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Crypto Beyond Capitalism: Part 5

In , agents are trained on a and mechanism. The is rewarded for correct moves and punished for the wrong ones. In doing so, the agent tries to minimize wrong moves and maximize the right ones. Reinforcement Learning (RL) reward punishment agent Source In this article, we’ll look at some of the real-world applications of reinforcement learning. Applications in self-driving cars Various papers have proposed for . In self-driving cars, there are various aspects to consider, such as speed limits at various places, drivable zones, avoiding collisions — just to mention a few. Deep Reinforcement Learning autonomous driving Some of the autonomous driving tasks where reinforcement learning could be applied include trajectory optimization, motion planning, dynamic pathing, controller optimization, and scenario-based learning policies for highways. For example, parking can be achieved by learning automatic parking policies. Lane changing can be achieved using while overtaking can be implemented by learning an overtaking policy while avoiding collision and maintaining a steady speed thereafter. Q-Learning is an autonomous racing car that has been designed to test out RL in a physical track. It uses cameras to visualize the runway and a reinforcement learning model to control the throttle and direction. AWS DeepRacer Source Wayve.ai has successfully applied reinforcement learning to training a car on how to They used a deep reinforcement learning algorithm to tackle the lane following task. Their network architecture was a deep network with 4 convolutional layers and 3 fully connected layers. The example below shows the lane following task. The image in the middle represents the driver’s perspective. drive in a day. Source Industry automation with Reinforcement Learning In industry reinforcement, learning-based are used to perform various tasks. Apart from the fact that these robots are more efficient than human beings, they can also perform tasks that would be dangerous for people. robots A great example is the use of AI agents by . This led to a 40% reduction in . The centers are now fully controlled with the AI system without the need for human intervention. There is obviously still supervision from data center experts. The system works  in the following way: Deepmind to cool Google Data Centers energy spending Taking snapshots of data from the data centers every five minutes and feeding this to deep neural networks It then predicts how different combinations will affect future energy consumptions Identifying actions that will lead to minimal power consumption while maintaining a set standard of safety criteria Sending and implement these actions at the data center The actions are verified by the local control system. Reinforcement Learning applications in trading and finance Supervised models can be used for predicting future sales as well as predicting . However, these models don’t determine the action to take at a particular stock price. Enter Reinforcement Learning (RL). An RL agent can decide on such a task; whether to hold, buy, or sell. The RL model is evaluated using market benchmark standards in order to ensure that it’s performing optimally. time series stock prices This automation brings consistency into the process, unlike previous methods where analysts would have to make every single decision. for example has a sophisticated reinforcement learning based platform that has the ability to make . It computes the reward function based on the loss or profit of every financial transaction. IBM financial trades Reinforcement Learning in NLP (Natural Language Processing) In NLP, RL can be used in , and just to mention a few. text summarization question answering, machine translation The authors of this Eunsol Choi, Daniel Hewlett, and Jakob Uszkoreit propose an RL based approach for question answering given long texts. Their method works by first selecting a few sentences from the document that are relevant for answering the question. A slow RNN is then employed to produce answers to the selected sentences. paper Source A combination of supervised and reinforcement learning is used for . The paper is fronted by Romain Paulus, Caiming Xiong & Richard Socher. Their goal is to solve the problem faced in while using Attentional, RNN-based encoder-decoder models in longer documents. The authors of this paper propose a neural network with a novel intra-attention that attends over the input and continuously generates output separately. Their training methods are a combo of standard supervised word prediction and reinforcement learning. abstractive text summarization in this paper summarization Source On the side of machine translation, , propose a reinforcement learning based approach to simultaneous . The interesting thing about this work is that it has the ability to learn when to trust the predicted words and uses RL to determine when to wait for more input. authors from the University of Colorado and the University of Maryland machine translation Source Researchers from Stanford University, Ohio State University, and Microsoft Research have fronted Deep RL for use in . The deep RL can be used to model future rewards in a Conversations are simulated using two virtual agents. Policy gradient methods are used to reward sequences that contain important conversation attributes such as coherence, informativity, and ease of answering. dialogue generation chatbot dialogue. Source More NLP applications can be . found here Reinforcement Learning applications in healthcare In healthcare, patients can from policies learned from RL systems. RL is able to find optimal policies using previous experiences without the need for previous information on the mathematical model of biological systems. It makes this approach more applicable than other control-based systems in healthcare. receive treatment RL in healthcare is categorized as in chronic disease or critical care, automated medical diagnosis, and other general domains. dynamic treatment regimes(DTRs) Source In DTRs the input is a set of clinical observations and assessments of a patient. The outputs are the treatment options for every stage. These are similar to states in RL. Application of RL in DTRs is advantageous because it is capable of determining time-dependent decisions for the best treatment for a patient at a specific time. The also enables improvement of long-term outcomes by factoring the delayed effects of treatments. use of RL in healthcare RL has also been used for the discovery and generation of optimal DTRs for chronic diseases. You can dive deeper into RL applications in healthcare by exploring this . paper Reinforcement Learning applications in engineering In the engineering frontier, Facebook has developed an — . The platform uses reinforcement learning to optimize large-scale production systems. Facebook has used Horizon internally: open-source reinforcement learning platform Horizon to personalize suggestions deliver more meaningful notifications to users optimize video streaming quality. Horizon also contains workflows for: simulated environments a distributed platform for data preprocessing training and exporting models in production. A classic example of reinforcement learning in video display is serving a user a low or high bit rate video based on the state of the video buffers and estimates from other machine learning systems. Horizon is capable of handling production-like concerns such as: deploying at scale feature normalization distributed learning serving and handling datasets with high-dimensional data and thousands of feature types. Reinforcement Learning in news recommendation User preferences can change frequently, therefore to users based on reviews and likes could become obsolete quickly. With reinforcement learning, the RL system can track the reader’s return behaviors. recommending news Construction of such a system would involve obtaining news features, reader features, context features, and reader news features. News features include but are not limited to the content, headline, and publisher. Reader features refer to how the reader interacts with the content e.g clicks and shares. Context features include news aspects such as timing and freshness of the news. A reward is then defined based on these user behaviors. Reinforcement Learning in gaming Let’s look at an application in the frontier, specifically . Using reinforcement learning, AlphaGo Zero was able to learn the game of Go from scratch. It learned by playing against itself. After 40 days of self-training, Alpha Go Zero was able to outperform the version of Alpha Go known as that has defeated . It only used black and white stones from the board as input features and a single neural network. A simple tree search that relies on the single neural network is used to evaluate positions moves and sample moves without using any rollouts. gaming AlphaGo Zero Master world number one Ke Jie Monte Carlo Real-time bidding— Reinforcement Learning applications in marketing and advertising In this , the authors propose with multi-agent reinforcement learning. The handling of a large number of advertisers is dealt with using a clustering method and assigning each cluster a strategic bidding agent. To balance the trade-off between the competition and cooperation among advertisers, a Distributed Coordinated Multi-Agent Bidding (DCMAB) is proposed. paper real-time bidding In marketing, the ability to accurately target an individual is very crucial. This is because the right targets obviously lead to a high return on investment. The study in this paper was based on — the largest e-commerce platform in China. The proposed method outperforms the state-of-the-art single-agent reinforcement learning approaches. Taobao Reinforcement Learning in robotics manipulation The use of deep learning and reinforcement learning that have the ability to grasp various objects — even those unseen during training. This can, for example, be used in building products in an assembly line. can train robots This is achieved by combining large-scale distributed optimization and a variant of called . QT-Opt support for continuous action spaces makes it suitable for robotics problems. A model is first trained offline and then deployed and fine-tuned on the real robot. deep Q-Learning QT-Opt Google AI applied this approach to where 7 real-world robots ran for 800 robot hours in a 4-month period. robotics grasping Source In , the QT-Opt approach succeeds in 96% of the grasp attempts across 700 trials grasps on objects that were previously unseen. Google AI’s previous method had a 78% success rate. this experiment Final thoughts Whereas reinforcement learning is still a very active research area significant progress has been made to advance the field and apply it in real life. In this article, we have barely scratched the surface as far as application areas of reinforcement learning are concerned. Hopefully, this has sparked some curiosity that will drive you to dive in a little deeper into this area. If you want to learn more check out this — no pun intended, and as well. awesome repo this one See also: How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug The Best Reinforcement Learning Papers from the ICLR 2020 Conference This article was originally written by Derrick Mwiti and posted on the Neptune blog . You can find more in-depth articles for machine learning practitioners there.

Reinforcement Learning: 10 Real Reward & Punishment Applications

Jules Verne, John Brunner, Arthur Clarke, William Gibson, George Orwell — it’s a short list of writers who predicted the future in their books. They’ve written about social and technical changes that will take place in human society. Here we are, facing those changes good or bad. Path of The Prophet The 3rd of October, 2007 year. A new prophecy has been made. Jason Calacanis in his own blog raised the topic about the future of the internet and named it . Twelve years ago Net was just a force that finally found power to impact business, social and other life areas. How did things change for these years? Web 3.0 "Web 3.0 is defined as the creation of high-quality content and services produced by gifted individuals using Web 2.0 technology as an enabling platform." ( source ) Now, you don’t have to be a computer engineer to build your own website. Ready-made solutions for creating a web-site are a strong opponent to custom-developed pages. Think about website promotion? Need articles? The neural network can generate readable and meaningful texts. Give it some time and nobody will notice that information is provided by something inhuman. Does it sound dark enough? Let’s brighten the colors. You can never lose touch with your friends, relatives, co-workers. You have access to all world information in your pocket and find it in seconds. You can order food, clothes and even buy a car sitting on your couch. Pretty good, huh? So, we made the new industrial revolution using Web 2.0. Every revolution has its own cost. Cost of the connection We’ve built a powerful data sharing container around social media, news, business, sports and now we’re paying for this. We should pay with our time, attention. Fast and everlasting connection to the Net is good. But are we able to “taste” everything that is delivered to us using the Internet? That’s why in every phone, OS we have a Do Not Disturb feature. Developers customize them year by year to create something like a spam filter or a tool for important information. The Internet created big opportunities for business. We quickly receive new e-mails and immediately answer them, discuss current affairs in messengers and quickly make decisions, jointly create documents in office suites and monitor the work of the business using management systems and CRM. But are all management issues resolved with current tools? Also it made people defocused. One of the most common troubles for employees is to find a necessary document. So, It turned out that, the weakest part of the data distribution system is a human, creator of the system. Salvation Data Market How does Web 3.0 should solve these issues? This concept focuses on the main purpose of the Internet — to serve people as a personal assistant. Now the worldwide web knows about us pretty much. To get the help we should share our information with the Internet, but instead of it, we are afraid of security issues. “Big brothers” like Facebook, Google, Yandex and others are watching us. They easily sell our data or open it to governments, even if you are an exemplary citizen. Who owns the information, owns the world now. But the real data owner is you and the third generation of the Internet will return this ownership to you. The market is over-flooded with goods, services. You need them, but the thing you need the most is frank advice. That’s why crowd marketing is so good. That’s why digital marketing tries to help instead of selling you something. In the future you’ll be able to ask the Internet which car suits your needs best, what food will help you to be healthy and attractive like you can ask Netflix about the next movie today. These tips will be even better than friends’ ones because they are the professionals’ choices made based on your preferences or features. It is necessary to collect information for the user, not about him. Data Consumption How does a new generation of the internet should impact your data consumption? People want end-to-end navigation to work for all entities and pages that can be linked to each other. Imagine, when you open the browser, you can see the custom dashboard, where all the main operational information flows — news, promotions and discounts, blog posts, vacancies and much more filtered data. It accumulates according to the interests of the user, and not just a common shaft from all sources. Even more up-to-date data should come to the curtain of a mobile phone, according to those clearly formulated subscriptions that we did not just on a specific site, but in its specific section, block, page. In the end, we want to reuse the data we see. New Web Is Yours Requests for a new web are obvious, users have waited for more comfort so long. We can’t cope with the flow of information from the Internet very often. Web 3.0 is already somewhere nearby, it is needed, and a little more time will pass and the solution of the above problems will become inevitable. Let’s return to the beginning of the article for one second. Do famous writers give us drawings or schemes of future devices? Of course, no. They gave us something more useful. They gave us a vision and a sense of things. How to build them is up to us. So, a new look at the Internet is our choice. New Web, safe and useful, is mine and your choice. Everyone should do steps to make our working and personal web-environment the thing we want. How is it possible? Analyze the data across the Web and you’ll understand the same thing that we understood a while ago. There is no better format for data than tables and documents for data saving. We invented the tools for powerful interaction with these data-forms. Then we “teach” them to be user-friendly. You shouldn’t be a scientist to use the service. Focus on creating content, read and watch the content you like without interruptions or long searching. Make money, when you share knowledge with others. What will be in the future? Remember how people switched from cellphones to smartphones. At first, it was hard to believe in these technologies for many of us. Old-fashioned ways were more comfortable and it seemed unrealistic to have a 6-inch phone in your pocket. Time passed, our mindsets changed and we’re using phones of the new generation up to 90% more times a day. Will be great to make a revolution in the data world. We already created a platform with powerful filtering tools and give people the ability to share content and sell access to valuable information. won’t spy on you . We “to provide the best services” No chance. You’re able to set preferences, change and transform methods of consuming the information you’re interested in. So, we will return data ownership taken from you by “Big Brothers”. We will be your data filter, and your “Do Not Disturb Mode”.

The Future of the Internet Through the Web 3.0 Lens

According to WHO “The enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinction of race, religion, political belief, economic or social condition” Smart Health in Smart cities Density in the urban population has posed new challenges to healthcare such as high demand for more hospitals, medical personnel, and medical resources in sustainable cities. provide an infrastructure for many other local and regional government and private functions such as smart health, smart mobility, smart commerce, and smart communication to name a few. Smart cities Smart health in smart cities has been defined by as Solanas et al Smart health is the provision of health services by using the context-aware network and sensing infrastructure of smart cities. Integration of IoT in medical science has made healthcare ubiquitous and pervasive. By providing outpatient care at homes, not only does the cost of healthcare decrease but also the independence of patients and their relatives increase. Soon, the healthcare sector will evolve from centralized hospitals to .  The IoT integration to smart health benefits doctors, patients, and healthcare workers in smart hospitals, smart home care, and robot surgeons. ubiquitous and pervasive Internet of Things (IoT) in Healthcare Market is Expected to Grow at a CAGR of 29.9% reaching $322.2 billion by 2025. Smart health is an application of IoT and is enabled via sensors, actuators(implanted medical devices), smartphones and cloud services. The sensors collect clinical data such as blood pressure, blood glucose level, pO2 level and send it to a remote healthcare provider. The remote health care provider can even send commands to the actuators implanted in the human body like insulin infusion pumps, pacemakers and other cardiac devices. These devices use wireless remote controls, connect to the internet, relay patient data, and deliver critical therapies on command from remote healthcare providers. Privacy in Smart Health With the comfort and efficiency provided by smart health, there come the issues of privacy and security as well. The health records of patients is confidential information which must not be leaked at any time. But, in a smart health environment where third party cloud services are employed, there are chances of security vulnerabilities. The protection of huge amounts of data that contains highly personal information of citizens is an inescapable challenge for researchers.The data protection in smart health is especially critical, as it can lead to life threatening outcomes. In appropriate values of patients vitals can result in wrong medical prescriptions which can lead to serious health problems. Along with data protection, unauthorized access to medical devices may result in fatal consequences. An example of a misused can result in hyperglycemia/hypoglycemia. Similarly, can alter the normal functioning of devices and even has potential to kill. insulin infusion pump unauthorized access to cardiac devices Usage Control in Smart Health IoT and cloud-enable smart health accredits healthcare personnel to view and update patients data. Similarly patients can view and update their care records in cloud services. The access to the healthcare records must be controlled through usage control policies. E-g a general physician has the right to view the records of a patient, but he is restricted to update them by usage control policies. The model, suggested in my previous article, is a step towards the protection of data collected from smart city infrastructure. The components of SmartUCON are mapped onto the smart health scenario and their explanation is given below: Usage Control Subjects in the case of smart health scenarios are doctors, healthcare providers, and medical staff, who need access to patient’s data for monitoring, diagnostics, and treatment. Patients also become subjects when they need to view their medical records as they are only allowed to access specific information with conditions of not modifying even their records. Subjects: In the case of smart health data owners are patients. The physiological information of patients to be accessed by subjects may include glucose level sensing, ECG, Blood pressure monitoring, body temperature monitoring, and oxygen saturation monitoring, etc. Objects: There are several tasks related to patient care e-g keeping and managing records of physiological data, adding or updating records, accessing patient's current body parameters, administering drugs, etc. SmartUCON utilizes the RBAC model of access control, therefore, roles must be assigned to Subjects (S) with Permission (P) to access only permitted attributes by following Conditions (C) and Obligations (O). Examples of roles in this scenario are roles of healthcare providers which can be a nurse, a senior doctor, a junior doctor, pharmacist, intern, healer, etc. All of these roles are authorized to access only the permitted data according to assigned roles. Roles: Since SmartUCON uses the RBAC model which states that the user's roles are subject attributes. However the attributes whether it be subject attribute or object attribute, an update must be forced and controlled by the system administrator. Attributes: In smart health, obligations are forced upon the access of sensitive data of patients where subjects are restricted to access information designated to their roles. Obligations: The example of conditions in smart health are confidentiality, locality, abstraction, and timestamp of patient data. Conditions: Rights are the privilege that roles of smart health hold and practice. The rights contain a set of functions for data usage. Rights: The encryption module of utilizes lightweight encryption techniques to secure data retrieved from the sensing layer of smart cities' infrastructure. This ensures that even the system administrator and cloud server remains unaware of the assignment of roles to subjects SmartUCON An example of a very simple use case incorporating different roles in healthcare systems and their rights on objects is given below: The smart health example contains three different roles; Patient, Doctor, and Pharmacist, that have rights to access objects based on their roles. Here is the formal description of transaction flow in use case; ROLES= {Patient, Doctor, Pharmacist} OBJECTS (O) = {Patients Records, Prescription Drugs, Diagnosis}. RIGHTS (R) = {View, Add, Prescribe, Enter, Dispense} Example of Doctors accessible operations: if ROLES(Doctor)==true; allowed_operation= {View, Add, Prescribe, Enter}. Similarly for other roles Conditions and Obligations must be fulfilled to grant access. Conclusion In addition to preventing unauthorized access, be aware that even authorized users can also be potential threats. Usage control provides fine grained access to data and resources. provides capabilities to configure for different level authorities and easy to track the records of events. Access control in smart cities If threat actors can’t access your network, the amount of damage they’ll be able to do will be limited. Yet, keeping in mind the sensitivity of smart health to vulnerabilities, usage control should be accompanied by strong defense mechanisms like encryption, anti malwares, intrusion detection and network segmentation.

Understanding Usage Control in Smart Cities: Smart Health Use Case

In recent months, individuals across the globe have shifted to a remote way of life, including working from home, virtual dating, drive-by birthday parties and now, even doctor’s appointments. The healthcare sector quickly implemented changes to provide a more remote experience to comply with social distancing regulations. To decrease the number of face-to-face doctor’s appointments and adhere to social distancing limitations and regulations, the announced they “will not impose penalties for noncompliance” with the regulatory requirements under the HIPAA Rules against covered health care providers in connection with the good faith provision of telehealth during the COVID-19 nationwide public health emergency. Department of Health and Human Services (HHS) The shift to virtual appointments has allowed different healthcare providers to implement some popular video communication applications including Zoom, Skype, WebEx, and GoToMeeting. Telehealth visits can be a successful tool for remotely observing and treating patients with symptoms who are more comfortable and safe staying home. While telehealth has many benefits for patients, it comes with its challenges for healthcare providers – especially the traditional healthcare companies that did not have a telehealth program in place. Similar to scaling an entire company remotely, implementing telehealth programs also takes time to establish, including installing the necessary technology, recruiting and training doctors about the best virtual practices and teaching the patient how best to use the platform. Privacy risks are inherent to any digital process including telehealth. With the sudden surge of telehealth visits, medical professionals, providers, IT teams and even patients need to think more about their privacy, but now in a virtual setting. The healthcare industry’s hyper-sensitive regulations on record sharing present itself as a problem for telehealth platforms. By sharing sensitive data over a virtual communications app, it creates an opportunity for hackers to access the conversation. Cybercriminals can act as flies on the wall of vulnerable and privileged conversations between doctor and patient, since even seemingly secure systems  may sometimes be easily breached and expose a trove of valuable patient information. Hospitals and healthcare providers need to double down on IT and cybersecurity to fight off potential privacy breaches. HIPAA Solves Privacy Risks Paired with Secure Solutions Healthcare has always been an attractive target for hackers trying to breach patients’ sensitive records. While medical data and records are some of the most delicate pieces of information out there, the healthcare industry has the right compliance regulations in place. HIPAA and other regulations have been around for years but since the HHS weakened the previous compliance regulations, telehealth is now a more attractive target for hackers. Now that the government has become more lenient with telehealth regulation rules, enforcing HIPAA with telehealth communication is the right move to provide better privacy for patients. The (HIPAA) requires medical providers to adopt data security in order to protect their patients’ information from disclosure. The HIPAA regulation requirement of encryption initially sounds a bit confusing, however, it’s more simple than suggested. The HIPAA encryption requirements for transmission security state that covered entities should “implement a mechanism to encrypt PHI whenever deemed appropriate.” Health Insurance Portability and Accountability Act In other words, the majority of healthcare organizations are required to be and each provider needs to have some level of security for PHI. Healthcare providers are required to encrypt their data unless they can justify why they can’t implement encryption and can provide an equal alternative. HIPAA compliant Tasked with choosing the best way to store, access and back up electronically protected health information, many healthcare technology companies and providers are looking at cloud computing. They are adopting cloud-based Network as a Service technology as an alternative to traditional hardware-based solutions, as it offers scalability, affordability and increased compatibility with cloud storage environments. But remember, the security service you choose must be and and have signed multiple HIPAA BAAs. With these checks in place, a Network as a Service solution can offer a highly effective solution for any organization’s HIPAA compliance needs. SOC 2 type 2 compliant ISO 27001-compliant Secure Telehealth for a Secure Future of Healthcare As we are experiencing global social distancing, telehealth is quickly evolving, as is the way that it presents a remote option for healthcare services. However, potential privacy and security risks could decrease its value in moving forward. Soon, we should begin seeing more government bodies authorize and create federal telehealth privacy and security protocols which will help healthcare providers avoid risks to their patients and better show the numerous benefits telehealth has to offer. With the help of the government and best privacy and security tools in place, telehealth will have that added security to fight off hackers, and defend its security model.

With Telehealth on the Rise, Privacy Regulations are Imperative

This paper is under CC 4.0 license. available on arxiv Authors: (1) Kota Chin, University of Tsukuba, National Institute of Information and Communications Technology Japan; (2) Keita Emura, Kanazawa University, Japan National Institute of Information and Communications Technology Japan; (3) Kazumasa Omote, University of Tsukuba National Institute of Information and Communications Technology Japan. Table of Links Abstract & Introduction Preliminaries Proposed Anonymous Yet Accountable Contract Wallet System Implementation Conclusion & References V. CONCLUSION In this paper, we proposed an anonymous yet accountable contract wallet system based on account abstraction and accountable ring signatures. The proposed system is implemented using Solidity for zkSync. Moreover, we discussed potential of the proposed system, e.g., medical information sharing and asset management. Since the current implementation results using Solidity show the required costs are expensive, our result here might be regarded as somewhat conceptual. However, to the best of our knowledge, no previous implementation result is known that confirms the cost to run an accountable ring signature scheme in Solidity to date, and we believe that our result can be seen as an important stepping stone to provide anonymity and accountability simultaneously in blockchain systems. Investigating other applications of the proposed system will be left to future work. The underlying account ring signature scheme does not provide post-quantum security due to the discrete logarithm-based construction. Thus, it is difficult to accept the current construction as a platform to manage large amounts of assets due to the progress of quantum computing. Because a post-quantum accountable ring signature scheme has been proposed in [7], it would be interesting to employ the scheme, precisely, how to implement it using Solidity is left to future work. The authors would like to thank Dr. Miyako Ohkubo (NICT) for her invaluable comments and suggestions. This work was supported by JSPS KAKENHI Grant Numbers JP21K11897 and JP22H03588. Acknowledgment: REFERENCES [1] Arrest of suspected developer of Tornado Cash. https://www.fiod.nl/arrest-of-suspected-developer-of-tornado-cash/. August 12, 2022. [2] StarkNet. https://starkware.co/starknet/. [3] zkSync. https://zksync.io/. [4] Jean-Philippe Aumasson, Daniel J. Bernstein, Ward Beullens, Christoph Dobraunig, Maria Eichlseder, Scott Fluhrer, Stefan-Lukas Gazdag, Andreas H ¨ulsing, Panos Kampanakis, Stefan K ¨olbl, Tanja Lange, Martin M. Lauridsen, Florian Mendel, Ruben Niederhagen, Christian Rechberger, Joost Rijneveld, Peter Schwabe, and Bas Westerbaan. 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Anonymous, Accountable Contract Wallet System With Account Abstraction: Conclusion & References

Introduction Remember when Femtech was a brand-new term nobody knew? It feels like we have been using this word forever, but it has been around for less than a decade! Yet the history of products, technologies, and services addressing health issues specific or prevalent to women started long ago, ages before this umbrella term was first used in 2016 to address this rapidly evolving market. Since then, this industry has had major plot twists, from Maven Clinic becoming the first unicorn in 2021 to venture capital infusions shaking up the game; the sector is sizzling hot! Femtech And let us give a thumbs-up to the growing acceptance and issues. It is no longer a niche but a booming market, with its size estimated at over $55 billion in 2022 and . de-stigmatization of women’s health expected to surpass $100 billion by 2030 Why is it important to me? I am a digital tech specialist coming from the Artificial Intelligence/ Machine Learning field with a rich background in building and successfully deploying voice recognition, AI cameras, and virtual assistant products catering to millions. Lately, I have shifted my focus, stepping into the intriguing world of Femtech and healthcare drawn by the promise of AI to simplify and enhance healthcare solutions, changing stagnant approaches to women’s healthcare through daring initiatives. With this article providing a thoughtful inquiry into how advanced technologies like LLMs shape modern healthcare and Femtech, I invite you to join me on this exciting journey. Together, we will take a look at how boosts the Femtech market and sift through the new directions and strategies reshaping this burgeoning sector. Our aim will be not only to learn about the tendencies of the industry but also to identify key patterns that could offer thrilling opportunities. And so, if you are curious like me about how novel technologies improve healthcare experiences for women and what strategic footholds exist in the Femtech market, let us dive in and explore. AI/ML Current trends and thrilling developments in Femtech In the last few years, the Femtech sector has been undergoing a transformative shift. Gone are the days when Femtech products predominantly focused on menstrual cycle tracking and fertility planning. Today, the industry is blossoming with more specialized solutions, from advanced home-testing kits to cutting-edge wearables. Technological advances have attracted a range of strong players in a multitude of niche markets, each aiming to revolutionize different aspects of women’s health. 1. From Basic Tracking to Comprehensive Health Solutions Take Sonio, a Paris-based Femtech firm. They recently received FDA clearance for their AI product, Sonio Detect, which aims to redefine prenatal ultrasounds. What sets Sonio apart is its manufacturer-independent tool that simplifies the screening process while raising its efficiency and accuracy, offering an easy-to-use yet powerful solution. San Francisco startup Mira is pioneering another monumental shift in Femtech by embedding lab technology into a handheld fertility monitor. With a user base exceeding 76,000, Mira’s advanced fluorescent detector goes beyond traditional ovulation kits. The technology allows for daily health checks and has accumulated an impressive amount of data — over 8.5 million hormone data points and analysis of more than 670,000 menstrual cycles. With AI-driven insights into hormone fluctuations, Mira is offering more personalized and accurate predictions than ever before, quite literally putting a mini hormone lab in the palms of its users. Recently, The Inc. placed Mira at number 437 on its 2023 list of fastest-growing private companies in the US, ranking it the highest among Femtech companies. More complex products that require significant medical research are also quickly gaining momentum. Teal Health exemplifies this with its recent $8.8 million seed funding round, backed by prominent investors like Emerson Collective and Serena Ventures. The funds aim to build a comprehensive platform, starting with the crucial issue of cervical cancer. women’s telemedicine 2. The Rise of Wearable Innovations Wearable technology is another frontier where Femtech is making significant strides. One famous example is Bloomlife, a clinically validated diagnostic device for pregnant women that allows future mothers to track contractions, giving them a better awareness of their progress. Movano Health has captured attention with its Evie Ring — a wearable gadget currently under examination by the FDA. Upon receiving FDA clearance, the Evie Ring will have the potential to set new standards in women’s health monitoring, offering an integrated, round-the-clock health monitoring and tracking solution right at the user’s fingertips. Ava, a fertility and pregnancy tracking bracelet with an accuracy rate of over 90%, has already been cleared by the FDA. Established in 2014, well before we even started using the term ‘Femtech,’ Ava is a prime example of a successful Femtech startup. Having attracted over $42 million in funding, the company reported to have helped over 70 thousand women in 36 countries to get pregnant by 2020. 3. The Expansion to Different Age Groups One of the noteworthy trends in Femtech recently is the surge in startups focusing on menopause. According to Female Insights, over 30% of companies in this sector were founded in the last three years alone. This uptick is accompanied by increasing diversity in the types of solutions being offered to tackle menopausal symptoms — from mobile apps that track hormones and symptoms and platforms facilitating user interaction and community building to consumer goods and therapeutic medications. According to Pubmed, by 2030, up to 1.2 billion women will be in or approaching menopause globally, with 47 million new women joining every year. By 2025, it is estimated that over 1 billion people worldwide—about 12% of the population—will be going through menopause. Financially, the menopause product market was valued at $15 billion in 2020 and is projected to skyrocket to $23 billion by 2028. These figures illustrate a maturing market with significant financial potential, underscoring why it has become a magnet for innovation and investment. An Irish startup, IdentifyHer, offers menopausal women sensor solutions. Controlled through a smartphone app, the sensor passively gathers information on menopause symptoms without interfering with the wearer's daily activities. The app also makes use of the data gathered to track progress and assess the effectiveness of lifestyle changes. The collected data is presented in periodic reports and can be used by medical professionals to diagnose and treat menopausal symptoms. AI-driven Midday is another customized menopause product developed by US-based startup Lisa Health that offers comprehensive management of the menopause journey. Midday’s solution, including a wearable device, an AI-based symptom tracking system, and a decision support tool for hormone therapy, uses advanced tracking to determine the menopause stage and anticipate symptoms. The app is used by doctors to manage the entire menopause process and provide decision support, while users are provided with a variety of therapeutic options and informational resources to give them personalized insights and get them ready for every stage of menopause. 4. Going Beyond Just Health Although Femtech is considered, first of all, a rising star in the Medtech industry, the possibilities of women-oriented digital technology products are not limited to healthcare alone. Recently, the term started to see new applications, one of which is Women’s Safety. Indian startup Shakti Wearables manufactures Shakti, a women’s defensive wearable device that increases women's safety in dangerous situations. When the wearer activates the button on the device, a small, non-lethal current is released, electrifying an attacker, while the internal insulation of the bangle keeps the wearer safe; this gives women a chance to flee their attackers and call for assistance. Additionally, Shakti sends the wearer's most important three contacts a WhatsApp message with their most recent live location. Another interesting personal safety app is being developed by Australian startup Safely, offering women peer-to-peer support in tense or frightening circumstances. With their contacts, users of the app can create private groups or circles for internal communication and location sharing. In case of emergency, users can also switch on the high-alert mode and notify each of their designated guardians. The app then uses live monitoring, records live video and audio, sends GPS updates, and saves all of this data to the cloud for later use as evidence. Additionally, users receive safety alerts from locations of interest to help them plan their commute or travel safely. Expanding healthcare’s reach with new technologies The world of healthcare is rapidly evolving, especially when it comes to Femtech. This dynamic transformation is powered by modern innovative technologies that are reshaping how women engage with healthcare and how healthcare providers offer services tailored to them. 1. The Personalization Revolution Fueled by LLMs One of the most disruptive advancements in this domain is the rise of Large Language Models (LLMs) like ChatGPT and GPT-4. Far from being a mere tech fad, LLMs are revolutionizing healthcare, with Femtech embracing this new technology. LLMs have the potential to serve as virtual nursing assistants, supporting busy doctors in responding to patients' basic queries about medications or health conditions. As you will see from the examples below, LLMs are proving to be instrumental in facilitating the creation of highly promising Femtech products, from personalized consulting products to innovative community engagement tools. One of the most intriguing examples of the implementation of this technology in Femtech is Soula, a Cyprus-based startup that provides women with support during pregnancy, childbirth, and motherhood with its "AI-powered" assistant. Powered by Med-PaLM, an LLM developed by Google to generate answers to medical questions, the app is expected to be able to respond to inquiries about pregnancies and maternity issues and provide information on a range of health-related concerns. It will also be able to answer questions about diet, sleep, physical activity, and emotional self-care, as well as track fetal movement and kick frequency. Mexican startup Kuna utilizes an LLM-powered chatbot to give women a secure way to access information and advice on parenting and sexual health. The startup's , Kuna Chat, offers details on sex, reproduction, pregnancy, and parenthood. The startup also provides online consultations with experts for more complex or difficult questions. In this way, Kuna encourages sex education for the uneducated while offering trustworthy, practical, and concrete answers to users' queries. AI-powered chatbot 2. Cost-Effective Health Management An extraordinarily important aspect of building successful medical products is to make services affordable to the customer, reaching broader but less solvent user segments and geographies. In this vein, Femtech offers not only personalized but also cost-effective healthcare solutions. Apps often provide free basic versions, with premium services available through subscription fees. Quite obviously, the use of LLMs trained on medical data sets and specialized in specific product cases can provide a boost to the more affordable cost of the products themselves. For example, the Ava fertility bracelet mentioned in the previous section of this article costs around $249 in the U.S., and the accompanying app is free with the bracelet purchase. When considering that the average cost of infertility treatment in the U.S. is around $11,000—often not covered by basic healthcare plans—Femtech presents a more affordable starting point for many women. 3. Breaking Barriers in Remote and Rural Areas Digital technology in Femtech is also crucial for improving healthcare access in remote and rural areas, especially in low- or middle-income countries in Africa and Asia — two regions with the highest female populations. Femtech provides innovative solutions such as virtual prenatal visits and remote monitoring, to manage their health more cost-effectively, regardless of their location. empowering women One successful example of such a product is M♡THer – a virtual care product facilitating the diagnosis of gestational diabetes mellitus by measuring blood glucose levels, blood pressure, weight, and more. GDM affects up to 20% of pregnant women, leading to various birth complications, and M♡THer, developed by the Australia E-Health Research Centre, has already benefited thousands of women in remote areas of the region. 4. A Multifaceted Approach to Chronic Diseases Femtech is not solely about reproductive health; it also targets female-specific chronic diseases through a mix of prevention, societal change, and technology. Mobile apps and wearable devices make it easier to manage and monitor conditions over time, a vital component of tackling chronic diseases that affect women disproportionately. To name an example of a health issue specific to women, it is estimated that 1 in 10 women have endometriosis – the growth of endometrial tissue outside of the uterus that can cause significant problems from pelvic pain to infertility. DotLab, a US-based medical diagnostics company, pioneers medical tests in this area, applying machine learning mechanisms to diagnose and assess this currently incurable disease. On the other side of the Atlantic Ocean, Kheiron Medical Technologies, a UK Medtech startup, combines data science and radiology to help with the diagnosis of breast cancer, another terrible disease affecting predominantly women. According to published clinical trial results, the company’s breakthrough technology achieves previously unseen heights in malignancy detection in breast cancer screening. Applied Conclusions Recent years have shown rapid exponential growth in the number of Femtech companies and products, both through the use of innovative technologies and through the discovery of more and more new applications and solutions. With the Femtech market estimated to reach an imposing $100 billion globally by 2030, it is one of the areas of the modern technology economy that is expected to provide maximum lucrative opportunities for new bright ideas and prosperous businesses in the coming years. While deciding whether to follow this attractive route, aspiring Femtech entrepreneurs should consider the following market characteristics. While there are highly competitive niches in the Femtech market, specifically well-established fields like menstrual cycle and pregnancy tracking, there are also areas that are more attractive for entry. Examples of such areas include app-controlled wearables and menopause management. The largest share of the global women’s health market surprisingly pertains to chronic women’s health conditions (forecast to reach $218 billion by 2027), and only 1 percent of existing Femtech companies address this area. Did you hear that? That is an opportunity knocking. AI and specifically Large Language Models can be a powerful boost for Femtech products as (a) with proper customization, LLMs assist in scaling the product or service itself and strengthen the core part of the product, and (b) they provide magnificent tools for community development, and the community is said to be the most important part of a product! Organic communities built around products are both a way of monetization and a tool that increases retention, helping build durable community-led growth. Taking into account the fact that the Femtech market is rapidly developing with a simultaneous increase in competition, and given the general market conditions (in particular, subscription applications revenues are currently declining; even if positive dynamics have remained in the Medtech segment, it has become more difficult to attract and retain users), companies need to be cost-efficient and provide a highly personalized, secure service to survive and rise above the competitors. A relevant and extremely effective tool employed by more and more technology startups for achieving these goals, again, is AI technologies. Whether it's addressing questions about menstrual health, pregnancy, or chronic conditions, LLMs can fill the information gap quickly and efficiently. Statistically, only 6% of Femtech companies used AI in 2021, mostly in diagnostics and data analytics, and while this share has seen stable growth over the years, there is enough room for new inspired ideas and success stories in this area. All in all, the advent of heralds an exciting era for Femtech entrepreneurs. LLMs have the potential to revolutionize user engagement and content delivery, supporting busy healthcare professionals by handling preliminary patient communication, serving as virtual nursing assistants or health advisors, and simply bringing a personalized touch to technology. With cutting-edge AI technologies charging Femtech products, humanity has the chance to drastically improve the efficiency of the entire healthcare system catering to women. Finally, there is a chance to overcome the millennia-old barriers plaguing this area of medicine, such as high costs, gender bias, stigmatization of women's health-related topics, and a lack of resources. Large Language Models Powered by the latest technological advancements, Femtech waits for its next hero — and it may be you!

Beyond Wellness: How Femtech is Shaping the Future of Women's Healthcare

সুস্থতার বাইরে: কীভাবে ফেমটেক মহিলাদের স্বাস্থ্যসেবার ভবিষ্যত গঠন করছে

Beyond Wellness: Wie Femtech die Zukunft der Frauengesundheit gestaltet

Más allá del bienestar: cómo Femtech está dando forma al futuro de la atención médica de las mujeres

Au-delà du bien-être : comment Femtech façonne l'avenir des soins de santé des femmes

कल्याण से परे: कैसे फेमटेक महिलाओं के स्वास्थ्य देखभाल के भविष्य को आकार दे रहा है

ウェルネスを超えて: フェムテックが女性のヘルスケアの未来をどのように形作るか

웰니스를 넘어서: Femtech가 여성 건강 관리의 미래를 형성하는 방법

Além do bem-estar: como a Femtech está moldando o futuro da saúde feminina

За пределами здоровья: как Femtech формирует будущее женского здравоохранения

Wellness'ın Ötesinde: Femtech Kadın Sağlığı Hizmetlerinin Geleceğini Nasıl Şekillendiriyor?

Ngoài sức khỏe: Femtech đang định hình tương lai của ngành chăm sóc sức khỏe phụ nữ như thế nào

超越健康：Femtech 如何塑造女性医疗保健的未来

Despite the advancement of ’ capabilities over the past year, adoption has been slowed by terrible user experience. Users are required to complete a complicated and onerous series of steps—download a wallet, learn about gas costs, obtain tokens to pay gas, save seed phrases, and more. This poses a significant hurdle for users new to the blockchain, or those who are just uncomfortable with holding crypto. Often, they just give up. dApps To solve this problem, on Flow have emerged. With this approach, users can easily sign up for dApps using credentials they are already comfortable with (social logins, email accounts). This allows them to get started quickly, without needing to understand the complexities of wallets or blockchain. walletless dApps In this two-part series, we’ll explore how walletless dApps on Flow work. We'll look at some use cases and walk you through the steps of building and deploying your wallet-less web3 dApp using the Flow Wallet API and account abstraction. Here in part one, we'll focus on building the backend for our walletless dApp. In part two, we'll wrap up the walkthrough by building the front end. Content Overview How Flow Solves Web3 Onboarding Use Cases of Account Abstraction Building a Sample Walletless Login dApp Set up Flow Wallet API application Configure Environment Variables Update the File docker-compose.yml Test the Flow Wallet API Application How Flow Solves Web3 Onboarding Flow is a highly scalable blockchain with a design philosophy that prioritizes : easy user logins, mobile-ready, fast development time, 99.99% up-time, and more. It’s made for dApps that have real-world usage mainstream use Account abstraction (AA) on Flow falls right into this philosophy. AA, in combination with hybrid custody, creates a experience for users. What does this mean? walletless onboarding Typically, accounts on a blockchain are owned either by a user with a private key—called an externally owned account (EOA)—or by a smart contract—called a contract account. Account abstraction combines these ideas. It allows the user to control the wallet while abstracting away the idea of the wallet altogether by letting the contract also have control. On Flow, this is called Hybrid Custody. Use Cases of Account Abstraction There are lots of advantages to this “account delegation.” Since one account (the child/EOA account) can delegate control to another account (the parent/app account), developers can provide users with a seamless onboarding and in-app experience while giving them a sense of actual ownership and self-sovereignty. This is particularly useful for new users who are not familiar with the intricacies of blockchain technology and can help increase adoption and engagement with the application. For example, developers can build apps that create wallets for users, manage keys and transactions, and even abstract away the blockchain altogether. But in the end, the user still has control and ownership over any assets in the wallet. Other use cases include: Key recovery with multi-sig transactions An account may be set up to require multiple signatures to complete a transaction. This creates a wide variety of new use cases and enhances the usability of Web3 apps.\ Gasless experiences with sponsored transactions The stress that comes with paying fees—before newbies can use a Web3 app and execute a transaction—can be a barrier. Sponsored transactions give room for developers to subsidize these charges on behalf of users. Seamless experiences with bundled transactions Flow’s Cadence programming language, which introduces new features to smart contracts, also separates contracts from transactions. It supports bundling transactions from several contracts into a single transaction at the protocol level. Social logins with wallet-less onboarding + hybrid custody Flow enables its developers to deliver a familiar experience while progressively exposing new users to the benefits of Web3. Users can realize the benefits of both custodial and non-custodial experiences, which is what hybrid custody entails. All this together makes it much easier for users to get started with dApps. web3 Building a Sample Walletless Login dApp So let’s next walk through creating a walletless dApp. We’ll build a dApp that integrates Google social login/signup and creates a Flow wallet for the user on signup time. Here is a quick breakdown of what we’re going to do in part one of this walkthrough: Set up a dockerized Flow Wallet API application to use the Flow Testnet Test the Flow Wallet API Application Then, in part two: Create a new Next.js application Set up Prisma for backend user management Build the Next.js application frontend functionality Test our Next.js application Are you ready? Let’s go! Set up Flow Wallet API application To make our lives easier, we’ll use the . This is a REST HTTP service that allows developers to quickly integrate wallet functionality into dApps. This API was developed in Golang, so knowledge of Go will help you, though proficiency is unnecessary to make this app work! The Flow Wallet API is currently not maintained. However, at the time of writing, the API was working perfectly. Flow Wallet API Clone the project folder with the following command: $ git clone https://github.com/flow-hydraulics/flow-wallet-api.git To facilitate the development, we will use . If you don't have it installed on your OS, you can find instructions for it at this link: Docker https://docs.docker.com/engine/install/ From the terminal, navigate to the newly created directory. flow-wallet-api I recommend taking the time to review the folder structure. This API was very well done and is a complete code with unit tests included! To make this article as short as possible, we will focus on executing the code. MacOS M1 chip config Note: If your computer is a Mac using an M1 chip, you will need to change the file in the folder. If you are not using an M1, you can skip this section. Dockerfile docker/wallet/ In , edit the first line of code. We will change the Golang version used by the container to: Dockerfile FROM golang:1.20rc1-alpine3.17 AS dependencies You will also need to change the value of the field: GOARCH GOARCH=arm64 That's it! These are the required changes in Mac M1 operating systems. Configure Environment Variables Before we can spin up the Flow Wallet API, we need to make some configuration changes. The first step is to rename the file to . The application will use this file to import the environment variables. .env.example .env Within the file, we need to change some values. .env Flow network fields Since we will be running the application over Flow’s Testnet network, we need to change the environment variables and . In the file, comment out the following lines: FLOW_WALLET_ACCESS_API_HOST FLOW_WALLET_CHAIN_ID .env # emulator
# FLOW_WALLET_ACCESS_API_HOST=localhost:3000
# FLOW_WALLET_CHAIN_ID=flow-emulator Then, uncomment the following lines: # testnet
FLOW_WALLET_ACCESS_API_HOST=access.testnet.nodes.onflow.org:9000
FLOW_WALLET_CHAIN_ID=flow-testnet With this change, we’ve modified the configuration of the application to use the Testnet network. Flow admin account fields To use the Testnet, we need to create a Testnet account and update the and fields inside the file. FLOW_WALLET_ADMIN_ADDRESS FLOW_WALLET_ADMIN_PRIVATE_KEY .env To create a Flow Testnet account, you must first install the Flow CLI on your operating system. You can find instructions on how to do this . here With Flow CLI installed, run the command: $ flow keys generate This command will generate an asymmetric public-private key pair. Save the private key somewhere. Then, copy the public key—we will use it in the next step to create an account on the Flow blockchain! Create a Flow Testnet account Open the browser on the Flow faucet: https://testnet-faucet.onflow.org/ In the first input, paste in the public key you just generated. Leave the rest with the default settings, then perform the CAPTCHA verification and click the Create Account button. This will generate a Testnet Address. Copy the address and use it as a value for the field in the file. Also, update the field with the private key that you generated in the previous step. FLOW_WALLET_ADMIN_ADDRESS .env FLOW_WALLET_ADMIN_PRIVATE_KEY In my case, it would look like this: FLOW_WALLET_ADMIN_ADDRESS=0x7cc7be2796e8cf29
FLOW_WALLET_ADMIN_PRIVATE_KEY=73bc408436c14befd74cb01fa4c54217c6c860ff97df1a77a3063a2807c7067f We’re ready! Our Testnet account has been created. This is the admin account that will execute, sign, and pay for the creation of new wallets for our application. Proposal key field Within an account, it is possible to have several proposal keys. As we will call several transactions with the same account to avoid concurrency problems, we will need to create new proposal keys for the admin account. You can read more about proposal keys . here We’ll change the configuration in to create 10 new proposal keys. For our tests, this is a sufficient amount. However, if you start having concurrency problems when executing transactions, you can change this value and restart the application. .env FLOW_WALLET_ADMIN_PROPOSAL_KEY_COUNT=10 Idempotency middleware field We’ll also add a property to the file to disable idempotency middleware. Since we are just testing the application (and using it to facilitate our post requests), we will leave it disabled by setting the following value to . However, it is recommended to activate it in a production environment. .env true FLOW_WALLET_DISABLE_IDEMPOTENCY_MIDDLEWARE=true Update the File docker-compose.yml Since we made some changes to our (including using the Testnet network and disabling idempotency middleware), We can remove a few lines of unnecessary code in the file. Our application will not be using the and containers. So, we can remove them. .env docker-compose.yml redis emulator After removing them, your file will look like this: docker-compose.yml version: "3.9"

networks:
  private:

services:
  db:
    image: postgres:13-alpine
    environment:
      POSTGRES_DB: wallet
      POSTGRES_USER: wallet
      POSTGRES_PASSWORD: wallet
    networks:
      - private
    ports:
      - "5432:5432"
    healthcheck:
      test:
        [
          "CMD-SHELL",
          "pg_isready --username=${POSTGRES_USER:-wallet} --dbname=${POSTGRES_DB:-wallet}",
        ]
      interval: 10s
      timeout: 5s
      retries: 10

  api:
    build:
      context: .
      dockerfile: ./docker/wallet/Dockerfile
      target: dist
      network: host # docker build sometimes has problems fetching from alpine's CDN
    networks:
      - private
    ports:
      - "3000:3000"
    env_file:
      - ./.env
    environment:
      FLOW_WALLET_DATABASE_DSN: postgresql://wallet:wallet@db:5432/wallet
      FLOW_WALLET_DATABASE_TYPE: psql
    depends_on:
      db:
        condition: service_healthy that we also removed some lines from the service: Note api and in the environment section. FLOW_WALLET_ACCESS_API_HOST FLOW_WALLET_CHAIN_ID Lines for and in the section redis emulator depends_on We spin up our containers with the following command: $ docker compose up Test the Flow Wallet API Application When we run the above command, Docker will spin up the following: : This is the database container where all data will be stored, including the users’ private keys. Remember that we are only running the application in a test environment, using the . If you use this API in production (Mainnet), protect the users’ private keys. One option is to use key management system services. The Flow Wallet API has easy and fast integration with Google KMS and AWS KMS. flow-wallet-api-db-1 PostgreSQL Flow Testnet network : This is an application in Golang that connects to the Flow network and performs actions such as creating wallets, transactions, scripts, and much more. flow-wallet-api-api-1 ! Our application runs through these two Docker containers, We can make calls using (the default) port . Done 3000 All endpoints available in the application can be found in the documentation . here Check the Application’s Health The endpoint to check if the application is healthy is ready. /v1/health/ We send a curl request to check this endpoint, and this is what we receive as a response: $ curl -X GET -i http://localhost:3000/v1/health/ready


HTTP/1.1 200 OK
Vary: Accept-Encoding
Date: Fri, 05 May 2023 17:27:05 GMT
Content-Length: 0 With the response, we are assured that our application is up and running. 200 Create a Wallet Creating a new wallet via the API is very easy! We send a POST request to the endpoint. /v1/accounts $ curl -X POST http://localhost:3000/v1/accounts

{
  "jobId":"2876f90d-d9ec-4007-935b-4aba3cb8e45e",
  "type":"account_create",
  "state":"INIT",
  "error":"",
  "errors":null,
  "result":"",
  "transactionId":"",
  "createdAt":"2023-05-05T17:29:34.800299551Z",
  "updatedAt":"2023-05-05T17:29:34.800299551Z"
} Under the hood, the API creates an asymmetric key pair and executes a transaction on the Flow blockchain to create an account. Jobs Since a transaction takes a few seconds and can fail, the API creates . jobs These jobs are records/units that are stored in the database. Each time a transaction is called or queued, a job is created. The status of the transaction is stored in this job. Notice how the transaction returned by our call above is . The API is monitoring the account creation transaction. state INIT Get Job Data and Account Addresses To get the updated state of this job, we can send a GET request to the endpoint. /v1/jobs/{JOB_ID} $ curl -X GET \
  http://localhost:3000/v1/jobs/2876f90d-d9ec-4007-935b-4aba3cb8e45e

{
  "jobId":"2876f90d-d9ec-4007-935b-4aba3cb8e45e",
  "type":"account_create",
  "state":"COMPLETE",
  "error":"",
  "errors":null,
  "result":"0xb23d449bc23d9d04",
  "transactionId":
    "ab68527578c323b68caf9d1b1533ebf4a3486f22b0d6f7df4339c57e48d9c4ca",
  "createdAt":"2023-05-05T17:29:34.800299Z",
  "updatedAt":"2023-05-05T17:29:47.507803Z"
} We see the result, with a newly created address. It is also possible to check all addresses created by the application using the endpoint. /v1/accounts $ curl -X GET http://localhost:3000/v1/accounts

[
  {
    "address":"0xb23d449bc23d9d04",
    "keys":null,
    "type":"custodial",
    "createdAt":"2023-05-05T17:29:47.504906Z",
    "updatedAt":"2023-05-05T17:29:47.504906Z"
  },
  {
    "address":"0x7cc7be2796e8cf29",
    "keys":null,
    "type":"custodial",
    "createdAt":"2023-05-05T17:25:51.847239Z",
    "updatedAt":"2023-05-05T17:25:51.847239Z"
  }
] With our wallet API working, we can start implementing the application that will send requests to the wallet API to create new accounts. We'll cover this in part two of our walkthrough. Also published here.

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Abstractive Health

abstractivehealth.com

11-50 employees

Since 2022

COMPANY RANKING

ABSTRACTIVE HEALTH

EVERGREEN INDEX #1470

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