SLU 模型做法：

• 1 第一步是词汇分析，也就是分词，然后词性标注。本文用的是HanLP做词性分析。

• 2 第二步是槽位边界检测。这个任务看成一个用BILOU进行序列标注的。我们用了基于字和词的序列标注。基于字的 版本是用一个window为7的CRF，用此法特征和词典特征，另外基于词的的CRF模型是window size为5的词法特征，词性特征和词典特征。词典特征是指“当前字词是否 prefix/infix/suffix 在实体词典中某个条目关系。”每个CRF输出n(3)个输出，这整个2n个输出用到下一步。用基于字的序列标注是为了弥补分词效果差带来的可能影响。

• 3 第三部是槽位类型识别。用的是LR+L正则分类器，预测出的slot，上下文的字词，上下文的词性标注作为特征。

• 4 第四步是槽位纠正。这个是为了解决因为ASR导致的错误识别造成的结果。用的是一个基于搜索的方法。鉴于已经有各种槽位类型的词典，如果一个预测出来的槽位s类型T没有在对应的槽位词典中，那么就用s作为查询词来在根据最小编辑距离来查询槽位词典中的记录。这个操作会进行两次，一个是s作为中文字符，另一个是s作为拼音来查询。最好的结果是从这两个查处的结果中重新排序后得到的。

• 5 最后一步是意图分类。用的是XGBoost及其默认参数。用到的特征是单词token，query length，以及前面步骤预测出来的槽位。

规则的具体形式是,"如果query q被列在了一个特定的词汇表L，并且其前面的queries和它们预测出来的领域标签满足特定条件，那么q就可以被打上一个特定的意图标签，并且对于短的命令来说，整个query q是当作对应于L的一个槽位类型".所有规则按照优先级顺序组织的。

一个query首先经过基于规则的无明显意图词的判定过程，如果是空的话那就意味含有IISPs基于模型的组件会继续来处理，否则的话也就意味着query被看作是只有实体的，那么规则的结果就作为最终结果直接返回。

可以把预料文本分成2类，一类是有明显的预示意图的词语，另一类是没有。

How do we augment our ability as humans to set objectives? How do we observe that process? How do we gain insight into hidden aspects and drivers of setting intention? How do we recognize our own framings? How do we re-frame? If the Anthropocene Epoch means anything, it is that our own emoto-cognitive lenses make all the difference.

Human-system/Tool-system fine-grained intersections and compositionalities. There are UX and UI levels here. There are likely further levels having to do with intentionalities of (semi-) autonomous Tool Systems, how they are to be guided by Human intentionalities, and - most importantly - how humans fully ascertain and guide their own intentionalities.

SLU有俩事，意图识别+填槽。

实践中的困难：

• 1 意图分类的复杂性
• 2 世界知识
• 3 用户状态

任务型人机对话系统中的认知技术

用一个带有copy机制的简单seq2seq框架超过现有最好的真实DSTC2 7个点。

A Copy-Augmented Sequence-to-Sequence Architecture GivesGood Performance on Task-Oriented Dialogue

Task-oriented dialogue focuses on conversational agents that participate in dialogues with user goals on domain-specific topics. In contrast to chatbots, which simply seek to sustain open-ended meaningful discourse, existing task-oriented agents usually explicitly model user intent and belief states. This paper examines bypassing such an explicit representation by depending on a latent neural embedding of state and learning selective attention to dialogue history together with copying to incorporate relevant prior context. We complement recent work by showing the effectiveness of simple sequence-to-sequence neural architectures with a copy mechanism. Our model outperforms more complex memory-augmented models by 7% in per-response generation and is on par with the current state-of-the-art on DSTC2, a real-world task-oriented dialogue datase

这个地方的NLU和NLG都是用基于模版的模型。

症状数据抽取，BIO格式。每个中文字符标注为“B","I","O".每个抽取出的症状根据病人真实情况打标为“True","False"。3人2个都标过的才有效，第三人评判。Cohhen kappa 相关性来作为标注标准。

In this paper, we make a move to builda dialogue system for automatic diagno-sis. We first build a dataset collected froman online medical forum by extractingsymptoms from both patients’ self-reportsand conversational data between patientsand doctors. Then we propose a task-oriented dialogue system framework tomake the diagnosis for patients automat-ically, which can converse with patients tocollect additional symptoms beyond theirself-reports. Experimental results on ourdataset show that additional symptoms ex-tracted from conversation can greatly im-prove the accuracy for disease identifica-tion and our dialogue system is able tocollect these symptoms automatically andmake a better diagnosis

从一个在线医疗论坛抽取来病人的病情自述以及和医生的对话过程作为训练数据，结果表明从对话过程获得的病情描述能大幅提高医生对疾病的诊断，并且论文的对话系统能够有效的收集到这些信息帮助诊断。

在生物医药领域很缺数据，为了解决这个问题，常识了基于模版，术语大的数据扩展技术。先在大的数据集上用ELMo来构建词向量。把任务评估分成两个子任务来进行，slot-filling和意图分类。

偏应用的一篇文章，结果也说明不了什么

Natural language understanding for task oriented dialog in the biomedical domain in a low resources context

PyDial: A Multi-domain Statistical Dialogue System Toolkit

一个开源的端到端的统计对话系统工具。

• http://pydial.org
• https://github.com/claycollier/pydial
• https://bitbucket.org/dialoguesystems/pydial.git

其总的架构包含Sematic Decode,Belief Tracker,Policy Reply System,Language generator. 整体来说整个系统都支持了基于规则的判断过程，也融合了模型的支持。源码值得一看的。

对于填槽任务，输入是一个有一系列词组成的语句，输出是每个词对应的slot/concept IDs。在统计SLU系统里，这个任务可以看作是：给定词序列L,SLU的目标是找到一个slot 序列来最大化后验概率P(S/L).

Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding

双向的 Jordan-type网络对槽最好用

Using Recurrent Neural Networksfor Slot Filling in Spoken Language Understanding

非常简单的介绍，可以看作是一个文献阅读索引

detail :

• http://opendialogue.miulab.tw

• https://www.csie.ntu.edu.tw/~yvchen/doc/OpenDialogue_Tutorial_ISCSLP18.pdf

• https://www.csie.ntu.edu.tw/~yvchen/doc/OpenDialogue_Tutorial_IJCNLP.pdf

Open-Domain Neural Dialogue Systems

Open-Domain Neural Dialogue Systems

Open-Domain Neural Dialogue Systems

用到的数据集是一个coffee ordering的对话过程的数据，31567通对话，142412对会话。数据只有用正则匹配出来的填充的标签信息。

agent model 构建前先预训练。网络结构和user model一样，但是输入和输出反转，同时也把之前对话中已经填充的槽位信息作为输入。但是这俩部分信息并不是简单的直接拼接在一起，而是来学习适合的attention 权重来更好的利用注意力机制。此外任何其他额外的语义意图标签都不必用。

User model.直接用双向的LSTM，以agent的utterance作为X，对应的用户的utterance作为Y。

这个认识是说在通常场景中用户先表达出意图然后回答agent的一个个问题来具体自己的需求。

用户通常是被动的，偶尔的有一轮问题。换句话说用户基本上都是在一轮中回答由agent提出的问题。所以可以基于一个用户只需要考虑一轮回答来给出回复这样的假设来构建user model,,让agent model来处理多轮对话。

吐槽现有机器人比较low都是手工规则，强化学习只适用有限的几个场景。所以受用户和agent角色的不对称关系造了samia。首先是用户模型不是规则或者排序而是seq2seq，然后基于用户模型来用强化学习构建agent。

ntegrating User and Agent Models: A Deep Task-Oriented Dialogue System Weiyan Wang, Yuxiang WU, Yu Zhang, Zhongqi Lu, Kaixiang Mo, Qiang Yang (Submitted on 10 Nov 2017) Task-oriented dialogue systems can efficiently serve a large number of customers and relieve people from tedious works. However, existing task-oriented dialogue systems depend on handcrafted actions and states or extra semantic labels, which sometimes degrades user experience despite the intensive human intervention. Moreover, current user simulators have limited expressive ability so that deep reinforcement Seq2Seq models have to rely on selfplay and only work in some special cases. To address those problems, we propose a uSer and Agent Model IntegrAtion (SAMIA) framework inspired by an observation that the roles of the user and agent models are asymmetric. Firstly, this SAMIA framework model the user model as a Seq2Seq learning problem instead of ranking or designing rules. Then the built user model is used as a leverage to train the agent model by deep reinforcement learning. In the test phase, the output of the agent model is filtered by the user model to enhance the stability and robustness. Experiments on a real-world coffee ordering dataset verify the effectiveness of the proposed SAMIA framework.

Deep Reinforcement Learning for Dialogue Generation

Recent neural models of dialogue generationoffer great promise for generating responsesfor conversational agents, but tend to be short-sighted, predicting utterances one at a timewhile ignoring their influence on future out-comes. Modeling the future direction of a di-alogue is crucial to generating coherent, inter-esting dialogues, a need which led traditionalNLP models of dialogue to draw on reinforce-ment learning. In this paper, we show how tointegrate these goals, applying deep reinforce-ment learning to model future reward in chat-bot dialogue. The model simulates dialoguesbetween two virtual agents, using policy gradi-ent methods to reward sequences that displaythree useful conversational properties: infor-mativity, coherence, and ease of answering (re-lated to forward-looking function). We evalu-ate our model on diversity, length as well aswith human judges, showing that the proposedalgorithm generates more interactive responsesand manages to foster a more sustained conver-sation in dialogue simulation. This work marksa first step towards learning a neural conversa-tional model based on the long-term success ofdialogues.

Dialog System & Technology Challenge 6 Overview of Track 1 - End-to-End Goal-Oriented Dialog learning

End-to-end dialog learning is an important research subject inthe domain of conversational systems. The primary task consistsin learning a dialog policy from transactional dialogs of a givendomain. In this context, usable datasets are needed to evaluatelearning approaches, yet remain scarce. For this challenge, atransaction dialog dataset has been produced using a dialogsimulation framework developed and released by Facebook AIResearch. Overall, nine teams participated in the challenge. Inthis report, we describe the task and the dataset. Then, we specifythe evaluation metrics for the challenge. Finally, the results ofthe submitted runs of the participants are detailed.

A COMPARATIVE STUDY OF RECURRENT NEURAL NETWORK MODELS FOR LEXICAL DOMAIN CLASSIFICATION

Intent Detection for code-mix utterances in task oriented dialogue systems

Intent detection is an essential component of taskoriented dialogue systems. Over the years, extensiveresearch has been conducted resultingin many state of the art modelsdirected towards resolving user’sintents indialogue. A variety of vector representation for user utterances have been explored for the same. However, these models and vectorization approaches have more so been evaluated in a single language environment. Dialogue systems generally have to deal with queries in different languages and most importantly Code-Mix form of writing. Since Code-Mix texts are not bounded by a formal structure they are difficult to handle. We thus conduct experiments across combinations of models and various vector representations for Code-Mix as well as multi-language utterancesand evaluate how these models scale to a multi-languageenvironment. Our aim is to find the best suitable combination of vector representation and models for the process of intent detection for code-mix utterances. We have evaluated the experiments on two different dataset consisting of only Code-Mix utterances and the otherdataset consisting of English, Hindi, and Code-Mix( English-Hindi) utterances

Sequence-to-Sequence Learning for Task-oriented Dialogue with Dialogue State Representation

Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems

Improving Semantic Parsing for Task Oriented Dialog

Semantic parsing using hierarchical representations has recently been proposedfor task oriented dialog with promising results. In this paper, we present three dif-ferent improvements to the model: contextualized embeddings, ensembling, andpairwise re-ranking based on a language model. We taxonomize the errors pos-sible for the hierarchical representation, such as wrong top intent, missing spansor split spans, and show that the three approaches correct different kinds of errors.The best model combines the three techniques and gives 6.4% better exact matchaccuracy than the state-of-the-art, with an error reduction of 33%, resulting in anew state-of-the-art result on the Task Oriented Parsing (TOP) dataset

对话系统任务综述与基于POMDP的对话系统

MXNet设计和实现简介

• Communication 通常的存储都是kv,更新粒度是单个数值，但是ML算法通常的数据集类型是matrix，vector，tensor，更新的是part matrix或者vector，所以可以更进一步优化通信数据类型。

• Fault tolerance

A.T.E.’s Concentrated Solar Thermal system has provided solutions for commercial and industrial applications. Solar thermal technology is used in it which uses solar energy from the sun. This technology directly impacts costs and increase your savings by being energy-efficient. The system also has many benefits like easy to install, longer life cycle, user friendly etc.

Language as a symbol. Relationship to memory.

This is aimed at System Design, from a distributed systems perspective.

<big>评：</big><br/><br/> 传统数据库与区块链式处理，哪个才是更佳的业务模式？这个问题的回答早已在我们的日常工作中得以体现，但却迫于某种难以逾越的权力边界而成了难言之隐。「事中容错，事后一致」是一种颇为崇高的境界，甚至可以从中一窥理想社会的光耀图景，但人们目前尚未能大规模应用这套 workflow，究其原因，并非目标遥远，而是由于决策权被少部分人掌控着，和数据打交道的主体只是把数据当作本职工作，并未主动贡献、积极参与。系统使用方背上的不是「数据必须一次性正确输入」的包袱，他们直面的，是将权利拱手让人后的自责，是与民主开放的理想世界背道而驰的困惑。

In the field of comfort cooling, Indirect Evaporative Cooling (IEC) is an innovative and revolutionary change. The question is how eco-friendly are they? Evaporative cooling systems are upgraded by regular research and development for them to remain eco-friendly. Being cost effective and reliable is another important requirement to meet the varied industry demands.

Description of information processing challenges and breakdowns.

Uncertainty -- not enough information

Complexity -- too much information

Ambiguity -- lack of clear meaning

Equivocality -- multiple meanings

Muhren and Walle use the 6 of the 9 most relevant design premises for the future information system design guidelines for DERMIS, another crisis management system

Information focus (dealing with complexity)

Crisis memory (creating historical frames of reference)

Exceptions as norms (support changing frames of reference in fluid, unpredictable scenario)

Scope and nature of crisis (support adaptable management depending on type of crisis)

Information validity and timeliness (synergy of coping with uncertainty and creating frames of reference from relevant, known information)

Free exchange of information (synergy of social context and creating useful/sharable frames of reference)

Bookmarked as a reminder to get these papers which could be helpful for the participatory design study.

Description of problem in integrating sensemaking (interpretive information process) into structured data systems.

information =/= data

Muhren and Walle also state that there are "few studies that use Sensemaking as an analytical lens for the design of information technology."

Definition of sensemaking and how the process interacts with ambiguity and equivocality in framing information.

"Sensemaking is about coping with information processing challenges of ambiguity and equivocality by dealing with frames of reference."

Definition of decision making and how this process interacts with uncertainty and complexity in information.

"Decision making is about coping with information processing challenges of uncertainty and complexity by dealing with information"

Strategies for sensemaking

Interaction between sensemaking and decision making

Description of how the seven properties interact to foster sensemaking.

"Weick distinguishes between seven properties of Sensemaking"

Description of information problems in crisis environments.

Sensemaking frame used in this study relies on work by Weick, et al.

Description of developing the design agenda, values, and needs assessment

Cites Value Sensitive Design

Description of design opportunities

Has this design challege been adequately addressed in CSCW (and CHI, for that matter) in the last 30-ish years?

What role does system meta data (version control, user history, etc.) play in bounding the visibility of decision making?

This also seems to be an area ripe for more collaborative design approaches (participatory, reflective, feminist, etc.)

This seems to still be a major challenge in information system design as well as collaborative workflow. Even if the information/meta context is made available, do people use it?

Two different camps on how to resolve this problem:

1) Change more flexible social activity/protocols to better align with technical limitations 2) Make systems more adaptable to ambiguity

This passage helps to explain the emphasis in CSCW papers on time/duration as a system design concern for workflow coordination (milliseconds between MTurk hits) versus time/representation considerations for system design

Elizabeth Cox describes her experience as an adult learner and how technology has positively impacted that experience. She specifically mentions a few learning management systems and online tools and how they were excellent at making the course content available any time and any place. Rating: 5/5

Federalism has the capability of being both bad and good. It just depends who you ask. On one side the advantages of fedaralism is it creates more effectiveness and makes the government stable. On the other hand federalism is risky it gets expensive, lead to a complex tax system and is slow in responses to crisis.

This is precisely the results I would expect. However, I completely disagree with their interpretation.

People with high blood pressure (BP) can tolerate a reduction in BP without instigating compensatory mechanisms. People with normal or low BP would invoke compensation by the sympathetic nervous system in response to alpha blockade. This would counteract the depressant effects of adrenergic antagonism. Indeed, adrenaline and noradrenaline elevate in response to standing, which I find to be an obvious prediction. Thus, the lack of benefit from prazosin in these subjects may be mediated by an increase in adrenergic receptor activation other than the apha1-adrenoreceptor; in particular, the beta-adrenergic receptors are likely at fault. Propranolol, a beta-blocker, is used for PTSD, so this mechanism seems well substantiated.

The study apparently found benefit for patients with BP over 110 (with more benefit for higher BP). Thus, I would conclude that systolic pressure below 110 induce compensation.

Good source to use on closing digestive system unit- explains energy well

Magic School Bus Reference Video:)

ORC Lesson Plan- good tool/questions for evaluating info Look on slide 32

Cool example of a digestive system dance:)

music standard to integrate into digestive system lesson (the class creates a song plus sings it for the digestive system)

Music standard " I can collaborate with others to compose or arrange a musical work for a specific purpose"

Good site option for students to do research on

A good website option to give students for online collaborative inquiry (when having students research, list this website)

Game Link for Lesson Plan activity on ipads or computer. Students pick the food they want and the game takes them through an interactive tutorial

Good source/page to find other links from for digestive system

So often we consider technology as being about particular things, but it can be much more fruitful when thinking of it as a system.

SC State Standard G-3.1.4 One of main lesson topics (digestive system)

1 of 2 Main Science/Health Objectives 3rd Grade health standard

The takeaway from the article: Choose document-oriented database only when the data can be treated as a self-contained document

Operating System Concepts

调度是操作系统的基本功能, 几乎所有的计算机资源在使用前均先调度

Evaporative Cooling System for different industrial applications from HMX. Your search for indirect evaporative cooling and direct evaporative cooling systems ends at HMX.

Hm? Aren’t they likely to encounter Content Management Systems, Enterprise Resource Planning, Customer Relationship Management, Intranets, etc.? Granted, these aren’t precisely the same think as LMS. But there’s quite a bit of continuity between Drupal, Oracle, Moodle, Sharepoint, and Salesforce.

Therein lies the rub. Mandated systems are a radically different thing from “systems which are available for use”. This quote from the aforelinked IHE piece is quite telling:

“I want somebody to fight!” Crouch said. “These things are not cheap -- 300 grand or something like that? ... I want people to want it! When you’re trying to buy something, you want them to work at it!”

In the end, it’s about “procurement”, which is quite different from “adoption” which is itself quite different from “appropriation”.

In context, these other dimensions of “online presence” in Higher Education take a special meaning. Reminds me of WPcampus. One might have thought that it was about using WordPress to enhance learning. While there are some presentations on leveraging WP as a kind of “Learning Management System”, much of it is about Higher Education as a sector for webwork (-development, -design, etc.).

An interesting case where Stephen’s tone might have drowned a useful discussion. FWIW, flexible roles and permissions are among the key things in my own personal “spec list” for a tool to use with learners, but it’s rarely possible to have that flexibility without also getting a very messy administration. This is actually one of the reasons people like WordPress.

While this whole bit is over-the-top, à la @mfeldstein67, must admit that my initial reaction was close to that. For a very similar reason. Still haven’t had an opportunity to use Canvas with learners, but the overall workflow for this type of feature really does make a big difference. The openness aspect is very close to gravy. After all, there are ways to do a lot of work in the open without relying on any LMS. But the LMS does make a huge difference in terms of such features as quickly grading learners’ work.

Sounds like part of the problem Vitrine technologie-éducation has been tackling with Ceres, a Learning Object Repository with a Semantic core.

This from a university which had dropped Blackboard for iTunes U.

As good a summary of Learning Analytics as any.

Is that about the colours favoured by both platforms? Does sound like it weakens the point (going from comparing fruits to comparing one citrus with another). The point, eventually, is that Canvas and Moodle occupy a similar space: course-based “learning” management systems.

Oh? Not xAPI?

Norman's Law of eLearning Tool Convergence

Any eLearning tool, no matter how openly designed, will eventually become indistinguishable from a Learning Management System once a threshold of supported use-cases has been reached.

Emphasis on “still”. Direction of change?

Have yet to notice much discussion of the relationships between SIS (Student Information Systems), CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), and LMS (Learning Management Systems).

Finally game companies figured out reporting system to decrease cheating. However, some inevitable problems are still no ignorable. Although reporting system has made cheating detection more accurate and effective, there could also be some malicious reports that can have honest players be banned. So each solution has its advantages and disadvantages and both of them would be deadly.

The Mackenzie River is a major river system in northwestern North America. It is exceeded only in basin size by the Mississippi-Missouri system. The entire Mackenzie River system is 2,635 miles long and passes through many lakes before emptying into the Beaufort Sea of the Arctic Ocean. The Mackenzie River alone is 1,025 miles long when measured from Great Slave Lake. It begins at Great Slave Lake where the elevation is 512 feet above sea level. Great Slave Lake can be as deep as 2,000 feet in certain places. It is filled with clear water on the eastern side and shallow, murky water on the western side. The headwaters of the Mackenzie River include numerous large rivers. The drainage basins of the Mackenzie River include the Liard River, Peace River, and Athabasca River. The ice that forms on the Mackenzie River over the winter months begins the break up in early to mid-May in the southern sections. Ice covering some portions of the Mackenzie River can break up as late as the end of May. The Mackenzie River basin is home to a very small and sparse population despite the natural resources available in this area. This area is home to muskrat, marten, beaver, lynx, and fox. Pulpwood and other small conifer trees can be found here. Petroleum and natural gas are usually the underlying reason larger settlements have formed in this area (Robinson 1999). 

References

Robinson, J. Lewis. 1999. Mackenzie River. July 26. Accessed May 2017, 2017. https://www.britannica.com/place/Mackenzie-River#ref466063.

The oil discovered in the Prudhoe Bay oil field in the North Slope region of Alaska in 1968 was the “largest oil field discovered in North America.” In 1969, a Trans-Alaska pipeline to transport oil from the North Slope was proposed by the Trans-Alaska Pipeline System. The Trans-Alaska Pipeline System was comprised of three major oil corporations. Despite many other ideas and suggestions to transport this oil, the oil industry reached a consensus in favor of the pipeline proposal of the Trans-Alaska Pipeline System (Busenberg, 2013). Construction of the Alyeska oil pipeline, also known as the Alaska pipeline or trans-Alaska pipeline, began in 1975. This pipeline was built by the Alyeska Pipeline Service Company, a group that was made up of seven different oil companies. In certain regions, the pipeline is buried underground, but where there is permafrost, the pipeline is constructed above the ground. The pipeline crosses over 800 river and streams and passes through three mountain ranges. The first oil was delivered from Prudhoe Bay to Valdez on June 20, 1977. This oil had to travel through the 789 mile long pipeline to reach its destination (Alaska Public Lands Information Centers, n.d.). See below for a link to “Pipeline! The story of the building of the trans-Alaska pipeline” video posted on YouTube by the Alaska National Parks service. 

https://www.youtube.com/watch?v=WmO6loYsm4Q

References

Alaska Public Lands Information Centers. (n.d.). The Trans-Alaska Pipeline. Retrieved from Alaska Public Lands Information Centers: https://www.alaskacenters.gov/the-alyeska-pipeline.cfm

Busenberg, G. J. (2013). The Trans-Alaska Pipeline System. In G. J. Busenburg, Oil and Wilderness in Alaska (pp. 11-43). Georgetown University Press.

This map shows the 800-mile Trans-Alaska Pipeline System (TAPS), also called the Alyeska Pipeline, that was built in the 1970s with 11 pumping stations that transports crude oil from Prudhoe Bay to Port Valdez. The pipeline cost around $8 billion to build. The link below provides facts on the pipeline provided by the Alyeska Pipeline Service Company: http://www.alyeska-pipe.com/TAPS/PipelineFacts

About the Trans-Alaska Pipeline System. Accessed April 30, 2017. http://www.treasure-hunt.alaska.edu/ch5/info_pipeline.html.

The Great Slave Lake was found in 1771 by Samuel Hearne (Ernst). Many others passed through during the Klondike Gold Rush in 1896-1899, but the region surrounding the Great Slave Lake remained greatly unoccupied. In 1930, a radioactive uranium mineral called pitchblende, or uraninite, was discovered on the shore of the Great Slave Lake and incentivized colonizers. 1934, gold was discovered on Yellowknife Bay, which led to a Yellowknife community settlement. Today, additional communities in this region include Hay River, Fort Resolution, Fort Providence, and Behchoko. The Great Slave Lake is the fifth largest lake is North America and is part of the Mackenzie River System. The Lake gets its name from a tribe of Native Americans called Slavery First Nations (National Geographic). This tribe fished for sustenance and did not explore farther than their immediate surroundings. Their neighbors, the Cree, thought the tribe was weak and often called them awonak, which means slaves. Explorer Peter Pond named the lake the Slave Lake in 1785 and then the Great Slave Lake in 1790. The Lake is known for its variety of types of fish, including trout, pike, and Arctic grayling. The Great Slave Lake is covered in snow and ice 8 months out of the year. The Great Slave Lake region is also the home to the largest intact forest in the world, the Boreal Forest, which contains evergreens, bogs, shallow lakes, and ponds (Pala). This Great Slave Lake cove is the habitat for caribou, waterfowl, beavers, and many fish species.

Ernst, Chloe. "The History and Sites of Great Slave Lake: A Visitor's Guide.” PlanetWare.com. Accessed April 06, 2017. http://www.planetware.com/northwest-territories/great-slave-lake-cdn-nt-ntgs.htm.

National Geographic, February 2002, 1. Global Reference on the Environment, Energy, and Natural Resources (accessed April 5, 2017). http://find.galegroup.com/grnr/infomark.do?&source=gale&idigest=6f8f4a3faafd67e66fa023866730b0a1&prodId=GRNR&userGroupName=bucknell_it&tabID=T003&docId=A83374988&type=retrieve&PDFRange=%5B%5D&contentSet=IAC-Documents&version=1.0.

Pala, Christopher. "Forests forever. (Forest conservation in Canada)." Earth Island Journal, September 22, 2010.

Again, this implies that the professor should care. One of the principles behind my grading system is that I don't. People are welcome to do whatever they want and they get the same grade, unless they do exceptional work.

This also implies that grades are somehow the currency of learning and that if you are getting good grades without learning, then you are somehow "winning."

This is a misunderstanding of grades. They are really the bits of an expert system that converts qualitative evaluation of individual performances into a final score that helps people categories graduates. So they are secondary to the actual learning and performance.

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In the late 1800s and early 1900s northern explorers depended on the indigenous people. The natives knew the land, the climate, and the wildlife. Because of their knowledge, the indigenous northerners served as local guides in this harsh and uninviting place. The native people also served as interpreters for researchers and were a lifeline for those that had little-to-no knowledge of how to survive in that kind of environment. However, they were not always seen as important figures. As southern technologies became more and more prominent in the far north, native peoples were pushed aside. “The airplane and helicopter strained relations among researchers and northerners. These technologies relieved field-workers from establishing extensive and regular relationships with locals as guides, interpreters, and informants. Permafrost scientists in particular could produce knowledge about the Arctic environment without Inuit expertise and apply that research in governmental construction projects without consulting locals” (108). The Inuits began to view the government scientists as pests, “they arrived in summer ‘in lusty swarm’ and were just as annoying” (108). Many researchers come during the warm months and gather information that allows them to cut ties to the indigenous people. The use of modern technology in the north forces Inuit to work menial jobs and completely change their way of life in order to survive in the modernizing landscape. While the industrial system has brought many valuable things to them, the Inuit are no longer needed or heard. If it is in the best interest of the oil industry, a pipeline would be built right over their homeland, even if they are still on it.

Annotation drawn form Stuhl, Andrew. Unfreezing the Arctic: Science, Colonialism, and the Transformation of Inuit Lands. Chicago: The University of Chicago Press, 2016.