternative for PageRank? WWW 2007 Wherefore Art Thou R3579X? Anonymized Social Networks, Hidden Patterns, and ... WWW 2008 Fast Algorithms for Top-k Personalized PageRank Queries WWW 2009 Towards Context-Aware Search by Learning A Very Large Variable Length Hidden ... WWW 2010 Sampling Community Structure Stochastic Models for Tabbed Browsing Tracking the Random Surfer Empirically Measured Teleportation Parameters in ... WWW 2011 Limiting the Spread of Misinformation in Social Networks Estimating Sizes of Social Networks via Biased Sampling CELF++ Optimizing the Greedy Algorithm for Influence Maximization in Social ... WWW 2012 The Role of Social Networks in Information Diffusion Analyzing Spammer s Social Networks for Fun and Profit Human Wayfinding in Information Networks Optimizing Budget Allocation Among Channels and Influencers Recommendations to Boost Content Spread in Social Networks WWW 2013 Subgraph Frequencies Mapping the Empirical and Extremal Geography of Large ... Estimating Clustering Coefficients and Size of Social Networks via Random Walk Spectral Analysis of Communication Networks Using Dirichlet Eigenvalues WWW 2014 How to Influence People with Partial Incentives An Upper Bound based Greedy Algorithm for Mining Top-k Influential Nodes in ... ポスター Maximizing the Long-term Integral Influence in Social Networks Under the ... ポスター WWW 2015 Path Sampling A Fast and Provable Method for Estimating 4-Vertex Subgraph ... ✔The K-clique Densest Subgraph Problem ASIM A Scalable Algorithm for Influence Maximization under the Independent ... ✔Scalable Methods for Adaptively Seeding a Social Network WWW 2017 Why Do Cascade Sizes Follow a Power-Law? Exact Computation of Influence Spread by Binary Decision Diagrams ACM International Conference on Web Search and Data Mining WSDM 2010 TwitterRank Finding Topic-sensitive Influential Twitterers Learning Influence Probabilities In Social Networks WSDM 2013 On the Streaming Complexity of Computing Local Clustering Coefficients Influence Diffusion Dynamics and Influence Maximization in Social Networks ... From Machu_Picchu to rafting the urubamba river Anticipating information ... WSDM 2015 Negative Link Prediction in Social Media On Integrating Network and Community Discovery The Power of Random Neighbors in Social Networks International Conference on Weblogs and Social Media ICWSM 2010 ICWSM - A Great Catchy Name Semi-Supervised Recognition of Sarcastic ... ICWSM 2011 4chan and /b/ An Analysis of Anonymity and Ephemerality in a Large Online ... 人工知能 + ... AAAI Conference on Artificial Intelligence AAAI 2007 Extracting Influential Nodes for Information Diffusion on a Social Network AAAI 2008 Minimizing the Spread of Contamination by Blocking Links in a Network AAAI 2010 EWLS A New Local Search for Minimum Vertex Cover AAAI 2011 Simulated Annealing Based Influence Maximization in Social Networks Nonnegative Spectral Clustering with Discriminative Regularization AAAI 2012 Exacting Social Events for Tweets Using a Factor Graph Time-Critical Influence Maximization in Social Networks with Time-Delayed ... Two New Local Search Strategies for Minimum Vertex Cover AAAI 2013 Sensitivity of Diffusion Dynamics to Network Uncertainty Spectral Rotation versus K-Means in Spectral Clustering Fast and Exact Top-k Algorithm for PageRank workshop Negative Influence Minimizing by Blocking Nodes in Social Networks AAAI 2014 New Models for Competitive Contagion Influence Maximization with Novelty Decay in Social Networks Rounded Dynamic Programming for Tree-Structured Stochastic Network Design Theory of Cooperation in Complex Social Networks AAAI 2015 Two Weighting Local Search for Minimum Vertex Cover AAAI 2016 Approximate K-Means++ in Sublinear Time AAAI 2018 Risk-Sensitive Submodular Optimization International Joint Conference on Artificial Intelligence IJCAI 2001 Link Analysis, Eigenvectors and Stability IJCAI 2009 Efficient Estimation of Influence Functions for SIS Model on Social Networks IJCAI 2011 Fast Approximate Nearest-Neighbor Search with k-Nearest Neighbor Graph IJCAI 2015 Influence Maximization in Big Networks An Incremental Algorithm for ... Non-monotone Adaptive Submodular Maximization IJCAI 2017 Robust Quadratic Programming for Price Optimization International Conference on Artificial Intelligence and Statistics AISTATS 2012 On Bisubmodular Maximization AISTATS 2018 Random Warping Series A Random Features Method for Time-Series Embedding International Workshop on Internet and Network Economics WINE 2007 Competitive Influence Maximization in Social Networks A Note on Maximizing the Spread of Influence in Social Networks WINE 2010 Threshold Models for Competitive Influence in Social Networks IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology WI-IAT 2009 From Dango to Japanese Cakes Query Reformulation Models and Patterns WI-IAT 2014 Lazy Walks Versus Walks with Backstep Flavor of PageRank Conference on Uncertainty in Artificial Intelligence UAI 2010 Maximizing the Spread of Cascades Using Network Design International Conference on Antonomous Agents and Multiagent Sytems AAMAS 2015 Dynamic Influence Maximization Under Increasing Returns to Scale AAMAS 2016 Robust Influence Maximization (Lowalekar+) KES (International Conference on Knowledge-Based Intelligent Information and Engineering Systems) Prediction of Information Diffusion Probabilities for Independent Cascade Model ISMIS (International Conference on Foundations of Intelligent Systems) Learning Diffusion Probability based on Node Attributes in Social Networks 機械学習 + ... Conference on Neural Information Processing Systems NIPS 2003 Learning with Local and Global Consistency NIPS 2004 An Application of Boosting to Graph Classification NIPS 2009 Random Walks with Random Projections NIPS 2013 http //connpass.com/event/4728/ Scalable Influence Estimation in Continuous-Time Diffusion Networks Distributed Representations of Words and Phrases and their Compositionality DeViSE A Deep Visual-Semantic Embedding Model A Gang of Bandits Similarity Component Analysis One-shot learning by inverting a compositional causal process Inverse Density as an Inverse Problem The Fredholm Equation Approach Approximate Bayesian Image Interpretation using Generative Probabilistic ... Playing Atari with Deep Reinforcement Learning Scalable kernels for graphs with continuous attributes More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server NIPS 2014 Tight Bounds for Influence in Diffusion Networks and Application to Bond ... NIPS 2015 A Structural Smoothing Framework For Robust Graph-Comparison Anytime Influence Bounds and the Explosive Behavior of Continuous-Time ... Learnability of Influence in Networks A Submodular Framework for Graph Comparison https //stanford.edu/~jugander/NetworksNIPS2015/ ワークショップ NIPS 2016 Joint M-Best-Diverse Labelings as a Parametric Submodular Minimization Fast and Provably Good Seedings for k-Means Maximizing Influence in an Ising Network A Mean-Field Optimal Solution Budgeted stream-based active learning via adaptive submodular maximization Computing and maximizing influence in linear threshold and triggering models The Power of Optimization from Samples NIPS 2017 Stochastic Submodular Maximization The Case of Coverage Functions Robust Optimization for Non-Convex Objectives The Importance of Communities for Learning to Influence International Conference on Machine Learning ICML 2003 ✔Marginalized Kernels Between Labeled Graphs ICML 2011 Uncovering the Temporal Dynamics of Diffusion Networks Preserving Personalized Pagerank in Subgraphs ICML 2012 Influence Maximization in Continuous Time Diffusion Networks ICML 2014 Efficient Label Propagation ICML 2015 ✔Yinyang K-Means A Drop-In Replacement of the Classic K-Means with ... ACML (Asian Conference on Machine Learning) 2009 Learning Continuous-Time Information Diffusion Model for Social Behavioral ... 高性能計算 + ... IEEE International Parallel & Distributed Processing Symposium IPDPS 2016 Rabbit Order Just-in-time Parallel Reordering for Fast Graph Analysis PDPTA (International Conference on Parallel and Distributed Processing Techniques and Applications) Latent Feature Independent Cascade Model for Social Propagation 通信ネットワーク + ... IEEE International Conference on Computer Communications INFOCOM 2007 On a Routing Problem Within Probabilistic Graphs ... INFOCOM 2012 Approximate Convex Decomposition Based Localization in Wireless Sensor Networks INFOCOM 2013 2.5K-Graphs from Sampling to Generation Maximizing Submodular Set Function with Connectivity Constraint Theory and ... A Graph Minor Perspective to Network Coding Connecting Algebraic Coding ... INFOCOM 2014 Information Diffusion in Mobile Social Networks The Speed Perspective A General Framework of Hybrid Graph Sampling for Complex Network Analysis INFOCOM 2015 Assessing Attack Vulnerability in Networks with Uncertainty INFOCOM 2016 Cost-aware Targeted Viral Marketing in Billion-scale Networks INFOCOM 2017 Why approximate when you can get the exact? Optimal Targeted Viral Marketing ... WASA (Wireless Algorithms, Systems, and Applications) Minimum-Cost Information Dissemination in Social Networks 情報検索 + ... ACM International Conference on Research and Development in Information Retrieval SIGIR 2014 The Role of Network Distance in LinkedIn People Search Influential Nodes Selection A Data Reconstruction Perspective IMRank Influence Maximization via Finding Self-Consistent Ranking 自然言語処理 + ... Meeting of the Association for Computational Linguistics ACL 2011 Word Alignment via Submodular Maximization over Matroids ACL 2013 A user-centric model of voting intention from Social Media グラフィクス・ビジョン・HCI + ... ACM SIGCHI Conference on Human Factors in Computing Systems IEEE Conference on Computer Vision and Pattern Recognition CVPR 2014 Spectral Graph Reduction for Efficient Image and Streaming Video Segmentation superpixelでグラフを小さくして画像分割とかを効率化 SBP (International Workshop on Social Computing and Behavioral Modeling) 2009 Finding Influential Nodes in a Social Network from Information Diffusion Data Manuscript+Technical report Random-walk domination in large graphs problem definitions and fast solutions Lazier Than Lazy Greedy ✔A Fast and Provable Method for Estimating Clique Counts Using Turan s Theorem ジャーナルトップジャーナル KAIS (Knowledge and Information Systems) Efficient algorithms for influence maximization in social networks IPL (Information Processing Letters) A Fast and Practical Bit-Vector Algorithm for the Longest Common Subsequence ... Internet Mathematics Link Evolution Analysis and Algorithms Towards Scaling Fully Personalized PageRank Algorithms, Lower Bounds, and ... TKDD (Transactions on Knowledge Discovery from Data) 2009 Blocking Links to Minimize Contamination Spread in a Social Network TKDE 2013 Clustering Large Probabilistic Graphs 普通のジャーナル Computational Social Networks Efficient influence spread estimation for influence maximization under the ... Computers and Mathematics with Applications A practical bounding algorithm for computing two-terminal reliability based ... Dynamics of Information Systems Algorithmic Approaches Minimum-Risk Maximum Clique Problem Information Sciences Super mediator - A new centrality measure of node importance for information ... Minimizing the expected complete influence time of a social network Maximizing the spread of influence ranking in social networks 連続時間マルコフ連鎖を取り入れたICモデル JCO (Journal of Combinatorial Optimization) 2012 The complexity of influence maximization problem in the deterministic linear ... JSAC (IEEE Journal on Selected Areas in Communications) 2013 On Budgeted Influence Maximization in Social Networks SNAM (Social Network Analysis and Mining) 2012 On minimizing budget and time in influence propagation over social networks TPDS (IEEE Transactions on Parallel and Distributed Systems) IMGPU GPU-Accelerated Influence Maximization in Large-Scale Social Networks フォーカス外 Maximizing the Extent of Spread in a Dynamic Network ICEC (International Conference on Electronic Commerce) Maximizing Influence in a Competitive Social Network A Follower s Perspective WISE 2013 A Novel and Model Independent Approach for Efficient Influence Maximization ... 国内会議人工知能学会 JSAI Resampling-based Predictive Simulation for Identifying Influential Nodes ... Finding Important Users for Information Diffusion Influence analysis of information diffusion focusing on directed networks Proposal of AIDM Agent-based Information Diffusion Model Predicting Japanese General Election in 2013 with Twitter Considering ... Which Targets to Contact First to Maximize Influence over Social Network 他分野 Econometrica The Network Origins of Aggregate Fluctuations PLoS ONE Social Network Sensors for Early Detection of Contagious Outbreaks Proceedings of the National Academy of Sciences PNAS Dynamic social networks promote cooperation in experiments with humans Spectral Redemption Clustering Sparse Networks Physical Review Letters First Passage Time for Random Walks in Heterogeneous Networks Adaptation and Optimization of Biological Transport Networks Locating the Source of Diffusion in Large-Scale Network Enhanced Flow in Small-World Networks Science Quantifying Long-Term Scientific Impact Control Profiles of Complex Networks Nature Communications Griffiths phases and the stretching of criticality in brain networks A scaling law for random walks on networks Influence maximization in complex networks through optimal percolation 2024-04-23 23 09 41 (Tue)

ICS_ui_meil - BdM+-仕様

https://w.atwiki.jp/icsuimeil/pages/15.html

ID指定法 @ID にてID指定例 @ICS_ui_meil オワコン @ICS_shell_fire 指定されたID→@ICS_ui_meil,@ICS_shell_fire ヘッダー本体→オワコン @ID での指定はヘッダー内ならどこでも可能 ID指定以外での方法例 @ICS_ui_meil オワコン ID指定必要なコマンドがあるので推奨しません爆撃種類通常ただ単に繰り返すだけ改行改行補助を繰り返すことにより改行爆撃実現公式普通に公式RTするだけ検索公式検索し検索内容すべてを公式無差別公式TLから無差別に公式有差別公式爆撃リプ欄から公式非公式非公式返信を繰り返す無言無言を繰り返すふぁぼ普通にふぁぼするだけフォローこれだけ特有のやり方(user検索から) ログイン中のすべての垢でフォロー使用言語 PHP HTML CSS JavaScript 画面確認環境 Google Chrome Android 非推奨環境ガラケー

naruhodo-digital @ ウィキ - html5

https://w.atwiki.jp/naruhodo-digital/pages/13.html

1.HTML5とは簡単に言うと、動画とか音楽にも対応した新しいhtml。現在、ホームページ記述の主流となっている「HTML 4.01」の新バージョンとして開発された言語。HTML4とは上位互換であり、HTML5対応のブラウザからは見ることができる。逆に、HTML5専用ページを対応していないブラウザで見ようとしても、表示することが出来ない（基本的に何も表示されないが、ページによっては対応していませんという旨のメッセージが表示される） ⇨Flashだとここまでできる! HTML5とFlashの機能比較 ⇨http //clockmaker.jp/blog/2010/02/flash-vs-html5/ 2.今までと何が違うのか 2.1　プラグインなしに動画が再生できる。現在のhtml4では、FlashやSilverlightといったプラグインが仲介することで動画の再生が実現されている。html5では、 video タグにより動画再生を可能にする。これにより、flashが対応していないiphoneで動画が再生できるなど、身近な部分でも期待することができる。 2.1.1　動画再生の問題点動画再生の問題点に、コーデックの問題がある。現在のデファクトスタンダードとなっているflashでは、プラグイン自体に複数の動画コーデックをサポートしているため問題ないが、html5では、表示するブラウザごとにサポートの必要がある。特に、大きな問題となっているのは、「H.264」をプッシュするAppleやMicrosoftと、オープンビデオにはオープンコーデックがふさわしいとしているMozilla（firefox）やOperaとの対立である。「H.264」はライセンス料が必要であるため、営利団体でないMozillaなどは特に難色を示す。逆にオープンソースの「Ogg Theora」では、品質（圧縮効率）が劣るとして、両者の足並みは揃わない。 ⇨「Google、H.264サポート中止」の背景を探る／ascii.jp ⇨http //ascii.jp/elem/000/000/581/581646/ ⇨Flashだとここまでできる! HTML5とFlashの機能比較 ⇨http //clockmaker.jp/blog/2010/02/flash-vs-html5/