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sources/tech/20180914 A day in the life of a log message.md
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A day in the life of a log message
======
Navigating a modern distributed system from the perspective of a log message.
![](https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/plane_travel_world_international.png?itok=jG3sYPty)
Chaotic systems tend to be unpredictable. This is especially evident when architecting something as complex as a distributed system. Left unchecked, this unpredictability can waste boundless amounts of time. This is why every single component of a distributed system, no matter how small, must be designed to fit together in a streamlined way.
[Kubernetes][1] provides a promising model for abstracting compute resources—but even it must be reconciled with other distributed platforms such as [Apache Kafka][2] to ensure reliable data delivery. If someone were to integrate these two platforms, how would it work? Furthermore, if you were to trace something as simple as a log message through such a system, what would it look like? This article will focus on how a log message from an application running inside [OKD][3], the Origin Community Distribution of Kubernetes that powers Red Hat OpenShift, gets to a data warehouse through Kafka.
### OKD-defined environment
Such a journey begins in OKD, since the container platform completely overlays the hardware it abstracts. This means that the log message waits to be written to **stdout** or **stderr** streams by an application residing in a container. From there, the log message is redirected onto the node's filesystem by a container engine such as [CRI-O][4].
![](https://opensource.com/sites/default/files/uploads/logmessagepathway.png)
ithin OpenShift, one or more containers are encapsulated within virtual compute nodes known as pods. In fact, all applications running within OKD are abstracted as pods. This allows the applications to be manipulated in a uniform way. This also greatly simplifies communication between distributed components, since pods are systematically addressable through IP addresses and [load-balanced services][5] . So when the log message is taken from the node's filesystem by a log-collector application, it can easily be delivered to another pod running within OpenShift.
### Two peas in a pod
To ensure ubiquitous dispersal of the log message throughout the distributed system, the log collector needs to deliver the log message into a Kafka cluster data hub running within OpenShift. Through Kafka, the log message can be delivered to the consuming applications in a reliable and fault-tolerant way with low latency. However, in order to reap the benefits of Kafka within an OKD-defined environment, Kafka needs to be fully integrated into OKD.
Running a [Strimzi operator][6] will instantiate all Kafka components as pods and integrate them to run within an OKD environment. This includes Kafka brokers for queuing log messages, Kafka connectors for reading and writing from Kafka brokers, and Zookeeper nodes for managing the Kafka cluster state. Strimzi can also instantiate the log collector to double as a Kafka connector, allowing the log collector to feed the log messages directly into a Kafka broker pod running within OKD.
### Kafka inside OKD
When the log-collector pod delivers the log message to a Kafka broker, the collector writes to a single broker partition, appending the message to the end of the partition. One of the advantages of using Kafka is that it decouples the log collector from the log's final destination. Thanks to the decoupling, the log collector doesn't care whether the logs end up in [Elasticsearch][7], Hadoop, Amazon S3, or all of them at the same time. Kafka is well-connected to all infrastructure, so the Kafka connectors can take the log message wherever it needs to go.
Once written to a Kafka broker's partition, the log message is replicated across the broker partitions within the Kafka cluster. This is a very powerful concept on its own; combined with the self-healing features of the platform, it creates a very resilient distributed system. For example, when a node becomes unavailable, the applications running on the node are almost instantaneously spawned on healthy node(s). So even if a node with the Kafka broker is lost or damaged, the log message is guaranteed to survive as many deaths as it was replicated and a new Kafka broker will quickly take the original's place.
### Off to storage
After it is committed to a Kafka topic, the log message waits to be consumed by a Kafka connector sink, which relays the log message to either an analytics engine or logging warehouse. Upon delivery to its final destination, the log message could be studied for anomaly detection, queried for immediate root-cause analysis, or used for other purposes. Either way, the log message is delivered by Kafka to its destination in a safe and reliable manner.
OKD and Kafka are powerful distributed platforms that are evolving rapidly. It is vital to create systems that can abstract the complicated nature of distributed computing without compromising performance. After all, how can we boast of systemwide efficiency if we cannot simplify the journey of a single log message?
--------------------------------------------------------------------------------
via: https://opensource.com/article/18/9/life-log-message
作者：[Josef Karásek][a]
选题：[lujun9972](https://github.com/lujun9972)
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/jkarasek
[1]: https://kubernetes.io/
[2]: https://kafka.apache.org/
[3]: https://www.okd.io/
[4]: http://cri-o.io/
[5]: https://kubernetes.io/docs/concepts/services-networking/service/
[6]: http://strimzi.io/
[7]: https://www.elastic.co/

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sources/tech/20190219 5 Good Open Source Speech Recognition-Speech-to-Text Systems.md
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[#]: collector: (lujun9972)
[#]: translator: (luuming)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (5 Good Open Source Speech Recognition/Speech-to-Text Systems)
[#]: via: (https://fosspost.org/lists/open-source-speech-recognition-speech-to-text)
[#]: author: (Simon James https://fosspost.org/author/simonjames)
5 Good Open Source Speech Recognition/Speech-to-Text Systems
======
![](https://i0.wp.com/fosspost.org/wp-content/uploads/2019/02/open-source-speech-recognition-speech-to-text.png?resize=1237%2C527&ssl=1)
A speech-to-text (STT) system is as its name implies; A way of transforming the spoken words via sound into textual files that can be used later for any purpose.
Speech-to-text technology is extremely useful. It can be used for a lot of applications such as a automation of transcription, writing books/texts using your own sound only, enabling complicated analyses on information using the generated textual files and a lot of other things.
In the past, the speech-to-text technology was dominated by proprietary software and libraries; Open source alternatives didn’t exist or existed with extreme limitations and no community around. This is changing, today there are a lot of open source speech-to-text tools and libraries that you can use right now.
Here we list 5 of them.
### Open Source Speech Recognition Libraries
#### Project DeepSpeech
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 15 open source speech recognition][1]
This project is made by Mozilla; The organization behind the Firefox browser. It’s a 100% free and open source speech-to-text library that also implies the machine learning technology using TensorFlow framework to fulfill its mission.
In other words, you can use it to build training models yourself to enhance the underlying speech-to-text technology and get better results, or even to bring it to other languages if you want. You can also easily integrate it to your other machine learning projects that you are having on TensorFlow. Sadly it sounds like the project is currently only supporting English by default.
It’s also available in many languages such as Python (3.6); Which allows you to have it working in seconds:
```
pip3 install deepspeech
deepspeech --model models/output_graph.pbmm --alphabet models/alphabet.txt --lm models/lm.binary --trie models/trie --audio my_audio_file.wav
```
You can also install it using npm:
```
npm install deepspeech
```
For more information, refer to the [project’s homepage][2].
#### Kaldi
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 17 open source speech recognition][3]
Kaldi is an open source speech recognition software written in C++, and is released under the Apache public license. It works on Windows, macOS and Linux. Its development started back in 2009.
Kaldi’s main features over some other speech recognition software is that it’s extendable and modular; The community is providing tons of 3rd-party modules that you can use for your tasks. Kaldi also supports deep neural networks, and offers an [excellent documentation on its website][4].
While the code is mainly written in C++, it’s “wrapped” by Bash and Python scripts. So if you are looking just for the basic usage of converting speech to text, then you’ll find it easy to accomplish that via either Python or Bash.
[Project’s homepage][5].
#### Julius
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 19 open source speech recognition][6]
Probably one of the oldest speech recognition software ever; It’s development started in 1991 at the University of Kyoto, and then its ownership was transferred to an independent project team in 2005.
Julius main features include its ability to perform real-time STT processes, low memory usage (Less than 64MB for 20000 words), ability to produce N-best/Word-graph output, ability to work as a server unit and a lot more. This software was mainly built for academic and research purposes. It is written in C, and works on Linux, Windows, macOS and even Android (on smartphones).
Currently it supports both English and Japanese languages only. The software is probably availbale to install easily in your Linux distribution’s repository; Just search for julius package in your package manager. The latest version was [released][7] around one and half months ago.
[Project’s homepage][8].
#### Wav2Letter++
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 21 open source speech recognition][9]
If you are looking for something modern, then this one is for you. Wav2Letter++ is an open source speech recognition software that was released by Facebook’s AI Research Team just 2 months ago. The code is released under the BSD license.
Facebook is [describing][10] its library as “the fastest state-of-the-art speech recognition system available”. The concepts on which this tool is built makes it optimized for performance by default; Facebook’s also-new machine learning library [FlashLight][11] is used as the underlying core of Wav2Letter++.
Wav2Letter++ needs you first to build a training model for the language you desire by yourself in order to train the algorithms on it. No pre-built support of any language (including English) is available; It’s just a machine-learning-driven tool to convert speech to text. It was written in C++, hence the name (Wav2Letter++).
[Project’s homepage][12].
#### DeepSpeech2
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 23 open source speech recognition][13]
Researchers at the Chinese giant Baidu are also working on their own speech-to-text engine, called DeepSpeech2. It’s an end-to-end open source engine that uses the “PaddlePaddle” deep learning framework for converting both English & Mandarin Chinese languages speeches into text. The code is released under BSD license.
The engine can be trained on any model and for any language you desire. The models are not released with the code; You’ll have to build them yourself, just like the other software. DeepSpeech2’s source code is written in Python; So it should be easy for you to get familiar with it if that’s the language you use.
[Project’s homepage][14].
### Conclusion
The speech recognition category is still mainly dominated by proprietary software giants like Google and IBM (which do provide their own closed-source commercial services for this), but the open source alternatives are promising. Those 5 open source speech recognition engines should get you going in building your application, all of them are still under heavy development by time. In few years, we expect open source to become the norm for those technologies just like in the other industries.
If you have any other recommendations for this list, or comments in general, we’d love to hear them below!
**
Shares
--------------------------------------------------------------------------------
via: https://fosspost.org/lists/open-source-speech-recognition-speech-to-text
作者：[Simon James][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://fosspost.org/author/simonjames
[b]: https://github.com/lujun9972
[1]: https://i0.wp.com/fosspost.org/wp-content/uploads/2019/02/hero_speech-machine-learning2.png?resize=820%2C280&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 16 open source speech recognition)
[2]: https://github.com/mozilla/DeepSpeech
[3]: https://i0.wp.com/fosspost.org/wp-content/uploads/2019/02/Screenshot-at-2019-02-19-1134.png?resize=591%2C138&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 18 open source speech recognition)
[4]: http://kaldi-asr.org/doc/index.html
[5]: http://kaldi-asr.org
[6]: https://i2.wp.com/fosspost.org/wp-content/uploads/2019/02/mic_web.png?resize=385%2C100&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 20 open source speech recognition)
[7]: https://github.com/julius-speech/julius/releases
[8]: https://github.com/julius-speech/julius
[9]: https://i2.wp.com/fosspost.org/wp-content/uploads/2019/02/fully_convolutional_ASR.png?resize=850%2C177&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 22 open source speech recognition)
[10]: https://code.fb.com/ai-research/wav2letter/
[11]: https://github.com/facebookresearch/flashlight
[12]: https://github.com/facebookresearch/wav2letter
[13]: https://i2.wp.com/fosspost.org/wp-content/uploads/2019/02/ds2.png?resize=850%2C313&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 24 open source speech recognition)
[14]: https://github.com/PaddlePaddle/DeepSpeech

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[#]: collector: (lujun9972)
[#]: translator: ( )
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Blockchain 2.0 – Ongoing Projects (The State Of Smart Contracts Now) [Part 6])
[#]: via: (https://www.ostechnix.com/blockchain-2-0-ongoing-projects-the-state-of-smart-contracts-now/)
[#]: author: (editor https://www.ostechnix.com/author/editor/)
Blockchain 2.0 – Ongoing Projects (The State Of Smart Contracts Now) [Part 6]
======
![The State Of Smart Contracts Now][1]
Continuing from our [**earlier post on smart contracts**][2], this post aims to discuss the state of Smart contracts, highlight some current projects and companies currently undertaking developments in the area. Smart contracts as discussed in the previous article of the series are programs that exist and execute themselves on a blockchain network. We explored how smart contracts work and why they are superior to traditional digital platforms. Companies described here operate in a wide variety of industries however most of them deal with identity management systems, financial services, crowd funding systems etc., as these are the areas thought to be most suitable for switching to blockchain based data base systems.
### Open platforms
Platforms such as **Counterparty** [1] and **Solidity(Ethereum)** are fully public building blocks for developers to create their own smart contracts. Wide spread developer participation in such projects have allowed these to become de facto standards for developing smart contracts, designing your own cryptocurrency token systems, and creating protocols for the blockchains to function. Many commendable projects have derived from them. **Quorum** , by JP Morgan, derived from Ethereum, is an example. **Ripple** is another example for the same.
### Managing financial transactions
Transferring cryptocurrencies over the internet is touted to be the norm in the coming years. The shortfalls with the same are:
* Identities and wallet addresses are anonymous. The payer doesn’t have any first recourse if the receiver does not honor the transaction.
* Erroneous transactions if any will cannot be traced.
* Cryptographically generated hash keys are difficult to work with for humans and human errors are a prime concern.
Having someone else take in the transaction momentarily and settle it with the receiver after due diligence is preferred in this case.
**EscrowMyEther** [3] and **PAYFAIR** [4] are two such escrow platforms. Basically, the escrow company takes the agreed upon amount and sends a token to the receiver. Once the receiver delivers what the payer wants via the same escrow platform, both confirm and the final payment is released. These are used extensively by freelancers and hobbyist collectors online.
### Financial services
Developments in micro-financing and micro-insurance projects will improve the banking infrastructure for much of the world’s poor and unbanked. Involving the poorer “unbanked” sections of the society is estimated to increase revenues for banks and institutions involved by **$380 billion** [5]. This amount supersedes the savings in operational expenses that can be expected by switching to blockchain DLT for banks.
**BankQu Inc.** based in Midwest United States goes by the slogan “Dignity through identity”. Their platform allows for individuals to setup their own digital identity record where all their transactions will be vetted and processed real time on the blockchain. Overtime the underlying code records and builds a unique online identity for its users allowing for ultra-quick transactions and settlements. The BankQu case studies exploring more about how they’re helping individuals and companies this way is available [here][3].
**Stratumn** is helping insurance companies offer better insurance services by automating tasks which were earlier micromanaged by humans. By automation, end to end traceability, and efficient data privacy methods they’ve radically changed how insurance claims are settled. Improved customer experience along with significant cost reductions present a win-win situation for clients as well as firms involved[6].
A similar endeavor is being run on a trial basis currently by the French Insurance firm, **AXA**. The product _**“fizzy”**_ allows users to subscribe to its service for a small fee and enter their flight details. In case, the flight gets delayed or comes across some other issue, the program automatically scours online databases, checks with the insurance terms and credits the insurance amount to the user’s account. This eliminates the need for the user or the customer to file a claim after checking with the terms manually and in the long-run once such systems become mainstream, increase accountability from airlines[7][8].
### Keeping track of ownership rights
It is theoretically possible to track media from creation to end user consumption utilizing timestamped blocks of data in a DLT. Companies **Peertracks** and **Mycelia** are currently helping musicians publish content without worrying about their content being stolen or misused. They help artists sell directly to fans and clients while getting paid for their work without having to go through rights and record labels[9].
### Identity management platforms
Blockchain based identity management platforms store your identity on a distributed ledger. Once an account is setup, it is securely encrypted and sent to all the participating nodes after. However, as the owner of the data block only the user has access to the data. Once your identity is established on the network and you begin transactions, an automated program within the network will verify all previous transactions associated with your account, send it for regulatory filings after checking requirements and execute the settlement automatically provided the program deems the transaction legitimate. The upside here being that since the data on the blockchain is tamper-proof and the smart contract checks the input with zero bias (or subjectivity), the transaction doesn’t, as previously mentioned, require oversight or approval from anyone and is taken care of instantaneously.
Start-ups like **ShoCard** , **Credits** , and **OneName** are currently rolling out similar services and are currently in talks with government and social institutions for integrating them into mainstream use.
Other independent projects by developers like **Chris Ellis** and **David Duccini** have respectively developed or proposed alternative identity management systems such as **“[World Citizenship][4]”** , and **[IDCoin][5]** , respectively. Mr Ellis even demonstrated the capabilities of his work by creating passports on the a blockchain network[10][11] [12][5].
### Resource sharing
**Share & Charge (Slock.It)** is a European blockchain start-up. Their mobile app allows homeowners and other individuals who’ve invested their money in setting up a charging station share their resource with other individuals who’re looking for a quick. This not only allows owners to get back some of their investment, but also allows EV drivers to access significantly more charging points in their near-by geographical area allowing for suppliers to meet demands in a convenient manner. Once a “customer” is done charging their vehicle, the hardware associated creates a secure time stamped block consisting of the data and a smart contract working on the platform automatically credits the corresponding amount of money into the owners account. A track of all such transactions is recorded and proper security verifications kept in place. Interested readers can take a look [here][6], to know the technical angle behind their product[13][14]. The company’s platforms will gradually enable users to share other products and services with individuals in need and earn a passive income from the same.
The companies we’ve looked at here, comprise a very short list of ongoing projects that make use of smart contracts and blockchain database systems. Platform such as these help in building a secure “box” full of information to be accessed only by the users themselves and the overlying code or the smart contract. The information is vetted in real time based on a trigger, examined, and the algorithm is executed by the system. Such platforms with minimal human oversight, a much-needed step in the right direction with respect to secure digital automation, something which has never been thought of at this scale previously.
The next post will shed some light on the **different types of blockchains**. Click the following link to know more about this topic.
* [**Blockchain 2.0 – Public Vs Private Blockchain Comparison**][7]
**References:**
* **[1][About | Counterparty][8]**
* **[2] [Quorum | J.P. Morgan][9]
**
* **[3][Escrow My Ether][10]**
* **[4][PayFair][11]**
* **[5] B. Pani, “Blockchain Powered Financial Inclusion,” 2016.**
* **[6][STRATUMN | Insurance Claim Automation Across Europe][12]**
* **[7][fizzy][13]**
* **[8][AXA goes blockchain with fizzy | AXA][14]**
* **[9] M. Gates, “Blockchain. Ultimate guide to understanding blockchain bitcoin cryptocurrencies smart-contracts and the future of money.pdf.” 2017.**
* **[10][ShoCard Is A Digital Identity Card On The Blockchain | TechCrunch][15]**
* **[11][J. Biggs, “Your Next Passport Could Be On The Blockchain | TechCrunch][16]**
* **[12][OneName – Namecoin Wiki][17]**
* **[13][Share&Charge launches its app, on-boards over 1,000 charging stations on the blockchain][18]**
* **[14][slock.it – Landing][19]**
--------------------------------------------------------------------------------
via: https://www.ostechnix.com/blockchain-2-0-ongoing-projects-the-state-of-smart-contracts-now/
作者：[editor][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/译者ID)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.ostechnix.com/author/editor/
[b]: https://github.com/lujun9972
[1]: https://www.ostechnix.com/wp-content/uploads/2019/04/State-Of-Smart-Contracts-720x340.png
[2]: https://www.ostechnix.com/blockchain-2-0-explaining-smart-contracts-and-its-types/
[3]: https://banqu.co/case-study/
[4]: https://github.com/MrChrisJ/World-Citizenship
[5]: https://github.com/IDCoin/IDCoin
[6]: https://blog.slock.it/share-charge-smart-contracts-the-technical-angle-58b93ce80f15
[7]: https://www.ostechnix.com/blockchain-2-0-public-vs-private-blockchain-comparison/
[8]: https://counterparty.io/platform/
[9]: https://www.jpmorgan.com/global/Quorum
[10]: http://escrowmyether.com/
[11]: https://payfair.io/
[12]: https://stratumn.com/business-case/insurance-claim-automation-across-europe/
[13]: https://fizzy.axa/en-gb/
[14]: https://group.axa.com/en/newsroom/news/axa-goes-blockchain-with-fizzy
[15]: https://techcrunch.com/2015/05/05/shocard-is-a-digital-identity-card-on-the-blockchain/
[16]: https://techcrunch.com/2014/10/31/your-next-passport-could-be-on-the-blockchain/
[17]: https://wiki.namecoin.org/index.php?title=OneName
[18]: https://blog.slock.it/share-charge-launches-its-app-on-boards-over-1-000-charging-stations-on-the-blockchain-ba8275390309
[19]: https://slock.it/

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[#]: url: ( )

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[#]: translator: (luuming)
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[#]: collector: (lujun9972)
[#]: translator: (wxy)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (Blockchain 2.0 – Ongoing Projects (The State Of Smart Contracts Now) [Part 6])
[#]: via: (https://www.ostechnix.com/blockchain-2-0-ongoing-projects-the-state-of-smart-contracts-now/)
[#]: author: (editor https://www.ostechnix.com/author/editor/)
区块链 2.0：智能合约如今的发展（六）
======
![The State Of Smart Contracts Now][1]
继续我们的[前面的关于智能合约的文章][2]，这篇文章旨在讨论智能合约的形势，重点介绍目前正在该领域进行开发的一些项目和公司。如本系列前一篇文章中讨论的，智能合约是在区块链网络上存在并执行的程序。我们探讨了智能合约的工作原理以及它们优于传统数字平台的原因。这里描述的公司分布于各种各样的行业中，但是大多涉及到身份管理系统、金融服务、众筹系统等，因为这些是被认为最适合切换到基于区块链的数据库系统的领域。
### 开放平台
诸如 [Counterparty][8] 和 Solidity（以太坊）等平台是完全公用的构建模块，开发者可以以之创建自己的智能合约。大量的开发人员参与此类项目使这些项目成为开发智能合约、设计自己的加密货币令牌系统以及为区块链创建协议以实现功能的事实标准。许多值得称赞的项目都来源于它们。摩根大通派生自以太坊的 [Quorum][9]，就是一个例子。而瑞波是另一个例子。
### 管理金融交易
通过互联网转账加密货币被吹捧为在未来几年的常态。与此相关的不足之处是：
* 身份和钱包地址是匿名的。如果接收方不履行交易，则付款人没有任何第一追索权。
* 错误交易（如果无法追踪任何交易）。
* 密码生成的哈希密钥很难用于人类，人为错误是主要关注点。
在这种情况下，可以让其他人暂时接受该交易并在接受尽职调查后与接收方结算。
[EscrowMyEther][10] 和 [PAYFAIR][11] 是两个这样的托管平台。基本上，托管公司采用商定的金额并向接收方发送令牌。一旦接收方通过相同的托管平台提供付款人想要的内容，两者都会确认并最终付款。 这些得到了自由职业者和业余爱好者收藏家广泛在线使用。
### 金融服务
小额融资和小额保险项目的发展将改善世界上大多数贫穷或没有银行账户的人的银行金融服务。据估计，社会中较贫穷的“无银行账户”人群可以为银行和机构的增加 3800 亿美元收入 [^5]。这一金额取代了通过银行切换到区块链 DLT 预期可以节省的运营费用。
位于美国中西部的 BankQu Inc. 的口号是“通过身份而尊严”。他们的平台允许个人设置他们自己的数字身份记录，其中所有交易将在区块链上实时审查和处理。在底层代码上记录并为其用户构建唯一的在线标识，从而实现超快速的交易和结算。BankQu 案例研究探讨了他们如何以这种方式帮助个人和公司，可以在[这里][3]看到。
[Stratumn][12] 正在帮助保险公司通过自动执行早期由人类微观管理的任务来提供更好的保险服务。通过自动化、端到端可追溯性和高效的数据隐私方法，他们彻底改变了保险索赔的结算方式。改善客户体验以及显著降低成本，为客户和相关公司带来双赢局面。
法国保险公司 [AXA][14] 目前正在试行类似的努力。其产品 [fizzy][13] 允许用户以少量费用订阅其服务并输入他们的航班详细信息。如果航班延误或遇到其他问题，该程序会自动搜索在线数据库，检查保险条款并将保险金额记入用户的帐户。这样就消除了用户或客户在手动检查条款后提出索赔的要求，并且一旦这样的系统成为主流，就不需要长期提出索赔，增加了航空公司的责任。
### 跟踪所有权
理论上可以利用 DLT 中的带时间戳的数据块来跟踪从创建到最终用户消费的媒体。Peertracks 公司 和 Mycelia 公司目前正在帮助音乐家发布内容，而不必担心其内容被盗或被滥用。他们帮助艺术家直接向粉丝和客户销售，同时获得工作报酬，而无需通过权利和唱片公司 [^9]。
### 身份管理平台
基于区块链的身份管理平台可以将你的身份存储在分布式分类帐本中。设置帐户后，会对其进行安全加密，然后将其发送给所有参与节点。但是，作为数据块的所有者，只有该用户才能访问该数据。一旦你在网络上建立身份并开始交易，网络中的自动程序将验证与你的帐户关联的先前所有的交易，在检查要求后将其发送给监管备案，并在程序认为交易合法时自动执行结算。这里的好处是，由于区块链上的数据是防篡改的，而智能合约以零偏差（或主观性）检查输入，如前所述，交易不需要任何人的监督或批准，并且需要小心是即刻生效的。
像 [ShoCard][15] 、[Credits][16] 和 [OneName][17] 这样的初创公司目前正在推出类似的服务，目前正在与政府和社会机构进行谈判，以便将它们整合到主流用途中。
开发商的其他独立项目如 Chris Ellis 和 David Duccini 分别开发或提出了替代的身份管理系统，分别是 “[世界公民][4]”和 [IDCoin][5]。Ellis 先生甚至通过在区块链网络上创建护照来证明他的工作能力。
### 资源共享
[Share & Charge][18] ([Slock.It][19]) 是一家欧洲的区块链初创公司。他们的移动应用程序允许房主和其他个人投入资金建立充电站与其他正在寻找快速充电的人分享他们的资源。这不仅使业主能够收回他们的一些投资，而且还允许 EV 司机在其近地域获得更多的充电点，从而允许供应商以方便的方式满足需求。一旦“客户”完成对其车辆的充电，相关的硬件就会创建一个由数据组成的安全时间戳块，并且在该平台上工作的智能合约会自动将相应的金额记入所有者账户。记录所有此类交易的跟踪并保持适当的安全验证。有兴趣的读者可以看一下[这里][6]，了解他们产品背后的技术角度。该公司的平台将逐步使用户能够与有需要的个人分享其他产品和服务，并从中获得被动收入。
我们在这里看到的公司，以及一个很短的正在进行中的项目的清单，这些项目利用智能合约和区块链数据库系统。诸如此类的平台有助于构建一个安全的“盒子”，其中包含仅由用户自己和上面的代码或智能合约访问的信息。基于触发器对信息进行实时审查、检查，并且算法由系统执行。这样的平台具有最小化的人为监督，这是在安全数字自动化方面朝着正确方向迈出的急需的一步，这在以前从未被考虑过如此规模。
下一篇文章将阐述不同类型的区块链。单击以下链接以了解有关此主题的更多信息。
* [区块链 2.0：公有链与私有链的比较][7]
[^5]: B. Pani, “Blockchain Powered Financial Inclusion,” 2016.
[^9]: M. Gates, “Blockchain. Ultimate guide to understanding blockchain bitcoin cryptocurrencies smart-contracts and the future of money.pdf.” 2017.
--------------------------------------------------------------------------------
via: https://www.ostechnix.com/blockchain-2-0-ongoing-projects-the-state-of-smart-contracts-now/
作者：[editor][a]
选题：[lujun9972][b]
译者：[wxy](https://github.com/wxy)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://www.ostechnix.com/author/editor/
[b]: https://github.com/lujun9972
[1]: https://www.ostechnix.com/wp-content/uploads/2019/04/State-Of-Smart-Contracts-720x340.png
[2]: https://linux.cn/article-10956-1.html
[3]: https://banqu.co/case-study/
[4]: https://github.com/MrChrisJ/World-Citizenship
[5]: https://github.com/IDCoin/IDCoin
[6]: https://blog.slock.it/share-charge-smart-contracts-the-technical-angle-58b93ce80f15
[7]: https://www.ostechnix.com/blockchain-2-0-public-vs-private-blockchain-comparison/
[8]: https://counterparty.io/platform/
[9]: https://www.jpmorgan.com/global/Quorum
[10]: http://escrowmyether.com/
[11]: https://payfair.io/
[12]: https://stratumn.com/business-case/insurance-claim-automation-across-europe/
[13]: https://fizzy.axa/en-gb/
[14]: https://group.axa.com/en/newsroom/news/axa-goes-blockchain-with-fizzy
[15]: https://techcrunch.com/2015/05/05/shocard-is-a-digital-identity-card-on-the-blockchain/
[16]: https://techcrunch.com/2014/10/31/your-next-passport-could-be-on-the-blockchain/
[17]: https://wiki.namecoin.org/index.php?title=OneName
[18]: https://blog.slock.it/share-charge-launches-its-app-on-boards-over-1-000-charging-stations-on-the-blockchain-ba8275390309
[19]: https://slock.it/

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日志消息的一日之旅
======
> 从一条日志消息的角度来巡览现代分布式系统。
![](https://opensource.com/sites/default/files/styles/image-full-size/public/lead-images/plane_travel_world_international.png?itok=jG3sYPty)
混沌系统往往是不可预测的。在构建像分布式系统这样复杂的东西时，这一点尤其明显。如果不加以控制，这种不可预测性会无止境的浪费时间。这就是为什么分布式系统的每个组件，无论多小，都必须设计成以简化的方式组合在一起。
[Kubernetes][1] 为抽象计算资源提供了一个很有前景的模型 —— 但即使是它也必须与 [Apache Kafka][2] 等其他分布式平台协调一致，以确保可靠的数据传输。如果有人要整合这两个平台，它会如何运作？此外，如果你通过这样的系统跟踪像日志消息这么简单的东西，它会是什么样子？本文将重点介绍来自[OKD][3] 内运行的应用程序的日志消息如何通过 Kafka 进入数据仓库（OKD 是为 Red Hat OpenShift 提供支持的 Kubernetes 的原初社区发行版）。
### OKD 定义的环境
这样的旅程始于 OKD，因为该容器平台完全覆盖了它抽象的硬件。这意味着日志消息等待由驻留在容器中的应用程序写入 stdout 或 stderr 流。从那里，日志消息被容器引擎（例如 [CRI-O][4]）重定向到节点的文件系统。
![](https://opensource.com/sites/default/files/uploads/logmessagepathway.png)
在 OpenShift 中，一个或多个容器封装在称为 pod 的虚拟计算节点中。实际上，在 OKD 中运行的所有应用程序都被抽象为 pod（豆荚）。这允许应用程序以统一的方式操纵。这也大大简化了分布式组件之间的通信，因为 pod 可以通过 IP 地址和[负载均衡服务][5]系统地寻址。因此，当日志消息由日志收集器应用程序从节点的文件系统获取时，它可以很容易地传递到在 OpenShift 中运行的另一个 pod。
### 在豆荚里的两个豌豆
为了确保可以在整个分布式系统中无处不在地传播日志消息，日志收集器需要将日志消息传递到在 OpenShift 中运行的 Kafka 集群数据中心。通过 Kafka，日志消息可以以可靠且容错的方式低延迟传递给消费应用程序。但是，为了在 OKD 定义的环境中获得 Kafka 的好处，Kafka 需要完全集成到 OKD 中。
运行 [Strimzi 操作子][6]将实例化所有 Kafka 组件为 pod，并将它们集成在 OKD 环境中运行。 这包括用于排队日志消息的 Kafka 代理，用于从 Kafka 代理读取和写入的 Kafka 连接器，以及用于管理 Kafka 集群状态的 Zookeeper 节点。Strimzi 还可以将日志收集器双实例化作为 Kafka 连接器，允许日志收集器将日志消息直接提供给在 OKD 中运行的 Kafka 代理 pod。
### 在 OKD 内的 Kafka
当日志收集器 pod 将日志消息传递给 Kafka 代理时，收集器会写到单个代理分区，并将日志消息附加到该分区的末尾。使用 Kafka 的一个优点是它将日志收集器与日志的最终目标分离。由于解耦，日志收集器不关心日志最后是放在 [Elasticsearch][7]、Hadoop、Amazon S3 中的某个还是全都。Kafka 与所有基础设施连接良好，因此 Kafka 连接器可以在任何需要的地方获取日志消息。
一旦写入 Kafka 代理的分区，该日志消息就会在 Kafka 集群内的代理分区中复制。这是它的一个非常强大的概念；结合平台的自愈功能，它创建了一个非常灵活的分布式系统。例如，当节点变得不可用时，节点上运行的应用程序几乎立即在健康节点上生成。因此，即使带有 Kafka 代理的节点丢失或损坏，日志消息也能保证存活在必要多的节点上，并且新的 Kafka 代理将快速原位取代。
### 存储起来
在日志消息被提交到 Kafka 主题后，它将等待 Kafka 连接器接收器使用它，该接收器将日志消息中继到分析引擎或日志记录仓库。在传递到其最终目的地时，可以分析日志消息以进行异常检测，可以查询日志以立即进行根本原因分析，或用于其他目的。无论哪种方式，日志消息都由 Kafka 以安全可靠的方式传送到目的地。
OKD 和 Kafka 是正在迅速发展的功能强大的分布式平台。创建能够在不影响性能的情况下抽象出分布式计算的复杂特性的系统至关重要。毕竟，如果我们不能简化单条日志消息的旅程，我们怎么能夸耀全系统的效率呢？
--------------------------------------------------------------------------------
via: https://opensource.com/article/18/9/life-log-message
作者：[Josef Karásek][a]
选题：[lujun9972](https://github.com/lujun9972)
译者：[wxy](https://github.com/wxy)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://opensource.com/users/jkarasek
[1]: https://kubernetes.io/
[2]: https://kafka.apache.org/
[3]: https://www.okd.io/
[4]: http://cri-o.io/
[5]: https://kubernetes.io/docs/concepts/services-networking/service/
[6]: http://strimzi.io/
[7]: https://www.elastic.co/

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[#]: collector: (lujun9972)
[#]: translator: (luuming)
[#]: reviewer: ( )
[#]: publisher: ( )
[#]: url: ( )
[#]: subject: (5 Good Open Source Speech Recognition/Speech-to-Text Systems)
[#]: via: (https://fosspost.org/lists/open-source-speech-recognition-speech-to-text)
[#]: author: (Simon James https://fosspost.org/author/simonjames)
5 款不错的开源语音识别/语音文字转换系统
======
![](https://i0.wp.com/fosspost.org/wp-content/uploads/2019/02/open-source-speech-recognition-speech-to-text.png?resize=1237%2C527&ssl=1)
<ruby>语音文字转换<rt>speech-to-text</rt></ruby>（STT）系统就像它名字所蕴含的那样，是一种将说出的单词转换为文本文件以供后续用途的方式。
语音文字转换技术非常有用。它可以用到许多应用中，例如自动转录，使用自己的声音写书籍或文本，用生成的文本文件和其他工具做复杂的分析等。
在过去，语音文字转换技术以专有软件和库为主导，开源替代品并不存在或是有严格的限制并且没有社区。这一点正在发生改变，当今有许多开源语音文字转换工具和库可以让你立即使用。
这里我列出了 5 个。
### 开源语音识别库
#### DeepSpeech 项目
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 15 open source speech recognition][1]
该项目由 Firefox 浏览器背后的组织 Mozilla 团队开发。它 100% 自由并且使用 TensorFlow 机器学习框架实现。
换句话说，你可以用它训练自己的模型获得更好的效果，甚至可以用它转换其它的语言。你也可以轻松的将它集成到自己的 Tensorflow 机器学习项目中。可惜的是项目当前默认仅支持英语。
它也支持许多编程语言，例如 Python（3.6）。可以让你在数秒之内获取：
```
pip3 install deepspeech
deepspeech --model models/output_graph.pbmm --alphabet models/alphabet.txt --lm models/lm.binary --trie models/trie --audio my_audio_file.wav
```
你也可以通过 npm 安装它：
```
npm install deepspeech
```
想要获得更多信息，请参考[项目主页][2]。
#### Kaldi
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 17 open source speech recognition][3]
Kaldi 是一个用 C++ 写的开源语音识别软件，并且在 Apache 公共许可下发布。它可以运行在 Windows，macOS 和 Linux 上。它的开发始于 2009。
Kaldi 超过其他语音识别软件的主要特点是可扩展和模块化。社区提供了大量的三方模块可以用来完成你的任务。Kaldi 也支持深度神经网络，并且在它的网站上提供了[出色的文档][4]。
虽然代码主要由 C++ 完成，但它通过 Bash 和 Python 脚本进行了封装。因此，如果你仅仅想使用基本的语音到文字转换功能，你就会发现通过 Python 或 Bash 能够轻易的完成。
[Project’s homepage][5].
#### Julius
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 19 open source speech recognition][6]
可能是有史以来最古老的语音识别软件之一。它的发展始于 1991 年的京都大学，之后在 2005 年将所有权转移到了一个独立的项目组。
Julius 的主要特点包括了执行实时 STT 的能力，低内存占用（20000 单词少于 64 MB），输出<ruby>最优词<rt>N-best word</rt></ruby>/<ruby>词图<rt>Word-graph</rt></ruby>的能力，当作服务器单元运行的能力和很多东西。这款软件主要为学术和研究所设计。由 C 语言写成，并且可以运行在 Linux，Windows，macOS 甚至 Android（在智能手机上）。
它当前仅支持英语和日语。软件或许易于从 Linux 发行版的仓库中安装。只要在软件包管理器中搜索 julius 即可。最新的版本[发布][7]于大约一个半月之前。
[Project’s homepage][8].
#### Wav2Letter++
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 21 open source speech recognition][9]
如果你在寻找一个更加时髦的，那么这款一定适合。Wav2Letter++ 是一款由 Facebook 的 AI 研究团队于 2 个月之前发布的开源语言识别软件。代码在 BSD 许可下发布。
Facebook 描述它的库是“最快<ruby>最先进<rt>state-of-the-art</rt></ruby>的语音识别系统”。构建它时的想法使其能在默认情况下对性能进行优化。Facebook 最新的机器学习库 [FlashLight][11] 也被用作 Wav2Letter++ 的底层核心。
Wav2Letter++ 需要你先为所描述的语言建立一个模型来训练算法。没有任何一种语言（包括英语）的预训练模型，它仅仅是个机器学习驱动的文本语音转换工具，它用 C++ 写成，因此命名为 Wav2Letter++。
[Project’s homepage][12].
#### DeepSpeech2
![5 Good Open Source Speech Recognition/Speech-to-Text Systems 23 open source speech recognition][13]
中国巨头百度的研究人员也在开发他们自己的语音文字转换引擎，叫做“DeepSpeech2”。它是一个端对端的开源引擎，使用“PaddlePaddle”深度学习框架进行英语或汉语的文字转换。代码在 BSD 许可下发布。
引擎可以训练在任何模型之上，并且可以用于任何想要的语言。模型并未随代码一同发布。你要像其他软件那样自己建立模型。DeepSpeech2 的源代码由 Python 写成，如果你使用过就会非常容易上手。
[Project’s homepage][14].
### 总结
语音识别领域仍然主要地由专有软件巨头所占据，比如 Google 和 IBM（它们为此提供了闭源商业服务），但是开源同类软件很有前途。这 5 款开源语音识别引擎应当能够帮助你构建应用，随着时间推移，它们会不断地发展。在几年之后，我们希望开源成为这些技术中的常态，就像其他行业那样。
如果你对清单有其他的建议或评论，我们很乐意在下面听到。
--------------------------------------------------------------------------------
via: https://fosspost.org/lists/open-source-speech-recognition-speech-to-text
作者：[Simon James][a]
选题：[lujun9972][b]
译者：[译者ID](https://github.com/LuuMing)
校对：[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译，[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://fosspost.org/author/simonjames
[b]: https://github.com/lujun9972
[1]: https://i0.wp.com/fosspost.org/wp-content/uploads/2019/02/hero_speech-machine-learning2.png?resize=820%2C280&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 16 open source speech recognition)
[2]: https://github.com/mozilla/DeepSpeech
[3]: https://i0.wp.com/fosspost.org/wp-content/uploads/2019/02/Screenshot-at-2019-02-19-1134.png?resize=591%2C138&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 18 open source speech recognition)
[4]: http://kaldi-asr.org/doc/index.html
[5]: http://kaldi-asr.org
[6]: https://i2.wp.com/fosspost.org/wp-content/uploads/2019/02/mic_web.png?resize=385%2C100&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 20 open source speech recognition)
[7]: https://github.com/julius-speech/julius/releases
[8]: https://github.com/julius-speech/julius
[9]: https://i2.wp.com/fosspost.org/wp-content/uploads/2019/02/fully_convolutional_ASR.png?resize=850%2C177&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 22 open source speech recognition)
[10]: https://code.fb.com/ai-research/wav2letter/
[11]: https://github.com/facebookresearch/flashlight
[12]: https://github.com/facebookresearch/wav2letter
[13]: https://i2.wp.com/fosspost.org/wp-content/uploads/2019/02/ds2.png?resize=850%2C313&ssl=1 (5 Good Open Source Speech Recognition/Speech-to-Text Systems 24 open source speech recognition)
[14]: https://github.com/PaddlePaddle/DeepSpeech