214 Matching Annotations
  1. May 2019
    1. inferiority of your connexions? — to congratulate myself on the hope of relations, whose condition in life is so decidedly beneath my own?”

      Connexions - "Relationship by family ties, as marriage or distant consanguinity. Often with a and plural" (OED).

      Technically, Mr. Darcy and the Bennet family are from the same class, the gentry, but he has better connections. Mr. Darcy is related to Lady Catherine De Bourgh who holds the highest title a woman can have within the Gentry class. Comparatively, the Bennet's are related to the Gardiners, who are in a class below the gentry, the professional class.

  2. Apr 2019
    1. Yup, three years later, I have the exact same problem still with my otherwise great Surface Book 1. This has made me distrust Microsoft hardware, and yes, windows machines as a whole. Especially as my previous Dell laptop was also unreliable in this regard. Such a shame, because otherwise it's a fantastic piece of kit.

    1. It wouldn’t be an overstatement if I say that the manufacturing company’s success is directly proportional to the logistics and warehouse management of the enterprise company.

      It wouldn’t be an overstatement if I say that the manufacturing company’s success is directly proportional to the logistics and warehouse management of the enterprise company.

  3. Mar 2019
  4. arxiv.org arxiv.org
    1. EVALUATING PREREQUISITE QUALITIES FOR LEARNING END-TO-END DIALOG SYSTEMS

    1. The goal here is explicitly not to improve the state of the art in the narrow domain of restaurantbooking, but to take a narrow domain where traditional handcrafted dialog systems are known toperform well, and use that to gauge the strengths and weaknesses of current end-to-end systemswith no domain knowledge

      本文的目标不是来提升在狭窄的酒店预定领域的效果,而是用一个传统的手工系统就有较好系统来对比没有领域知识的end-to-end系统的优劣。

      MEMORYNETWORKS

    2. Unsurprisingly, perfectly coded rule-based systems can solve the simulated tasks T1-T5 perfectly,whereas our machine learning methods cannot. However, it is not easy to build an effective rule-based

      最终结果说明,在给出的任务中基于规则的毫无疑问效果比模型的好,但是对于在真实场景的真实问题来说,MemNN效果更好

    3. SUPERVISEDEMBEDDINGMODELS

      和现在的架构很像

    4. We implemented a rule-based system for this task in the followingway. We initialized a dialog state using the 3 relevant slots for this task: cuisine type, location andprice range. Then we analyzed the training data and wrote a series of rules that fire for triggers likeword matches, positions in the dialog, entity detections or dialog state, to output particular responses,API calls and/or update a dialog state. Responses are created by combining patterns extracted fromthe training set with entities detected in the previous turns or stored in the dialog state. Overall webuilt 28 rules and extracted 21 patterns. We optimized the choice of rules and their application priority(when needed) using the validation set, reaching a validation per-response accuracy of 40.7%. Wedid not build a rule-based system forConciergedata as it is even less constrained.

      先用word匹配和正则等制定一个规则系统来作为baseline.

    5. LEARNING END-TO-END GOAL-ORIENTED DIALOG

    1. A Network-based End-to-End Trainable Task-oriented Dialogue System

      这个end-to-end的系统,在意图识别的阶段用的是cnn+LSTM 在状态管理(belief state tracking)也用的LSTM,在policy的时候自定义了一套算法,将前面的几个输出向量做了个线性模型,输出。

    2. Finally, the policy network output is generated bya three-way matrix transformation,

      策略生成是用前面的特征向量加和乘积

    3. a distributed representationgenerated by an intent network and a probabilitydistribution over slot-value pairs called the beliefstate

      造出来的一个belief state的概念:

      由intent网络生成的分布式表示和对slot-value组的概率表示叫做belief stat。

    1. An End-to-End Trainable Neural Network Model withBelief Tracking for Task-Oriented Dialog

    1. In learning such neural network based dialogmodel, we propose hybrid offline training and on-line interactive learning methods. We first let theagent to learn from human-human conversationswith offline supervised training. We then improvethe agent further by letting it to interact with usersand learn from user demonstrations and feedbackwith imitation and reinforcement learning.

      模型训练思路:

      • 1 首先离线有监督学习 人和人的对话数据
      • 2 然后让模型和人交互,基于反馈和模仿用强化学习来学习

      为了解决样本效率问题,提出了learning-from-user and learning-from-simulationl两个方案。

    2. We design neural net-work based dialog system that is able to ro-bustly track dialog state, interface with knowl-edge bases, and incorporate structured queryresults into system responses to successfullycomplete task-oriented dialog.

      基于神经网络的端到端的网络模型能够健壮的跟踪对话状态,和知识库交互,用结构化的信息来成功的完成任务驱动型对话。

    3. End-to-End Learning of Task-Oriented Dialogs

      端到端的task类型对话的鼻祖

    1. These system components areusually trained independently, and their optimiza-tion targets may not fully align with the overallsystem evaluation criteria (e.g. task success rateand user satisfaction). Moreover, errors made inthe upper stream modules of the pipeline propa-gate to downstream components and get amplified,making it hard to track the source of errors

      传统pipeline方案的问题点: 1 流程比较复杂,每步骤独立训练,但是流程输入和输出有依赖,错误放大,难以跟进。

    2. Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue Systems

      一个混合学习过程,在人类的指导教育和反馈下增强强化学习的过程

    1. Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

      用一个模型来解决两个不同类型的问题,intent detect是分类,填槽是序列标注。都用基于attention机制的RNN来搞定了

    2. or joint modeling of intent detection and slot filling, weadd an additional decoder for intent detection (or intent clas-sification) task that shares the same encoder with slot fillingdecoder.

      本文为了对intent和slot-filling联合建模,额外添加了一个decoder来进行意图检测。

    3. The attentionmechanism later introduced in [12] enables the encoder-decodermodel to learn a soft alignment and to decode at the same time.

      本文中用到的attention-RNN算法。

      D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine trans-lation by jointly learning to align and translate,”arXiv preprintarXiv:1409.0473, 2014

    1. We present a general solution towards building task-orienteddialogue systems for online shopping, aiming to assist on-line customers in completing various purchase-related tasks,such as searching products and answering questions, in a nat-ural language conversation manner. As a pioneering work, weshow what & how existing natural language processing tech-niques, data resources, and crowdsourcing can be leveragedto build such task-oriented dialogue systems for E-commerceusage. To demonstrate its effectiveness, we integrate our sys-tem into a mobile online shopping application. To the bestof our knowledge, this is the first time that an dialogue sys-tem in Chinese is practically used in online shopping scenariowith millions of real consumers. Interesting and insightful ob-servations are shown in the experimental part, based on theanalysis of human-bot conversation log. Several current chal-lenges are also pointed out as our future directions

      整体来说,无法验证,没有任何实质的创新点。

      说是构建了一个第一个中文电商机器人对话系统(really?)

      M = (I, C, A)

      I是intent,C是product category, A是商品attribute。 M是根据用户Query得到的信息的表示。

      意图分类:PhraseLDA 1000个topic

      产品分类: a CNN-based approach that resembles (Huang et al. 2013)and (Shen et al. 2014

    2. Main actions that areconsidered in the online shopping scenario include

      在购物场景中主要的行为有:

      • Recommendation
      • Comparison
      • Opinion Summary
      • Question Answering
      • Proactive Questioning
      • Chit-chat
    3. To deal with the problem we mentioned, our work focuson using three kinds of data resources that are common tomost E-commerce web service provider or easily crawledfrom webs, including: (i) product knowledge base, which isprovided by the E-commerce partner and contains structuredproduct information; (ii) search log, which is closely linkedwith products, natural language queries and user selectionbehaviors (mouse click); (iii) community sites, where userpost their intents in natural language and can be used to minepurchase-related intents and paraphrases of product-relatedterms. Besides, we show that crowd sourcing is necessary tobuild such AI bot

      为了解决所谓的问题:

      • 1 结构化商品信息
      • 2 用户的搜索日志
      • 3 社区网站,挖掘购买意图和产品相关的词
    1. The Sogou Spoken Language Understanding System for the NLPCC 2018 Evaluation

    2. The first step is lexical analysis, i.e. word segmentation and part-of-speech (POS)tagging. The words and POS labels are used as features in the subsequent models. Forthe shared task we used HanLP [1] as our Chinese lexical analyzer.

      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。

    3. Each rule is of the form “if thequery q is listed in a particular lexicon L, and the preceding queries and their predicteddomain labels satisfy certain conditions, then q is assigned a certain intent label and,with the exception of short commands, the entire q is regarded as a slot of the typecorresponding to L.” The rules are arranged in sequential order in accordance with theirpriorities

      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。

    4. Figure 1 shows the framework of our SLU system, which consists of the context-dependent rules for entity-only queries and the context-independent model for querieswith IISPs. The entire system feeds the query to the rules first. If the rule-based compo‐nent returns null result, that means the query is judged to contain IISPs and the model-based component will continue to process it. Otherwise, it means the query is regardedas entity-only and the result of the rules is returned as the final output

      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。

    5. s in real use cases of dialog systems, the queries in the shared task can be roughlydivided into two kinds, viz. queries with intent-indicating salient phrases and querieswithout. By intent-indicating salient phrase (IISP) it is meant a phrase in the query thatshows the intent of the query. E.g. the phrase “” in the query “” andthe phrases “” in the query “” are IISPs.

      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。

    1. Retrieval-based MethodsRetrieval-based methods choose a response from candidateresponses. The key to retrieval-based methods is message-response matching. Matching algorithms have to overcomesemantic gaps between messages and responses [28].

      基于检索的是从候选的回复中选出一个。检索式的关键是message-response的匹配。

      B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neu-ral network architectures for matching natural lan-guage sentences. InAdvances in neural informationprocessing systems, pages 2042–2050, 2014.

      单轮的匹配 match(X,Y) = X^TAy

      X:message的向量表示, y:回复的向量表示。

      H. Wang, Z. Lu, H. Li, and E. Chen. A dataset for re-search on short-text conversations. InProceedings ofthe 2013 Conference on Empirical Methods in NaturalLanguage Processing, pages 935–945, Seattle, Wash-ington, USA, October 2013. Association for Compu-tational Linguistics

      Z. Lu and H. Li. A deep architecture for matchingshort texts. InInternational Conference on Neural In-formation Processing Systems, pages 1367–1375, 2013.

      B. Hu, Z. Lu, H. Li, and Q. Chen. Convolutional neu-ral network architectures for matching natural lan-guage sentences. InAdvances in neural informationprocessing systems, pages 2042–2050, 2014

      M. Wang, Z. Lu, H. Li, and Q. Liu. Syntax-based deepmatching of short texts.InIJCAI, 03 2015

      Y. Wu, W. Wu, Z. Li, and M. Zhou. Topic augmentedneural network for short text conversation.CoRR,2016

      多轮匹配

    2. TASK-ORIENTED DIALOGUESYSTEMSTask-oriented dialogue systems have been an important branchof spoken dialogue systems. In this section, we will reviewpipeline and end-to-end methods for task-oriented dialoguesystems.

      任务型对话系统整体来说可以分为两类:

      • 1 pipeline,也就是包含SLU+DST+PL+NLG
      • 2 end-to-end
    3. 2.2 End-to-End Methods

      在传统的task-oriented对话系统中,尽管有很多特定领域的人工定制,很难推广其他领域,更进一步的是pipeline的方法有两个局限。

      • 一个是信用分配问题,一个用户的反馈很难传播到上游每个组件中。
      • 另一个是问题流程的相互依赖。一个组件的输入依赖上一个组件的输出。一部分变动其他都得动。(这个真的是问题么?)

      这俩文章介绍来一种基于网络的end-to-end的可训练的task-oriented对话系统,方法是把对话系统学习看成从对话历史到回复响应的mapping,并用encoder-decoder模型来训练整个模型。不过这个系统是以有监督的方式训练的,不仅需要大量的训练数据,并且由于在训练数据中缺乏对对话控制的探索也不能找到一个鲁棒的好策略。

      • A network-based end-to-end trainable task-oriented di-alogue system
      • Learningend-to-end goal-oriented dialog.

      下文中,首次提出了一个联合训练dialogue state tracking和policy learning来优化得到更鲁棒的系统行为。

      • Towards end-to-end learn-ing for dialog state tracking and management us-ing deep reinforcement learning

      task-oriented系统经常需要query外部知识库,前面的系统是通过发出一个符号请求到知识库基于属性来获得条目。

    4. TASK-ORIENTED DIALOGUESYSTEMS

      一个典型的pipeline方法构建的task-oriented对话系统包含四部分:

      • Language understanding.NLU/SLU,目标是解析理解用户输入为intent,slot

      • Dialogue state tracker. 根据当前对话输入信息结合历史信息给出当前会话状态。

      • Dialogue policy learning.基于当前对话状态给出接下来要采取的行动

      • Natural language generation(NLG). 将映射的选择的动作行为转换生成对应的输出回复。

    5. 2.1.3 Policy learning

      策略学习 基于前面state tracker的状态表示,策略学习(policy learning)是来生成下一个可用的系统行动。无论是监督学习或者强化学习都可以用来优化策略学习。 H. Cuayhuitl, S. Keizer, and O. Lemon. Strategic di-alogue management via deep reinforcement learning.arxiv.org, 2015.

      通常都用一个基于规则的agent来初始化系统。 Z. Yan, N. Duan, P. Chen, M. Zhou, J. Zhou, andZ. Li. Building task-oriented dialogue systems for on-line shopping. InAAAI Conference on Artificial Intel-ligence, 2017

      然后用监督学习来基于规则生成的规则来学习。Building task-oriented dialogue systems for on-line shopping. 强化学习,Strategic di-alogue management via deep reinforcement learning.结果据说比很多系统,rule based,superviesed都好

    6. A statistical dialog system

      状态管理。

      统计对话系统维护了一个对真实状态基于多重假设来描述的分布,以应对噪声场景和歧义。

      • S. Young, M. Gai, S. Keizer, F. Mairesse, J. Schatz-mann, B. Thomson, and K. Yu. The hidden informa-tion state model: A practical framework for pomdp-based spoken dialogue management. 在DSTC比赛中结果形式是每轮对话中每个slot的一个概率分布。各种统计学方法如下:
      • 规则集, Z. Wang and O. Lemon. A simple and generic belieftracking mechanism for the dialog state tracking chal-lenge: On the believability of observed information. InSIGDIAL Conference, pages 423–432, 2013
      • CRF S. Lee and M. Eskenazi. Recipe for building robustspoken dialog state trackers: Dialog state trackingchallenge system description. InSIGDIAL Conference,pages 414–422, 2013

        S. Lee. Structured discriminative model for dialogstate tracking. InSIGDIAL Conference, pages 442–451, 2013

      H. Ren, W. Xu, Y. Zhang, and Y. Yan. Dialog statetracking using conditional random fields. InSIGDIALConference, pages 457–461, 2013.

      • maximum entropy model J. Williams. Multi-domain learning and generaliza-tion in dialog state tracking. InSIGDIAL Conference,pages 433–441, 2013.

      • web-style ranking J. D. Williams. Web-style ranking and slu combina-tion for dialog state tracking

      深度学习的状态管理。用一个滑动窗口来在任意数量可能值上输出一个概率序列。 M. Henderson, B. Thomson, and S. Young. Deep neu-ral network approach for the dialog state tracking chal-lenge. InProceedings of the SIGDIAL 2013 Confer-ence, pages 467–471, 2013

      多领域的RNN状态跟进模型: B. Thomson, M. Gasic, P.-H. Su, D. Vandyke, T.-H. Wen, and S. Young. Multi-domain dialog state tracking using recurrent neuralnetworks.

      基于neural belief tracker(NBT)来检测slot-value对。 Neural belief tracker: Data-driven dia-logue state tracking.

    7. Dialogue State Tracking

      跟进对话状态是保障dialog system的robust的核心。主要目标是预测每轮对话的用户目标。经典的状态结构通常叫做slot-filling 或者 sematic frame.

      传统用手工规则的方法: D. Goddeau, H. Meng, J. Polifroni, S. Seneff, andS. Busayapongchai. A form-based dialogue managerfor spoken language applications. InSpoken Language,1996. ICSLP 96. Proceedings., Fourth InternationalConference on, volume 2, pages 701–704. IEEE, 1996

      基于规则的方法倾向于常见的错误,然后很多结果并不是想要的。 J. D. Williams. Web-style ranking and slu combina-tion for dialog state tracking. InSIGDIAL Conference,pages 282–291, 2014

    8. Slot filling

      填槽这个问题更多的是看成一个序列标注的问题。句子中的每个词都打上一个语义标签。输入是由词组成的句子,输出是每个词对应的slot/concept IDs.

      DBN 类的处理:

      • A Deoras and R. Sarikaya. Deep belief network basedsemantic taggers for spoken language understanding.

        L. Deng, G. Tur, X. He, and D. Hakkani-Tur. Use ofkernel deep convex networks and end-to-end learningfor spoken language understanding

      RNN:

      • G. Mesnil, X. He, L. Deng, and Y. Bengio. Investi-gation of recurrent-neural-network architectures andlearning methods for spoken language understanding.Interspeech, 2013.
      • K. Yao, G. Zweig, M. Y. Hwang, Y. Shi, and D. Yu.Recurrent neural networks for language understand-ing. InInterspeech, 2013
      • R. Sarikaya, G. E. Hinton, and B. Ramabhadran.Deep belief nets for natural language call-routing
      • K. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, andY. Shi. Spoken language understanding using longshort-term memory neural networks. InIEEE Insti-tute of Electrical & Electronics Engineers, pages 189 –194, 2014
    9. Language Understanding

      目标是根据一个用户utterance/query 得到其对应的语义slot。slots是预先根据场景定于的。通常来说有两种类型的表示,一个是句子级别的类别,例如用户的意图和utterance的类别。另外一个是单词级别的信息抽取,例如命名实体和槽位填充。

      意图识别是根据一句话来检测用户的意图。 基于深度学习的意图识别: L. Deng, G. Tur, X. He, and D. Hakkani-Tur. Use ofkernel deep convex networks and end-to-end learningfor spoken language understanding. InSpoken Lan-guage Technology Workshop (SLT), 2012 IEEE, pages210–215. IEEE, 2012

      G. Tur, L. Deng, D. Hakkani-T ̈ur, and X. He. Towardsdeeper understanding: Deep convex networks for se-mantic utterance classification. InAcoustics, Speechand Signal Processing (ICASSP), 2012 IEEE Interna-tional Conference on, pages 5045–5048. IEEE, 2012.

      D. Yann, G. Tur, D. Hakkani-Tur, and L. Heck. Zero-shot learning and clustering for semantic utteranceclassification using deep learning. 2014.

      尤其是这个用CNN来抽取query vector进行query分类。 H. B. Hashemi, A. Asiaee, and R. Kraft. Query intentdetection using convolutional neural networks. InIn-ternational Conference on Web Search and Data Min-ing, Workshop on Query Understanding, 2016

      P.-S. Huang, X. He, J. Gao, L. Deng, A. Acero, andL. Heck. Learning deep structured semantic modelsfor web search using clickthrough data. InProceedingsof the 22nd ACM international conference on Confer-ence on information & knowledge management, pages2333–2338. ACM, 2013

      Y. Shen, X. He, J. Gao, L. Deng, and G. Mesnil.Learning semantic representations using convolutionalneural networks for web search. InProceedings of the23rd International Conference on World Wide Web,pages 373–374. ACM, 2014.

    1. setting up objectives

      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.

    2. A composite process, remember, is organized from both human processes and computer processes

      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.

  5. Feb 2019
    1. Spoken language understanding (SLU) comprises two tasks, intent identification andslot filling. That is, given the current query along with the previous queries in the samesession, an SLU system predicts the intent of the current query and also all slots (entitiesor labels) associated with the predicted intent. The significance of SLU lies in that eachtype of intent corresponds to a particular service API and the slots correspond to theparameters required by this API. SLU helps the dialog system to decide how to satisfythe user’s need by calling the right service with the right information

      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
    1. 对话管理也可以看成是一个分类任务,即每个对话状态和一个合适的对话动作相对应.和其它有监督的学习任务一样,分类器可以从标注的语料库中训练得到.但是,在某状态下系统应该选择的动作不能仅仅是模仿在训练数据中同一状态对应的动作,而应该是选择合适的动作能够导致一个成功的对话.因此,把对话过程看成是一个决策过程更为合适,从而根据对话的整体成功来优化动作的选择过程[32].因而这是一个规划问题,并且可以用强化学习[33]方法学习获得最优的结果
    2. 对话系统从本体构成和业务逻辑角度,可分为领域任务型和开放型的信息交互.领域任务型系统针对具体应用领域,具有比较清晰的业务语义单元的定义、本体结构以及用户目标范畴,例如航班查询、视频搜索、设备控制等等,这类交互往往是以完成特定的操作任务作为交互目标;而开放型信息交互则不针对特定领域,或说面向非常广泛的领域,交互目标并非业务任务,而是满足用户其它方面的需求,例如开放的百科问答、聊天等.它虽然能一定程度上显示人工智能的力量,但因其并不专注于帮助人解决现实任务问题,其实际使用范围较为狭窄.近年来,随着移动终端的高速发展,面向任务的自然人机对话系统和相关的认知控制理论得到了越来越多的学术和产业界重视,这也是本文讨论的重点
    3. 任务型人机对话系统中的认知技术

    1. We com-plement recent work by showing the effec-tiveness of simple sequence-to-sequenceneural architectures with a copy mecha-nism. Our model outperforms more com-plex memory-augmented models by 7% inper-response generation and is on par withthe current state-of-the-art on DSTC2, areal-world task-oriented dialogue dataset

      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。

    2. 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

    1. Both NLU and NLG are implementedwith template-based models

      这个地方的NLU和NLG都是用基于模版的模型。

    2. Symptom ExtractionWe follow the BIO(begin-in-out) schema for symptom identification(Figure 1). Each Chinese character is assigned alabel of ”B”, ”I” or ”O”. Also, each extractedsymptom expression is tagged withTrueorFalseindicating whether the patient suffers from thissymptom or not. In order to improve the anno-tation agreement between annotators, we createtwo guidelines for the self-report and the conver-sational data respectively. Each record is anno-tated by at least two annotators. Any inconsis-tency would be further judged by the third one.The Cohen’s kappa coefficient between two anno-tators are71%and67%for self-reports and con-versations respectively

      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。

    3. 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

      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。

    1. To overcome this issue, weexplore data generation using templates and terminologies and data augmentationapproaches. Namely, we report our experiments using paraphrasing and wordrepresentations learned on a large EHR corpus with Fasttext and ELMo, to learn aNLU model without any available dataset. We evaluate on a NLU task of naturallanguage queries in EHRs divided in slot-filling and intent classification sub-tasks.On the slot-filling task, we obtain a F-score of 0.76 with the ELMo representation;and on the classification task, a mean F-score of 0.71. Our results show that thismethod could be used to develop a baseline system

      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

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

    1. 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。

      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。

  6. www.iro.umontreal.ca www.iro.umontreal.ca
    1. For the slot filling task, the input is the sentence consisting of a sequence of words, L, and the output is a sequence of slot/concept IDs, S, one for each word. In the statistical SLU systems, the task is often formalized as a pattern recognition problem: Given the word sequence L, the goal of SLU is to find the semantic representation of the slot sequence 푆that has the maximum a posterioriprobability 푃(푆|퐿).

      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).

    2. Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding

    3. bi-directional Jordan-type network that takes into account both past and future dependencies among slots works best

      双向的 Jordan-type网络对槽最好用

    4. Using Recurrent Neural Networksfor Slot Filling in Spoken Language Understanding

    1. the dataset used in our experiment hasonly the tags of filled information slots extracted by patternmatching between dialogue log and final order information

      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。

    2. the agent model swaps the in-put and output sequences, and it also takes the tag of filledinformation slots as an input which is extracted from dia-logue in previous turns by pattern matching with the orderinformation in ground truth

      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。

    3. By directly learningfrom the raw dialogue logs, the network takes the agent ut-Figure 2: The network structure: encoder-decoder structurewith the attention mechanismteranceXa:xa1;xa2;:::;xanas the input sequence and takescorresponding user utteranceYu:yu1;yu2;:::;yumas the tar-get sequence.

      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。

    4. In the task-oriented dialogues, a user usually firstly showsthe intention to the agent and then answers the agent’s ques-tions one by one to specify the demand.

      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。

    5. we propose a uSer andAgent Model IntegrAtion (SAMIA) framework inspired byan observation that the roles of the user and agent models areasymmetric. Firstly, this SAMIA framework model the usermodel as a Seq2Seq learning problem instead of ranking ordesigning rules. Then the built user model is used as a lever-age to train the agent model by deep reinforcement learning.In the test phase, the output of the agent model is filtered bythe user model to enhance the stability and robustness. Ex-periments on a real-world coffee ordering dataset verify theeffectiveness of the proposed SAMIA framework.

      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。

    6. 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.

    1. 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.

    1. 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.

    1. 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

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

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

    1. 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

    1. Engineering Challenges
      • 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

    1. 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.

  7. Jan 2019
    1. For large-scale software systems, Van Roy believes we need to embrace a self-sufficient style of system design in which systems become self-configuring, healing, adapting, etc.. The system has components as first class entities (specified by closures), that can be manipulated through higher-order programming. Components communicate through message-passing. Named state and transactions support system configuration and maintenance. On top of this, the system itself should be designed as a set of interlocking feedback loops.

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

    1. 计算机领域在分布式处理过程中追求高效、一致。对错误数据记录的修复和更正,通常会另行设计一套机制来保证。相对传统数据库,区块链由于需要保证事后数据的不可篡改,引入了共识机制,为错误的出现和修复提供更多的容忍度。这一重要思想通常被许多区块链设计者所忽略,众多项目纷纷追求提高短交易及确认速度,这会导致弱化甚至牺牲其他节点对数据的验证过程。同时,更早更快的确认也会带来问题。参与生成数据的节点需要满足生成数据不能出错等更严苛要求,导致现在很多区块链项目的在落地过程中出现困难。因为系统使用方会背上了数据必须一次性正确输入的包袱,需要非常保守和谨慎地选择上链数据。最终,区块链落地应用范围的狭窄,许多存在出错可能性的数据难以结合区块链的优点参与业务升级改造。

      <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。

    1. 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.

  8. wendynorris.com wendynorris.com
    1. Zack [42] distinguished these four termsaccording to two dimensions: the nature of what is being processed and the consti-tution of the processing problem.The nature of what is being processed is either information or frames of ref-erence. With information, we mean “observations that have been cognitively pro-cessed and punctuated into coherent messages” [42]. Frames of reference [4, p.108], on the other hand, are the interpretative frames which provide the context forcreating and understanding information. There can be situations in which there is alack of information or a frame of reference, or too much information or too manyframes of reference to process.

      Description of information processing challenges and breakdowns.

      Uncertainty -- not enough information

      Complexity -- too much information

      Ambiguity -- lack of clear meaning

      Equivocality -- multiple meanings

    2. Ta b l e 3DERMIS design premises [29]

      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)

    3. For our research design, we drew on Walsham [33] and Klein and Myers [13],who provide comprehensive guidelines on how to conduct interpretive case studyresearch in the IS domain.

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

    4. The problems of managing information and managing frames of reference are“tightly linked in a mutually interacting loop” and require “managing informationand the systems that provide it” [42]. IS have been generally designed to overcomethe information problems from Table 1. Most IS are aimed at either storing and re-trieving information to reduce uncertainty, such as database management systemsand document repositories, or at analyzing and processing large amounts of infor-mation to reduce complexity, such as decision support systems [31]. However, aswe have previously discussed, information related strategies are not always helpfulin coping with a variety of potential meanings.Problems of interpretation and the creation and management of frames of refer-ence, which aids Sensemaking, have generally not been taken into account whendesigning IS. Most IS currently seem tointend the opposite because they aim atreplacing or suppressing the possibility tomake sense of situations.

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

      information =/= data

    5. there is scarce research on how IS can support informa-tion processing challenges—specifically related to Sensemaking—in crisis manage-ment [14]

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

    6. Sensemaking is about contextual rationality, built out of vaguequestions, muddy answers, and negotiated agreements that attempt to reduce ambi-guity and equivocality. The genesis of Sensemaking is a lack of fit between whatwe expect and what we encounter [40]. With Sensemaking, one does not look at thequestion of “which course of action should we choose?”, but instead at an earlierpoint in time where users are unsure whether there is even a decision to be made,with questions such as “what is going on here, and should I even be asking this ques-tion just now?” [40]. This shows that Sensemaking is used to overcome situationsof ambiguity. When there are too many interpretations of an event, people engagein Sensemaking too, to reduce equivocality.

      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."

    7. Decision making is traditionally viewed as a sequential process of problem classifi-cation and definition, alternative generation, alternative evaluation, and selection ofthe best course of action [26]. This process is about strategic rationality, aimed atreducing uncertainty [6, 36]. Uncertainty can be reduced through objective analysisbecause it consists of clear questions for which answers exist [5, 40]. Complex-ity can also be reduced by objective analysis, as it requires restricting or reducingfactual information and associated linkages [42]

      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"

    8. The central problem requiring Sensemaking ismostly that there are too many potential meanings, and so acquiring informationcan sometimes help but often is not needed. Instead, triangulating information [34],socializing and exchanging different points of view [20], and thinking back of pre-vious experiences to place the current situation into context, as the retrospectionproperty showed us, are a few strategies that are likely to be more successful forSensemaking.

      Strategies for sensemaking

    9. Just as the information processing challenges from Table 1 are not mutually ex-clusive, Sensemaking and decision making cannot be separated, but instead operatesimultaneously. Meaning must be established and then sufficiently negotiated priorto acting on information [42]: Sensemaking shapes events into decisions, and deci-sion making clarifies what is happening [40].

      Interaction between sensemaking and decision making

    10. Weick et al. [41, p. 419] formulate a gripping conclusion on what the sevenSensemaking properties are all about: “Taken together these properties suggest thatincreased skill at Sensemaking should occur when people are socialized to makedo, be resilient, treat constraints as self-imposed, strive for plausibility, keep show-ing up, use retrospect to get a sense of direction, and articulate descriptions thatenergize. These are micro-level actions. They are small actions. But they are smallactions with large consequences.”

      Description of how the seven properties interact to foster sensemaking.

    11. The seven different properties of Sensemaking can be captured by the acronym SIRCOPE: Social context, Identity construction, Retrospection, Cue extraction, Ongo-ing projects, Plausibility, and Enactment [17–21, 37–39]

      "Weick distinguishes between seven properties of Sensemaking"

    12. Crisis environments are characterized by various types of information problemsthat complicate the response, such as inaccurate, late, superficial, irrelevant, unreli-able, and conflicting information [30, 32]. This poses difficulties for actors to makesense of what is going on and to take appropriate action. Such issues of informationprocessing are a major challenge for the field of crisis management, both concep-tually and empirically [19].

      Description of information problems in crisis environments.

    13. We use the theory of Sensemaking to study exactly this: how people makesense of their environment, and how they give meaning to what is happening. Sense-making is a crucial process in crises, as the manner and thereby the success of howone deals with crucial events is determined by the grasp one has of a situation.

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

    1. Value Sensitive Design (VSD) emphasizes consideration of stakeholder values when making design decisions [5]. Applying this rationale to the goal of leveraging the capacity of digital workers during crisis events, we identify design solutions that fit the underlying community dynamics, including current work practices, organizational structures, and motivations of digital volunteer work.

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

      Cites Value Sensitive Design

    2. Our research reveals several design opportunities in this space. Importantly, informed by the empirical findings presented here, we argue for situating solutions within current work practices and infrastructures.

      Description of design opportunities

    1. By ignoring the diversity and discord of the ‘goals’ of theparticipants involved, the differentiation of strategies, and the incongruence of theconceptual frames of reference within a cooperating ensemble, much of the currentCSCW research evades the problem of how to provide computer support for peoplecooperating through the establishment of a common information space.

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

    2. On the one hand, the visibility requirement is amplified by this divergence. Thatis, knowledge of the identity of the originator and the situational context motivat-ing the production and dissemination of the information is required so as to enableany user of the information to interpret the likely motives of the originator. On theother hand, however, the visibility requirement is moderated by the divergence ofinterests and motives. A certain degree of opaqueness is required for discretionarydecision making to be conducted in an environment charged with colliding inter-ests. Hence,visibility must be bounded.

      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.)

    3. Thus, a computer-basedsystem supporting cooperative work involving decision making should enhancethe ability of cooperating workers to interrelate their partial and parochial domainknowledge and facilitate the expression and communication of alternative perspec-tives on a given problem. This requires a representation of the problem domainas a whole as well as a representation, in some form, of the mappings betweenperspectives on that problem domain.

      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?

  9. Dec 2018
    1. Theproblem, then, was centered by social scientists in the process of design. Cer-tainly, many studies in CSCW, HCI, information technology, and informa-tion science at least indirectly have emphasized a dichotomy betweendesigners, programmers, and implementers on one hand and the social ana-lyst on the other.

      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

    2. In particular, concurrency control problems arise when the software, data,and interface are distributed over several computers. Time delays when ex-changing potentially conflicting actions are especially worrisome. ... Ifconcurrency control is not established, people may invoke conflicting ac-tions. As a result, the group may become confused because displays are incon-sistent, and the groupware document corrupted due to events being handledout of order. (p. 207)

      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

  10. Nov 2018
    1. An Adult Learner Reflects on Technology in Higher Education

      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

  11. Oct 2018
  12. Aug 2018
    1. CONCLUSIONS: These findings suggest that higher standing BP is a biomarker that helps identify persons with combat PTSD who are likely to benefit from prazosin. These results also are consistent with α1AR activation contributing to PTSD pathophysiology in a subgroup of patients.

      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.

  13. Jul 2018
    1. I can demonstrate a relationship between music and another subject in my schoo

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

    2. I can collaborate with others to composeor arrange a musical work for a specific purpose.

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

    1. It takes around 24 hours for your dinner to wind its way through the nine-metre-long digestive tract. On its trip, it’s mixed with acids and digestive juices, and squeezed and squelched until all the nutrients that the body needs are absorbed.

      Good site option for students to do research on

    1. Your digestive (say: dye-JES-tiv) system started working even before you took the first bite of your pizza. And the digestive system will be busy at work on your chewed-up lunch for the next few hours — or sometimes days, depending upon what you've eaten. This process, called digestion,

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

    1. Digestion interactive game for kids

      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

    1. At least as far as these gentlemen were concerned, this was a talk about the future of technology. Taking their cue from Elon Musk colonizing Mars, Peter Thiel reversing the aging process, or Sam Altman and Ray Kurzweil uploading their minds into supercomputers, they were preparing for a digital future that had a whole lot less to do with making the world a better place than it did with transcending the human condition altogether and insulating themselves from a very real and present danger of climate change, rising sea levels, mass migrations, global pandemics, nativist panic, and resource depletion. For them, the future of technology is really about just one thing: escape.

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

    1. Identify the major parts of the digestive system(e.g., esophagus, intestine, mouth, stomach

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

    2. D-3.1.1Identify the basic parts of the respiratory system (e.g., lungs, bronchioles, diaphragm)

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

    1. Perelman says his Babel Generator also proves how easy it is to game the system. While students are not going to walk into a standardized test with a Babel Generator in their back pocket, he says, they will quickly learn they can fool the algorithm by using lots of big words, complex sentences, and some key phrases - that make some English teachers cringe. "For example, you will get a higher score just by [writing] "in conclusion,'" he says.
  14. May 2018
  15. Apr 2018
    1. The takeaway from the article: Choose document-oriented database only when the data can be treated as a self-contained document

    1. The baseline here is detecting technical issues (counting errors, service availability, etc) but it's also worth monitoring business issues (such as detecting a drop in orders).
  16. Mar 2018
    1. Operating System Concepts

    2. Scheduling of this kind is a fundamental operating-system function.Almost all computer resources are scheduled before use

      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

    Tags

    Annotators

  17. Feb 2018
  18. Dec 2017
  19. Nov 2017
    1. an environment unlike anything they will encounter outside of school

      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.

    2. mandate the use of "learning management systems."

      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”.

    3. institutional demands for enterprise services such as e-mail, student information systems, and the branded website become mission-critical

      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.).

    4. Five Arguments against the Learning Management System
    1. “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! [Instructure] just didn’t.”
    2. two quarters of pilot courses on Instructure’s Canvas platform
    3. To the surprise of those behind the initiative, about two-thirds of faculty members said they were satisfied with the Blackboard system, deployed on campus in 1999.
    1. the terrible, horrible, no-good university administrators are trying to build a panopticon in which they can oppress the faculty
    2. If you recall your LMS patent infringement history, then you'll remember that roles and permissions were exactly the thing that Blackboard sued D2L over.
    3. (At the time, Stephen Downes mocked me for thinking that this was an important aspect of LMS design to consider.)

      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.

    4. Do you know what the feature set was that had faculty from Albany to Anaheim falling to their knees, tears of joy streaming down their faces, and proclaiming with cracking, emotion-laden voices, "Finally, an LMS company that understands me!"?

      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.

    5. Why, they would build an LMS. They did build an LMS. Blackboard started as a system designed by a professor and a TA at Cornell University. Desire2Learn (a.k.a. Brightspace) was designed by a student at the University of Waterloo. Moodle was the project of a graduate student at Curtin University in Australia. Sakai was built by a consortium of universities. WebCT was started at the University of British Columbia. ANGEL at Indiana University.
    6. Let's imagine a world in which universities, not vendors, designed and built our online learning environments.
    7. the backbone of for a distributed network of personal learning environments
    8. the tools shouldn’t dictate the choice