{"id":9090,"date":"2016-09-27T01:00:00","date_gmt":"2016-09-26T17:00:00","guid":{"rendered":"http:\/\/swarma.org\/%swarma-paper%\/"},"modified":"2018-12-11T21:39:26","modified_gmt":"2018-12-11T13:39:26","slug":"%e9%9a%90%e7%a7%81%e4%bf%9d%e6%8a%a4%e3%80%81%e4%bf%a1%e6%81%af%e5%85%b1%e4%ba%ab%e7%b3%bb%e7%bb%9f%e5%8f%8a%e5%ba%a6%e9%87%8f%e7%b3%bb%e7%bb%9f%e5%a4%8d%e6%9d%82%e6%80%a7%e9%80%9a%e7%94%a8%e6%a1%86","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=9090","title":{"rendered":"\u9690\u79c1\u4fdd\u62a4\u3001\u4fe1\u606f\u5171\u4eab\u7cfb\u7edf\u53ca\u5ea6\u91cf\u7cfb\u7edf\u590d\u6742\u6027\u901a\u7528\u6846\u67b6 | \u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c3\u671f"},"content":{"rendered":"
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1. \u5728\u53c2\u4e0e\u793e\u4f1a\u611f\u77e5\u4e2d\u7684\u81ea\u6211\u8c03\u8282\u4fe1\u606f\u5171\u4eab\uff08<\/span>Self-regulatory information sharing in participatory social sensing\uff09<\/span><\/p>\n


<\/span><\/strong><\/span><\/p>\n

\"\u96c6\u667a\"
FromEPJ Data Science 2016 5:14 April 3, 3:00 PM<\/span><\/p>\n

By Evangelos Pournaras, Jovan Nikolic, Pablo Vel\u00e1squez, Marcello Trovati, Nik Bessis and Dirk Helbing<\/span><\/p>\n

(Translated by -\u960e\u8d6b, Edited by <\/span>Jake<\/span><\/a> <\/span>)<\/span><\/p>\n


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\u5728\u793e\u4f1a\u611f\u77e5\u7684\u5e94\u7528\u4e2d\uff0c\u53c2\u4e0e\u6027\u4e00\u76f4\u53d7\u5230\u9690\u79c1\u5a01\u80c1\u7684\u6311\u6218\u3002\u5927\u89c4\u6a21\u8bbf\u95ee\u516c\u6c11\u7684\u6570\u636e\u5c31\u4f7f\u5f97\u76d1\u63a7\u548c\u53ef\u80fd\u9020\u6210\u793e\u4f1a\u5206\u79bb\u73b0\u8c61\u7684\u6b67\u89c6\u884c\u4e3a\u5f97\u5230\u5141\u8bb8\uff1b\u800c\u5b83\u7684\u597d\u5904\u662f\u4e3a\u66f4\u77e5\u60c5\u7684\u51b3\u7b56\u63d0\u4f9b\u6240\u9700\u7684\u7cbe\u786e\u8ba1\u7b97\u4e0e\u5206\u6790\uff0c\u66f4\u6709\u6548\u7684\u653f\u7b56\u548c\u7531\u7269\u8054\u7f51\u6280\u672f\u652f\u6301\u7684\u6280\u672f-\u793e\u4f1a-\u7ecf\u6d4e\u7cfb\u7edf\u7684\u8c03\u8282\u3002<\/span><\/p>\n


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\u4ee5\u524d\u7684\u5de5\u4f5c\u8981\u4e48\u7740\u773c\u4e8e\u9690\u79c1\u4fdd\u62a4\uff0c\u8981\u4e48\u7740\u773c\u4e8e\u5927\u6570\u636e\u5206\u6790\uff0c\u672c\u6587\u4e0e\u8fd9\u4e9b\u5de5\u4f5c\u4e0d\u540c\uff0c\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u79cd\u81ea\u6211\u8c03\u8282\u7684\u4fe1\u606f\u5171\u4eab\u7cfb\u7edf\u4e3a\u8fd9\u4e2a\u9694\u9602\u642d\u5efa\u6865\u6881\u3002\u6211\u4eec\u901a\u8fc7\u5c06\u4fe1\u606f\u5171\u4eab\u5efa\u6a21\u4e3a\u4e00\u79cd\u8ba1\u7b97\u5e02\u573a\u4e2d\u7684\u4f9b\u6c42\u7cfb\u7edf\u4ee5\u8fbe\u5230\u6b64\u76ee\u7684\u3002\u516c\u6c11\u5904\u4e8e\u4f9b\u65b9\uff0c\u4ed6\u4eec\u5728\u6240\u5171\u4eab\u7684\u4fe1\u606f\u5c42\u9762\u505a\u51fa\u53d7\u6fc0\u52b1\u800c\u53c8\u81ea\u4e3b\u7684\u51b3\u7b56\u3002\u6570\u636e\u6c47\u96c6\u5904\u4e8e\u9700\u6c42\u65b9\uff0c\u5b83\u4eec\u4e3a\u4e86\u7cbe\u786e\u7684\u5206\u6790\uff0c\u4e3a\u516c\u6c11\u63a5\u53d7\u6240\u9700\u7684\u6570\u636e\u63d0\u4f9b\u62a5\u916c\u3002<\/span><\/p>\n


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\u8fd9\u4e2a\u7cfb\u7edf\u7528\u6765\u81ea\u4e24\u4e2a\u5e94\u7528\u9886\u57df\u7684\u4e24\u4e2a\u771f\u5b9e\u6570\u636e\u96c6\u6765\u8fdb\u884c\u5b9e\u8bc1\u5730\u8bc4\u4f30\uff1a\uff081\uff09\u667a\u80fd\u7f51\u683c\u548c\uff082\uff09\u624b\u673a\u611f\u5e94\u3002\u6211\u4eec\u7684\u5b9e\u9a8c\u7ed3\u679c\u53ef\u4ee5\u91cf\u5316\u51fa\u5728\u4e0d\u540c\u7684\u5b9e\u9a8c\u8bbe\u5b9a\u4e0b\u9690\u79c1\u4fdd\u62a4\u3001\u5206\u6790\u7684\u51c6\u786e\u6027\u53ca\u63d0\u4f9b\u62a5\u916c\u5f15\u53d1\u7684\u6210\u672c\u4e4b\u95f4\u7684\u6743\u8861\u3002<\/span><\/p>\n


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\u8c03\u67e5\u7ed3\u679c\u663e\u793a\u9690\u79c1\u4fdd\u62a4\u662f\u5426\u9ad8\uff0c\u53d6\u51b3\u4e8e\u53c2\u4e0e\u7684\u516c\u6c11\u6570\u91cf\u548c\u5f52\u7eb3\u7684\u6570\u636e\u7684\u7c7b\u578b\u3002\u6b64\u5916\uff0c\u6c47\u603b\u6570\u636e\u5206\u6790\u80fd\u591f\u5bb9\u5fcd\u9ad8\u5ea6\u5c40\u90e8\u9519\u8bef\uff0c\u5bf9\u6574\u4f53\u51c6\u786e\u5ea6\u6ca1\u6709\u91cd\u5927\u5f71\u54cd\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u5c40\u90e8\u9519\u8bef\u53ef\u4ee5\u88ab\u62b5\u6d88\u6389\u3002\u6211\u4eec\u53ef\u4ee5\u4f18\u5316\u62a5\u916c\u7684\u516c\u5e73\u7a0b\u5ea6\u4ee5\u4f7f\u5f97\u80fd\u591f\u5171\u4eab\u66f4\u591a\u91cd\u5927\u610f\u4e49\u4fe1\u606f\u7684\u516c\u6c11\u4f1a\u63a5\u53d7\u66f4\u9ad8\u7684\u62a5\u916c\u3002\u6240\u6709\u8fd9\u4e9b\u8c03\u67e5\u7ed3\u679c\u6fc0\u52b1\u4e86\u4e00\u79cd\u771f\u6b63\u7684\u53bb\u4e2d\u5fc3\u5316\u7684\u3001\u5408\u4e4e\u9053\u5fb7\u89c4\u8303\u7684\u6570\u636e\u5206\u6790\u7684\u8303\u5f0f\u51fa\u73b0\u3002<\/span><\/p>\n<\/blockquote>\n


<\/p>\n

<\/span>  \u539f\u6587<\/strong><\/p>\n

Participation in social sensing applications is challenged by privacy threats. Large-scale access to citizens\u2019 data allow surveillance and discriminatory actions that may result in segregation phenomena in society. On the contrary are the benefits of accurate computing analytics required for more informed decision-making, more effective policies and regulation of techno-socio-economic systems supported by \u2018Internet-of Things\u2019 technologies. In contrast to earlier work that either focuses on privacy protection or Big Data analytics, this paper proposes a self-regulatory information sharing system that bridges this gap. This is achieved by modeling information sharing as a supply-demand system run by computational markets. On the supply side lie the citizens that make incentivized but self-determined decisions about the level of information they share. On the demand side stand data aggregators that provide rewards to citizens to receive the required data for accurate analytics. The system is empirically evaluated with two real-world datasets from two application domains: (i) Smart Grids and (ii) mobile phone sensing. Experimental results quantify trade-offs between privacy-preservation, accuracy of analytics and costs from the provided rewards under different experimental settings. Findings show a higher privacy-preservation that depends on the number of participating citizens and the type of data summarized. Moreover, analytics with summarization data tolerate high local errors without a significant influence on the global accuracy. In other words, local errors cancel out. Rewards can be optimized to be fair so that citizens with more significant sharing of information receive higher rewards. All these findings motivate a new paradigm of truly decentralized and ethical data analytics.<\/span><\/p>\n


<\/p>\n

\u539f\u6587\u94fe\u63a5\uff1a<\/span><\/strong><\/p>\n

http:\/\/dx.doi.org\/10.1140\/epjds\/s13688-016-0074-4<\/a><\/span><\/p>\n


<\/p>\n

2. \u4e00\u79cd\u5ea6\u91cf\u7cfb\u7edf\u590d\u6742\u6027\u7684\u901a\u7528\u6846\u67b6\uff08A general framework for measuring system complexity\uff09<\/span><\/h2>\n

\"\u96c6\u667a\"<\/span>From onlinelibrary.wiley.com April 3, 12:47 AM<\/span><\/p>\n

By Mahmoud Efatmaneshnik and Michael J. Ryan<\/span><\/p>\n

(Translated by – \u9f9a\u529b\uff0cedited by \u5085\u6e25\u6210)<\/span><\/p>\n


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\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\uff0c\u6211\u4eec\u7684\u52a8\u673a\u57fa\u4e8e\u6b64\u524d\u7684\u7cfb\u7edf\u590d\u6742\u6027\u5ea6\u91cf\u65b9\u6cd5\u90fd\u6ca1\u6709\u76f4\u63a5\u89e3\u51b3\u6700\u672c\u8d28\u7684\u95ee\u9898\u2014\u2014\u4efb\u4f55\u7279\u5b9a\u7684\u7269\u8d28\u6216\u4e8b\u7269\u7684\u590d\u6742\u6027\u5177\u6709\u4e00\u4e2a\u5341\u5206\u663e\u8457\u7684\u4e3b\u89c2\u6210\u5206\uff0c\u5176\u590d\u6742\u6027\u7a0b\u5ea6\u53d6\u51b3\u4e8e\u89c2\u5bdf\u8005\u91c7\u7528\u7684\u53c2\u8003\u7cfb\u3002<\/span><\/p>\n


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\u56e0\u6b64\uff0c\u4efb\u4f55\u8bd5\u56fe\u901a\u8fc7\u79fb\u9664\u7cfb\u7edf\u7684\u4e3b\u89c2\u6027\u6765\u5ea6\u91cf\u5176\u590d\u6742\u6027\u7684\u65b9\u6cd5\uff0c\u90fd\u4e0d\u80fd\u89e3\u51b3\u8fd9\u4e00\u975e\u5e38\u91cd\u8981\u7684\u65b9\u9762\uff1b\u53cd\u4e4b\uff0c\u5bf9\u4e8e\u7eaf\u7cb9\u4e3b\u89c2\u7684\u590d\u6742\u6027\u5ea6\u91cf\u65b9\u6cd5\u4e5f\u5b58\u5728\u5408\u7406\u7684\u62c5\u5fe7\uff0c\u7b80\u5355\u6765\u8bb2\uff0c\u56e0\u4e3a\u5982\u679c\u53c2\u8003\u7cfb\u672c\u8eab\u4e5f\u662f\u590d\u6742\u548c\u4e3b\u89c2\u7684\uff0c\u90a3\u4e48\u8fd9\u79cd\u65b9\u6cd5\u5fc5\u7136\u662f\u4e0d\u53ef\u9a8c\u8bc1\u7684\u3002\u6211\u4eec\u901a\u8fc7\u5f15\u5165\u4e3b\u89c2\u7b80\u5355\u6027\uff08subjective simplicity\uff09\u7684\u6982\u5ff5\u6765\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\u2014\u2014\u867d\u7136\u4e00\u4e2a\u5408\u7406\u548c\u53ef\u9a8c\u8bc1\u7684\u4e3b\u89c2\u590d\u6742\u6027\u7a0b\u5ea6\u503c\u96be\u4ee5\u76f4\u63a5\u5ea6\u91cf\uff0c\u4f46\u662f\u5728\u4e00\u4e2a\u7ed9\u5b9a\u7684\u4e0a\u4e0b\u6587\u4e2d\u5ea6\u91cf\u4ec0\u4e48\u662f\u201c\u7b80\u5355\u201d\u5374\u662f\u53ef\u80fd\u7684\uff0c\u4ee5\u6b64\u201c\u7b80\u5355\u201d\u4f5c\u4e3a\u53c2\u8003\uff0c\u53ef\u4ee5\u5c06\u4e3b\u89c2\u590d\u6742\u6027\u5f53\u4f5c\u5230\u201c\u7b80\u5355\u201d\u7684\u8ddd\u79bb\u3002\u7136\u540e\uff0c\u6211\u4eec\u63d0\u51fa\u4e00\u4e2a\u5e7f\u6cdb\u7684\u3001\u9002\u7528\u4e8e\u4efb\u4f55\u9886\u57df\u7684\u590d\u6742\u6027\u5ea6\u91cf\u65b9\u6cd5\uff0c\u5e76\u63d0\u4f9b\u4e86\u4e00\u4e9b\u5982\u4f55\u5c06\u5b83\u5e94\u7528\u5230\u5de5\u7a0b\u7cfb\u7edf\u4e2d\u7684\u4f8b\u5b50\u3002<\/span><\/p>\n<\/blockquote>\n


<\/p>\n

<\/span>  \u539f\u6587\uff1a<\/span><\/strong><\/p>\n

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In this work, we are motivated by the observation that previous considerations of appropriate complexity measures have not directly addressed the fundamental issue that the complexity of any particular matter or thing has a significant subjective component in which the degree of complexity depends on available frames of reference. Any attempt to remove subjectivity from a suitable measure therefore fails to address a very significant aspect of complexity. Conversely, there has been justifiable apprehension toward purely subjective complexity measures, simply because they are not verifiable if the frame of reference being applied is in itself both complex and subjective. We address this issue by introducing the concept of subjective simplicity\u2014although a justifiable and verifiable value of subjective complexity may be difficult to assign directly, it is possible to identify in a given context what is \u201csimple\u201d and, from that reference, determine subjective complexity as distance from simple. We then propose a generalized complexity measure that is applicable to any domain, and provide some examples of how the framework can be applied to engineered systems.<\/span><\/p>\n


<\/span><\/p>\n

\u539f\u6587\u94fe\u63a5\uff1a<\/span><\/strong><\/p>\n

<\/a>http:\/\/dx.doi.org\/10.1002\/cplx.21767<\/a><\/p>\n

\"\u96c6\u667a\"<\/a><\/p>\n

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\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u5c0f\u7ec4\u7ffb\u8bd1\u4f5c\u54c1\u96c6\u9526<\/span><\/strong><\/span><\/p>\n

\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c1\u671f<\/a>
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\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c2\u671f<\/a><\/span><\/h1>\n

\u590d\u6742\u6587\u6458\u7ffb\u8bd1\u7b2c3\u671f<\/a><\/p>\n

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\u57ce\u5e02\u51b3\u5b9a\u672a\u6765<\/a><\/p>\n

\u5927\u6570\u636e\u5982\u4f55\u9020\u6210\u865a\u5047\u4fe1\u5fc3<\/a><\/span><\/h1>\n

\u4e00\u4e2a\u751f\u7269\u4f53\u80fd\u5426\u5927\u5982\u661f\u7cfb\uff1f<\/a><\/p>\n

\u5411\u81ea\u7136\u5b66\u4e60\u5982\u4f55\u8ba9\u65e0\u4eba\u673a\u7740\u9646<\/a><\/p>\n

\u7f51\u7edc\u4e8c\u5143\u53ca\u4e09\u5143\u5173\u7cfb\u4e2d\u7684\u540c\u6027\u504f\u597d<\/a><\/p>\n

<\/a>\u4eba\u5de5\u667a\u80fd\u53ef\u4ee5\u5e2e\u6211\u4eec\u627e\u5230\u4e0b\u4e00\u4e2a\u4f18\u826f\u7684\u65b0\u6750\u6599\u5417\uff1f<\/a><\/span><\/p>\n

\u300a\u81ea\u7136\u300b\u6742\u5fd7\u5bf9\u8bdd\u5218\u6148\u6b23\uff1a\u300a\u4e09\u4f53\u300b\u5199\u4f5c\u3001\u7075\u611f\u4ee5\u53ca\u4e2d\u56fd\u79d1\u5e7b<\/span><\/a>
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\u7f16\u8f91\uff1aDandelion<\/span><\/p>\n

\"\u96c6\u667a\"<\/p>\n

\"\u96c6\u667a\"<\/p>\n

\u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n

\n
\"\u96c6\u667a\"
<\/section>\n<\/section>\n

\u957f\u6309\u8bc6\u522b\u4e8c\u7ef4\u7801\uff0c\u5173\u6ce8\u96c6\u667aClub\uff0c<\/span><\/p>\n

\u8ba9\u6211\u4eec\u79bb\u79d1\u5b66\u63a2\u7d22\u66f4\u8fd1\u4e00\u6b65\u3002<\/span><\/p>\n

\u25bc\u70b9\u51fb\u9605\u8bfb<\/span>\u66f4\u591a\u6587\u6458<\/span><\/p>\n

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\u59cb\u53d1\u4e8e\u5fae\u4fe1\u516c\u4f17\u53f7\uff1a \u96c6\u667a\u4ff1\u4e50\u90e8 <\/p>\n<\/blockquote>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

1. \u5728\u53c2\u4e0e\u793e\u4f1a\u611f\u77e5\u4e2d\u7684\u81ea\u6211\u8c03\u8282\u4fe1\u606f\u5171\u4eab\uff08Self-regulatory information sharing in participatory social sensing\uff09 FromEPJ Data Science 2016 5:14 April 3, 3:00 PM By Evangelos…<\/p>\n","protected":false},"author":1,"featured_media":9091,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[27],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/9090"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=9090"}],"version-history":[{"count":1,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/9090\/revisions"}],"predecessor-version":[{"id":9100,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/9090\/revisions\/9100"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/9091"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9090"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9090"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9090"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=9090"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}