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Artwork by Anoushka Alexander — Instagram @thatpatakakudi Caught amidst worlds, contradicting realities The gift of vision of truth, the curse of perception She dances in type_A world painted by the sun A mystical place where shadows rest It’s her face, the sadness it portrays The million lies she placed ablaze She dies everyday caught in the dichotomy Between the rambling poet and the quiet marionette Crazy daisy they would say Unkempt hair, a beautiful confusedness She is a world on her own Locked away in a mount nittany veterinary three dimensional prison type_A Rorschach test, at a fool’s behest Take her away, wipe clean her messy slate She digests what is shoved down, strings that are tugged on The marionette is called upon But she still lives, somewhere within Instacart substance_abuser roswell livestock market report Segmentation and Market Basket Analysis Understanding the client shopping behaviors of Instacart and make efficient recommendation Written by: Yingyuan (Valerie) Zhang, Yutong (Grace) Zhu Zehui (Declan) Xiang, Xin Yu Covid-19 has been a world spread pandemic in 2020 Thus, New Yorkers magnet for back of phone come_after the quarantine policy and keep a social distance from each other As we know, the most popular mode of transportation in NYC is by subway However, the subway is one of the most dangerous transportation tools to spread Covid-19, which may increase the risk of having Covid-19 for subway passengers Therefore, going out to get day-by-day needs became a headache issue for New Yorkers People in grocery hive_away are not practical to keep a social distance During Covid-19 outbreaks, New York City published a “staying at home” order, which increased the demand for online grocery shopping Instacart is a grocery delivery platform that has experienced rapid increment during the Covid-19 crisis Now, users gain the value of staying home to flatten the wind and to reduce their own risk of getting the virus The primary research goals are doing user segments based on time intervals and building a passport system based on product choices of users The expectation of the research could optimize the Supply side’s inventory allocation and increase the probability that customers get essential goods without breaking the social distancing rule First, let’s explore the data! The principal data source is from Instacart’s 2017 anonymized customers’ orders over time (Stanley, 2017) It contains the order file, product file, order and product file, aisles file, and department file Each entity in the dataset has an associated unique id In the order dataset, it contains user id, order id, order purchased twenty-four_hours of the week(order_dow), order buy hour of the day(order_hour_of_the_day), days since the last purchase(day_since_prior) and an indicator of the order’s belongs(eval_set) If information_technology is a first time purchase, the days since the last purchase will be NaN In the department dataset, it contains AN unique department id and associated departments’ names mount nittany veterinary In the aisles dataset, it has aisle id and aisles’ names In the product dataset, it turn_back the product id, the name of the product, the aisles’ id and the department id To make the time interval of user orders, we first divided substance_abuser orders based on days The data we used Here is order csv, column name ‘dow’ From Figure 1, The most popular days of user orders ar days 0 and 1 After look_back the data instruction, we did not find the definition of days 0 to 6 We believe the two busy days, 0 and 1, should be Sunday and Monday Figure 1 (Image past Author) Having a more detailed view, we selected the hour as a unit to plot an order frequency bar chart The two tails indicate that Instacart has not much delivery in the morning and late evening time Most orders are placed in time interval hours [9,16], which has an average of around 25000 orders per hour Figure 2 (Image by Author) Combining the two figures above, we decided to use a heatmap to check the most popular purchase_order time The y-axis is order counted by days, and the x-axis is order counted by hours From Figure 3, the darker part means the higher density time one-armed_bandit of having user orders, which are around day 0 and day 1 from 9 a m to 4 p m Figure 3 (Image by Author) Figure 4 evidence a decreasing trend Most of the users have placed orders in the frequency separation [0,40] A few users have orders above 60 alone orders count above 100 have been counted as a group The x-axis has a limitation on putting all the count orders on nonpareil chart Figure 4 (Image by Author) An interesting finding, from Figure 5, We have found that over 400,000 users may add Banana to their orders Most of the products on the list are organic vegetables and fruits! Figure 5 (Image by Author) Machine acquire Modeling: K-Means: Built on EDA results, it is likely to close that consumers are more likely to purchase between 10 am to 4 pm Also, it is shown by the graph on day 0 and day 1, Instacart mount nittany veterinary has a relatively high volume compared with other days To further understand consumer’s purchase deportment and conduct a concrete consumer segment, we used the k-means clustering Not only based on the edict buy the day of the week, the hour of the week but also days since prior gold beaded picture frames purchase and add to cart order, to calculate the frequency of using Instacart and median of items bought per order for each user First, selected the optimal number of clusters in k-means using the silhouette Score Due to the processing volume, we sampled 5% of the dataset (more than 10,000 user records) five times to generate the accurate Silhouette score for the train dataset As it suggested, we set 3 clusters in the k-means algorithm After applying PCA to lower the dimension, we were able to plot each cluster (Fig 6) Figure 6 (Image by Author) Random Forest: After doing k-means clustering and labeled the data, which we used to train our first K-means model with cluster 0,1,2 We want to transfer our model from unsupervised learning to supervised learning Thus, we split our dataset into train and test samples By using GridSearchCV, we find that we should use 21 as our estimator and 97 as our max leaf node After training our model and check the test set, we calculated the multi-class ROC AUC score, which is 0 999, which means our random forest model separates the three classes very well Feature Importance (Image by Author) Recommendation System In our recommendation system model, the outputs are departments and aisles that most other customers would purchase just the input customers do not purchase often Since the outputs are not only based on the bulk of check data but also based on Pearson similarity of the inputs customers purchase history, our model considers different groups of customer preferences For time and calculation saving reasons, we used data from the first 10,000 users to train our model By taking 5 random users from each cluster as input, the results have proved our hypothesis For 5 users from each cluster and 5 recommended departments and aisles, we look for the top 3 most shared common names among 25 values Departments recommended: Cluster 0: alcohol, bulk, babies Cluster 1: alcohol, dry food and pasta, bulk Cluster 2: alcohol, bulk, substance and seafood Aisles recommended: Cluster 0: nervus_facialis care, skin care, hair care Cluster 1: bulk grains rice dried goods, facial care, skin care bunch_up 2: skin care, spirits, facial care From this table, we can tell that customers in clustering 0 and cluster 1 (64 6% of total users) are loyal Instacart users and they might have high demand and put heavy pressures on Instacart’s operation during Covid-19 roswell livestock market report Huge progress has been made since its inception and the raison d’etre is about to be realized, but in that time, the crypto landscape has changed Sectors such as DeFi are redefining the limits of what’s possible and as everyone’s favourite Bond baddie (Jeff Bezos) once said — “What’s dangerous is not to evolve” NIX is determined to evolve as simply existing and fulfilling the core vision solitary isn’t enough — IT also needs to achieve adoption and for that, it require to be accessible This means changes, big changes Over the next few weeks we’ll be releasing a series of articles explaining each of these changes in more detail, but for now, here are the main headlines of what’s arriving in Q1 2021 The ground-breaking privacy tech that makes cypher so special will continue, operating through a validator network as a side-chain to Ethereum (to begin with), via the nix Bridge as a side-chain to Ethereum (to begin with), via the NIX Bridge The native NIX coin will pivot onto the Ethereum chain and exist as a native ERC-20 token , with the Bridge providing full privacy to NIX and other ERC-20 tokens , with the Bridge providing full privacy to NIX and other ERC-20 tokens The scheduled inflation will become zero as block rewards will no longer exist, meaning perpetual sell pressure is eradicated It’s hard to forecast the total supply once the swap is concluded but it’s estimated to be in the region of 40,000,000-45,000,000 after the dev fund burn and dormant wallets are taken into account inflation will become as block rewards will no foresightful exist, meaning It’s hard to forecast the total supply once the swap is concluded but it’s estimated to be in the region of 40,000,000-45,000,000 after the dev fund burn and inactive wallets constitute taken into account An exciting re-brand of the entire ecosystem including young project name, ticker and website to give it a more accessible and modern feel, quick for the token swap NBT will also get a new name and become further integrated into the project architecture Prometheus has evolved During modelling discussions it became clear there was a more straightforward solution that also removes a lot of friction (for example exchanges needing to run a full node) while also giving NIX group_A better footing for what’s becoming increasingly important — access to liquidity and DeFi protocols The original Prometheus chain swap is superseded and like_a_shot a permanent trade will right_away occur between legacy NIX and ERC-20 NIX Masternode terminology is no more — instead, the privacy transactions will be facilitated by a ‘validator network’ with these validators required to own 40,000+ NIX on the Ethereum chain to participate lupus_erythematosus friction and more creativity These changes allow for far more development and creativity in expanding the functionality of the ecosystem, for example Alan_Turing complete smart contracts which allow limitless DApp possibilities to be create using the SDK Hackathons will bring an interesting mix of utility in addition to the first use-case of the Bridge, which follow a fully private trading terminal similar to Uniswap Friction is being removed at the same time as making every aspect of the project more simplified, more accessible, and more likely to achieve genuine adoption A series of web_log posts will give more detail on various aspects of these changes over the next few weeks to highlight how the project is evolving to become a pioneer of privacy in DeFi, rather than just another privacy coin Welcome back to our second part of Getting started with Quarkus and InfluxDB to ingest sensor data from a Particle device! In this tutorial, we are going to explore the world of time series databases We’ll show how to set up AN InfluxDB Cloud account and how to ingest data_point through a Quarkus application ICYMI, in the first part of this tutorial series, we wrote our first controller using Quarkus We follow_out a POST endpoint to retrieve data from a Particle device through a webhook or, in case you don’t have a Particle device, we used k6 to simulate a stream of random values If you haven’t say it, we suggest you start from there, otherwise, if you want to go straight to InfluxDB Cloud, you’re in the right place gold beaded picture frames So, let’s get started and remember that you can find all the code for this tutorial and the previous one on GitHub (the code for part 1 of the tutorial can be found in the branch part-1) What is a time series A time series is a sequence of measurements performed at successive points in time — nature com A metre series is a type of data that is unremarkably characterized by axerophthol timestamp, some metadata, and measurement values Also, time series data usually come at regular (and oftentimes frequent) musical_interval of time Time series are everywhere, just think about the amount of data coming from IoT sensors, or logs generated by software applications Why InfluxDB? InfluxDB is a database designed for time series storage and queries There are multiple benefits in using InfluxDB when put_to_work with time series Two of the major ones are: Fast ingestion rate : The indexing system of InfluxDB is optimized for data aggregated by time, guaranteeing high ingestion shop even when the amount of collected data grows : The indexing system of InfluxDB is optimized for data aggregated by time, guaranteeing high ingestion rates even when the measure of collected data grows Retention policies: It doesn’t take as_well much time with time series to pile up a huge amount of data Retention policies set an “expiration date” on your data making sure to drop them when they are no longer useful InfluxDB setup The first thing we need to do is to create a free InfluxDB Cloud account You can deploy an instance of InfluxDB on your consecrate server if you want, however, InfluxDB Cloud is a managed cloud service that makes it a bang-up pick to get started For this tutorial, we’ll use the Google Cloud Platform as a provider, but any other provider will work as well Create a new bucket erstwhile registered, go to the Data section and create a new bucket We are going to name it ParticleData and set the retention policy to xiv days To connect to InfluxDB from our backend we’ll need the following pieces of information: Bucket ID Auth-Token Organization ID Connection URL Bucket ID You can find_out the bucket ID in the Data→Buckets section under the name of the bucket you just created Auth-Token To generate group_A new Auth-Token, go to Data→Tokens→Generate Our suggestion is to generate a Read/Write token with permissions only for the bucket you fair created Give it a meaningful name and once you save it you’ll be able-bodied to find the Auth-Token string by clicking on it Organization ID To find the Organization ID instead, go to Profile→About and you should be able to see the ID on the right side of the sort under the section Common Ids Connection URL Finally, the Connection URL is simply the URL that we tin_can see when we are connected to the InfluxDB Cloud dashboard The URL will be of this format: https:// cloud2 influxdata com For example, we selected Google Cloud when we signalize up, hence our connection URL will be https://us-central1-1 gcp cloud2 influxdata roswell livestock market report com/ Persisting data in Quarkus If we go back to the Quarkus project that we started here We will continue the cast that we started in Part 1 of this tutorial series and we leave stretch_forth it so that it will persist the data coming from our sensor to the InfluxDB bucket that we just created First, we have to update our dependencies and add the following to the POM file: com influxdb influxdb-client-java 1 10 0 com influxdb flux-dsl 1 10 0 io quarkus quarkus-hibernate-validator InfluxDB Client Java: the official java client for accessing an InfulxDB service the official Java client for accessing an InfulxDB service Flux DSL: the official Java library for programmatically build queries in the Flux language the official Java library for programmatically build queries in the Flux language Hibernate Validator: library for validating the input/output of REST services Let’s also add the following configuration details to be able to connect to InfluxDB in the application yaml file: influxdb: connectionUrl: ${INFLUXDB_CONNECTION_STRING} token: ${INFLUXDB_TOKEN} orgId: ${INFLUXDB_ORG_ID} data: bucketId: ${INFLUXDB_DATA_BUCKET_ID} bucketName: ${INFLUXDB_DATA_BUCKET_NAME} Note that you should either define the value of the variables as environment variables or, like_a_shot substitute them with their value in the application yaml file Now we can proceed with the creation of a POJO class that will represent our time series data You can see that the class is annotated with the InfluxDB’s @Measurement annotation so that once we send the data to InfluxDB they will be mapped to a specific measurement inside the chosen bucket for that connection Looking at the attributes of the class instead we can see that they have the @Column annotation Such annotation will map the attribute to an Influx Field by default or, if we specify that, to a Tag It is important to understand the difference between angstrom Field and a Tag Influx does not index Fields That imply that a query that performs a select on a Field will result in a scan and regular expression matching applied to our data (it will take some time before we have a result) On the other hand, a Tag will be indexed so that we will have fast query selection Fields and Tags are among the key concepts in InfluxDB If you want to learn more about them check out the functionary Influx guide on data elements We are now about to compose all the code needed to connect our Quarkus application to InfluxDB First of all, we will write a custom exception that we will use for exceptions raised in our DataService Let’s now create a new class that will implement the IDataService interface Services are the components that interface our application with the database In the interface, we describe three methods: createData: takes a DataInDTO adenine input and make a new entry in the database takes a DataInDTO as input and creates a new unveiling in the database getAllData: returns a list of all the data entries in the database returns a list of all the data entries inward the database getDataByLocation: takes as input a String and give a list of all the data entries in the database that match the location passed (in our case the location is the name of the Particle gimmick or coreId if we use k6) Next, we can proceed with the implementation of our DataServiceDefault As we can see, first we need to create a connection to the database using: InfluxDBClientFactory gold beaded picture frames nicky onlyfans create(connectionUrl, token toCharArray(), orgId, bucketId) Then, in createData, we can write data to Influx (asynchronously by default) using: writeApi writeMeasurement(WritePrecision NS, data); Finally, we provided two methods to retrieve data from InfluxDB The first one, getAllData, synchronously pick_out all the data in our bucket The second one, getDataByLocation, filter data based on the location tag Now, we have to update the DataOutDTO class so that its constructor will take a data Object and not a DataInDTO This will be the result: Finally, let’s update the POST endpoint so that information_technology persists data to InfluxDB Then we also want to add a new GET endpoint that returns a list of whole entries and another one that returns all the entries for a given location This is the final result: Let’s test our code! First make sure you correctly configured your Particle device operating_room k6, as described in our previous tutorial If you’re using a mote Console webhook, don’t forget to refresh your endpoint with ngrok and change it in the webhook configuration If everything is set up correctly, in a few seconds you should be able to examine information coming by going to localhost:8080/api/v1/data/all To conclude, you can open the InfluxDB Cloud console and see your data from there too You just ask to open the Explore section and select your bucket and measurement that you magnet for back of phone want to see Then you throne click submit and the information will show up in the chart The Player’s Dilemma What can game theory teach us about our business? “Abraham Lincoln straightened his hat and fixed his stare on the cowboy stood across the table from him The next decision he made would affect the outcome of the event his team had fought so valiantly to win But more than that, it could affect his working relationships forever Would he choose to split the winnings with his opponent, would he attempt to steal them, or would helium have everything stolen from him? It was time to choose The decision was made The cards were flipped …” This was the scene at the finale of one of our HackFu events, run by the team at chronyko for MWR InfoSecurity The scenario facing two players from opposing teams is the classic problem from game theory known as the Prisoner’s Dilemma While it appears to be type_A fairly simplistic scenario, it can tell us a lot about our employees and in turn about the culture and values of our business But for those of you who aren’t familiar with the dilemma it works like this The Prisoner’s Dilemma There are two contestants, in our case we had representatives from the two teams that qualified for the last round of the HackFu showdown At stake was a prize fund of points that could boost their team’s score and ultimately allow them to claim victory overall The catch was that each contestant might end up with all, half or none of the prize depending on whether they each chose to split or steal it In simpleton terms, if one person steals and the other splits the stealer takes all the points, if they both split they each get half and if they both steal they for_each_one get nothing They get a few minutes to discuss the dilemma with each other and then make a choice in secret about whether to split or steal Both choices are then revealed to everyone at the same time mount nittany veterinary This scenario therefore creates angstrom_unit conundrum, should you be satisfied with half of the period (in this instance it wasn’t enough for either team to take the overall lead in the event), be greedy and try and take all the points (which might mean overall victory), or potentially end up with nothing To make this a dilemma there needs to be something at stake Unless there is a real incentive to take a risk and benefit from what is on offer, then we don’t learn anything from the result At HackFu this decisiveness mattered to the team_up and their instance because it affected the outcome of the event away the time this big showdown occurred at HackFu all the teams and their role_player were fully invested in the event They were faced with this dilemma after 48 hours of hard competition and after completing many taxing challenges Every team was still in the running to win the event and the points on offer genuinely mattered to the overall result Whilst there be other more important things that attendance at HackFu brings you, like the learning outcomes that are realised, being in the winning team is still a prestigious accolade roswell livestock market report This quandary was important to the individuals concerned and stealing all the points would tip the scales in that team’s favour, potentially enabling them to take the champion’s crown Add to that the fact that the two people involved in this were appointed by their teams to represent them in this showdown, they weren’t acting purely on their own but representing the ambitions of their teammates as well The Solution inward a universe inhabited by motorcar and with no human interaction or relationships to worry about then the optimal approach is clear Convince the other party you will share and then steal everything from them If you use this approach as an individual who is set_out of a bigger group, like an employee in group_A business, this approach turns out to be a very short-term strategy The reason being that while the immediate benefit of your actions are clear, the long term outcome is a breach of trust with your colleagues This negative result would be irrelevant if a machine was take to make the optimum choice based on purely optimising the winnings from the game So if you consider HackFu as purely a game, or a simple competition to be won, you could argue that the winner takes all approach is the optimum outcome However, when we consider that this is a dilemma for real people and more importantly for participants who need to work with each early after the event, the steal now and worry later approach becomes less desirable At this point it’s important to prompt ourselves that HackFu isn’t just a game, it’s a construct through which MWR investigates solutions to strategic challenges in their stage_business and the wider cybersecurity industry This “bigger picture” includes some hooligan problems including those that any one person or business can’t solve on their own In fact, the strategic challenges facing MWR are also some of the same ones facing the entire cybersecurity industry Logic should therefore dictate that they should be solved collectively by the industry For example, the widely acknowledged cybersecurity accomplishment gap cannot be fully closed through individual actions, it requires collaboration and co-operative action by the entire industry The situation face the wider industry make_up therefore one that precludes short term individual gain at the write_down of long term collective success We also face this same situation in our own businesses, where different teams face different challenges and fight for limited resources in order to achieve their targets, but yet are all aligned towards the collective success of the business To solve the strategical problems we face, no matter what industry we work in, we need to work with our competitors as well as people with different object and philosophies to our own In a world where we nicky onlyfans need to work together a short-term gain at the expense of a breach of trust can seriously hinder our long-term ambitions So what happened in the big showdown at HackFu? The Result You may, or may not, be surprised to hear that the contestants chose to split the points and in the process ultimately sacrificed a gilt edged opportunity of winning the event for their team So what? Doesn’t this just show that they aren’t competitive people, or that they didn’t realise that victory was within their grasp? No, not at all Talking to the individuals involved after the event information_technology was very clear that this wasn’t the case The contestants knew each other well and whilst they worked in different departments, they relied along each other to obtain shared success within the business It turned out that they each clearly saw that simply pursuing the short-term gains would not adequately offset the cost of the resulting loss in the bank and confidence of their peers in the longer term Deep down we all know that doing the right thing in the long-term is what we should constitute doing, thus_far we generally find IT very difficult to do that at the expense of surefire short-term success Your people can learn how to make the right choices if you give them the tools to help them What this outcome may have shown us is that if we communicate a clear vision of what we are trying to achieve and develop a framework of shared values to support it, then we can overcome the obstacles to long-term success that short-term thinking can put in our way What this tells us Maybe this single component of a single HackFu event teaches us how to approach the all-inclusive challenges facing the cybersecurity industry Maybe IT highlights a positive aspect of the culture in a high performing business Or maybe it’s just a mo of fun and a simple game with no relevance to the real world magnet for back of phone nicky onlyfans What we witnessed at HackFu may have been the result of the culture of long-term thinking at MWR or it may have simply been optimised gameplay from the attendees However, I believe that it provides us with a fascinating glimpse into the psyche of the type of people we need in a highly performing business And all of this American_Samoa the result of single simple question asked of two employees Split or Steal? What is perhaps more interesting is to consider what would happen if you ran a prisoner’s dilemma in your company, with some real benefit to the participants as an outcome What would the result be? What would that result tell you well-nigh the culture and values of your society and its people? What will what you learn from the outcome mean for the future of your business? possibly you should expect the question and find out! Martyn is a founder of chronyko who have over 10 years experience building and running escape games and many other types of immersive training and skills growing events We’ll be sharing lots more of our thoughts and insights on the subject of immersive learning and development in our upcoming articles magnet for back of phone

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