Doyensec was founded in 2017 by John Villamil and Luca Carettoni and they are its only stakeholders

Doyensec

COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 24 of 42. 22. The 12 March 2008 Kleiman emails 415. There are two different versions of the 12 March 2008 email from Dr Wright to David Kleiman. The first was the Third Pleaded Example in COPA’s Particulars of Claim, contained in {ID_001318}. The second is {ID_000465}, produced in Dr Wright’s original disclosure. Both are alleged by COPA to be forgeries. They differ only in the domain from which the email was supposedly sent and in part of Dr Wright’s explanations. 416. Understandably, COPA pleaded the Third Pleaded Example because it was one of the few publicly available documents when COPA commenced this claim. Although the Third Pleaded Example (i.e. {ID_001318}) comes first procedurally, {ID_000465} appears to be the original email and was the focus of the expert analysis. 417. I explain each version first, and then set out the detail concerning ID_000465. (a) (Particulars of Claim at [28] – [29] {A/2/10}) The Third Pleaded Example ID_001318 418. In the Kleiman litigation in the United States involving Dr Wright (which is still ongoing), it is alleged that Dr Wright stole a substantial amount of Bitcoin and related intellectual property assets from a company named W&K Info Defence Research, LLC, after the death of its founder, David Kleiman, by forging documents (the “Kleiman Litigation”). In those proceedings Wright asserts that he sent an email to David Kleiman on 12 March 2008 which shows him to be the originator of the idea of Bitcoin. This version is the copy forwarded to Ira Kleiman (brother of David Kleiman) on 6 March 2014, when Dr Wright was seeking to persuade the Kleiman family to accept his claim to have created the Bitcoin system in collaboration with David Kleiman {ID_001318}. The email he claims to have forwarded is as follows: (b) COPA’s reason for alleging forgery. 419. The domain used by the sender of this email is ‘information-defense.com’, which was not created until 23 January 2009. For that reason, COPA allege that the email could not have been sent in 2008 as claimed from the “craig.wright@information-defense.com” email address and is a forgery. (c) Dr Wright’s Explanations 420. In his Defence, Dr Wright claimed that he sent an email to Mr Kleiman on 12 March 2008 and that “the body of the email [cited in COPA’s Particulars of Claim] is the same as the email which Dr Wright sent on 12 March 2008”. However, he claimed that the header was different and that he believed “that the difference has arisen as a result of the original email being moved from one exchange server to another”: Defence [50] {A/3/17}. In his fourth statement (from para. 93), he gave a supposed technical explanation for the email address having changed in this email as the result of mailboxes being moved from server to server: {E/4/31}. 421. In Appendices B and C to Wright11, Dr Wright changed his account entirely. He claimed that he had written an email to Mr Kleiman on 12 March 2008 which contained the first two paragraphs of the email above, but not the third paragraph. He accordingly claimed that all versions of the email in disclosure (all of which include the third paragraph) are fake. He said that the document had been faked by his enemies for deployment against him. See: {CSW/2/44}; {CSW/3/7}. 422. Dr Wright’s final explanation was an assertion that the authentic email is on a QNAP server, to which he does not have access. Continue Reading Here. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings.

This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series. You can jump to any part of this filing here. This part is 24 of 42. COPA v. Wright, Court Filing, retrieved on January 29, 2024, is part of HackerNoon’s Legal PDF Series . You can jump to any part of this filing here . This part is 24 of 42. HackerNoon’s Legal PDF Series HackerNoon’s Legal PDF Series here here 22. The 12 March 2008 Kleiman emails 415. There are two different versions of the 12 March 2008 email from Dr Wright to David Kleiman. The first was the Third Pleaded Example in COPA’s Particulars of Claim, contained in {ID_001318}. The second is {ID_000465}, produced in Dr Wright’s original disclosure. Both are alleged by COPA to be forgeries. They differ only in the domain from which the email was supposedly sent and in part of Dr Wright’s explanations. 416. Understandably, COPA pleaded the Third Pleaded Example because it was one of the few publicly available documents when COPA commenced this claim. Although the Third Pleaded Example (i.e. {ID_001318}) comes first procedurally, {ID_000465} appears to be the original email and was the focus of the expert analysis. 417. I explain each version first, and then set out the detail concerning ID_000465. (a) (Particulars of Claim at [28] – [29] {A/2/10}) The Third Pleaded Example ID_001318 (a) (Particulars of Claim at [28] – [29] {A/2/10}) The Third Pleaded Example ID_001318 418. In the Kleiman litigation in the United States involving Dr Wright (which is still ongoing), it is alleged that Dr Wright stole a substantial amount of Bitcoin and related intellectual property assets from a company named W&K Info Defence Research, LLC, after the death of its founder, David Kleiman, by forging documents (the “Kleiman Litigation”). In those proceedings Wright asserts that he sent an email to David Kleiman on 12 March 2008 which shows him to be the originator of the idea of Bitcoin. This version is the copy forwarded to Ira Kleiman (brother of David Kleiman) on 6 March 2014, when Dr Wright was seeking to persuade the Kleiman family to accept his claim to have created the Bitcoin system in collaboration with David Kleiman {ID_001318}. The email he claims to have forwarded is as follows: (b) COPA’s reason for alleging forgery. (b) COPA’s reason for alleging forgery. 419. The domain used by the sender of this email is ‘information-defense.com’, which was not created until 23 January 2009. For that reason, COPA allege that the email could not have been sent in 2008 as claimed from the “craig.wright@information-defense.com” email address and is a forgery. (c) Dr Wright’s Explanations (c) Dr Wright’s Explanations 420. In his Defence, Dr Wright claimed that he sent an email to Mr Kleiman on 12 March 2008 and that “the body of the email [cited in COPA’s Particulars of Claim] is the same as the email which Dr Wright sent on 12 March 2008”. However, he claimed that the header was different and that he believed “that the difference has arisen as a result of the original email being moved from one exchange server to another”: Defence [50] {A/3/17}. In his fourth statement (from para. 93), he gave a supposed technical explanation for the email address having changed in this email as the result of mailboxes being moved from server to server: {E/4/31}. 421. In Appendices B and C to Wright11, Dr Wright changed his account entirely. He claimed that he had written an email to Mr Kleiman on 12 March 2008 which contained the first two paragraphs of the email above, but not the third paragraph. He accordingly claimed that all versions of the email in disclosure (all of which include the third paragraph) are fake. He said that the document had been faked by his enemies for deployment against him. See: {CSW/2/44}; {CSW/3/7}. 422. Dr Wright’s final explanation was an assertion that the authentic email is on a QNAP server, to which he does not have access. Continue Reading Here. Continue Reading Here . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 29, 2024, judiciary.uk is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. judiciary.uk judiciary.uk

Two Emails, One Controversy: Unravelling Craig Wright’s Correspondence with David Kleiman 

Film Trailer Generation via Task Decomposition: Conclusions and References

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: উপসংহার এবং রেফারেন্স

Filmtrailer-Generierung durch Aufgabenzerlegung: Schlussfolgerungen und Referenzen

Generación de avances de películas mediante descomposición de tareas: conclusiones y referencias

Génération de bandes-annonces de films via la décomposition de tâches : conclusions et références

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: निष्कर्ष और संदर्भ

タスク分解による映画予告編生成: 結論と参考文献

작업 분해를 통한 영화 예고편 생성: 결론 및 참고 자료

Geração de trailer de filme por meio de decomposição de tarefas: conclusões e referências

Генерация трейлеров к фильму посредством декомпозиции задач: выводы и ссылки

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Sonuçlar ve Referanslar

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Kết luận và tài liệu tham khảo

通过任务分解生成电影预告片：结论与参考文献

The Corner Pub.

By Anton Trukhanov Among all the big technology trends popular today, Artificial Intelligence (AI) is probably the most universally welcomed and praised in the business world. The majority of businesses across industries recognize the enormous potential of this technology and are willing to adopt it as soon as possible. What are the biggest obstacles to AI implementation? Even though companies and decision-makers of the business world believe in AI’s big future and the power of this technology, many of them are not able to start implementing AI just yet. Costs and the lack of high-qualified talent are the main reasons. According to a by Forbes, 83% of companies say AI is a strategic priority for them today yet, there is not enough data science talent. And 40% of businesses by Deloitte said that the AI technologies and expertise are too expensive for them to implement at this point. report surveyed A widening AI talent gap is probably the biggest challenge for businesses wanting to utilize AI technologies in 2021. As companies are rushing to join the new high-tech innovations race, the demand for all tech specialists has increased with the supply unable to catch up. And in the AI field, this is especially the case. According to a by MMC Ventures, the demand for AI talent has more than doubled in 2018-2019 and continues to increase, with 60% of all AI specialists being absorbed by companies in the technology and financial services sectors. The pool of AI talent, however, remains small. report As AI demands advanced competencies in mathematics, statistics, and programming; the entry threshold level for this profession is really high, and educational institutions aren’t able to produce capable and qualified graduates fast enough to satisfy the business demand. Why is low-code the gateway to AI for businesses? And this is where low-code development platforms such as LANSA come in. Due to the above-mentioned challenges, in the last several years, companies were forced to jump on using low-code solutions as a way to build tools and applications that would allow them to utilize the power of AI and other latest tech innovations, quickly and without extensive financial resources. Low-code platforms turned out to be exactly the solution businesses needed, especially in the time of a significant economic and social turbulence such as the COVID-19 epidemic, when factors like flexibility, rapid customization, and scalability play an increasingly important role. According to a by Brandessence Market Research (indicating LANSA as one of the key market players), the volume of the global low-code development platform market was $12.85 bln in 2020, and it is expected to reach $65.15 bln by 2027 with a CAGR of 26.1% over the forecast period. report And the need to integrate AI and ML technologies into their daily operations is one of the biggest drivers of the rapidly increasing adoption of low-code platforms by companies all around the globe. What are the advantages of low-code AI solutions? Visual low-code and no-code platforms make AI technologies instantly accessible even to small and medium-sized companies. They allow businesses to build various kinds of custom applications and integrate different elements of AI and machine learning (ML) into them through third-party APIs. To understand why low-code platforms are undeniably the future of enterprise AI solutions development, let’s look at some of the core advantages of low-code AI compared to the traditional way of developing AI-focused solutions. 1. Much lower costs Low-code platforms basically enable companies to create fully-functional AI-powered solutions without the need to hire expensive AI experts, software developers, and other technical specialists. With low-code platforms, the cost of implementation of AI into daily business operations is much lower. 2. High accessibility Low-code makes AI solutions a lot more accessible on a number of levels, not just the financial. Visual and simple drag-and-drop solutions are more accessible to non-technical people and programming beginners, making AI less complex and thus, less intimidating for them. 3. High usability The simple user-friendly interface of low-code platforms also allows more people within an organization to collaborate on the creation and implementation of a custom AI-powered solution and contribute their perspective to the project, which often results in a more functional and effective final product. 4. Quick development and deployment Another major advantage of low-code platforms is speed. As they make the whole process of development and deployment of enterprise apps with AI a lot faster, this allows organizations to be more flexible and experiment with different versions or approaches to a problem. Which, again, results in higher quality in the end. 5. Great scalability Accessing sophisticated AI through third-party APIs also makes your solutions extremely scalable at no additional cost as the server power will automatically increase or decrease according to the current load and needs. 6. Gateway to other tech innovations. Finally, low-code platforms are probably the best way for companies, SMEs especially, to start utilizing other high-tech innovations as well, including Big data, IoT (Internet of Things), robotics, embedded systems, and so on. Low-code allows businesses to build solutions combining these and other technologies in a way that would serve their specific needs and requirements. How to create powerful AI apps with Visual LANSA? As you may know, is a powerful development environment pushing the boundaries of what we have traditionally considered as low-code. LANSA allows organizations to build anything from a basic application to handle tasks or send feedback to complex supply chain apps. When it comes to AI, multiple powerful AI solutions can be integrated into apps and tools made in Lansa’s low-code builder by using third-party REST APIs. Visual LANSA Let us give you a few examples of such APIs that can be easily integrated into LANSA solutions. Google Cloud Natural Language API Able to inspect the given text and identify the prevailing emotional opinion. Google Cloud Vision API Can detect and extract information about entities in an image, across a broad group of categories. Image Similarity API Compares two images and returns a value that tells you how visually similar they are. Super Resolution API Uses machine learning to clarify, sharpen, and upscale the photo without losing its content and defining characteristics. Fast Style Transfer API Applies artistic style to an image quickly by pre-training on specific style examples. DenseCap API Captions an image by labeling every object the model detects is present within the image. Content Moderation API Analyzes images and videos to detect the presence of adult content, hate symbols, guns, and offensive words. Image Colorization API Allows to colorizing black and white images or videos using the image colorization API. And these are just several examples of available third-party APIs that allow you to integrate powerful AI capabilities into your tools created in LANSA. Also published on https://lansa.com/blog/app-development/the-future-of-ai-is-low-code/.

AI for Businesses: The Future is Low-Code

Couple of months back, I was listening to Eric Evan’s pod cast on Software radio where he mentioned that actor model is a great fit for modeling aggregate root in domain driven , I was like what, wait? ain’t OO languages the only way to model concepts like aggregate, sagas, domain service, repositories or bounded Contexts in Domain driven design ? engineering design After hours of googling and days of reading and weeks of asking questions on forums, I realized that modeling DDD concepts is more explicit and easy in Elixir because of the language constructs it provides. For example using GenServer for aggregate root, OTP application for each Bounded Context, putting all of these Bounded Context together as an umbrella App. What is an Aggregate Root ? Essentially aggregate is pattern in domain driven design, Aggregate is a cluster of your domain objects which can be treated as a singe unit. Let’s say, you are writing a blog engine and you have identified some domain models like Blog Post Comment Like A Blog contains multiple Posts, a post contains multiple Comments and Likes, while defining relationship between domain models, it is a good practice to define relationship in such a way that object graph is acyclic, that is, there are no cycles. for example a blog might have posts but post should not contain reference to blog, similarly blog might contain comments but comment should not contain reference to post. Blog, Post, Comment and Likes can be considered as a collection of objects which defines a aggregate in our application. Next thing is to identify a model among these models and promote it to be root (called aggregate root) and make sure that all the operations/access on these models are happening through the aggregate root. In our case we can promote Blog model to be aggregate root, and make sure that we only access Post, like Blog.Post or Comments like Blog.Post.Comment, this approach guarantees that our models are being modified only via our aggregate roots and we are not breaking any invariant. DDD and Phoenix 1.3 If you have read about latest changes in Phoenix framework (1.3), you would realize that it forces developer to think about their models upfront and it encourages the concepts like Bounded Context and aggregate, wherein user need to specify context (more in line with Bounded context in DDD) to access resource (there are no models anymore). For example Blog, Post, Comment and Likes can be grouped together and accessed by using a Blog Context object. For detailed information take a look at this by Chris McCord at ElixirConf 2017. keynote Phoenix framework is awesome, but it is not my application ( look if not convinced), I like to think of Phoenix as only a web interface for my application and should only be responsible for exposing endpoints to interact with my application with minimal business logic. Following this approach means that I am going to move out my business logic from Phoenix app and put it somewhere else, to achieve this I will use Umbrella project and cut out our Bounded Context in our application into different OTP application within single Umbrella project. here Aggregate root as Elixir Process Let’s consider an application where we are storing and retrieving user information such as name, age, gender and educational details, so we have following models. NOTE: If you want to look the the entire repository, you can find it here . BasicInfo module containing name, age and gender fields. defmodule Bhaduli.User.BasicInfo dodefstruct [name: nil, age: nil, gender: nil] def new(basic_info) do%Bhaduli.User.BasicInfo{name: basic_info.name, age: basic_info.age, gender: basic_info.gender}endend EducationalDetails module containing graduation, senior_secondary and intermediate fields defmodule Bhaduli.User.EducationalDetails dodefstruct graduation: nil, senior_secondary: nil, intermediate: nil def new(educational_details) dostruct(Bhaduli.User.EducationalDetails, educational_details)endend User module which contains user_id, basicInfo and EducationalDetails model. defmodule Bhaduli.User do alias Bhaduli.User.{BasicInfo, EducationalDetails} defstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] ... User Module as Aggregate root I have got three models, basicInfo, educationalDetails and User. I am going to make User as my aggregate root and expose methods to save and retrieve EducationalDetails and BasicInfo as shown below. defmodule Bhaduli.User do alias Bhaduli.User.{BasicInfo, EducationalDetails} defstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] def update(id, %BasicInfo{} = basic_info) doend def update(id, %EducationalDetails{} = educational_details) doend def get_basic_info(id) doend def get_educational_details(id) doend def get(id) doend So we have got basic skeleton for User aggregate, but still we haven’t really done anything interesting. Like I said earlier I want to model my aggregate root as a long running elixir process and way to do that is to use GenServer. But before we do that I want to explain how entire flow should work. Each user will be an Elixir process, for example If a new user is created with user_id “Erin” than we would create a new process with user_id “Erin”. If we try to get user (user_id “Mustang”) for the first time , we will fetch it from database and spawn a new process with given user_id. Now we will have a total of two user processes one each for “Erin” and “Mustang”. If an existing user (user_id “Envy”) is updated and a process for that user has not been created yet than we are going to spawn new process and load its state from Database and update it. Now we will have three processes each for “Erin”, “Mustang” and “Envy”. We will keep one process for each user and keep it loaded in memory. If user process is already running we will read from it and return otherwise we go to database and spawn a new process. We will add a supervisor for user process so that every time a process crashes we create a new one and rehydrate it from database. Process Registry Even though I have outlined overall flow of application, there are few things that needs to be addressed first. Whenever a new process is created, we get back a PID. Since we are creating one process for each user, soon we will have thousands of processes . Anyone who want to get user data will have to know PID of each user process in order to interact with it. Having a supervisor for user process makes our application fault tolerant but also adds a complexity because every time a process crashes supervisor is going to create a new one and this new process will have a different PID than the one that had crashed. Though we can hand-roll our process registration mechanism,but it will soon get hard to track all of these processes as we intend to create thousands of them. On top of it, since supervisor is going to create a new process for every crashed one, propagating new process Id to the client would add more complexity. Luckily we have a module in Elixir called Registry, which allows us to map PID to any given string/atom, so that we don’t have to deal with cryptic PIDs. In our case we want to client to be able to interact with our application only by using user_id, so we would be mapping PID to user_id. Registry can be started with , once registry is started we need some kind of mechanism which will map our user_id to the PID of spawned process and way to do that is to implement a function that returns a tuple, based on a value we provide. Registry.start_link/3 :via Our function, along with the Registry module, will allows us to spawn a process for a given and make subsequent calls on the process using the same id, rather than a PID. via_tuple user_id Our :via_tuple function def via_tuple(user_id) do{:via, Registry, {:user_process_registry, user_id}}end GenServer provides the plumbing to make use of this tuple and automatically handle the registration of our name ( ) to the actual process. :via user_id we will use this via_tuple function while spawning a new user process(User module) #User.ex def start_link(name) doGenServer.start_link(__MODULE__, %Bhaduli.User{user_id: name}, name: via_tuple(name))end Aggregate Root as GenServer In order to make User aggregate an Elixir process, I am going to use GenServer behavior in User module. In our main application file, start the registry. Here we are starting the registry by giving it a unique name as `user_process_registry` and supervising it. defmodule Bhaduli douse Application def start(_type, _args) doimport Supervisor.Spec, warn: false children = [ supervisor(Bhaduli.UserSupervisor, [])]opts = [strategy: :one_for_one, name: Bhaduli.Supervisor]Supervisor.start_link(children, opts)endend supervisor(Registry, [:unique, :user_process_registry]), User Supervisor, we are defining a Supervisor for user module by giving it a simple_one_for_one strategy and module name. defmodule Bhaduli.UserSupervisor douse Supervisor def start_link doSupervisor.start_link(__MODULE__,nil, name: :user_sup)end def init(_) dochildren = [worker(Bhaduli.User, [])]supervise(children, strategy: :simple_one_for_one)endend Now, we will use GenServer behaviour in User module and alias BasicInfo and Educational Details. defmodule Bhaduli.User do use GenServer alias Bhaduli.User.{BasicInfo, EducationalDetails} ... Next we will define a User struct to contain user_id, basic_info and educational_details. defmodule Bhaduli.User do use GenServer alias Bhaduli.User.{BasicInfo, EducationalDetails} :user_process_registry @registry defstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] Let’s write a start_link/3 method in User module which will be called by User supervisor while starting User process. Here we are passing three arguments to start_link function __MODULE__, this is the module name. User struct as initial argument. Third parameter in alias for the spawned process, which allows us to access this process with given alias, we are using function because GenServer provides the plumbing to make use of this tuple and automatically handle the registration of our alias ( ) to the actual process. via_tuple :via user_id #User.ex def start_link(name) doGenServer.start_link(__MODULE__, %Bhaduli.User{user_id: name}, name: via_tuple(name))end Now comes the interesting part, we are defining a callback which will be called when GenServer is started. It is this method which will be responsible for loading the User state from database. init #User.ex def init(%Bhaduli.User{} = user) doGenServer.cast(self(), {:populate_user}){:ok, user}end Inside we are calling function, this callback goes to and fetches the data from database, If a user is found it is returned otherwise and error is returned. init handle_callback UserRepository #User.ex def handle_cast({:populate_user}, user) docase Bhaduli.UserRepository.get(user.user_id) do{:error, _} ->   {:noreply, user}{:ok, user} ->   {:noreply, user}endend It is important to note that GenServer won’t start until method returns. for this reason we are using method instead of because can load data asynchronously and init method don’t have to wait for it to finish. init handle_cast handle_call handle_cast Once we have done all this plumbing we can get the PID of any process for a given user_id from Registry as shown below. [{pid, _}] = Registry.lookup( , id) @registry Getting basic_info for a given user_id def get_basic_info(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:basic_info})end @registry We are using for getting basic_info as we are going to wait for reply. Interesting thing to note here is that we are not making database call (Guess why ?) handle_call def handle_call({:basic_info}, pid, state) do{:reply, state.basic_info, state}end Similarly for educational_details def get_educational_details(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:educational_details})end @registry def handle_call({:educational_details}, pid, state) do{:reply, state.educational_details, state}end For updating basic_info and educational details we are going to use as we don’t need to wait for response from GenServer callback. handle_cast def update(id, %BasicInfo{} = basic_info) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_basic_info, basic_info})end @registry def update(id, %EducationalDetails{} = educational_details) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_educational_details, educational_details})end @registry #Callbacks def handle_cast({:update_basic_info, basic_info}, user) douser = %Bhaduli.User{user | basic_info: basic_info}{:noreply, user}end def handle_cast({:update_educational_details, educational_details}, user) douser = %Bhaduli.User{user | educational_details: educational_details}{:noreply, user}end Putting it all together. #User.exdefmodule Bhaduli.User douse GenServeralias Bhaduli.User.{BasicInfo, EducationalDetails} :user_process_registrydefstruct [user_id: nil, basic_info: %Bhaduli.User.BasicInfo{}, educational_details: %Bhaduli.User.EducationalDetails{}] @registry def start_link(name) doGenServer.start_link(__MODULE__, %Bhaduli.User{user_id: name}, name: via_tuple(name))end def init(%Bhaduli.User{} = user) doGenServer.cast(self(), {:populate_user}){:ok, user}end def update(id, %BasicInfo{} = basic_info) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_basic_info, basic_info})end @registry def update(id, %EducationalDetails{} = educational_details) do[{pid, _}] = Registry.lookup( , id)GenServer.cast(pid, {:update_educational_details, educational_details})end @registry def get_basic_info(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:basic_info})end @registry def get_educational_details(id) do[{pid, _}] = Registry.lookup( , id)GenServer.call(pid, {:educational_details})end @registry def get(id) docase Registry.lookup( , id) do[{pid, _}] -> Registry.lookup( , id)GenServer.call(pid, {})[]      -> {:error, :not_found}end @registry @registry end def handle_cast({:update_basic_info, basic_info}, user) douser = %Bhaduli.User{user | basic_info: basic_info}{:noreply, user}end def handle_cast({:update_educational_details, educational_details}, user) douser = %Bhaduli.User{user | educational_details: educational_details}{:noreply, user}end def handle_cast({:populate_user}, user) docase Bhaduli.UserRepository.get(user.user_id) do{:error, _} ->   {:noreply, user}{:ok, user} ->   {:noreply, user}enddef handle_cast({:update_basic_info, basic_info}, user) douser = %Bhaduli.User{user | basic_info: basic_info}{:noreply, user}end def handle_cast({:update_educational_details, educational_details}, user) douser = %Bhaduli.User{user | educational_details: educational_details}{:noreply, user}end end def handle_call({:basic_info}, pid, state) do{:reply, state.basic_info, state}end def handle_call({:educational_details}, pid, state) do{:reply, state.educational_details, state}end def handle_call({}, pid, state) do{:reply, state, state}end def via_tuple(user_id) do{:via, Registry, {:user_process_registry, user_id}}end end Code for this can be found . This application is part of an Umbrella project and in next post I’ll show how this application is used from a Phoenix application. here About Author About Author : Naveen Negi is software engineer based out of Wolfsburg Germany. He believes in software craftsmanship. He is also an avid blogger, reader, and a polyglot developer. In his free time he tries to explore different spiritual paradigm and the one which entices him most is Samakhya school of philosophy. LinkedIn profile: http://linkedin.com/in/nav301186

Elixir: Domain Driven Design with Actor Model

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Image by: Sébastien Thibault Data brokers’ millions of dollars in lobbying spending in 2020 rivaled that of some Big Tech firms By Alfred Ng and Maddy Varner The data brokers who’ve made fortunes from collecting and sharing millions of people’s personal information tend to fly under the radar. Names like LiveRamp or RELX might not be familiar to most Americans, but they’re making themselves known on Capitol Hill. Collectively, data broker spending on lobbying in 2020 rivaled the spending of individual Big Tech firms like Facebook and Google. The Markup searched lobbying disclosures in the U.S. Senate’s database and the watchdog Center for Responsive Politics’ tool for the names of companies that registered as data brokers in or . Those states are the only two that require companies to annually disclose that they collect, sell, or share people’s personal information without having a direct relationship to them. Lobbying Disclosure Act OpenSecrets Vermont California All in all, we found 25 companies whose combined spending on federal lobbying totaled $29 million in 2020. Many of the top spenders were not pure data brokers but companies that nonetheless have massive data operations. Oracle, which has spent the past decade acquiring companies that collect data, spent the most by far, with disclosure documents showing $9,570,000 spent on federal lobbying. For comparison, of the Big Tech firms with heavy lobbying presences, Facebook spent $19,680,000, Amazon $18,725,000, and Google $8,850,000 in the same period, according to the Center for Responsive Politics. Public Citizen, a consumer advocacy group, found that Big Tech spent . $108 million collectively on lobbying in 2020 Oracle has its own data collection arm but has also built its portfolio by buying up companies like , Compendium, and Crosswise. The companies, which were acquired in 2012, 2013, and 2016, respectively, take data from a variety of sources. DataRaker gets data from millions of smart meters and sensors for utilities companies, while Compendium delivers targeted ads. Crosswise allows Oracle to track people across devices, claiming to process data from billions of devices every month. DataRaker Oracle also acquired , in 2014, which connected offline purchases to online profiles. Additionally, Oracle combines datasets from more than 75 other data brokers, which it calls “the world’s largest collection of third-party data.” Datalogix Our report comes as the data broker industry is not only growing but also facing scrutiny for the first time. California recently passed a statewide privacy law that establishes an agency focused on regulating data privacy issues. Virginia and Maine have also passed regulations to protect people’s online information. California’s and Virginia’s privacy laws hit at the core of what data brokers do by requiring companies to delete data collected about people upon request and allowing people to prevent their personal information from being used for targeted advertising. Maine’s privacy law prevents internet service providers from sharing personal information with data brokers until people give “express, affirmative consent.” And the past year also saw concerns raised about how well these brokers protect their massive troves of data—Oracle, for instance, after security researchers found were left exposed on a server. suffered a data breach billions of records from its BlueKai data collection The Markup contacted all 25 companies for comment on their lobbying activities. Several companies, like Inmar Intelligence and LiveRamp denied being data brokers, though they had self-identified as such to California and/or Vermont regulators. “Inmar Intelligence does not generally consider itself a data broker though one of our entities, Inmar-OIQ, LLC, is registered as such in a couple of states,” Holly Pavlika, a corporate marketing senior vice president for Inmar Intelligence, said. The industry itself is hard to define—many companies, including tech giants, make money off of personal data, though the technical details of how they use that data vary. So The Markup relied on companies that self-reported to and as members of the industry. The list, 480 companies long, shows just how pervasive it has become for companies to traffic in personal information. Vermont California The list includes businesses that are primarily data brokers, like CoreLogic, which claims to have collected data on 99 percent of property and homeowners in the U.S. and spent $215,000 on lobbying in 2020; and Acxiom, which spent $360,000 on lobbying in 2020 for issues related to data security and privacy. Acxiom’s InfoBase boasts of datasets on more than 250 million people and collects information including location data, purchases, interests, life events, and behaviors, according to its marketing material. Our list also includes credit monitoring services like TransUnion, Equifax, and Experian, which each spent about $1.4 million lobbying in 2020 on issues related to credit score reform, like the of 2020, the Credit Access and Inclusion Act of 2019, and the of 2019. TransUnion also owns subsidiaries like Callcredit, Iovation, and Signal, which has been used for collecting and profiling users for gambling apps, . Protecting Your Credit Score Act Clarity in Credit Score Formation Act according to The New York Times Notably, our tally also does not include lobbying by trade associations that data brokers are a part of, such as the Interactive Advertising Bureau and the Digital Advertising Alliance. Of the data brokers who spent the most on lobbying, Oracle far outspent its peers Oracle, the biggest spender, used lobbyists to advocate on issues like annual defense and intelligence budgets and “competition and antitrust in the mobile telecommunications and digital advertising industries,” according to . public records The company didn’t respond to requests for comment. The second largest spender was Accenture—a technology and consulting company that boasts a marketing and analytics branch called . According to its , the company can combine datasets from sources including people’s purchase history and location to help its clients build customer profiles for advertising. Accenture Interactive marketing materials In 2020, the company spent $3,250,000 lobbying on issues like artificial intelligence, the , and COVID-19 contact tracing, according to public records. Digital Dollar When The Markup reached out to Accenture, the company pointed us to two statements CEO Julie Sweet made on data privacy legislation in 2018. The statements called for a federal privacy law that would preempt state laws. PricewaterhouseCoopers, a major accounting firm, spent $2,820,000 lobbying the federal government in 2020, third most among registered data brokers. The total amount 25 data brokers spent on federal lobbying in 2020. $29M PWC collects data from advertising networks and data analytics partners including personal information like names and addresses, which it uses to help clients personalize advertising campaigns, . according to the company’s privacy statement Spending went to monitoring privacy legislation and accounting and auditing issues. PWC didn’t respond to a request for comment. Other big spenders included Deloitte, which was awarded a $106 million contract to build a development environment for the Joint Artificial Intelligence Center this summer. The company spent $2,400,000 on federal lobbying in 2020, including on bills like the and the . Department of Defense Artificial Intelligence in Government Act Artificial Intelligence Initiative Act Deloitte is an auditing and advisory company but provides services like , which uses information including people’s hobbies, interests, and financial data to generate a health risk prediction score and help life insurance companies figure out how likely people are to buy their products. PredictRisk provided by data brokers And RELX, which spent $2,375,000 on federal lobbying in 2020, including on data privacy bills like the , the , and the ; and on bills related to data privacy and COVID-19, including the and the . Data Accountability and Trust Act Information Transparency & Personal Data Control Act Data Broker Accountability and Transparency Act Covid-19 Consumer Data Protection Act Exposure Notification Privacy Act RELX is a major data broker that owns companies like LexisNexis and ThreatMetrix, which has customers in law enforcement, insurance, and financial services. ThreatMetrix alone boasts tracking on 4.5 billion devices, from RELX in 2018. according to a statement Deloitte and RELX didn’t respond to a request for comment. In addition, many other data brokers spent lower amounts on lobbying efforts An Industry Increasingly Under Fire Some of the bills targeted by lobbyists would have regulated the data broker industry, though disclosures do not specify whether they lobbied for or against the bills. The Data Accountability and Trust Act looked to establish security standards and require postbreach audits for data brokers and also prohibit collecting information under false pretenses. The Information Transparency & Personal Data Control Act would have required data brokers to get consent to collect sensitive data and go through an annual privacy audit. The Data Broker Accountability and Transparency Act of 2020 wanted to mandate opt-outs from data brokers and for the FTC to create a national list of data brokers. While lobbying records don’t always list specific bills, filings show that RELX paid lobbyists to address all three bills. Deloitte records show that some of its lobbying efforts went toward addressing the Data Accountability and Trust Act. Both COVID-19 data privacy bills looked to provide stronger controls over data related to contact tracing. None of the bills named in this story passed Congress, except for the National Defense Authorization Act, which was enacted. Some of the companies that showed up in lobbying records faced other sorts of pressure during 2020. For instance, Apple and Google from their app stores last December following a series of reports from on how the location data broker was providing information to contractors who passed that information to the U.S. military. X-Mode spent $30,000 on lobbying in the last quarter of 2020, during the height of the public pressure. banned X-Mode’s trackers Motherboard Both and , another location data broker, which spent $160,000 on lobbying, are facing scrutiny from Congress over their location data sales. Sen. Ron Wyden, a Democrat from Oregon who signed on to , called the data broker industry “out of control,” in a statement to The Markup. X-Mode Venntel a letter calling for an investigation on Venntel “Americans are learning more every day about the secretive and shady data broker industry and they’re demanding new laws to protect our privacy,” Wyden said in the email to The Markup. “It’s no surprise data brokers are trying every avenue they can think of to ward off the common sense protections Americans desperately need.” -Sen. Ron Wyden X-Mode and Venntel didn’t respond to a request for comment. Some companies said they were lobbying to have a voice on legislation, including potential federal laws on privacy. “With increased momentum, attention and legislative activity in multiple states, we support a federal data privacy law that can rebalance the system and set standards that rebuild trust with the people providing the data—consumers,” Christine Travis, a senior communications director for LiveRamp, said in an email. “A federal approach to data privacy and security is better than a patchwork of state laws for all stakeholders.” When somebody shows up on the lobbying records, it just tells me they see a real threat to their business model. -Hayley Tsukayama, Electronic Frontier Foundation LiveRamp was the top lobbying spender among companies whose for advertising purposes. The company, which connects people’s activity across the web and from offline purchases for advertisers, spent $630,000 on lobbying in 2020 on issues such as the and Privacy Shield. primary focus is collecting data Augmenting Compatibility and Competition by Enabling Service Switching (ACCESS) Act Privacy Shield was a framework for transferring data between the European Union and the U.S. but was declared . invalid last July for failing to meet the EU’s privacy regulations Travis denied that LiveRamp is a data broker despite being listed on , insisting instead that the company is a “data connectivity platform.” California’s data broker registry Experts said scrutiny and oversight is fairly new to the data broker industry. “When somebody shows up on the lobbying records, or in meetings or in trade associations, it just tells me they’ve probably recently woken up to this issue and they see a real threat to their business model by privacy regulations,” Hayley Tsukayama, a legislative activist for the Electronic Frontier Foundation, said. Others, however, said Congress is not doing nearly enough to regulate the unwieldy industry. “What is widely understood now in Congress and among independent agencies like the FTC are the ways in which large tech companies like Facebook and Google are extracting data from consumers and using that to monopolize markets like the advertising industry,” Jane Chung, a Big Tech accountability advocate at the consumer advocacy group Public Citizen, said. “What a lot of people don’t understand is how data brokers fit into that ecosystem.” Originally published as " “ with the Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license The Little-Known Data Broker Industry Is Spending Big Bucks LobbyingCongress

Who are the Data Brokers Spending Big Bucks to Lobby Congress?

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