如果您想了解为什么我在2台不同的计算机上收到“AppEngine:runisonlyavailableforappengine-web.xmlbasedprojects”错误?的知识,那么本篇文章将是
如果您想了解为什么我在 2 台不同的计算机上收到“AppEngine:run is only available for appengine-web.xml based projects”错误?的知识,那么本篇文章将是您的不二之选。同时我们将深入剖析(2018, IJCAI, Yiping Song et al.)An Ensemble of Retrieval-Based and Generation-Based Human-Comput...、0-based or 1-based:VB6各种index小结(持续更新中)、1 Project Overview 1.1 Project Description Based on tensorflow and Flask, a web-based image sea...、5 Successful Business Models for Web-Based Open-Source Projects的各个方面,并给出实际的案例分析,希望能帮助到您!
本文目录一览:- 为什么我在 2 台不同的计算机上收到“AppEngine:run is only available for appengine-web.xml based projects”错误?
- (2018, IJCAI, Yiping Song et al.)An Ensemble of Retrieval-Based and Generation-Based Human-Comput...
- 0-based or 1-based:VB6各种index小结(持续更新中)
- 1 Project Overview 1.1 Project Description Based on tensorflow and Flask, a web-based image sea...
- 5 Successful Business Models for Web-Based Open-Source Projects
为什么我在 2 台不同的计算机上收到“AppEngine:run is only available for appengine-web.xml based projects”错误?
如何解决为什么我在 2 台不同的计算机上收到“AppEngine:run is only available for appengine-web.xml based projects”错误?
错误:
[ERROR] Failed to execute goal com.google.cloud.tools:appengine-maven-plugin:2.4.0:run (default-cli) on project TEST: appengine:run is only available for appengine-web.xml based projects,the service defined in: C:\\Users\\Sample\\Documents\\BE_Test\\target\\TEST-1.0-SNAPSHOT cannot be run by the dev appserver.
我目前在计算机 A (mac) 上使用“mvn clean appengine:run”运行此代码就好了。 但是在计算机 B(也是 mac)上我得到了错误,我在计算机 C(Windows)上得到了完全相同的错误。 我错过了什么?
(2018, IJCAI, Yiping Song et al.)An Ensemble of Retrieval-Based and Generation-Based Human-Comput...
论文提出了 :一种 检索式对话 与 生成式对话 结合的方法,主要用于单轮对话。
动机
把 检索式对话 与 生成式对话 优点结合起来。
* 检索式对话的优点是,只要语料库足够大,就能找到信息量丰富、对用户有帮助的回答,缺点是,它会受限于语料库的丰富程度,而且找到的回答是固定,不一定是一个正常的、合适的回答。
* 生成式对话的优点是,回答是生成的,比较灵活,缺点是,它容易生成一些通用的、没有什么信息的、对用户帮助不大的回答。 * 作者认为,检索式对话 与 生成式对话 可以相辅助,得到 灵活且对用户有帮助的回答
基本思路
论文方法包括3个模块:检索模块Retrieval Process、生成模块Multi-Seq2Seq Generator、重排序模块Re-ranker,算法步骤是: 1. 检索模块Retrieval Process:通过 现成的检索系统lucene 检索出 $k$个问答对,这$k$个问答对的问题 与 用户问题最相近 2. 生成模块Multi-Seq2Seq Generator:$k$ 个问答对的回答 与 用户问题 输入到一个end2end生成模型Multi-Seq2Seq Generator,生成回答 3. 重排序模块Re-ranker:对回答进行评分,选出最佳的回答。参加评分的回答包括 Retrieval Process中$k$个问答对的回答,和 Multi-Seq2Seq Generator生成的回答
生成模块Multi-Seq2Seq Generator
Multi-Seq2Seq Generator采用多源seq2seq的方式,并在其中加入两种attention机制、copy mechanism机制
- 多源seq2seq:用多个encoder,分别对 用户问题 与 $k$个问答对的回答 进行编码
- 两种attention机制分别是 句子级别的注意力sentence-level attention、字级别的注意力character-level attention
- 句子级别的注意力sentence-level attention:把 多个encoder 最后一个时间步的输出 进行线性变换,得到的向量作为 decoder的初始state
- 字级别的注意力character-level attention:对 多个encoder 每个时间步的输出 进行加权求和,得到的向量作为 decoder每个时间步的context
- copy mechanism机制:对 $k$个问答对的回答 进行编码的encoder 每个时间步的输出 进行 copy mechanism
重排序模块Re-ranker
对 回答 与 用户问题 进行评分,判断 回答 与 用户问题 的 合适程度。 主要思路是,对 回答 与 用户问题 进行特征工程,然后通过 GBDT进行 2分类,GBDT输出的概率越高,则表示 回答 越适合 用户问题 。
related work
检索式对话
目前大部分商用的对话系统 是检索式对话
- (2000, AAAI, Isbell et al.)Cobotin LambdaMOO: A social statistics agent
- 用信息检索的方法,从语料库中检索出 相关的 用户问题 和 对应的回答
- (2014, arXiv:1408.6988, Ji et al.)An information retrieval approach to short text conversation (引用量有41)
- 在query-reply matching中 使用 手工特征 和 神经网络
- (2016, SIGIR, Yan et al.)Learning to respond with deep neural networks for retrieval-based human-computer conversation system
- 在query-reply matching中 使用 手工特征 和 神经网络
- (2016, IJCAI, Li et al.)StalemateBreaker: A proactive content-introducing approach to automatic human-computer conversation
- 提出一个random walk-style algorithm,对候选回答进行排序
- 模型融合了其他知识,主要是从知识库中检索得到的、与context实体相关的知识
生成式对话
生成式对话越来越受到 学术界的重视
- (2011, EMNLP, Ritter et al.)Data-driven response generation in social media
- 将 query-reply transformation问题 视为 基于短语的机器翻译问题
- (2014, NIPS, Sutskeveret al.)Sequence to sequence learning with neural networks
- 首次提出 seq2seq模型的论文(发表单位:谷歌)
- (2015, arXiv:1506.05869, Vinyals and Le)A neural conversational model(引用量有292)
- 同样地,在对话系统中 使用了RNN
- (2015, NAACL-HLT, Sordoniet al.)A neural network approach to context-sensitive generation of conversational responses
- 同样地,在对话系统中 使用了RNN
- (2015, ACL, Shanget al.)Neural responding machine for short-text conversation
- 同样地,在对话系统中 使用了RNN
- (2016, AAAI, Serban et al.)Building end-to-end dialogue systems using generative hierarchical neural network models.
- 同样地,在对话系统中 使用了RNN
- (2016, NAACL-ACL, Zoph and Knight)Multi-source neural translation
- 使用 2个encoder 和 1个decoder,将 2种语言 翻译为 一种语言
普遍知道,RNN容易生成短小的、无意义的句子,所以有研究者在这个问题上进行探究
- (2016, NAACL, Li et al.)A diversity-promoting objective function for neural conversation models.
- 提出 一种基于互信息的优化目标,而不是用 传统的最大似然
- (2016, COLING, Mou et al.)Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation.
- 将 互信息最大的词 或 主题信息 等额外的内容 引入 对话生成中
- (2016, arXiv:1606.08340, Xing et al.) Topic augmented neural response generation with a joint attention mechanism.(引用量有34)
- 将 互信息最大的词 或 主题信息 等额外的内容 引入 对话生成中
- (2017, AAAI, Serban et al.)A hierarchical latent variable encoder-decoder model for generating dialogues.
- 采用变分编码器 捕获 用户问题的信息分布,在生成回答时 从这个信息分布中采样出一个向量,用于生成
- (2017, ACL, He et al.)Generating natural answers by incorporating copying and retrieving mechanisms in sequence-to-sequence learning.
- 在问答任务中使用 知识库
- (2017, ACL, Libovicky and Helcl)Attention strategies for multi-source sequence-to-sequence learning.
- 探索多源输入生成中的不同注意力策略
0-based or 1-based:VB6各种index小结(持续更新中)
版权声明:可以任意转载,转载时请务必以超链接形式标明如下文章原始出处和作者信息及本声明
出处:http://blog.csdn.net/slowgrace/archive/2009/04/23/4102056.aspx
总是被各种index是0-based还是1-based整得头晕,每次都得费不少时间去查,索性做个小结在这里。学到新的就添进来,遇到旧的就到这里来查:)
0-based
(1)数组:数组缺省是0-based。UBOUND函数的返回值也是0-based。
(2)TabIndex : 0-based。
(3)comboBox和ListBox:其下拉列表的Item的index是0-based,当它们具有多个Columns的时候,Columns的index也是基于0的。
(4)MSHFlexGrid:它的Col 和 Row 属性是从 0 开始计数的
(5)Forms 和 Controls 集合。Visual Basic 中旧的集合大多是基于 0 的。
1-based
(2)TabStrip:它的tab的index是1-based。
()集合collection的索引
1 Project Overview 1.1 Project Description Based on tensorflow and Flask, a web-based image sea...
1.项目介绍
在本项目中,我们实现了对人脸图片数据的三种处理:人脸互换(face swap)、人脸融合(face morph)以及基于特征向量的人脸处理(eigen face)。
1.1 人脸互换(face swap)
人脸互换部分主要实现的功能是,给定任意两张人脸图片,通过一系列操作,使两个人的脸部交换,这部分需要的问题有:
不同的人的脸部结构千差万别,同一个人也会因为角度、面部表情的不同而导致差别,即如何实现不同图片的人脸对齐
不同人脸的肤色、光照不同,即不同图片的面部亮度不同,在换脸后如何与整体亮度统一
不同人脸的纹理不同,比如老人的皱纹等,如何实现换脸后纹理的统一
参考文档和完整的文档和源码下载地址:
https://www.write-bug.com/article/2518.html
5 Successful Business Models for Web-Based Open-Source Projects
https://handsontable.com/blog/articles/2016/3/5-successful-business-models-for-web-based-open-source-projects
Even if you don’t imagine your open-source project becoming next year’s hottest unicorn,all but the smallest of open-source projects are always at risk of turning into abandonware. As your project grows,so too will the demands it places on your time and finances. At this point you will have to make a choice: either abandon it completely and hope that someone with more resources successfully forks it or begin exploring business models that Could provide the resources you need.
In this post I will explore five of the most common business models for web-based OSS projects,without comparing them to one another. But there are certain details you need to consider before deciding on a business model; so although I won’t debate which model is the best,I will highlight some of the points you need to look at.
Defining Success
The title for this post refers to Successful Business Models,but any time you use the term successful you need to quantify how it was measured: what metric did I use to determine that these were successful business models? Did I look at companies that used these business models and decide they were successful based on their revenue,number of employees,number of paying customers,or how many of these customers were Fortune 500 companies? What then of the number of times the project had been forked by other companies that ended up making money off it? Isn’t the number of contributors you have a measure of success too?
Defining success is problematic because although in simple terms it is the achievement of an aim or goal,what that aim or goal is is unique to each person or organization. First,as a developer,you must decide what success means to you.
In 2013 the founder of Automattic,Matt Mullenweg, wrote:
Automattic is healthy,generating cash,and already growing as fast as it can so there’s no need for the company to raise money directly – we’re not capital constrained.
and even though a year later Matt admitted to being wrong (after raising $160 million from investors),Automattic is still somewhat of a poster child for open-source success stories. Matt appears to be happy with what has been achieved so far,and they are still focused on open-source projects,with commercial ventures designed to sustain the company rather than generating massive revenue.
The Business Models
Peter Groen and Roger A. Maduro compiled a list of 16 open-source business models in 2012,and although the number has probably grown since,the key strategy to follow is keeping it familiar. Don’t be afraid to mix and match elements from the different models,but be wary of trying to create something brand new. Your potential clients are more likely to convert if they can clearly understand your model and/or plans,and it seems somewhat similar to what other companies offer.
That said,let’s take a look at some successful business models,where success is determined by popularity,and common use.
1. The Professional Services Model
This model has the software still completely open-source,and freely available to all customers,but services such as consulting,installation,support (basic and/or priority),and training are only available at a fee. Consulting services usually cover the management and implementation of the software within specific industries. Red Hat is a prime example of this model being used successfully.
2. The Software-as-a-Service (SaaS) Model
This model sees your software provided as a centrally hosted service that is only accessible via a paid subscription. Subscriptions are usually user,transaction volume,or time based. SugarCRMs commercial offering followed the SaaS model,and it was only recently that they started offering on-site deployments. Although Heroku follows the as-a-Service model,they offer a platform solution,rather than a software solution.
3. The Open Core Model
This model is similar to the Professional Services model in that the core software remains open-source,and continues to be developed. However,special features and modules that extend or enhance the core product are only available as commercial software,for a fee. Talend uses the open core model to sell value added features for its core modules.
4. The Proprietary Software Model
The terms of the GPL state that the source code of any program that uses GPLed parts needs to be made available under the same license terms. This can be problematic for any company that requires your OSS project to integrate with their own proprietary software. A way around this is to develop a closed-source version that is similar to your OSS version (and does not use any GPL libraries or source code),which is then licensed (for a fee) to enterprise clients. This differs from the next business model in that the open-source version continues to be developed and updated.
5. The Drug Pusher Model
A popular myth persists that drug dealers create a market by giving their product to customers for free,and once the customers are hooked,switching to charging exorbitant prices. Without wanting to speculate about their motivation,there are developers who follow a similar model,and it isn’t a myth. The project starts out as open-source,with regular development,and everything else needed to build traction. But then,once they have established a niche,the OSS project is completely abandoned,and a version similar to the OSS project is Now only available commercially. Tough luck for the open-source community,and anyone else who can’t afford the commercial product.
After the suspension of development on the Community Edition,it would be tempting to slot SugarCRM under the Drug Pusher model,but it is unlikely that this was the founders intention way back in 2004. They simply want to focus more heavily on growing their big enterprise client-base,and as you will eventually discover,each business model makes it easier to achieve specific goals.
Next Steps
Once you have set your goals,you still need to follow a few additional steps before compiling a list of possible business models:
- Define your customers. This should be as detailed as you can manage,and should address:
- what problems they have that your product can solve,
- what they would be willing to pay for your product,and
- what type of business model is most likely to appeal to them? It should be remembered that some businesses are reluctant to use cloud-based solutions,or have a specific need for on-site deployment of solutions.
- Define your competitors. This means not only kNowing who they are,but also what business model they follow,how much they charge,and what terms and conditions they apply to their products.
Only once you have done all of the above can you compile a list of most suited business models,which you can then discuss in detail with your team,community or advisors.
Conclusion
Choosing the right business model is no simple task,and kNowing that your customers won’t tolerate a big change to your model doesn’t make it easier. No model will be perfect from the beginning,but although small amendments and adjustments are fine,shifting to a completely different model later on will put your entire client-base at risk.
Finally,remember to keep the entire process familiar and easy,for your customers,not you. You can look at it as a business model,but they should see it as a simple solution to their problems.
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