日常前言
- 四月份花了一大部分时间去深入代码，把我们的双摄虚化流程解析了一遍。然后为了给组内分享，又花了相当一部分时间去做总结，画思维导图、作流程图等等，这其中学到了挺多东西的，尤其是对高通 Camera HAL 层的数据流部分，Camera Post Process 的前后节点都有了一个比较大概的了解，在跟踪数据流的时候没那么头晕了。
- 还有，总结、分享知识的时候，作图真的非常重要，一份填满大量文字的 PPT，可能讲 3 个小时都讲不完，最后听众也很难有所收获。然而如果有 70% ~ 90% 的篇幅用图片来直观表述，剩下的文字用于精炼、简洁地描述，这样可能 1~2 个小时就能搞定，并且听众至少也能留下一个比较整体的印象。
- 好吧，扯远了，回归这次的活动，这一期是物联网的主题，又是我不熟悉的领域，只能找一些介绍性的文章来翻译了。以及……上期又送来一个抱枕……这期要是再送公仔，那就把这些东西送给女盆友一宿舍当毕业礼物吧hh
- 这期采纳了四篇：
版权相关
翻译人：StoneDemo，该成员来自云+社区翻译社
原文链接：IoT in cognitive science and web proctoring
原文作者：（未找到作者信息）
IoT in cognitive science and web proctoring
题目：（物联网中的认知科学与网络监督）
IoT has been a buzz word for some time now and is being talked about in applying to all and sundry fields. But nowhere is there talk of applying it in cognitive science and web proctoring.
“物联网（IoT，Internet of Things）” 这个词已经流行了好一段时间，并且当前人们都在讨论如何将它应用于各种领域。但是，没有任何关于将其应用于认知科学（Cognitive science）和网络监督（Web proctoring）的讨论。
Cognitive science is all about measuring the effectiveness of a human in his cognitive endeavors. What we mean by cognition is related to how a human is learning, understanding and keeping new knowledge in memory.
认知科学，就是衡量一个人在其认知过程（Cognitive endeavors）中的有效性。我们所说的认知是指 “人类如何从记忆中学习、理解和保留新的知识”。
In massive open online courses (MOOC), students learn and take exams online. At present there is no way to properly proctor those online exams to be confident that the education imparted online is effective. Due to difficulties in effectively proctoring these online exams, there is a widely held perception that classroom-based education is somewhat more effective and desirable than online education. To change this perception, the effectiveness of proctoring has to be improved — this is where IoT comes into play.
在大型开放式网络课程（MOOC）中，学生在线学习并参加考试。目前尚无方法对在线考试进行适当的监督，以确保在线教育是有效的。人们普遍认为，由于难以有效监督这些在线考试，所以以课堂教学为主的教育比网络教育更有效、可取。为了改变这种看法，监管的有效性必须有所改善 —— 而此处便是物联网发挥其作用的所在。
I will explain how IoT can be an effective tool in cognitive science research and how IoT can help effectively proctor online courses in such a way to impart the badly needed credibility for online education.
接下来我将解释物联网如何成为认知科学研究中的一种有效工具，以及物联网如何以这种方式有效地帮助监督网络课程，从而给予在线教育正急需的可信度。
Cognitive science and IoT
（物联网与认知科学）
In cognitive science research, various sensors are fitted onto the subject and physiological parameters are measured and recorded. These parameters might be brainwaves measured through EEG or heartbeats, pulse rates, iris contraction, skin conductivity, etc. These parameters, when studied with other parameters which are related to how a person understood a certain topic or how the subject retained certain topic through a questionnaire, provide valuable insights. The sensors and sensor data are the subject of IoT wearables which can measure and send data through a wireless infrastructure to the cloud. If IoT technologies are adapted to the research of cognitive science, the data from various geographically different places can be collected, stored and studied with the help of the IoT cloud. Researchers will have valuable data for the asking and will actually “open source” the data to multiple stakeholders to conduct research effectively. At present, there is no such infrastructure which collects, stores and analyzes data from cognitive science related research.
A representative sensor pad for measuring EEG is shown below.
在认知科学的研究中，研究者将各种传感器安装到受试者身上，用以测量、记录生理参数。这些参数可能是通过 EEG（Electroencephalo-graph，脑电图） 或心跳，脉搏，虹膜收缩，皮肤电等方式测量的脑电波。在通过问卷调查来研究其它因素（关于人类如何理解一个特定话题，或者特定话题中的主题是如何保留下来的）时，前述的参数就能够提供一些有价值的见解。传感器和及其数据是物联网可穿戴设备的主题，它可以通过无线基础设施测量并发送数据到云端。如果将物联网技术适用于认知科学研究，则研究者就可以在物联网云的帮助下，收集、存储并且研究来自不同地域的不同地点的数据。研究者们将获得有价值的数据，并实际地 “开源” 数据给多个利益相关者进行有效的研究。目前，还没有用于收集、存储和分析认知科学相关研究数据的基础设施。
下图展示了一种具有代表性的，用于测量 EEG 的传感器垫（Sensor pad）。
Unfortunately, the data from the headset stays with the nearest receptor and is stored in a local server. If the data is put into the IoT pipeline towards an IoT cloud, the data can be leveraged and properly “open sourced” for the greater good.
不幸的是，头戴式耳机（Headset）中的数据与最最邻近接收器保持一致，并存储在本地服务器中。如果将数据放入物联网管道（IoT Pipeline）中，实现物联网云，则数据可以被利用，并且能够适当地 “开源” 以获取更大的利益。
Where IoT infrastructure fits in is where data collection and dissemination has crossed certain limits. The headset shown above for EEG-based cognitive science research can be purchased off the shelf and used by anybody. This means with the availability of cheap off-the-shelf sensor pods, people who are interested in quantifying themselves will start using them. It would be a pity if no effort is made to collect the data, store and analyze it in a central cloud-based structure (which is nothing but an IoT cloud).
数据的收集与传播已经存在某些局限性的所在，就是物联网基础设施所适合的地方。上面展示的用于（基于脑电的）认知科学研究的耳机可以现货购买，供任何人使用。这就意味着，有兴趣量化自身的人将开始使用这些现成便宜的传感器吊舱（Sensor pod）。如果不在收集数据上作出努力，并在基于云的中央结构（物联网云）中进行存储和分析，那就太遗憾了。
Where IoT fits in web proctoring and MOOC
（物联网与网络监管和 MOOC 相融合）
Another interesting scenario where the explosion of data occurs but is somehow ignored is web proctoring and massive open online courses. MOOC somehow lacks credibility when it comes to companies and employers accepting the certificates to be as credible as offline standard classroom course certificates. It is difficult to imagine a MOOC course conducted by Stanford to be as respected as the same course conducted in a classroom in Stanford. It all boils down to whether a student who took the course has sat for the exams in a manner in which the exams were properly proctored. In other words, it is difficult to proctor an online exam because the student is invisible and there are many ways to cheat in an online exam when compared to an offline one.
另一个有趣的场景是，发生了数据爆炸的情况，但却不知何故被忽略了，这就是网络监督和 MOOC 的情况。当涉及到到公司与雇主接受证书时，MOOC 由于某种原因而缺乏可信度（无法像线下标准课堂证书一般可信）。很难想象，斯坦福大学开设的 MOOC 课程能与其开设的线下课程一样受到重视。这一切都归结为一个学习过该课程的学生是否已经参加了（以一种适当的监督方式进行）的考试。换句话说，由于学生是不可见（Invisible）的，并且比起线下考试，在线考试中有很多方法可以作弊，所以很难对在线考试进行监督。
Sure, there are platforms where students are monitored by online proctors through web cameras. Here a number of students’ webcams are monitored by online proctors, but there are no tools to capture other parameters of students who are taking the online tests. It is easy to cheat in an online exam just by keeping another screen somewhere in the room to get answers to the online questions. True, there are 360 degree cameras, but there is no data to validate that these are effective in curbing online cheating.
当然，有一些平台可以通过使用网络摄像头（Web camera），使在线监考（Online proctor）可以对学生进行监控。这样，许多学生的网络摄像头受到在线监考的监控，但没有工具可以捕获参加在线测试的学生的其他参数。仅仅需要在房间中另一个屏幕上查找在线问题的答案，就很容易在线上考试中作弊。没错，360 度相机是有的，但没有数据可以证明它们在遏制在线作弊方面是有效的。
To end this menace and to increase the credibility of online courses and exams, a number of measures has to be taken. The most important one is to collect other kinds of data from the student along with webcam data, such as EEG, ECG, skin resistance and run analytics. One example is how the student reacts when he came across a questions which he feel is difficult. This can be achieved by carefully monitoring the above said sensors, namely EEG, ECG and skin resistance. Based on their inputs, the webcam data can be closely examined to see whether the student is trying to cheat or has already cheated, if the webcam data has been recorded.
为了消除这种威胁，并提高在线课程和考试的可信度，人们必须采取一些措施。其中最重要的就是从学生那端，连同摄像头数据一起，收集其他类型的数据（例如脑电图，心电图，皮肤电阻）并进行分析。举个例子，当学生遇难题时，他是如何反应的。这可以通过仔细监测上述传感器（即脑电图，心电图和皮肤电阻）来实现。根据这些输入，可以仔细检查摄像头数据（如果该数据已经被记录下来），以查看学生是否试图作弊或已经作弊。
Interestingly, only the webcam data is enough to measure the heartrate, and this data will provide a certain amount of input by way of whether the pulse is increasing or decreasing while a student attempts to answer a question.
有趣的是，只有网络摄像头的数据便足以测量心率，并且这些数据将通过学生尝试答题时脉搏的增减情况来提供一定数量的输入。
The whole gamut of online exams and courses can be brought under the IoT umbrella and all analytics can be brought to IoT cloud, stored and analyzed for a more effective MOOC and online certification. Eventually more data can be brought in with the help of sensors available in any modern laptops namely mics for voice recognition, fingerprint sensors for identity management, facial recognition, etc.
所有的网络考试和课程都可以归入物联网领域，所有分析都可以提供给物联网云，从而进行存储和分析以获得更有效的 MOOC 和在线认证。最终，借助任何现代笔记本电脑所提供的传感器（即用于语音识别的麦克风、用于身份识别管理的指纹传感器，以及面部识别等），可以得到更多的数据。
So where does it all lead to?
（那么，这一切会导致什么结果呢？）
Based on the details about how IoT can be leveraged for both cognitive science and MOOC, a question may come about why cognitive science and MOOC are banded together in this article. The reason is obvious: Both fields are measuring physiological data in one way or another and can immensely benefit from IoT infrastructure and practices.
根据物联网在认知科学和 MOOC 中如何得到充分利用的详细说明，你可能要问了：为什么本文要将认知科学和 MOOC 结合在一起？原因很明显：这两个领域都以各种方式测量生理数据，并且可以从物联网基础设施和实践中获得极大的利益。
Therefore IoT can and most probably will help many fields mature and become widely available to the masses. This article highlighted just two fields which are poles apart but suited for IoT to take over because of commonality of data from physiological sensors. Their applications might be different, but the underlying platform can be same, to benefit all.
因此，物联网能够，而且很可能会帮助许多领域走向成熟，并广泛地应用于群众。本文强调了两个截然不同的领域，但是由于具有从生理传感器中获取数据的共性，他们都适合与物联网相结合。他们的应用可能会有所不同，但底层平台却可以是相同的，并且都可以造福所有人。