用户:Hello World I'm Dad

未来 宁滥毋缺此用户认为条目数量先于品质。

记事 这位用户的维基生日是 2019年7月4日 H1N1这个用户曾是H1N1新型流感患者。 此用户经历过语义饱和。

观点 ♀＝♂这个用户认为两性除生理构造上应无任何差异。 这个用户反对版权。 自由 共享 知 这个用户认为知难行易。 唯我这个用户是唯我论者。 整残该用户反对整形文化。克隆人 这个用户重新诠释了自由的定义，并努力实践它。 ∞这个用户自认为拥有无限多的时间。 SLEEP is piddling!此用户视睡觉为浪费时间，故从不睡觉。 这个用户是虚无主义者。 这个用户是无神论者。 Copenhagen 这个用户支持量子力学的哥本哈根理论。 MWI 这个用户支持量子力学的多世界理论。 这个用户反对一切阶级制度 这个用户是利己主义者，不以损害自身利益的同情心施舍行为为乐。 这个用户既是利己主义又是无政府主义者，并且欣赏麦克斯·施蒂纳的思想。 PeTA这个用户讨厌善待动物组织。

喜好 绿色这个用户喜欢绿色。 转笔这个用户隔三岔五会转笔。 这个用户对心理学感兴趣。 这个用户对信息学感兴趣。 这个用户对电子学感兴趣。 这个用户对电脑程序感兴趣。 这个用户对混沌理论很着迷。 这位用户热爱维基百科。 这个用户喜欢理查德·费曼。

一般 7368这个用户来到这个世界上已有7368天了。 18-这个用户尚未成年。 160 cm这个用户身高160 cm。 这个用户戴眼镜。 ?这个用户是... 什么性别？ ♀＝♂这个用户认为两性除生理构造上应无任何差异。 这个用户从事广告欺诈以及点击欺诈，游走于法律的边陲地带。 这个用户自认是无性恋。 永不结婚这个用户是独身主义者，永不有配偶，永不结婚。 这个用户喜欢酷儿柏拉图式关系。 龙 这个用户是龙 这个用户喜欢下雨天  这个用户的性格悲观。 熬夜 (～﹃～)这个用户经常熬夜。 堆砌用户框已浪费掉这位用户很多时间，并因此损害了这位用户的身心健康。 这名用户运行Tor节点。 如果您使用Tor，您可能正通过此用户的电脑连接维基。 这个用户使用Telegram通讯。 这位用户使用Firefox对中文维基百科作出贡献。 无社会性这个用户自认是无社会性者，很少从社交行为中获取到快乐。

语言 zh-N 此用户的母语是汉语。 用户巴别信息 查看用户语言 en-3 This user is able to contribute with an advanced level of English. 用户巴别信息 查看用户语言 la-1 Hic usor simplice latinitate contribuere potest. 用户巴别信息 查看用户语言 Bureau-5 此用户指出，其“官话”水平已经对人民产生深远影响，可以和外宾进行亲切友好的会谈。 用户巴别信息 查看用户语言 py该用户会使用Python语言。 用户巴别信息 查看用户语言 JS该用户会用JavaScript语言。 C该用户会使用C语言。 用户巴别信息 查看用户语言 Java该用户会使用Java语言。 用户巴别信息 查看用户语言 Ruby该用户会使用Ruby。 用户巴别信息 查看用户语言

快速连结

广告欺诈

无性恋

点击欺诈

金橘子 (苹果)

大卫·杰

Glow（JavaScript函式库）

热图

永恒论

现在论

下载量农场

广告堆叠

Click Spam

Click Injection

应用程序内事件

规避技术

SDK spoofing

签名区

testHello World I'm Dad（留言） 2019年8月13日 (二) 13:32 (UTC)

点击欺诈

每点击付费模式	Pay-per-click advertising Main article: Pay-per-click PPC advertising is an arrangement in which webmasters (operators of websites), acting as publishers, display clickable links from advertisers in exchange for a charge per click. As this industry evolved, a number of advertising networks developed, which acted as middlemen between these two groups (publishers and advertisers). Each time a (believed to be) valid Web user clicks on an ad, the advertiser pays the advertising network, which in turn pays the publisher a share of this money. This revenue-sharing system is seen as an incentive for click fraud. The largest of the advertising networks, Google's AdWords/AdSense and Yahoo! Search Marketing, act in a dual role, since they are also publishers themselves (on their search engines).[2] According to critics, this complex relationship may create a conflict of interest. This is because these companies lose money to undetected click fraud when paying out to the publisher but make more money when collecting fees from the advertiser. Because of the spread between what they collect and pay out, unfettered click fraud would create short-term profits for these companies.[citation needed]
组织性点击欺诈 除了平台主个人的点击欺诈案件之外，许多大规模点击欺诈也正在发生。想从事大规模点击欺诈的人通常会使用模拟人类行为的自动程式去点击网页上的广告，然而这些点击看起来都像是来自同一个人、少量的电脑或是同一个地区，对于广告主和广告网络来说会十分可疑，若想从事大规模点击欺诈，只有一台电脑的话会很容易被发现。 一种规避透过IP特征侦测点击欺诈的机制的方法是将现有的用户流量转换成点击和曝光（impression）。欺诈者可以透过放置十分微小且一直重新载入的广告以达到对用户伪装的效果，并且和广告主保证所谓的“网页爬虫”浏览的都是正常的网页，而呈给用户点击欺诈用的网页。 细小广告以及其他利用用户的技术还能与透过奖励制造的流量并用，像是“有偿阅读”（Paid to Read）网站的会员在浏览网站或点击关键字的时候能得到少量的钱，而有些有偿阅读网站的管理者也是每点击付费的会员，他们可能会给经常搜寻的人寄特别多的广告邮件，因为关键字的每点击付费常常是网站的唯一收入。这被称为强迫搜寻，是一个在线上有偿购买用户行为的产业中不被赞同的行为。 组织性的点击欺诈可以透过使用非常多的电脑组成犯罪网络从而 使虚假流量的来源显示在许多位置。由于自动程式仍然无法完全模拟用户行为，点击欺诈网络可能会透过感染他人组成僵尸网络或是域名服务器快取污染等方法使得一般用户在不知情的情况下为他们制造收入。广告主、广告网络以及警察将会十分难以追查散布在各个国家的点击欺诈网络。 曝光数欺诈是被用于拉低对手的广告排序，当竞争对手的广告点击率过低的时候，他们就有可能受到惩罚，广告可能会遭到替换，使得出价较低的广告得以被换上。	Organization Click fraud can be as simple as one person starting a small Web site, becoming a publisher of ads, and clicking on those ads to generate revenue. Often the number of clicks and their value is so small that the fraud goes undetected. Publishers may claim that small amounts of such clicking is an accident, which is often the case.[citation needed] Much larger-scale fraud also occurs.[3] Those engaged in large-scale fraud will often run scripts which simulate a human clicking on ads in Web pages.[4] However, huge numbers of clicks appearing to come from just one, or a small number of computers, or a single geographic area, look highly suspicious to the advertising network and advertisers. Clicks coming from a computer known to be that of a publisher also look suspicious to those watching for click fraud. A person attempting large-scale fraud, from one computer, stands a good chance of being caught. One type of fraud that circumvents detection based on IP patterns uses existing user traffic, turning this into clicks or impressions.[5] Such an attack can be camouflaged from users by using 0-size iframes to display advertisements that are programmatically retrieved using JavaScript. It could also be camouflaged from advertisers and portals by ensuring that so-called "reverse spiders" are presented with a legitimate page, while human visitors are presented with a page that commits click fraud. The use of 0-size iframes and other techniques involving human visitors may also be combined with the use of incentivized traffic, where members of "Paid to Read" (PTR) sites are paid small amounts of money (often a fraction of a cent) to visit a website and/or click on keywords and search results, sometimes hundreds or thousands of times every day[6] Some owners of PTR sites are members of PPC engines and may send many email ads to users who do search, while sending few ads to those who do not. They do this mainly because the charge per click on search results is often the only source of revenue to the site. This is known as forced searching, a practice that is frowned upon in the Get Paid To industry. Organized crime can handle this by having many computers with their own Internet connections in different geographic locations. Often, scripts fail to mimic true human behavior, so organized crime networks use Trojan code to turn the average person's machines into zombie computers and use sporadic redirects or DNS cache poisoning to turn the oblivious user's actions into actions generating revenue for the scammer. It can be difficult for advertisers, advertising networks, and authorities to pursue cases against networks of people spread around multiple countries. Impression fraud is when falsely generated ad impressions affect an advertiser's account. In the case of click-through rate based auction models, the advertiser may be penalized for having an unacceptably low click-through for a given keyword. This involves making numerous searches for a keyword without clicking of the ad. Such ads are disabled[7] automatically, enabling a competitor's lower-bid ad for the same keyword to continue, while several high bidders (on the first page of the search results) have been eliminated. Hit inflation attack A hit inflation attack is a kind of fraudulent method used by some advertisement publishers to earn unjustified revenue on the traffic they drive to the advertisers’ Web sites. It is more sophisticated and harder to detect than a simple inflation attack. This process involves the collaboration of two counterparts, a dishonest publisher, P, and a dishonest Web site, S. Web pages on S contain a script that redirects the customer to P's Web site, and this process is hidden from the customer. So, when user U retrieves a page on S, it would simulate a click or request to a page on P's site. P's site has two kinds of webpages: a manipulated version, and an original version. The manipulated version simulates a click or request to the advertisement, causing P to be credited for the click-through. P selectively determines whether to load the manipulated (and thus fraudulent) script to U's browser by checking if it was from S. This can be done through the Referrer field, which specifies the site from which the link to P was obtained. All requests from S will be loaded with the manipulated script, and thus the automatic and hidden request will be sent.[8] This attack will silently convert every innocent visit to S to a click on the advertisement on P's page. Even worse, P can be in collaboration with several dishonest Web sites, each of which can be in collaboration with several dishonest publishers. If the advertisement commissioner visits the Web site of P, the non-fraudulent page will be displayed, and thus P cannot be accused of being fraudulent. Without a reason for suspecting that such collaboration exists, the advertisement commissioner has to inspect all the Internet sites to detect such attacks, which is infeasible.[8] Another proposed method for detection of this type of fraud is through use of association rules.[9]
操纵有机搜寻结果 影响一个网页在有机搜寻中的排名的一个重要因素就是点击率，计算方法是将点击数除以曝光数，或者说将一个搜寻结果被点击的次数除以搜寻结果被展示的次数。 和每点击付费欺诈相反，当你的竞争对手正在购买僵尸网络服务或是低价劳力以产生虚假点击时，点击率欺诈的目的就是将竞争对手的点击率降低，从而不断地降低他们的网站在搜索引擎最佳化中的排名。 更坏的点击率欺诈者或许还会在削弱对手的同时提升自己网站的排名，或是他的政治立场等。我们对于这个问题上演的规模还不清楚，但很明显有许多网站开发者都很在意网站在分析工具上的指标。	Manipulation of organic search results One major factor that affects the ranking of websites in organic search results is the CTR (Click-through Rate). That is the ratio of clicks to impressions, or in other words how many times a search result is clicked on, as compared to the number of times the listing appears in search results. In contrast to PPC fraud, where a competitor leverages the services of a botnet, or low cost labour, to generate false clicks, in this case the objective is to beggar thy competitor by making their CTR rate as low as possible, thereby diminishing their ranking factor (position from the top of search results). Bad actors will therefore generate false clicks on organic search results that they wish to promote, while avoiding search results they wish to demote. This technique can effectively create a cartel of business services controlled by the same bad actor, or be used to promote a certain political opinion etc. The scale of this issue is unknown, but is certainly evident to many website developers who pay close attention to the statistics in webmaster tools.
法律案件	Legal cases Lawsuits Disputes over the issue have resulted in a number of lawsuits. In one case, Google (acting as both an advertiser and advertising network) won a lawsuit against a Texas company called Auction Experts (acting as a publisher), which Google accused of paying people to click on ads that appeared on Auction Experts' site, costing advertisers $50,000.[10] Despite networks' efforts to stop it, publishers are suspicious of the motives of the advertising networks, because the advertising network receives money for each click, even if it is fraudulent. In July 2005, Yahoo settled a class-action lawsuit against it by plaintiffs alleging it did not do enough to prevent click fraud. Yahoo paid $4.5 million in legal bills for the plaintiffs and agreed to settle advertiser claims dating back to 2004[11] In July 2006, Google settled a similar suit for $90 million.[12][13] On March 8, 2006, Google agreed to a $90 million settlement fund in the class-action lawsuit filed by Lane's Gifts & Collectibles.[14] The class-action lawsuit was filed in Miller County, Arkansas, by Dallas attorneys Steve Malouf, Joel Fineberg, and Dean Gresham.[15] The expert witness for the Plaintiffs in the case was Jessie Stricchiola, an internet search expert who first identified instances of PPC fraud in 2001.[16] Michael Anthony Bradley In 2004, California resident Michael Anthony Bradley created Google Clique, a software program that he claimed could let spammers defraud Google out of millions of dollars in fraudulent clicks, which ultimately led to his arrest and indictment.[17] Bradley used technology that he created for his other companies that took him five years to develop. Using this technology, he was able to demonstrate that fraud was possible, and was impossible for Google to detect. Bradley notified Google of this security flaw, and was willing to work with them to close up some of these holes. However, Bradley was offered $500,000 for his software and technology by some of the world's top spammers. With this information, Bradley thought he could put a price of $100,000 on his technology, and offered to sell Google all rights to his technology, and they could make the Internet a better and safer place. When Bradley showed up to Google's offices, he demonstrated the software for them, and when they asked what he wanted, he had stated that he would consult for free if they wanted to purchase the rights to his technology. He explained the prior offer of $500,000 and said he knew he could get it, but would settle for $100,000 if they wanted to work together. Bradley returned to Google's offices and was met by United States Secret Service officers who were undercover. They kept asking him what he wanted, and they even pushed a check for $100,000 to him. Bradley stated that this felt like blackmail and was not comfortable with this, and pushed the money away. Just then the Secret Service came in and arrested him. Authorities said he was arrested while trying to extort $100,000 from Google in exchange for handing over the program.[18] Charges were dropped without explanation on November 22, 2006; both the US Attorney's office and Google declined to comment. Business Week suggests that Google was unwilling to cooperate with the prosecution, as it would be forced to disclose its click fraud detection techniques publicly.[19] Fabio Gasperini On June 18, 2016, Fabio Gasperini, an Italian citizen, was extradited to the United States on click fraud charges.[20] An indictment charged Gasperini with: two counts of computer intrusion one count of wire fraud one count of wire fraud conspiracy and one count of money laundering According to the U.S. government, Gasperini set up and operated a botnet of over 140,000 computers around the world. This was the first click fraud trial in the United States. If convicted of all counts, Gasperini risked up to 70 years in jail. Simone Bertollini, an Italian-American lawyer, represented Gasperini at trial. On August 9, 2017 a jury acquitted Gasperini of all the felony charges of the indictment. Gasperini was convicted of one misdemeanor count of obtaining information without a financial gain. Gasperini was sentenced to the statutory maximum of one year imprisonment, a $100,000 fine, and one year of supervised release following incarceration. Shortly after he was credited with time served and sent back to Italy. An appeal is currently pending.[21]
应对方案	Solutions Proving click fraud can be very difficult, since it is hard to know who is behind a computer and what their intentions are. When it comes to mobile ad fraud detection, data analysis can give some reliable indications. It is important to understand that abnormal metrics can hint at the presence of different types of frauds. To detect click fraud in ad campaign, advertisers can focus on the following attribution points: IP Address: As bots run similar scripts from the same server, any click fraud on mobile ads will indicate a high density of clicks coming from the same IP address or a range of similar IP addresses. Advertisers can also run check on IP addresses to verify their history with another fraud. Click Timestamp: Click timestamp maintains the time at which the click is made on the ad. The bot-based click fraud runs repeatedly to attempt clicking on the ads, which increases click frequency for that duration. A high range of clicks with almost similar timestamp points at the possibility of click fraud. A low duration and high frequency mean a high probability of fraud. Action Timestamp: Action timestamp is the time at which the user takes action on (or engages with) the app or website. With a bot-based click attack, there can be a similarity with action timestamp. As bot clicks on the advertisement and then performs the action on app or website without any delay, the advertiser can notice a low or almost no action timestamp. Often the best an advertising network can do is to identify which clicks are most likely fraudulent and not charge the account of the advertiser. Even more sophisticated means of detection are used,[22] but none are foolproof. The Tuzhilin Report[23] produced as part of a click fraud lawsuit settlement, has a detailed and comprehensive discussion of these issues. In particular, it defines "the Fundamental Problem of invalid (fraudulent) clicks": "There is no conceptual definition of invalid clicks that can be operationalized [except for certain obviously clear cases]." "An operational definition cannot be fully disclosed to the general public because of the concerns that unethical users will take advantage of it, which may lead to a massive click fraud. However, if it is not disclosed, advertisers cannot verify or even dispute why they have been charged for certain clicks." The PPC industry is lobbying for tighter laws on the issue. Many hope to have laws that will cover those not bound by contracts. A number of companies are developing viable solutions for click fraud identification and are developing intermediary relationships with advertising networks. Such solutions fall into two categories: Forensic analysis of advertisers' web server log files.This analysis of the advertiser's web server data requires an in-depth look at the source and behavior of the traffic. As industry standard log files are used for the analysis, the data is verifiable by advertising networks. The problem with this approach is that it relies on the honesty of the middlemen in identifying fraud. Third-party corroboration.Third parties offer web-based solutions that might involve placement of single-pixel images or Javascript on the advertiser's web pages and suitable tagging of the ads. The visitor may be presented with a cookie. Visitor information is then collected in a third-party data store and made available for download. The better offerings make it easy to highlight suspicious clicks, and they show the reasons for such a conclusion. Since an advertiser's log files can be tampered with, their accompaniment with corroborating data from a third party forms a more convincing body of evidence to present to the advertising network. However, the problem with third-party solutions is that such solutions see only part of the traffic of the entire network. Hence, they can be less likely to identify patterns that span several advertisers. In addition, due to the limited amount of traffic they receive when compared to middlemen, they can be overly or less aggressive when judging traffic to be fraud. In a 2007 interview in Forbes, Google click fraud czar Shuman Ghosemajumder said that one of the key challenges in click fraud detection by third-parties was access to data beyond clicks, notably, ad impression data.[24] Click fraud is less likely in cost per action models.
研究	Research The fact that the middlemen (search engines) have the upper hand in the operational definition of invalid clicks is the reason for the conflict of interest between advertisers and the middlemen, as described above. This is manifested in the Tuzhilin Report[23] as described above. The Tuzhilin report did not publicly define invalid clicks and did not describe the operational definitions in detail. Rather, it gave a high-level picture of the fraud-detection system and argued that the operational definition of the search engine under investigations is "reasonable". One aim of the report was to preserve the privacy of the fraud-detection system in order to maintain its effectiveness. This prompted some researchers to conduct public research on how the middlemen can fight click fraud.[25] Since such research is presumably not tainted by market forces, there is hope that this research can be adopted to assess how rigorous a middleman is in detecting click fraud in future law cases. The fear that this research can expose the internal fraud-detection system of middlemen still applies. An example of such research is that done by Metwally, Agrawal and El Abbadi at UCSB. Other work by Majumdar, Kulkarni, and Ravishankar at UC Riverside proposes protocols for the identification of fraudulent behavior by brokers and other intermediaries in content-delivery networks.

热图

种类	A heat map, atop a color bathymetric map, indicating the probable location of missing Malaysia Airlines Flight 370 based on a Bayesian method analysis of possible flight paths of the aircraft. Types There are different kinds of heat maps: Biology heat maps are typically used in molecular biology to represent the level of expression of many genes across a number of comparable samples (e.g. cells in different states, samples from different patients) as they are obtained from DNA microarrays. The tree map is a 2D hierarchical partitioning of data that visually resembles a heat map. A mosaic plot is a tiled heat map for representing a two-way or higher-way table of data. As with treemaps, the rectangular regions in a mosaic plot are hierarchically organized. The means that the regions are rectangles instead of squares. Friendly (1994) surveys the history and usage of this graph. A density function visualization is a heat map for representing the density of dots in a map. It enables one to perceive density of points independently of the zoom factor. Perrot et al (2015) proposed a way to use density function to visualize billions and billions of dots using big data infrastructure with Spark and Hadoop.
配色	Color schemes Many different color schemes can be used to illustrate the heat map, with perceptual advantages and disadvantages for each. Rainbow color maps are often used, as humans can perceive more shades of color than they can of gray, and this would purportedly increase the amount of detail perceivable in the image. However, this is discouraged by many in the scientific community, for the following reasons: The colors lack the natural perceptual ordering found in grayscale or blackbody spectrum colormaps. Common colormaps (like the "jet" colormap used as the default in many visualization software packages) have uncontrolled changes in luminance that prevent meaningful conversion to grayscale for display or printing. This also distracts from the actual data, arbitrarily making yellow and cyan regions appear more prominent than the regions of the data that are actually most important. The changes between colors also lead to perception of gradients that aren't actually present, making actual gradients less prominent, meaning that rainbow colormaps can actually obscure detail in many cases rather than enhancing it.
与等值线图的比较	Choropleth maps vis-à-vis heat maps Choropleth maps are sometimes incorrectly referred to as heat maps. A choropleth map features different shading or patterns within geographic boundaries to show the proportion of a variable of interest, whereas the coloration a heat map (in a map context) does not correspond to geographic boundaries.
实际软件应用	Software implementations Several heat map software implementations are freely available: R, a free software environment for statistical computing and graphics, contains several functions to trace heat maps, including interactive cluster heat maps (via the heatmaply R package). Gnuplot, a universal and free command-line plotting program, can trace 2D and 3D heat maps Google Fusion Tables can generate a heat map from a Google Sheets spreadsheet limited to 1000 points of geographic data. Dave Green's 'cubehelix' colour scheme provides resources for a colour scheme that prints as a monotonically increasing greyscale on black and white postscript devices Openlayers3 can render a heat map layer of a selected property of all geographic features in a vector layer. Highcharts, a JavaScript charting library, provides the ability create heat maps as a part of its solution.
图例	Lake effect snow – weather radar information is usually shown using a heat map. Human voice visualized with a spectrogram; a heat map representing the magnitude of the STFT. An alternative visualization is the waterfall plot. Example showing the relationships between a heat map, surface plot, and contour lines of the same data Combination of surface plot and heat map, where the surface height represents the amplitude of the function, and the color represents the phase angle. Score of each contiguous region of a dartboard (not to scale)