fabiochiu/medium-articles huggingface dataset:
Update 3 — Spoiler Warning: On-Demand Sneak Peak
Tags: Ethereum, Bitcoin, Cryptocurrency, Oracle, Defi

Modefi On-Demand Oracle Demo

We are extremely excited to share the very first visual demo of the eagerly awaited On-Demand Oracle system. In the demo you will see how it can be utilized to bring data from a UFC event. Once the event is completed and the data is validated by the Modefi network it will be available for anyone to use on the blockchain. This is one example of how the oracle system can be used.

Time to get your thinking caps on. While watching the video try and think of a possible use case that pops into your head. We will be holding a contest to see who can come up with the most unique use case.

Don’t forget your thinking cap!

Please keep in mind that this is the standard Oracle and several other Oracle formats will be added in the near future. One that we are particularly excited for can handle an entire tournament setup from start to finish. This will include teams, single entries, sponsors, entry fees, sponsored entries and winning settlements with customized settings and security for large and small tournaments alike. Alongside the tournaments, user generated prediction markets are coming!

Without further ado please enjoy the very first preview of the On-Demand Oracle! Full write-up below the video if you missed anything.

This demonstration will run through the process of requesting the results of the upcoming UFC 266 event. The data that will be validated and placed on chain would be the winner of:

Main Event: Volkanovski vs. Ortega

Prelim #6: Moraes vs. Dvalishvili

Prelim #7: Abdurakhimov vs. Daukausi

The event data will be available for use on-chain after match completion. Once on-chain the data can be used in smart contracts for any purpose. Allowing for unlimited possibilities and virtually unlimited use cases for users and data on-chain.

Check out the video below for the demonstration of the On-Demand Oracle
This isn’t Freedom, This is Criminal
By: Matthew Goodman
Tags: Coronavirus, Media, Covid 19, Lies, Graph

Lets start with with the most obvious and egregious:

Mistake 1 — Vertical Scale Inconsistencies

Next to the two green bars I have added is the easiest to find “mistake” in this plot. Here, the bottom bar corresponds to a difference of 5 thousand cases, and the second just above it represents a difference of 15 thousand cases. Even steps in the scale of a graph is what makes the location and position of the data within the plot meaningful. This “mistake” makes it appear as if the first 5,000 cases happened more quickly, and decreases the perceived steepness of the curve, minimizing threat.

Mistakes 2-6 -Horizontal Scale Inconsistencies

Much as the vertical scale is a little…deformed…the horizontal scale is similarly unevenly spaced. Lets look at the spacing in terms of days:

2/21 to 3/1 => 9 days (corrected! thank you!)

3/1 to 3/10 => 9 days

3/10 to 3/15 => 5 days

3/15 to 3/20 => 5 days

3/20 to 3/24 => 4 days

This has the effect of making the earlier cases appear more recent, and the more recent cases seem less sudden. The compound effect is, again, to make the overall trend of growth seem less sudden. In addition to these selection of dates, the locations of the dates themselves on the graph are similarly skewed.

Mistakes 7-8 — Label Locations

The last of the labeled dates is the 24th of March, you would probably infer that to mean that on that day, there were approximately 65,000 cases in the US. Sadly, this is the figure for the 25th of March. This shifting of the labels again favors the minimization of the ongoing impact by about ~18,000 cases.

Much as with the time axis, is rather hard to determine where to read the cases out on the vertical axis. If we take the centers of the numbers, we would be seeing a negative case count.

Needless to say, this again minimizes the apparent ongoing impact communicated by this figure.

If we instead give this a generous reading suggesting we read off near the number bottoms, the overall figure doesn't line up at all with the source data. Which brings us to . . .

Mistake #9 — Mis-attribution of the source

At the bottom of this screenshot is the suggestion that this data might come from the “John Hopkins Tracker” [sic], one of the best maintained sources for aggregated corona virus reporting. In addition to their strong digital accountability, primary source tracking, version history, and support community, they present clear terms for how the data may be used. They state specifically that: “…use of the Website in commerce is strictly prohibited.” It would be a clear violation for a entity supported by commercials to use it in this way… I assume they are getting their data from elsewhere, after all the graphs just don’t line up:

The green dotted line is data overlaid from the Johns Hopkins Tracker.
Your Partner has been in more relationships than you
Tags: Mathematics, Relationships, Love, Applied Mathematics, Graph

Now for the real calculation,

For every girl i, X_i is the number of relationships she has been in. For every guy j, she has been with Y_j is his count. So, her perception for average girls a guy dates is

i.e average of Y over all the guys she knows about i.e. X_i .

To calculate this quantity as an expectation, we need to sum this over every girl and every guy she has dated, i.e.

Step 3 because if we sum all the instance i that has edges with j, it is same as the degree of j. Now, from the lemma E(X) = E(Y)

The last part follows from the inequality E(X²) ≥ E(X)² and equality is in the case where variance of X is 0, surely not a case in dating world. As Y and X are interchangeable, the calculation for other general also follows.

E(average relationships of your partner) > E(your average relationships)

So, WE NEED TO TALK. Not about how many partners or relationship history, but math and science and it’s beauty.

Peace.

Tl;dr : When calculating average for partners, a partner with higher degree is calculated by partners multiple times and hence the abnormality in the averages.

Reference:
Writing Task 1 (graph)
By: Vita Muflihah F
Tags: Ielts, Cambridge Ielts, Graph, Ielts Preparation, Ielts Writing

Writing Task 1 (graph)

The chart below shows the amount spent on six consumer goods in four European countries.

Write a report for a university lecturer describing the information shown below. Vita Muflihah F Jul 6·1 min read

Cambridge 3 test 2

The graph compares the spending amount on the goods that bought by the consument in four European countries and is measured in thousand pounds sterling. Overall, it can be seen that all of country spend the average amount for each stuffs, unless Britain and France on photographic film.

Turning first to each country spent respectively in CDs, Toyo, and photographic films, whereas there was the highest number in Britain, it was around 165 to 170. The lowest number of those stuffs was Germany in approximately 148 pounds sterling. Italy and France saw an equal amount in Toyo, at more than 150 pounds sterling.

In contrast to this, each country spent personal stereos, tennis, and perfume in stable amount, it was around 140 to 150. Next, Britain was always be the highest for those categories, it was more than average and reached 160 pounds sterling. Germany experienced a stable amount in every stuffs, less than 155 pounds sterling.
Some States See Recovery in Unemployment Rate
By: Sarita Cavazos
Tags: Jobs, Covid 19, Unemployment Rate, Unemployment, Graph

Some States See Recovery in Unemployment Rate

After the peak of COVID-19’s toll on America’s Unemployment Rate, some states see recovering numbers Sarita Cavazos Jun 8·2 min read

A comparison of unemployment rates between April of 2020 and April of 2021 shows the American unemployment levels have significantly lowered, signaling a recovery from COVID-19 impacts.

Some states, like Nebraska and Massachusetts, experienced remarkable declines in their unemployment rates since last year. But others, such as Wyoming and Connecticut, experienced less significant fluctuations in the employment rate, most likely since they did not experience significant declines in the jobs at the start of the pandemic.

The Bureau of Labor and Statistics reported that 559,000 jobs were added in May, as the national unemployment rate fell to 5.8%. The spike in public desire to resume pre-pandemic activity (such as eating out and participating in public activities has further driven employment opportunities.

Lowering numbers in COVID-19 cases nationally have allowed lowering of COVID-19 related restrictions and regulations in many large cities and town. Combined with increasing vaccination rates, many states have either fully reopened businesses to the public or plan to do so in July.

60% of U.S. adults have received at least one dose of the COVID-19 vaccine, and availability for the vaccine expanded to all individuals 12 or older on May 12. This means that as states lower restrictions and transition into plans for reopening, public support won’t be far behind. As vaccination rates continue to rise, more individuals will feel comfortable not only returning to public activity but also to in-person employment, potentially encouraging a rapid approach back to a representation of pre-pandemic life.

This is also good news for President Biden, lowered unemployment rates ahead of the State of the Union address could mean decent presidential approval ratings. With many Democrats seeking reelection in the midterms in the fall of next year, decent presidential approval ratings combined with reasonable unemployment levels could mean maintained control of both the House and the Senate -which would be vital for future Biden policies and agendas.
Diagonal Traversal of Matrix using DFS
Tags: Arrays, Matrix, Depth First Search, Diagonal Traversal, Graph

I’ve encountered this problem in an Online Assessment(OA), the requirement was not exactly traversing diagonally, but I had to do something traversing diagonally.

Thinking about it during my OA hurt my brain, I had to think, after I put decent amount of thought into it, I was able to write a very messy code to do the job.

I wanted to give it another shot after the OA and ended up making it even messier but manageable, I wanted to use DFS as I was solving some questions related to Maze and paths in a maze.

Traversing Matrix diagonally

Above is the visual representation,

Now If we pick the first element 1 at (0,0) it has no valid neighbors diagonally

If we move onto element 2 at say i,j=(0,1) it as a valid neighbors at

(i+1, j-1) = (1,0)

For element 3 at i,j=(0,2), we have to move on until we find an invalid i,j by incrementing i (moving down) and decrementing j (moving left)

We would end up having (0,2)->(1,1) and (2,0)

We can add these to our stack until no valid neighbors and then print before popping them off.

With an objective to simplify it, I might’ve overdone it.

vector<vector<int>> matrix = { {1,2,3,4}, {5,6,7,8}, {9,10,11,12}, {13,14,15,16} };

This is the same matrix in the above png.

vector<vector<bool>> visited(matrix.size(), vector<bool>(matrix[0].size(), false)); //Just not to revisit the traversed vertices.

Core DFS Logic ahead

Output:
What are Dynamic Graphs and Why They are Interesting?
By: Rohith Teja
Tags: Biomedical, Data Science, Artificial Intelligence, Social Network, Graph

Use of Dynamic Graphs

Photo by Emily Morter on Unsplash

As I mentioned before, the dynamic graphs are useful in capturing the complex behaviour of real-world entities (like human beings in a social network, or a gene coding protein in a PPI network). We also add the time dimension to the dataset which explains the evolution of a process from start to finish.

In order to extract valuable information from dynamic graph data, we can employ machine learning techniques. Firstly, we need to learn numerical representations of the entities present in the dynamic graph and then we can use methods such as Graph Neural Networks to model several use cases like node classification, link prediction etc.

One very common task on such graphs is community detection, which in the context of PPI is known as complex detection. Basically, we use unsupervised machine learning techniques such as clustering to identify important groups at each timepoint. For the PPI network, this is interesting because the identification of important protein clusters/ complexes would result in better drug discovery for diseases.

We also have a graph alignment task where we aim to identify proteins or complexes that show variations among different types of samples (like healthy or infected cells). This is useful for biologists in finding the proteins on which they should focus when looking for treatments.

In the case of social networks, we can use dynamic graph analysis to identify the spammers in the network (spam detection). The core idea of dynamic graphs is the same but different domains have their own use cases and we can use machine learning models to cater for their needs.

Using Link Prediction, we can propose friend recommendations to users in a social network by analysing the growth of the network over time. For PPIs, we can provide a list of protein recommendations, meaning we want to propose a list of proteins that have the potential to form new interactions.
Relational 🆚 Document 🆚 Graph — Part 3
By: Gabor Galazzo
Tags: Neo4j, Analitycs, Big Data Engineering, Database, Graph

Here we are with the last part of this saga where we analyze which is the best data model/structure for our data application.

In this article, we will discover the Graph model (NoSQL plus a bit of Relational).

I strongly suggest reading previous articles about the Relational Model and NoSQL Databases to have a better understanding of what we are talking about, previous articles include also content that is very often cited in this reading.

So, read the previous articles if you didn’t! 😉

Score Recap:

Space complexity — Redundancy Consideration, Support data, Elaboration space …

Time complexity — Join complexity, Indexing, Parallel computing, …

Modelling Easiness— Concepting your database, model entities and relationship, projections, …

Querying Easiness— Query size, Think to Text steps, ORM, …

Scalability — Vertical, Horizontal, CAP Theorem, …

Consistency — BASE, ACID, CAP Theorem, PACELC Theorem, Concurrency, …

Worst Score: 1 — Best Score: 10

The Graph model

We discussed a lot about databases in previous articles, so in this one, I will not dig through some concepts that are transversal among all database paradigms.

In this article, we will examine another NoSQL paradigm: the Graph Model. The objective of this approach is to take advantage of the representation of data as a graph trying to mashup all advantages of the relational model and the document one.

A few definitions

Class or Entity: Something that we consider in our domain

Node or Vertex: An instance of an entity or a class

Relation or Edge: An association (directed or undirected) between two Nodes

Property: information associated with a Node or a Relation (the entity class and the relation name are properties)

Graph model

If we consider the Relational and the Document model, we may not see find big differences: in both of those paradigms, we can reconstruct the same database in the image above. Why should we use a graph database then?

The answer is Edges. Previous paradigms have an implicit representation for a relation, to manage cross entity dependency they use the foreign key strategy. The graph model represents those relations explicitly in memory by linking data together and from any point of view.

So, we take the advantage of the relational model for the ability to represent our domain from any point of view (the entity decomposition is very similar) and we want to add the concept of the document database to have unstructured or semi-structured data.
Blockchain Applications Landscape
By: Tal Elyashiv
Tags: Insights, Blockchain, Blockchain Application, Distributed Ledgers

OVERVIEW

Blockchain, tokenization, and the underlying Distributed Ledger technology (DLT) have transformational potential for any industry where transactions, data sharing across entities, trust, or regulatory compliance play a major role. As a result, over the last few years, many ideas, projects and players started working on and providing blockchain-based products and services targeting different industries and solving different problems.

We set out to create a high level picture of the current (early 2020) landscape in the blockchain domain from a business application perspective.

The difficulty in mapping the blockchain landscape is the high level of noise: products/projects that are simply a “technology in search of a problem”, rather than a solution that was born out of real business needs, the “cool but impractical” ideas, and also great solution that “ahead of their time” and cant fit the current ecosystem in the industry they are destined for.

There are many ways to map the universe of blockchain business applications and players, projects and ecosystems developed, and we had to choose one.

The approach we chose is heavily influenced by our investment focus, while still presenting an overall view of this domain. We chose to focus the mapping on business applications of blockchain and not on enabling technology infrastructure. Our mapping is also influenced by the level of current adoption and readiness for such applications in the relevant industry. Note that as a result only application clusters that passed our screening hurdles (traction, adoption, etc) made it to the list. With time, this map will naturally keep changing.

Beyond mapping the various Blockchain application clusters, we also describe the main value proposition for using blockchain technology in each cluster, as well as some of the players and projects in that space. Note that the list of players and projects in each cluster is not meant to be an all inclusive list, but merely to demonstrate the type of players/projects involved.

We divided the universe of blockchain applications to 3 main areas:

FINANCIAL: Blockchain applications that are targeting different aspects of the financial industry. From insurance and banking, through fundraising, payments, and money, to capital markets. CORPORATE PROCESSES: Blockchain applications that are targeting simplifying, enhancing, or solving major issues related to corporate processes and business interactions. OTHER: all other application clusters were mapped under this heading.

THE BIG PICTURE

SOME MORE DETAIL
Introducing: The Zillacracy Initiative
Tags: Tokenization, Blockchain, Cryptocurrency, Distributed Ledgers, Defi

As the Bitcoin halvening draws near, the world waits and watches in eager anticipation of parabolic price action. Meanwhile, regardless of candle color, builders continue to build. And as we round out the second month of 2020, Zilliqa continues to stay ahead of the curve, in its march towards growth. The first public network to implement sharding continually inches closer towards traction, and scaling the adoptability of blockchain usage.

Month after month, the core team members continue to hit their deliverables. It’s been nothing less than inspirational to watch the diligent grind of men and women from across the globe, working beyond the bounds of timezones and cultural differences or language barriers, to produce tangible and forward moving results.

Impact like that lasts. Beyond any executive’s tenure of office, or core team employment structure, or any single team member. When people band together over time with a single mission and a unified goal of building something, then their individual contributions, as well as the collective effects, are that much stronger, and linger much longer.

But, just as the core of any bright star produces energy from within, the brilliance and light of that energy is spun outward, as centrifugal force widens and enlarges the reach of its light. So too, as we observe the Zilliqa core continuing in its dizzying pace of refining and enhancing, we from among the community, can begin to energize and ionize one another towards action in many ways as well!

For that reason, a new community-based initiative has been given life: Zillacracy!

In an effort to harness the talents and gifts hidden among our vast, global community base, a fund has been set up, to reward community contributions towards the expansion of the Zilliqa ecosystem. This fund will be governed by a multi-sig wallet on the Zilliqa Network, with both community and core team members holding the keys, the former being the majority.

Projects will be open to the general public and rewards for milestone and project completion will be sent in $ZIL. HERE you can find a list of supported projects, both tech and non-tech. Also, If you’d like to contribute in other ways that may not yet be listed, we’d love to hear your new and innovative project pitches.

The life of a Project can be seen in this 5 Stage process

Now, whether you’re a longtime Zilliqan, or just recently discovering the potential of sharding on a live and working blockchain; we want you to be a part of this network’s success and expansion! Independent of marketplace trades, lies a core technology capable of improving efficiencies across our entire planet. But it needs to scale. Zilliqa does. So if you’d like to participate in making an early and profound impact within this space, then check out the Zillacracy, and let’s build something awesome!
Hyperledger Fabric 2.0 released
By: Rakesh Kumar
Tags: Blockchain, Distributed Ledgers, Smart Contracts, Hyperledger, Hyperledger Fabric

The Hyperledger Fabric which was initiated by Digital Asset and IBM has came up with a new version 2.0 of its enterprise distributed ledger technology (DLT) platform in January 2020.The main features of Fabric 2.0 are faster transactions, updated smart contract technology and streamlined data sharing.

What is a Hyperledger ?

It is an enterprise-grade, open-source distributed ledger framework launched by the Linux Foundation in December 2015.

What is a Hyperledger Fabric ?

It is modular blockchain framework of hyperledger consortium for developing applications or solutions which allows components such as, consensus and membership services, to be plug-and-play by preserving the privacy.

How Hyperledger Fabric Works

Traditional blockchain networks can’t support private transactions and confidential contracts that are of utmost importance for businesses. Hyperledger Fabric was designed in response to the present as a modular, scalable and secure foundation for offering industrial blockchain solutions.

Hyperledger Fabric is the open-source engine for blockchain and takes care of the most important features for evaluating and using blockchain for business use cases.

Within private industrial networks, the verifiable identity of a participant is a primary requirement. Hyperledger Fabric supports memberships supported permission; all network participants must have known identities. Many business sectors, such as healthcare and finance, are bound by data protection regulations that mandate maintaining data about the various participants and their respective access to various data points. Fabric supports such permission-based membership.

Fabric 2.0 and buried flaws of preceding version

The chaincodes i,e. the smart contracts can be tweaked by single organizations before being committed to the ledger, which ensures that everyone agrees on what data can be shared with each other. Data sharing has also been streamlined to work on a need-to-know basis. Also, several performance improvements were introduced, involving parallelization of tasks and more efficient program flows.

The biggest updates involve forcing agreement among the parties before any new data can be added to the ledger, known as decentralized governance of the smart contracts. It means that the entity will be prevented from writing to the ledger by the system until there is consensus among the parties involved in the transaction, a basic blockchain tenet.

This is a requirement because the beauty and the curse of the distributed ledger is that it is an immutable record. Once you have written something in the ledger, it becomes very difficult to change it without the agreement of all those involved in the contract. You want to make sure you get it right before you commit something to the ledger.
Distributed Databases, Distributed Ledger and Blockchain. Which is Which?
By: Rakiza, Aiman Nazaal
Tags: Bitcoin, Blockchain, Distributed Ledgers, Digital Transformation, Database

The Holy Grail

Few days ago I witnessed a heated discussion on one of my contacts’ LI page over the differences between distributed databases, distributed ledger and blockchain technology.

The discussion and the differing points of view weren’t, surprisingly, surprising at all! They, merely, reflect the bigger controversy in the field over these technologies, especially DLT and BC.

According to Fotune a Google search for “blockchain” returns 215 million websites, while “distributed ledger technology” returns only 1.3 million.

Some experts and publications tend to use the acronym DLT over blockchain to describe the technology from a wider perspective or to avoid getting into the different classifications of blockchain technology types, which is a thorny issue in its own right.

While businesses tended to use blockchain technology to benefit from the hype surrounding the technology, it seems that this trend is changing with the different levels of maturity attained in the market.

Further, blockchain as a term is more associated with public permissionless blockchains, especially bitcoin, which is not welcome by different governments nor its underlying technology.

Therefore, DLT is emerging as an alternative to be used by high-end firms and mainly financial institutions, which apply the technology in a centralised manner or with a different modus operandi diverging sharply from the main characteristics of a public permissionless blockchain.

Anyway. While I was conducting my habitual and ritual daily research routine on BC technology, I stumbled again upon an old study — though it was conducted in 2017 — which I read many months ago. It discusses BC technology, its emergence and ecosystem. It has many interesting information which may fill the gaps in knowledge for many non-techies like myself.

I have found a small section which addresses the differences between the three technologies, DD, DLT and BC. I, gladly, present what I have read with minimum to no editing.

Below is a framework that can be used to distinguish between traditional distributed databases, distributed ledgers, and blockchains. Distributed ledgers are a subset of distributed databases, and blockchains are a subset of distributed ledgers.

This is not an exhaustive list. It is just and attempt to show the main differences between each type.

What is described below is mainly about public blockchains.

*Image and table reproduced from Global Blockchain Benchmarking Study by Dr Garrick Hileman & Michel Rauchs 2017
RECAP: MJAC Crypto Compare Summit, London, 2019
By: Nat Cole
Tags: Events, Blockchain, Cryptocurrency, Distributed Ledgers, Digital Asset

An Insight to Regulation…

Speaking of the FCA, whose Consultation Paper CP19/3* — “Guidance on Cryptoassets”, was released back in January 2019. Attendees were able to get an up-to-date insight of the work the FCA has been doing since January, and also the view the FCA currently holds towards regulatory matters.

“Insights from the regulators” Panel @ Crypto Compare Digital Asset Summit, London. 2019

In a panel headed up by Lawrence Wintermeyer — Co-Founder, Elipses & Global Digital Finance. The FCA Innovate’s — Samantha Emery, Interim Head of Department, spoke of the FCA’s “…cautious, but open…” approach to DLT and crypto companies based within the UK. This was accompanied by the confirmation that both the FCA, and the Bank of England are also looking at the wider impact of the International crypto asset markets and regulations.

Bank of England Head of Notes Operations — Martin Etheridge, also chimed in to affirm that the BoE currently view crypto assets as a “non-threat” to the GBP, and that they are looking at possible future threats, or opportunities that DLT’s and cryptocurrencies may present. He also affirmed that the BoE “…is not currently using, or currently planning to use blockchain within our infrastructure…” and are also taking an “open” approach to the technologies, among ”other innovations”.

For the most part these views were also reverberated by Gillian Dorner, Deputy Director at HM Treasury.

As crypto regulation in the UK has always been a hot topic, it was good for attendees to hear about high level progressions within this subject.
Ethereum Wiki
By: Random Wiki
Tags: Vitalik Buterin, Ethereum, Distributed Ledgers, Wikipedia, Random

Vision: Decentralization of Wikipedia content and Tokenization of page contributions. Allowing for immutable, public information with fungible, liquid tokens allocated to the users who create and support the site. Providing site supporters digital asset representing their contributions toward the preservation and proliferation of history and information gathered by humanity.

Legacy contribution: Users who have supported the site’s survivability for years would be allocated any tokens pro-rata based on their User Group and contribution amount. Exact math and allocation process still to be defined.

Distributed Ledger Enablement and Reduced Overhead: Wikipedia currently runs on centralized servers for information which should be distributed freely where all users can contribute.

Decentralized wiki is a perfect use case for the Ethereum Network removing server costs, allowing for open contributions, enablement of privacy, and incentivizing open discourse and contribution through tokenization.

Having all edits on the Ethereum network would also provide point in time reporting of page edits and transparent auditability.

Governance and moderation can also be both automated or collective.

Random Wiki Contribution: If Wikipedia or the larger Wikimedia foundation would agree to a Dapp which is agreeable for a future platform. Random Wiki will contribute up to an ETH to initialize the network and allocate tokens to existing moderators and users of the site.

Thank you,

Random Wiki
9 critical elements that can bring autonomous cars to life 💁🚙
By: Ram Alagianambi
Tags: Self Driving Cars, Autonomous Cars, Uber, User Experience, Science Fiction

Items 4–7, the essentials

Pick-up: In the case of a human driver, the driver and passenger get an opportunity to confirm who’s who by looking at their profile picture and confirming their appearance. However, with regard to AVs, this might be done using biometric (speech, facial recognition) or mobile technologies.

Erika might use her voice to command the car, Kilo can recognize Erika’s voice as he gets the feed for what questions to ask and decode the speech to recognize if Erika is indeed the right person he was here to pick-up.

The car might also be able to sense based on Erika’s voice whether she is stressed or not and adjust the type of conversation it has accordingly.

Trip — the experience: While pick-up and drop-off are the beginning and the ending part of the trip where the customer gets to engage with the AV, the actual trip starts much earlier. Once inside the AV, the world of possibilities is limitless —

Once passengers are free from being judged by a human (driver), and the AV effectively belongs to the customer for the ride, then they can summon the AV to do things such as play music, launch work stations, call the office on a speakerphone, watch Netflix on a big screen, practice music, or even present the hot sandwich that was placed in the AV just before the trip.

Of course, if Erika has never been in an AV before there would be an onboarding experience where Erika could ease into it. The onboarding flow would perhaps start as a dumb video tutorial but futuristic AVs will showcase themselves using inbuilt controls such lights/sound/interactive modes that can guide/respond to passenger actions making sure they are comfortably onboard.

Drop off: The drop-off experience might entail a ritual of wiping any data from the AV that belong to the passenger(s). A typical example would be any interactions on the work station or audio/video recordings. Also, during each drop-off cycle, any of the biometric decoding for the passenger would be erased. This will ensure all the passenger information is protected.

Prior to the trip’s end, AVs would determine whether they need to run to the gas station to fuel up, cleaning station to clean up, etc. This ensures that the AV is ready for the next ride. In a typical, human-controlled driving network, the human driver orchestrates when the car is available vs. not on the network — they take care of cleaning/fueling, etc. In the case of AVs, the burden falls on the AVs, the network or both.

Diagnostics/Upgrades: The time between trips is an opportunity for appropriate upkeep of the systems. AVs needing attention or maintenance will be taken care of once they arrive at the scheduled maintenance stations. In some cases, they would be due for new additions or upgrades to their systems.

As an example, the new fuel cell system can be implanted to AVs resulting in a greater range and availability. Similarly, upgrading to the new infotainment center can elevate the passenger experience.
Roadmap to Uber-Like Taxi App Development
By: Shaikh N
Tags: Buildanapplikeuber, Ride Hailing Apps, Uber, Uberliketaxiapp, Taxiappdevelopers

People living in Alpha cities like New York, Dubai, London, or Mumbai have two biggest vows: busy traffic and lack of parking space.

They are less likely to buy cars and rather rely on ride hailing apps like Uber and Lyft. The advantages are many.

They don’t have to worry about car repairs, a permanent parking space, and driving themselves in busy traffic.

For investors seeking a fresh investment opportunity, investing in an app-based taxi booking service is a great idea if they are not investing building an Uber-clone of their own.

Even with dominant players like Uber and Lyft in the market, private-hire car business is still a thriving one as long as you stick to a staunch roadmap and learn from your competitors’ mistakes.

Staying relevant by bringing changes

What sets apart Microsoft and Google is the latter’s tendency to stay relevant to changes. The same goes with Uber and most of the modern-day startups. Like Uber, they don’t make many mistakes.

Despite being on the top of private-hire car business for many years now, Uber keeps reviving its brand identity. Did you notice Uber changed its logo thrice since its inception?

Booking apps are mushrooming everywhere. Mobile technology has made it pretty easy and possible to run a business through just an app.

But, the problem with mobile startups is such that there are plenty of good ideas already in the market. What is missing in the business is the uniqueness of idea.

Orignal Sorce : Uber-Like Taxi App Development
LOCAL & LONG DISTANCE YEOVIL TAXI SERVICE
By: Yeovil Taxis
Tags: Yeovil Taxis, Yeovil Taxi Service, Local Taxi Service, Uber

A2Z Taxi Service has been in the business long enough and offers cost-effective and 24 hours taxi services all through Somerset. You can look for instant and convenient booking on the phone, in person or online mode, avail pick and drop facility at any hour of the day, safety and comfort ensured along with the highly trained driver and keeping up to the strict guidelines of the authority we operate in. Book with us in advance, and get 100 % customer satisfaction at the best price. A2Z Taxis Yeovil is the name to trust for whenever you plan to visit in and around Somerset, Dorset, Devon and we will be more than happy to deliver the best of the service to you always. Feel free to contact us and we assure you of the best of the travel times. With years of experience in the transport & cab service industry, you can be sure that A2Z Taxis Yeovil will always give you the best service. Our aim is to offer a great alternative to the existing solutions, but in a simpler way. Rest assured that whatever requirement you might have, we are here to fulfil it, Twelve months a year at your service. We strive to take the stress out of travelling to and from the airport, giving you more time to enjoy your holiday or prepare for your business meetings. The door to door Taxi service.

Call now 07484 098722
Case study: Uber Eats new feature ‘Restaurant Highlights’
By: Katherine Ng
Tags: Design, Technology, Uber, Interaction Design, User Experience

User Scenarios

When I first enter the app I want to find a list of small business owners in the navigation system so I can easily sort through large corporations.

When I go through the list of recommended small business owners I want to be introduced to new local owners so I can support a diverse range of small businesses.

When I go through the list of recommended small business owners I want to be informed about their production, their ethics, and safety precautions so I can make informed decisions about the businesses I spend my money on.

When I go through the list of recommended small business owners I want to learn about different foods and ingredients they use so I can replicate the dish in my own way.

When I go through the list of recommended small business owners I want to save and edit items in my cart so I can checkout without having to return to the home page.

When I first enter the app I want to easily know what the restaurant serves so I can quickly find a meal that fulfills my needs.

When I first enter the app I want to find the app extension on the main navigation so I can quickly find the option of searching.

When I go through the list of recommended small business owners I want to sift through options easily so I can passively look through food options without getting overwhelmed.

When I go through the list of recommended small business owners I want to know what general food category the restaurant serves so I can quickly move on to the next option if it doesn’t match my current mood.

When I go through the list of recommended small business owners I want to sort and filter certain aspects so I can eliminate options I don’t want while passively searching.

When I go through the list of recommended small business owners I want to better see the ingredients in each dish so I can note what foods I am feeling driven towards in this moment.

When I go through the list of recommended small business owners I want to be able to add people to the order, like the restaurant and menu filter/search so I can use the existing features that I am familiar with in the extension page.

When I go through the list of recommended small business owners I want to see something other than the list of food options so I can be convinced to make my decision by other factors than just the food.

Flowcharts
Uber Eats Has Stories Now
By: Brittany Jezouit
Tags: Social Media, Uber, From The Editors, Business, Stories

Meghan Casserly, head of communications for Uber’s Delivery business, says that Stories were built around two insights: “First, that consumers find photos extremely helpful in deciding what to order, and second, that restaurants and other merchants were looking for ways to share more about what makes them unique — whether that’s photos, text updates like daily specials, or special promotions — with their customers through the app.”

Instagram launched its Stories feature in 2016, and it’s arguably changed the way users navigate the app — more in-the-moment updates, less polished content. Since then, it seems like every app has experimented with some version of its own Stories, including YouTube, Facebook, LinkedIn, Skype, Bumble, Airbnb, Google, and Spotify.

This isn’t another one of those “Stop Adding Stories To Your App” articles, though. Whether or not you’re a fan of Uber and Uber Eats, I think this is a cool feature. The “where should we order dinner?” decision-making process is convoluted: I often find myself bouncing between Google reviews, the restaurant’s website, and Instagram to get the latest information on specials, what’s new, or what’s good to order. If I’m already planning to place an order on Uber Eats, this update helps to make that customer journey a little more frictionless.

With this feature, restaurants can highlight their Instagram feeds on their Uber Eats account (this integrates with their static photo feed, not their Instagram Stories), as well as create new stories directly in Uber Eats. And it may help with conversion rates: according to Uber Eats, 13% of consumers who clicked through to a merchant Story placed an order from that same merchant in the same session.
In UK Food Delivery Battleground, Just Eat Takes #1 Spot with 45% Market Share
Tags: IPO, Food Delivery, Uber, UK, Deliveroo

In UK Food Delivery Battleground, Just Eat Takes #1 Spot with 45% Market Share

Uber Eats (27%) comes 2nd and Deliveroo (26%) 3rd, after both services grew slightly in the last year. Edison Follow Apr 1 · 3 min read

Key Takeaways:

UK food delivery service market share changed slightly before and after COVID-19: Uber Eats market share increased 9 percentage points Deliveroo increased 7, while Just Eat fell. Foodhub remained about the same.

The UK food delivery market (by customer spend) rose 128% in the last year.

Since our previous October 2020 U.S. Food Delivery Report we’ve seen how consumer demand and sales first began climbing in March of 2020 as pandemic restrictions came into place, reaching new heights and remaining steady since. On-demand food service platforms continue to expand to new markets and evolve. Across the pond, Amazon-backed Deliveroo has filed to IPO recently and is seeking to raise £1 billion (or $1.4 billion.) To see how third party food delivery service platforms sales are performing in the UK, Edison Trends analyzed over 200,000 transactions.

Which on-demand food platform has the highest overall market share across the UK?

Figure 1a: Chart shows weekly market share of spend for third-party food delivery services in the UK from December 30, 2019 — February 28, 2021, for Foodhub, Just Eat, Deliveroo, and Uber Eats, according to Edison Trends. Note: Due to rounding, numbers may not add to 100. Spend refers to what the customer spent on the order, excluding taxes, tips, and fees. This analysis was performed on over 200,000 UK-based transactions from 4 food delivery services: Just Eat, Foodhub, Uber Eats, and Deliveroo.

COVID-19 has left its mark on third party food delivery and pickup purchases in the United Kingdom. Social distancing caused a major shift in market share of spend starting the week of March 23, 2020, when many restaurants began closing. McDonald’s, which partners with Uber Eats and Just Eat, closed all UK-based restaurants and the two services saw their share of spend drop; subsequently, Just Eat reached the highest market share of spend it would see for the year, and Foodhub also briefly rose.

In 2020, stay-at-home restrictions went into effect on March 26th. McDonald’s began limited re-openings on May 13th, with all of their restaurants opening again on June 4th, after which market share for Uber Eats and Deliveroo returned to previous levels.

Between February 2020 and February 2021, the size of the market (by customer spend) rose 128%. As of the month of February 2021, Just Eat had 45% share of the money customers spent among these top selling food delivery services. Uber Eats was second with 27%, and Deliveroo had 26%. Looking at how market share has changed before and after COVID-19 (between the weeks of December 30, 2019 and February 22, 2021), Uber Eats market share increased 9 percentage points, Deliveroo increased 7, while Just Eat fell. Foodhub remained about the same.
Taxi’s out, Alfresco in — New life for South Terrace
By: Adin Lang
Tags: Fremantle, Urban Planning, Uber, Taxi, Rideshare

In an exciting move for Fremantle Markets, the City of Fremantle has approved alfresco seating on Fremantle’s iconic Cappuccino Strip by relocating the taxi rank and loading bay on South Terrace.

From the September 24th, Market traders will be allowed to spill out onto the pavement and enliven the dead spot on South Terrace. This proposal comes as a relief to Fremantle Markets traders who have been lobbying for an increase in alfresco seating to allow a space for customers to relax while they enjoy their retail experience at the markets.

Owner of Italiana: The Mediterranean Deli, Ersilia Festa, says being able to use the outdoor space could help realise her dream to give people a real experience of Italian culture. “It’s not just about food,” she says, “I want to provide music, art, painting and dancing.”

The taxi rank was created in 1999 as a place for customers to queue and wait for a taxi (at the time taxis in Perth were in very short supply). At times the line of people waiting for a taxi would extend from the Markets all the way back to the Sail and Anchor. The introduction of rideshare has seen the taxi rank switch in it’s purpose — it’s now a place where taxis queue for customers and this has been causing a conflict between cars, Market traders and pedestrians all vying for a space on the footpath.

Current: Pedestrians, cars and Market traders all trying to squeeze into the same space.

Where you can now find a taxi

There are three locations you will be able to catch a taxi from after the 24th of September listed below. Note: the City will review these locations and make changes/additions if required.

1. Esplanade Hotel, Marine Terrace.

Taxi rank outside Esplande Hotel, Marine Terrace

2. Adelaide Street, Walyalup Koort.

*This site is expected to get busier in the coming months as new businesses open and new apartments are completed.

Taxi rank outside Town Hall

3. Market Street, Fremantle Post Office

*Operational as a taxi zone between the hours of 6pm to 8am with the area being a loading zone and parking (as per existing use) at all other times.

Taxi rank outside Fremantle Post Office.

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Ola, Lyft, Rapido, Gojek, Grab, and Uber Bikes Ride Demand Forecasting | Data Science in Ride-Hailing (Transportation) Domain
By: Shaurya Uppal
Tags: Machine Learning, Analytics, Uber, Data Science, Predictions

“Life is not about waiting for the storms to pass: it’s about learning how to ride in the rain!”

Bike Ride Demand Forecasting (Source: Dribble)

Project Description

The taxi service (ride-hailing) industry is growing for the last couple of years and it is expected to grow in near future. Taxi drivers need to choose where to hang tight for passengers as they can get somebody quickly. Passengers also prefer a quick taxi service whenever needed. We all have faced problems with taxi booking requests, which sometimes cannot be fulfilled or the wait time for ride arrival is very long due to the unavailability of a nearby taxi. One should feel fortunate if you get a taxi booked in one go.

Taxi demand prediction has become extremely important for taxi-hailing (and e-haling) companies to understand their demand and optimize their fleet management.

To handle such issues, we would be building a model based on users’ ride request dataset, which would contain attributes: ride booking time, pickup point, and drop point latitude-longitude. This model would forecast the demand, for a particular time in different areas of the city which would help the company optimize taxi concentration to fulfill users’ demand.

Business Problem

Ride Haling Companies are suffering losses and losing out from their competition due to their inability to fulfill the ride requests of many users. To tackle this problem, you are asked to predict demand for rides in a certain region and a given future time window. This would help them allocate drivers more intelligently to meet the ride requests from users.