1,366 Matching Annotations
  1. Jul 2020
  2. Jun 2020
    1. Heat and work have signs (positive or negative), and the sign of each depends on whether the system we are considering is gaining or losing energy. In this class, if a process makes the system gain energy, qqq and/or www are positive; if the process makes the system lose energy, qqq and/or www are negative. We can put this information into four formal statements: If heat flows into a system, qqq is positive. If heat flows out of a system, qqq is negative If the surroundings do work on the system, www is positive. If the system does work, www is negative.

      Heat and work have signs (positive or negative), and the sign of each depends on whether the system we are considering is gaining or losing energy. In this class, if a process makes the system gain energy, q and/or w are positive; if the process makes the system lose energy, q and/or w are negative. We can put this information into four formal statements:

      • If heat flows into a system, q is positive.
      • If heat flows out of a system, q is negative
      • If the surroundings do work on the system, w is positive.
      • If the system does work, w is negative.
    1. Cumulatively, these studies suggest that the potential influence of informal mentors on mobility may be most pronounced for those youth who are facing a disadvantage of some kind (family structure, income, etc.) and/or are a racial ethnic minority. Concerning the focus of the present study, this literature would suggest that informal mentoring may be more strongly associated with upward mobility for low-income youth than for middle-or higher-income youth for whom informal mentoring is

      Suggests a stronger influence on disadvantaged or racial ethnic minority youth

    2. Persistent immobility also disproves the idea of the U.S. being a land of equal opportunity. Since the term "the American Dream" was first coined in 1931, it has become a persistent cultural ethos, a wish list of sorts, with a consistent main tenet being the idea that each generation can achieve more than their parents (Samuel, 2012). Yet we know this tenet of the American Dream is no longer true: the chances that a child earnsmore than their parents has decreased in the past 40 years, especially for low-income families

      chances of earning more than parents has decreased in past 40yrs for low-income families

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  3. May 2020
    1. Insight through making suggests that you’ll need to make simultaneous progress in theory-space and system-space to spot the new implications in their conjoined space. Effective system design requires insights drawn from serious contexts of use: you must constantly instantiate new theoretical ideas in new systems, then observe their impact in some serious context of use.

      Very powerful way of wording the implications of Insights through making and the need for serious contexts of use.

      You need to advance in theory-space as well as in system-space to spot the implications for their conjoined space.

      Pragmatically, you must constantly instantiate new theoretical ideas in the system, then observe the effects in some serious context of use.

  4. Apr 2020
    1. Programming languages and operating systems Stanford CoreNLP is written in Java; recent releases require Java 1.8+. You need to have Java installed to run CoreNLP. However, you can interact with CoreNLP via the command-line or its web service; many people use CoreNLP while writing their own code in Javascript, Python, or some other language. You can use Stanford CoreNLP from the command-line, via its original Java programmatic API, via the object-oriented simple API, via third party APIs for most major modern programming languages, or via a web service. It works on Linux, macOS, and Windows. License The full Stanford CoreNLP is licensed under the GNU General Public License v3 or later. More precisely, all the Stanford NLP code is GPL v2+, but CoreNLP uses some Apache-licensed libraries, and so our understanding is that the the composite is correctly licensed as v3+.
    1. Kerckhoffs’ Principle states that you should assume that your adversary knows as much about the system you use as you do. This is why – despite what I may have said on April Fools Day last year – security experts are skeptical of security systems that hide the details of how they operate. They are particularly skeptical of systems that derive their security from keeping the details of how they work secret. I could go on at great length about why openness about the system improves security. Indeed, my first draft of this article did go on at great length.
    1. School for many people is a place to get fed, a place to feel safe, a place to get encouraged. It’s a place to be around people who share your desire to learn. Now they are cut off from that, and some of that can’t be duplicated easily online.

      Yes, this is a problem. However... Schools weren't designed to be a safegaurd against poor parenting, but they're treated that way, as if they're a place to escape the idiots they live with.

      Schools shouldn't tolerate this. Instead, they should intervene. They should bring in a third party, someone/an organization specifically designed to help kids who come from broken homes, to help heal how they live when school's not in session. Any measure less than this signals, to me, a school system that's not paying attention to their student's emotional needs, which are, I believe, key to ensuring the child thrives throughout their school years.

    1. Relational databases are designed around joins, and optimized to do them well. Unless you have a good reason not to use a normalized design, use a normalised design. jsonb and things like hstore are good for when you can't use a normalized data model, such as when the data model changes rapidly and is user defined. If you can model it relationally, model it relationally. If you can't, consider json etc.
    2. Joins are not expensive. Who said it to you? As basically the whole concept of relational databases revolve around joins (from a practical point of view), these product are very good at joining. The normal way of thinking is starting with properly normalized structures and going into fancy denormalizations and similar stuff when the performance really needs it on the reading side. JSON(B) and hstore (and EAV) are good for data with unknown structure.
  5. Mar 2020
  6. Feb 2020
    1. I use this to keep information about processes that were running at any time during last five days. These are 1-min snapshots so something might get lost but I think it is good enough for me. I want to have some data available when I discover there was a peak in resource usage (I use munin for that). I haven't found a better way to keep track of past processes (tried psacct).
  7. Jan 2020
    1. with the Flickr architecture each shard would need to be updated or searched (or a search service would need to be created to collate that metadata—which is in fact what they do).

      Search service for collating data spread out in shards.

    2. Deconstructing a system into a set of complementary services decouples the operation of those pieces from one another. This abstraction helps establish clear relationships between the service, its underlying environment, and the consumers of that service. Creating these clear delineations can help isolate problems, but also allows each piece to scale independently of one another. This sort of service-oriented design for systems is very similar to object-oriented design for programming.

      Service-Oriented-Architecture (SOA)

  8. Dec 2019
    1. it is certainly more creditable to cultivate the earth for the sustenance of man, than to be the confidant, and sometimes the accomplice, of his vices; which is v1_117the profession of a lawyer

      (Deleted in 1831). Percy Shelley had suffered negative rulings by the English court system and Mary seems to share his moral judgment on the legal profession. This skepticism will soon be reinforced in the novel by the court's harsh treatment of Justine Moritz.

    1. Now using sudo to work around the root account is not only pointless, it's also dangerous: at first glance rsyncuser looks like an ordinary unprivileged account. But as I've already explained, it would be very easy for an attacker to gain full root access if he had already gained rsyncuser access. So essentially, you now have an additional root account that doesn't look like a root account at all, which is not a good thing.
    1. It is possible to do a successful file system migration by using rsync as described in this article and updating the fstab and bootloader as described in Migrate installation to new hardware. This essentially provides a way to convert any root file system to another one.
    2. rsync provides a way to do a copy of all data in a file system while preserving as much information as possible, including the file system metadata. It is a procedure of data cloning on a file system level where source and destination file systems do not need to be of the same type. It can be used for backing up, file system migration or data recovery.
    1. CloneZilla works perfectly. It produces small image files, has integrity check and works fast. If you want to use third device as image repository you should choose device-image when creating image of the first disk and then image-device when you restore it to second disk. If you want to use only two disks - you should use device-device mode. Optionally you may want generate new UUIDs, SSH-key (if SSH server installed), and change hostname.
  9. Nov 2019
    1. Twitter offers two distinct benefits to engaging learners. First of all, it allows learners to respond to classroom discussions in a way that feels right for them, offering shy or introverted students a chance to participate in the class discussion without having to speak in a public forum. Secondly, it allows students to continue the conversation after class is completed, posting relevant links to course material, and reaching out to you (the educator) with additional thoughts or questions.

      The article explains how social media, student learning through digital experience, and Learning Management Systems can be beneficial to the learner/student. Article Rating: 3/5

  10. Oct 2019
  11. Sep 2019
    1. While these are excellent frameworks for evaluating instructor-level change, our field is pivoting from an emphasis on 1:1 work or workshops to longer-term, systemic change initiatives

      Shift from one-off sessions to more sustained faculty development efforts

  12. Aug 2019
    1. tudies show that costs would be between $25 Trillion and $32 Trillion over 10 years. YouGov/Economist Poll, April 2-4, 2017      $32 Trillion sounds like quite a high number.

      The government seems that they are afraid of doing it because they might go in debt.

  13. May 2019
    1. Jillian Maynard, from the University of Hartford, and Jeremy Anderson, from Bay Path University, led a session on developing a strategic plan for OER on your home campus. I went to that one having previously selected a different one -- born to be wild, that’s me -- because upon closer reading, I made the connection to a law that NJ recently passed requiring public colleges to develop plans for OER (or “inclusive access”)

      This seems very much like what I was talking about yesterday with MinnState folks and reported on my blog.

    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.

  14. Apr 2019
  15. Mar 2019
  16. arxiv.org arxiv.org
    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. 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.

    1. 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. 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。

    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。

    1. 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。

    2. 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. 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. 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.