Extend

extend

We have exciting news 🥁 Due to overwhelming demand, we are extending the deadline for Startups of The Year 2024 nominations! It has been one month since the launch, and we've already surpassed 1,074,900 votes! Till now, we have 153,600 startups nominatedacross 98 industries and 2,900 cities worldwide. The competition we see this year is fierce - the nominated startups are not shying away from announcing their achievement, and we have observed thousands of shares on social media 🔥🔥 In this spirit, let's add more startups that deserve to be in the limelight - LET'S NOMINATE MORE THRIVING STARTUPS!!! This is the perfect opportunity (and time!) to nominate your favorite startup or yourself (we won't tell if you don't). Help us acknowledge and celebrate the remarkable achievements of the most innovative startups in 2024 and their profound impact on the tech industry. Nominate your fav startup NOW. Startups of the Year: How to Nominate? It’s easy as A.B.C! Visit our website and choose the region or industry you want to nominate from. Once on the page, scroll all the way down and you’ll see two buttons, one to nominate and one to claim. 2. Click on 'Nominate Startup' 3. Fill out the form and submit your nomination. We'll review each submission and choose the top contenders. Please note that you need a valid email that matches your company's URL. After clicking “Create Company”, you’ll see this message show up on your screen confirming your nomination 👇 5. Keep an eye on your inbox—we'll notify you if you're selected. Nominate your favorite startup NOW.
If you’re having trouble nominating on our website, try this link instead. Can’t find what you’re looking for? 💡 Use our search tool to find cities, regions, industries, and startups featured in Startups of The Year. The tool is conveniently located at the top of every page. P.S. Spread the word Don't keep this exciting news to yourself - share it with your friends, family, and colleagues, encourage them to participate in the nominations and voting process, and let's recognize the most innovative and impactful startups of 2024 together! P.P.S. It pays off to be nominated… Apart from validation and acknowledgment from the coolest tech publication on the planet, you get: Free interviews relevant to your industry, region, and funding.
A discounted publishing package designed for the nominated startups. Learn more about the startup package here. We can’t wait to see who you’re nominating next… The Team at HackerNoon About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of the Year Merch Shop here. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. We have exciting news 🥁 Due to overwhelming demand, we are extending the deadline for Startups of The Year 2024 nominations! we are extending the deadline for Startups of The Year 2024 nominations! Startups of The Year 2024 It has been one month since the launch , and we've already surpassed 1,074,900 votes ! Till now, we have 153,600 startups nominated across 98 industries and 2,900 cities worldwide. one month since the launch 1,074,900 votes 153,600 startups nominated The competition we see this year is fierce - the nominated startups are not shying away from announcing their achievement, and we have observed thousands of shares on social media 🔥🔥 thousands of shares on social media In this spirit, let's add more startups that deserve to be in the limelight - LET'S NOMINATE MORE THRIVING STARTUPS !!! LET'S NOMINATE MORE THRIVING STARTUPS This is the perfect opportunity (and time!) to nominate your favorite startup or yourself (we won't tell if you don't). Help us acknowledge and celebrate the remarkable achievements of the most innovative startups in 2024 and their profound impact on the tech industry. Nominate your fav startup NOW. Nominate your fav startup NOW . fav startup NOW fav startup NOW Startups of the Year: How to Nominate? It’s easy as A.B.C! Visit our website and choose the region or industry you want to nominate from. Once on the page, scroll all the way down and you’ll see two buttons, one to nominate and one to claim. Visit our website and choose the region or industry you want to nominate from. Once on the page, scroll all the way down and you’ll see two buttons, one to nominate and one to claim. 2. Click on 'Nominate Startup' 3. Fill out the form and submit your nomination. We'll review each submission and choose the top contenders. Please note that you need a valid email that matches your company's URL. Please note that you need a valid email that matches your company's URL. After clicking “Create Company”, you’ll see this message show up on your screen confirming your nomination 👇 After clicking “Create Company”, you’ll see this message show up on your screen confirming your nomination 👇 After clicking “Create Company”, you’ll see this message show up on your screen confirming your nomination 👇 5. Keep an eye on your inbox—we'll notify you if you're selected. Nominate your favorite startup NOW. If you’re having trouble nominating on our website, try this link instead. Nominate your favorite startup NOW. Nominate your favorite startup NOW . favorite startup NOW favorite startup NOW If you’re having trouble nominating on our website, try this link instead . this link instead this link instead Can’t find what you’re looking for? 💡 Use our search tool to find cities, regions, industries, and startups featured in Startups of The Year. The tool is conveniently located at the top of every page. P.S. Spread the word Don't keep this exciting news to yourself - share it with your friends, family, and colleagues, encourage them to participate in the nominations and voting process, and let's recognize the most innovative and impactful startups of 2024 together! P.P.S. It pays off to be nominated… Apart from validation and acknowledgment from the coolest tech publication on the planet, you get: Free interviews relevant to your industry, region, and funding. A discounted publishing package designed for the nominated startups. Learn more about the startup package here. Free interviews relevant to your industry, region, and funding. Free interviews A discounted publishing package designed for the nominated startups. Learn more about the startup package here . startup package here We can’t wait to see who you’re nominating next… The Team at HackerNoon About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview Evergreen Tech Company News Visit our FAQ page to learn more. FAQ page Download our design assets here . here Check out the Startups of the Year Merch Shop here . here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages Meet our sponsors: Wellfound: Join the #1 global, startup-focused community . At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free . Hubspot: Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection , businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform

Startups of The Year: Nominations Date Extended!

Стартапы года: дата вылучэння падоўжана!

Startups del año: ¡Fecha de nominaciones extendida!

Vuoden startup-yritykset: ehdokkuuden päivämäärää pidennetty!

Startups de l'année : la date des nominations est prolongée !

Startups do ano: prorrogada a data de nominacións!

Տարվա ստարտափներ. անվանակարգերի ժամկետը երկարաձգվել է:

Startup Tahun Ini: Tanggal Nominasi Diperpanjang!

今年のスタートアップ：ノミネート日が延長されました！

Ba Startups ya Mobu: Date ya ba nominations ebakisami!

Оны шилдэг стартапууд: Нэр дэвших хугацааг сунгалаа!

Bilawga Sannadka: Taariikhda Magacaabista waa la Kordhiyay!

سال کا آغاز: نامزدگی کی تاریخ میں توسیع!

Authors:
(1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk;
(2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Table of Links Abstract and 1 Introduction 2 Related Work 3 Framework 4 Main Results 5 A Case Study on Shortest-Path Distance 6 Conclusion and Discussion, and References 7 Proof of Theorem 1 8 Proof of Theorem 2 9 Procedure for Solving Eq. (6) 10 Additional Experiments Details and Results 11 Other Potential Applications 11 Other Potential Applications In addition to the initial dataset labelling problem discussed above, there are other potential applications of the proposed framework in other GNNs learning paradigms. Some examples include: 1. Fair k-shot learning: K-shot learning is a type of machine learning where the model is trained to classify new examples based on a small number of labelled examples in the training set. This type of learning is particularly useful in many situations where the amount of labelled data is limited. In addition to the active learning discussed earlier, k-shot learning can also be applied in choosing a representative experience replay buffer in incremental learning and transfer learning. 2. Fairness constraint: We can apply the derived results to ensure a fair predictive performance of GNNs for specified structural groups. As we have shown in the paper, the key factor of unfair predictive performance towards different structural groups is the distortion between structural distance and embedding distance. We can penalize the parameters with low distortion, which amounts to the following learning objective: The proposed framework has promising potential for studying and being applied to the above-mentioned problems. Further research into investigating the actual application results would be an interesting future work. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk; (2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Authors: Authors: (1) Junwei Su, Department of Computer Science, the University of Hong Kong and jwsu@cs.hku.hk; (2) Chuan Wu, Department of Computer Science, the University of Hong Kong and cwu@cs.hku.hk. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2 Related Work 2 Related Work 3 Framework 3 Framework 4 Main Results 4 Main Results 5 A Case Study on Shortest-Path Distance 5 A Case Study on Shortest-Path Distance 6 Conclusion and Discussion, and References 6 Conclusion and Discussion, and References 7 Proof of Theorem 1 7 Proof of Theorem 1 8 Proof of Theorem 2 8 Proof of Theorem 2 9 Procedure for Solving Eq. (6) 9 Procedure for Solving Eq. (6) 10 Additional Experiments Details and Results 10 Additional Experiments Details and Results 11 Other Potential Applications 11 Other Potential Applications 11 Other Potential Applications In addition to the initial dataset labelling problem discussed above, there are other potential applications of the proposed framework in other GNNs learning paradigms. Some examples include: 1. Fair k-shot learning: K-shot learning is a type of machine learning where the model is trained to classify new examples based on a small number of labelled examples in the training set. This type of learning is particularly useful in many situations where the amount of labelled data is limited. In addition to the active learning discussed earlier, k-shot learning can also be applied in choosing a representative experience replay buffer in incremental learning and transfer learning. 1. Fair k-shot learning: 2. Fairness constraint : We can apply the derived results to ensure a fair predictive performance of GNNs for specified structural groups. As we have shown in the paper, the key factor of unfair predictive performance towards different structural groups is the distortion between structural distance and embedding distance. We can penalize the parameters with low distortion, which amounts to the following learning objective: 2. Fairness constraint The proposed framework has promising potential for studying and being applied to the above-mentioned problems. Further research into investigating the actual application results would be an interesting future work. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Extending GNN Learning: 11 Additional Framework Applications

#blockchain-API Writing Contest Deadline Extended: Check Out dRPC’s Tips for Writing a Strong Entry 

#blockchain-API Skryfwedstryd Sperdatum verleng: Kyk na dRPC se wenke om 'n sterk inskrywing te skryf 

#blockchain-API Qillqañ atipt’asiw pachax jilxatayatawa: dRPC ukan iwxt’awinakap uñakipt’añäni, mä ch’aman qillqt’äwi qillqt’añataki 

Крайният срок за конкурс за писане на #blockchain-API е удължен: Вижте съветите на dRPC за писане на силен запис 

Se extiende la fecha límite para el concurso de redacción de API de blockchain: consulte los consejos de dRPC para escribir una propuesta sólida

Batas Waktu Kontes Penulisan #blockchain-API Diperpanjang: Simak Tips dRPC untuk Menulis Entri yang Kuat

#blockchain-API ライティング コンテストの締め切りが延長されました: 強力なエントリーを書くための dRPC のヒントをご覧ください

#blockchain-API rakstīšanas konkursa termiņš ir pagarināts: skatiet dRPC padomus, lai uzrakstītu spēcīgu pieteikumu 

#blockchain-API Letšatši la Mafelelo la Phadišano ya go Ngwala le Atološitšwe: Lekola dRPC’s Tips for Writing a Strong Entry 

#blockchain-API Qillqana atipanakuy p’unchaw mast’arisqa: Qhaway dRPC’s Yuyaychaykunata Sinchi Yaykuy qillqanapaq 

Срок подачи заявок на конкурс по написанию #blockchain-API продлен: ознакомьтесь с советами dRPC по написанию сильной заявки

#blockchain-API எழுதும் போட்டிக்கான காலக்கெடு நீட்டிக்கப்பட்டது: வலுவான நுழைவு எழுதுவதற்கான dRPC இன் உதவிக்குறிப்புகளைப் பாருங்கள் 

#blockchain-API ውድድር ጽሑፍ ናይ መወዳእታ ግዜ ተናዊሑ: dRPC’s ምኽርታት ንጽሕፈት ሓያል ምእታው ርአ 

Authors:
(1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel;
(2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Related Work Methodology Experimental Settings Results Conclusion and Limitations Bibliographical References 4. Experimental Settings Machine Translation. Our machine-translation architecture is based on a simple encoder-decoder framework, which we initialize using weights of the BERT model in focus.[5] For example, if we want our model to translate from Hebrew to Arabic, we might initialize the encoder with the model weights of mBERT and the decoder with the weights of CAMelBERT. To fine-tune the model on machine translation, we use the new “Kol Zchut” (in English, “All Rights”) Hebrew-Arabic parallel corpus[6] which contains over 4,000 parallel articles in the civil-legal domain, corresponding to 140,000 sentence-pairs in Arabic and Hebrew containing 2.13M and 1.8M words respectively. To the best of our knowledge, ours is the first work to report machine translation results using this resource; therefore, no established baseline or benchmark exists for comparison. As the corpus is provided without an official train/test split, we apply a random split with 80% of the data being allocated for training and the remaining 20% for testing, using the train_test_split function of scikit-Learn with a random seed of 42. We evaluate our HeArBERT-based translation against an equivalent system initialized using other models. The standard BLEU metric (Papineni et al., 2002) is employed to contrast the system’s generated translation with the sole reference translation present in the corpus. Each system is fine-tuned for the duration of ten epochs, and we report the best performance observed across all epochs. Baseline Language Models. To contrast HeArBERT with an equivalent model trained on texts in both Arabic and Hebrew scripts, we pre-train another model and tokenizer following the same procedure, but without the transliteration preprocessing for the Arabic data. This model is denoted with a subscript "NT" (no transliteration): HeArBERTNT. We compare our model with a number of existing models. The initial model, mBERT, was originally pre-trained on a range of languages, including both Arabic and Hebrew. We also chose a couple of monolingual Arabic language models, with specific versions from Hugging Face provided in a footnote. Specifically, we use CAMeLBERT[7] and GigaBERT[8]. In some experiments, we adopt a technique inspired by Rom and Bar (2021). This involves expanding the vocabulary of an existing Arabic language-model’s tokenizer by appending a Hebrew-transliterated version of each Arabic token and associating it with the original token identifier. We denote such extended models by adding “ET” (extended tokenizer) to the model name. All these models share the same architecture size as our proposed model. This paper is available on arxiv under CC BY 4.0 DEED license. [5] We use HuggingFace’s EncoderDecoderModel. [6] https://releases.iahlt.org/ [7] CAMeL-Lab/bert-base-arabic-camelbert-mix [8] lanwuwei/GigaBERT-v4-Arabic-andEnglish Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Authors: Authors: (1) Aviad Rom, The Data Science Institute, Reichman University, Herzliya, Israel; (2) Kfir Bar, The Data Science Institute, Reichman University, Herzliya, Israel. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Methodology Methodology Experimental Settings Experimental Settings Results Results Conclusion and Limitations Conclusion and Limitations Bibliographical References Bibliographical References 4. Experimental Settings Machine Translation. Our machine-translation architecture is based on a simple encoder-decoder framework, which we initialize using weights of the BERT model in focus.[5] For example, if we want our model to translate from Hebrew to Arabic, we might initialize the encoder with the model weights of mBERT and the decoder with the weights of CAMelBERT. To fine-tune the model on machine translation, we use the new “Kol Zchut” (in English, “All Rights”) Hebrew-Arabic parallel corpus[6] which contains over 4,000 parallel articles in the civil-legal domain, corresponding to 140,000 sentence-pairs in Arabic and Hebrew containing 2.13M and 1.8M words respectively. To the best of our knowledge, ours is the first work to report machine translation results using this resource; therefore, no established baseline or benchmark exists for comparison. As the corpus is provided without an official train/test split, we apply a random split with 80% of the data being allocated for training and the remaining 20% for testing, using the train_test_split function of scikit-Learn with a random seed of 42. We evaluate our HeArBERT-based translation against an equivalent system initialized using other models. The standard BLEU metric (Papineni et al., 2002) is employed to contrast the system’s generated translation with the sole reference translation present in the corpus. Each system is fine-tuned for the duration of ten epochs, and we report the best performance observed across all epochs. Baseline Language Models. To contrast HeArBERT with an equivalent model trained on texts in both Arabic and Hebrew scripts, we pre-train another model and tokenizer following the same procedure, but without the transliteration preprocessing for the Arabic data. This model is denoted with a subscript "NT" (no transliteration): HeArBERTNT. Baseline Language Models. We compare our model with a number of existing models. The initial model, mBERT, was originally pre-trained on a range of languages, including both Arabic and Hebrew. We also chose a couple of monolingual Arabic language models, with specific versions from Hugging Face provided in a footnote. Specifically, we use CAMeLBERT[7] and GigaBERT[8]. In some experiments, we adopt a technique inspired by Rom and Bar (2021). This involves expanding the vocabulary of an existing Arabic language-model’s tokenizer by appending a Hebrew-transliterated version of each Arabic token and associating it with the original token identifier. We denote such extended models by adding “ET” (extended tokenizer) to the model name. All these models share the same architecture size as our proposed model. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv [5] We use HuggingFace’s EncoderDecoderModel. [6] https://releases.iahlt.org/ [7] CAMeL-Lab/bert-base-arabic-camelbert-mix [8] lanwuwei/GigaBERT-v4-Arabic-andEnglish

Benchmarking HeArBERT: Comparative Analysis with Traditional and Extended Language Models

Controlling Automated Vehicles on Large Lane-free Roundabouts (Extended Version): References

A few days ago, I was fixing a flaky test, and it turned out I needed some unique and valid values within my factory. Laravel wraps FakerPHP, which we usually access through the fake() helper. FakerPHP comes with modifiers like valid() and unique(), but you can use only one at a time, so you can't do fake()->unique()->valid(), which is exactly what I needed. This made me think, what if we want to create our own modifier? For example, uniqueAndValid() or any other modifier. How can we extend the framework? Thinking Out Loud I will be dumping my train of thought. Before jumping into any over-engineered solution, I always want to check if there is a simpler option and understand what I'm dealing with. So, let's take a look at the fake() helper: function fake($locale = null)
{
    if (app()->bound('config')) {
        $locale ??= app('config')->get('app.faker_locale');
    }

    $locale ??= 'en_US';

    $abstract = \Faker\Generator::class.':'.$locale;

    if (! app()->bound($abstract)) {
        app()->singleton($abstract, fn () => \Faker\Factory::create($locale));
    }

    return app()->make($abstract);
} Reading the code, we can see that Laravel binds a singleton to the container. However, if we inspect the abstract, it's a regular class that does not implement any interface, and the object is created via a factory. This complicates things. Why? Because if it were an interface, we could just create a new class that extends the base \Faker\Generator class, add some new features, and rebind it to the container. But we don't have this luxury.


There is a factory involved. This means it's not a simple instantiation; there is some logic being run. In this case, the factory adds some providers (PhoneNumber, Text, UserAgent, etc.). So, even if we try to rebind, we will have to use the factory, which will return the original \Faker\Generator. Solutions 🤔? One might think, "What's stopping us from creating our own factory that returns the new generator as outlined in point 1?" Well, nothing, we can do that, but we won't! We use a framework for several reasons, one of them being updates. What will happen if FakerPHP adds a new provider or has a major upgrade? Laravel will adjust the code, and people who haven't made any changes won't notice anything. However, we would be left out, and our code might even break (most likely). So, yes, we don't want to go that far. So, What Do We Do? Now that we've explored the basic options, we can start thinking of more advanced ones, like design patterns. We don't need an exact implementation, just something familiar to our problem. This is why I always say it's good to know them. In this case, we can "decorate" the Generator class by adding new features while maintaining the old ones. Sounds good? Let's see how! First, let's create a new class, FakerGenerator: <?php

namespace App\Support;

use Closure;
use Faker\Generator;
use Illuminate\Support\Traits\ForwardsCalls;

class FakerGenerator
{
    use ForwardsCalls;

    public function __construct(private readonly Generator $generator)
    {
    }

    public function uniqueAndValid(Closure $validator = null): UniqueAndValidGenerator
    {
        return new UniqueAndValidGenerator($this->generator, $validator);
    }

    public function __call($method, $parameters): mixed
    {
        return $this->forwardCallTo($this->generator, $method, $parameters);
    }
} This will be our "decorator" (kinda). It is a simple class that expects the base Generator as a dependency and introduces a new modifier, uniqueAndValid(). It also uses the ForwardsCalls trait from Laravel, which allows it to proxy calls to the base object. This trait has two methods: forwardCallTo and forwardDecoratedCallTo. Use the latter when you want to chain methods on the decorated object. In our case, we will always have a single call. We also need to implement the UniqueAndValidGenerator, which is the custom modifier, but this is not the point of the article. If you are interested in the implementation, this class is basically a mixture of the ValidGenerator and UniqueGenerator that ship with FakerPHP, you can find it here. Now, let's extend the framework, in the AppServiceProvider: <?php

namespace App\Providers;

use Closure;
use Faker\Generator;
use App\Support\FakerGenerator;
use Illuminate\Support\ServiceProvider;

class AppServiceProvider extends ServiceProvider
{
    public function register(): void
    {
        $this->app->extend(
            $this->fakerAbstractName(), 
            fn (Generator $base) => new FakerGenerator($base)
        );
    }

    private function fakerAbstractName(): string
    {
        // This is important, it matches the name bound by the fake() helper
        return Generator::class . ':' . app('config')->get('app.faker_locale');
    }
} The extend() method checks if an abstract matching the given name has been bound to the container. If so, it overrides its value with the result of the closure, take a look: // Laravel: src/Illuminate/Container/Container.php

public function extend($abstract, Closure $closure)
{
    $abstract = $this->getAlias($abstract);

    if (isset($this->instances[$abstract])) {
         // You are interested here
        $this->instances[$abstract] = $closure($this->instances[$abstract], $this);

        $this->rebound($abstract);
    } else {
        $this->extenders[$abstract][] = $closure;

        if ($this->resolved($abstract)) {
            $this->rebound($abstract);
        }
    }
} That's why we defined the fakerAbstractName() method, which generates the same name as the fake() helper binds in the container. Recheck the code above if you missed it, I left a comment. Now, every time we call fake(), an instance of FakerGenerator will be returned, and we will have access to the custom modifier we introduced. Every time we invoke a call that does not exist on the FakerGenerator class, __call() will be triggered, and it will proxy it to the base Generator using the forwardCallTo() method. That's it! I can finally do fake()->uniqueAndValid()->randomElement(), and it works like a charm! Before we conclude, I want to point out that this is not a pure decorator pattern. However, patterns are not sacred texts; tweak them to fit your needs and solve the problem. Conclusion Frameworks are incredibly helpful, and Laravel comes with a lot of built-in features. However, they can't cover all the edge cases in your projects, and sometimes, you might hit a dead end. When that happens, you can always extend the framework. We've seen how simple it is, and I hope you understood the main idea, which applies beyond just this Faker example. Always start simple and look for the simplest solution to the problem. Complexity will come when it's necessary, so if basic inheritance does the trick, there's no need to implement a decorator or anything else. When you do extend the framework, ensure you don't go too far, where the loss outweighs the gain. You don't want to end up maintaining a part of the framework on your own. A few days ago, I was fixing a flaky test, and it turned out I needed some unique and valid values within my factory. Laravel wraps FakerPHP, which we usually access through the fake() helper. FakerPHP comes with modifiers like valid() and unique() , but you can use only one at a time, so you can't do fake()->unique()->valid() , which is exactly what I needed. unique valid fake() valid() unique() fake()->unique()->valid() This made me think, what if we want to create our own modifier? For example, uniqueAndValid() or any other modifier. How can we extend the framework? uniqueAndValid() extend Thinking Out Loud I will be dumping my train of thought. I will be dumping my train of thought. Before jumping into any over-engineered solution, I always want to check if there is a simpler option and understand what I'm dealing with. So, let's take a look at the fake() helper: fake() function fake($locale = null)
{
    if (app()->bound('config')) {
        $locale ??= app('config')->get('app.faker_locale');
    }

    $locale ??= 'en_US';

    $abstract = \Faker\Generator::class.':'.$locale;

    if (! app()->bound($abstract)) {
        app()->singleton($abstract, fn () => \Faker\Factory::create($locale));
    }

    return app()->make($abstract);
} function fake($locale = null)
{
    if (app()->bound('config')) {
        $locale ??= app('config')->get('app.faker_locale');
    }

    $locale ??= 'en_US';

    $abstract = \Faker\Generator::class.':'.$locale;

    if (! app()->bound($abstract)) {
        app()->singleton($abstract, fn () => \Faker\Factory::create($locale));
    }

    return app()->make($abstract);
} Reading the code, we can see that Laravel binds a singleton to the container. However, if we inspect the abstract, it's a regular class that does not implement any interface, and the object is created via a factory. This complicates things. Why? Because if it were an interface, we could just create a new class that extends the base \Faker\Generator class, add some new features, and rebind it to the container. But we don't have this luxury. There is a factory involved. This means it's not a simple instantiation; there is some logic being run. In this case, the factory adds some providers (PhoneNumber, Text, UserAgent, etc.). So, even if we try to rebind, we will have to use the factory, which will return the original \Faker\Generator. Because if it were an interface, we could just create a new class that extends the base \Faker\Generator class, add some new features, and rebind it to the container. But we don't have this luxury. Because if it were an interface, we could just create a new class that extends the base \Faker\Generator class, add some new features, and rebind it to the container. But we don't have this luxury. \Faker\Generator There is a factory involved. This means it's not a simple instantiation; there is some logic being run. In this case, the factory adds some providers (PhoneNumber, Text, UserAgent, etc.). So, even if we try to rebind, we will have to use the factory, which will return the original \Faker\Generator. There is a factory involved. This means it's not a simple instantiation; there is some logic being run. In this case, the factory adds some providers (PhoneNumber, Text, UserAgent, etc.). So, even if we try to rebind, we will have to use the factory, which will return the original \Faker\Generator . \Faker\Generator Solutions 🤔? One might think, "What's stopping us from creating our own factory that returns the new generator as outlined in point 1?" Well, nothing, we can do that, but we won't! We use a framework for several reasons, one of them being updates. What will happen if FakerPHP adds a new provider or has a major upgrade? Laravel will adjust the code, and people who haven't made any changes won't notice anything. However, we would be left out, and our code might even break (most likely). So, yes, we don't want to go that far. So, What Do We Do? Now that we've explored the basic options, we can start thinking of more advanced ones, like design patterns. We don't need an exact implementation, just something familiar to our problem. This is why I always say it's good to know them. In this case, we can "decorate" the Generator class by adding new features while maintaining the old ones. Sounds good? Let's see how! Generator First, let's create a new class, FakerGenerator : FakerGenerator <?php

namespace App\Support;

use Closure;
use Faker\Generator;
use Illuminate\Support\Traits\ForwardsCalls;

class FakerGenerator
{
    use ForwardsCalls;

    public function __construct(private readonly Generator $generator)
    {
    }

    public function uniqueAndValid(Closure $validator = null): UniqueAndValidGenerator
    {
        return new UniqueAndValidGenerator($this->generator, $validator);
    }

    public function __call($method, $parameters): mixed
    {
        return $this->forwardCallTo($this->generator, $method, $parameters);
    }
} <?php

namespace App\Support;

use Closure;
use Faker\Generator;
use Illuminate\Support\Traits\ForwardsCalls;

class FakerGenerator
{
    use ForwardsCalls;

    public function __construct(private readonly Generator $generator)
    {
    }

    public function uniqueAndValid(Closure $validator = null): UniqueAndValidGenerator
    {
        return new UniqueAndValidGenerator($this->generator, $validator);
    }

    public function __call($method, $parameters): mixed
    {
        return $this->forwardCallTo($this->generator, $method, $parameters);
    }
} This will be our "decorator" (kinda). It is a simple class that expects the base Generator as a dependency and introduces a new modifier, uniqueAndValid() . It also uses the ForwardsCalls trait from Laravel, which allows it to proxy calls to the base object. Generator uniqueAndValid() ForwardsCalls This trait has two methods: forwardCallTo and forwardDecoratedCallTo. Use the latter when you want to chain methods on the decorated object. In our case, we will always have a single call. This trait has two methods: forwardCallTo and forwardDecoratedCallTo . Use the latter when you want to chain methods on the decorated object. In our case, we will always have a single call. forwardCallTo forwardDecoratedCallTo We also need to implement the UniqueAndValidGenerator , which is the custom modifier, but this is not the point of the article. If you are interested in the implementation, this class is basically a mixture of the ValidGenerator and UniqueGenerator that ship with FakerPHP, you can find it here . UniqueAndValidGenerator ValidGenerator UniqueGenerator here Now, let's extend the framework, in the AppServiceProvider : extend AppServiceProvider <?php

namespace App\Providers;

use Closure;
use Faker\Generator;
use App\Support\FakerGenerator;
use Illuminate\Support\ServiceProvider;

class AppServiceProvider extends ServiceProvider
{
    public function register(): void
    {
        $this->app->extend(
            $this->fakerAbstractName(), 
            fn (Generator $base) => new FakerGenerator($base)
        );
    }

    private function fakerAbstractName(): string
    {
        // This is important, it matches the name bound by the fake() helper
        return Generator::class . ':' . app('config')->get('app.faker_locale');
    }
} <?php

namespace App\Providers;

use Closure;
use Faker\Generator;
use App\Support\FakerGenerator;
use Illuminate\Support\ServiceProvider;

class AppServiceProvider extends ServiceProvider
{
    public function register(): void
    {
        $this->app->extend(
            $this->fakerAbstractName(), 
            fn (Generator $base) => new FakerGenerator($base)
        );
    }

    private function fakerAbstractName(): string
    {
        // This is important, it matches the name bound by the fake() helper
        return Generator::class . ':' . app('config')->get('app.faker_locale');
    }
} The extend() method checks if an abstract matching the given name has been bound to the container. If so, it overrides its value with the result of the closure, take a look: extend() // Laravel: src/Illuminate/Container/Container.php

public function extend($abstract, Closure $closure)
{
    $abstract = $this->getAlias($abstract);

    if (isset($this->instances[$abstract])) {
         // You are interested here
        $this->instances[$abstract] = $closure($this->instances[$abstract], $this);

        $this->rebound($abstract);
    } else {
        $this->extenders[$abstract][] = $closure;

        if ($this->resolved($abstract)) {
            $this->rebound($abstract);
        }
    }
} // Laravel: src/Illuminate/Container/Container.php

public function extend($abstract, Closure $closure)
{
    $abstract = $this->getAlias($abstract);

    if (isset($this->instances[$abstract])) {
         // You are interested here
        $this->instances[$abstract] = $closure($this->instances[$abstract], $this);

        $this->rebound($abstract);
    } else {
        $this->extenders[$abstract][] = $closure;

        if ($this->resolved($abstract)) {
            $this->rebound($abstract);
        }
    }
} That's why we defined the fakerAbstractName() method, which generates the same name as the fake() helper binds in the container. fakerAbstractName() fake() Recheck the code above if you missed it, I left a comment. Recheck the code above if you missed it, I left a comment. Now, every time we call fake() , an instance of FakerGenerator will be returned, and we will have access to the custom modifier we introduced. Every time we invoke a call that does not exist on the FakerGenerator class, __call() will be triggered, and it will proxy it to the base Generator using the forwardCallTo() method. fake() FakerGenerator FakerGenerator __call() Generator forwardCallTo() That's it! I can finally do fake()->uniqueAndValid()->randomElement() , and it works like a charm! fake()->uniqueAndValid()->randomElement() Before we conclude, I want to point out that this is not a pure decorator pattern. However, patterns are not sacred texts; tweak them to fit your needs and solve the problem. Before we conclude, I want to point out that this is not a pure decorator pattern. However, patterns are not sacred texts; tweak them to fit your needs and solve the problem. Conclusion Frameworks are incredibly helpful, and Laravel comes with a lot of built-in features. However, they can't cover all the edge cases in your projects, and sometimes, you might hit a dead end. When that happens, you can always extend the framework. We've seen how simple it is, and I hope you understood the main idea, which applies beyond just this Faker example. Always start simple and look for the simplest solution to the problem. Complexity will come when it's necessary, so if basic inheritance does the trick, there's no need to implement a decorator or anything else. When you do extend the framework, ensure you don't go too far, where the loss outweighs the gain. You don't want to end up maintaining a part of the framework on your own.

Laravel Under The Hood - How to Extend the Framework

লারাভেল আন্ডার দ্য হুড - কীভাবে ফ্রেমওয়ার্ক প্রসারিত করবেন

Laravel unter der Haube – So erweitern Sie das Framework

Laravel Under The Hood: cómo ampliar el marco

Laravel Under The Hood - Comment étendre le framework

लारवेल अंडर द हुड - फ्रेमवर्क का विस्तार कैसे करें

Laravel の裏側 - フレームワークを拡張する方法

Laravel Under The Hood - 프레임워크를 확장하는 방법

Laravel Under The Hood - Como estender a estrutura

Laravel под капотом — как расширить фреймворк

Laravel Under The Hood - Çerçeve Nasıl Genişletilir

Laravel Under The Hood - Cách mở rộng khung

Laravel 底层原理 - 如何扩展框架

**LONDON, United Kingdom, May 16th, 2024/Chainwire/--**Talos, the premier provider of digital asset trading technology for institutions, and One Trading, a crypto trading venue headquartered and regulated in the European Union, announced an integration designed to expand liquidity access for institutions. As part of the collaboration, One Trading joins the Talos network of liquidity providers, expanding the possible destinations for their shared clients looking to achieve best execution. Talos users will now be able to access One Trading’s high-speed trading platform with a matching engine time of just 1 microsecond. In addition to expanding its potential reach to Talos’s institutional clients, One Trading also adopts the Talos trading platform as a sell-side client to support its OTC trading desk. The award-winning Talos trading platform will help One Trading efficiently source liquidity to better serve its clients’ needs. The multi-faceted relationship between the two firms highlights the different ways that the Talos network and platform can enhance a partner’s business. “We are thrilled to welcome One Trading into the Talos network of liquidity providers,” said Daniel Packham, VP and Head of Operations, EMEA. “They have built a leading regulated trading venue in Europe using cutting-edge technology to build an incredibly fast and secure exchange, holding themselves to the highest standards of security and transparency that are important to Talos and our institutional clients. In addition, as a client themselves using our trading platform, One Trading is a great example of how an OTC dealer can leverage Talos to help manage liquidity efficiently.” Commenting on the integration, Joshua Barraclough, CEO of One Trading, said, “Talos shares our goal of bridging the gap between traditional and crypto asset trading. That’s why we’re excited to integrate with Talos as a liquidity provider as well as a sell-side trading client. They provide us with the same institutional grade of technology that we seek to provide to our own clients.” One Trading is an EU-based trading venue built by a highly experienced ex-TradFi team. One Trading provides an institutional-grade digital asset exchange for both retail customers and institutional clients, with a focus on achieving market-leading execution speed, deep order books, and low fees. The exchange provides zero fees for both maker and taker transactions. One Trading operates a regulated spot trading venue and an OTC business with plans to roll out a MiFID II regulated derivatives business in the near future. As the Markets in Crypto Assets (MiCA) European regulation elevates the importance of best execution, Talos’s integration with One Trading will empower shared clients with an additional option for sourcing the best available liquidity. About Talos Talos provides institutional-grade technology that supports the full digital asset trading lifecycle, including liquidity sourcing, price discovery, trading, settlement, lending, borrowing, and portfolio management. Engineered by a team with unmatched experience building institutional trading systems, the Talos platform connects institutions to key participants in today’s digital asset ecosystem – exchanges, OTC desks, prime brokers, lenders, custodians, and more – through a single point of entry. By streamlining the entire trading process, Talos helps mitigate intermediary risk and facilitate best execution. For additional information, visit www.talos.com. Talos Disclaimer: Talos offers software-as-a-service products that provide connectivity tools for institutional clients. Talos does not provide clients with any pre-negotiated arrangements with liquidity providers or other parties. Clients are required to negotiate bilaterally and independently with liquidity providers and other parties. Talos is not a party to any of these arrangements. Services and venues may not be available in all jurisdictions. About One Trading One Trading is a leading European digital asset trading platform with a VASP registration in Italy with Organismo Agenti e Mediatori (OAM). The One Trading platform has various offerings: Exchange, Instant Trade, and an OTC desk. The Exchange is where registered customers can access the fastest trading venue in the world with zero fees — also boasting a transparent order book with deep liquidity, and charting tools for technical analysis. The team behind One Trading has strong ex-TradFi expertise and is focused on providing an unparalleled product experience. Instant Trade offers a simplified UI for trading a wide range of fiat, stablecoin, and altcoin pairs at 0% additional commission. One Trading retrieves the best prices for customers by plugging into a number of major liquidity providers with access to deep liquidity. These relationships have been established through the over-the-counter (OTC) offering and are typically only reserved for HNWs or institutional customers. Through Instant Trade, all trader types can access this unique trading mechanism through a simple UI. The OTC desk offers a high-touch trading team to work with clients, typically institutional partners, to facilitate trades of any size and digital asset, with access to deep pools of liquidity, rapid settlement, and large asset coverage. One Trading Disclaimer: This material is for informational purposes only and is not intended to provide legal, tax, financial, or investment advice. Past performance is not necessarily indicative of the future nor a reliable indicator of the likely performance of any investment. Recipients should consult their own advisors before making these types of decisions. One Trading has no responsibility or liability for any decision made or any other acts or omissions in connection with the Recipient’s use of this material. Contact Andy Keelaghan One Trading info@3webdigital.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LONDON, United Kingdom, May 16th, 2024/Chainwire/--**Talos, the premier provider of digital asset trading technology for institutions, and One Trading, a crypto trading venue headquartered and regulated in the European Union, announced an integration designed to expand liquidity access for institutions. As part of the collaboration, One Trading joins the Talos network of liquidity providers, expanding the possible destinations for their shared clients looking to achieve best execution. Talos users will now be able to access One Trading’s high-speed trading platform with a matching engine time of just 1 microsecond. In addition to expanding its potential reach to Talos’s institutional clients, One Trading also adopts the Talos trading platform as a sell-side client to support its OTC trading desk. The award-winning Talos trading platform will help One Trading efficiently source liquidity to better serve its clients’ needs. The multi-faceted relationship between the two firms highlights the different ways that the Talos network and platform can enhance a partner’s business. “We are thrilled to welcome One Trading into the Talos network of liquidity providers,” said Daniel Packham, VP and Head of Operations, EMEA. “We are thrilled to welcome One Trading into the Talos network of liquidity providers,” said Daniel Packham, VP and Head of Operations, EMEA. “They have built a leading regulated trading venue in Europe using cutting-edge technology to build an incredibly fast and secure exchange, holding themselves to the highest standards of security and transparency that are important to Talos and our institutional clients. In addition, as a client themselves using our trading platform, One Trading is a great example of how an OTC dealer can leverage Talos to help manage liquidity efficiently.” “They have built a leading regulated trading venue in Europe using cutting-edge technology to build an incredibly fast and secure exchange, holding themselves to the highest standards of security and transparency that are important to Talos and our institutional clients. In addition, as a client themselves using our trading platform, One Trading is a great example of how an OTC dealer can leverage Talos to help manage liquidity efficiently.” Commenting on the integration, Joshua Barraclough, CEO of One Trading, said, “Talos shares our goal of bridging the gap between traditional and crypto asset trading. That’s why we’re excited to integrate with Talos as a liquidity provider as well as a sell-side trading client. They provide us with the same institutional grade of technology that we seek to provide to our own clients.” “Talos shares our goal of bridging the gap between traditional and crypto asset trading. That’s why we’re excited to integrate with Talos as a liquidity provider as well as a sell-side trading client. They provide us with the same institutional grade of technology that we seek to provide to our own clients.” One Trading is an EU-based trading venue built by a highly experienced ex-TradFi team. One Trading provides an institutional-grade digital asset exchange for both retail customers and institutional clients, with a focus on achieving market-leading execution speed, deep order books, and low fees. The exchange provides zero fees for both maker and taker transactions. One Trading operates a regulated spot trading venue and an OTC business with plans to roll out a MiFID II regulated derivatives business in the near future. As the Markets in Crypto Assets (MiCA) European regulation elevates the importance of best execution, Talos’s integration with One Trading will empower shared clients with an additional option for sourcing the best available liquidity. About Talos Talos provides institutional-grade technology that supports the full digital asset trading lifecycle, including liquidity sourcing, price discovery, trading, settlement, lending, borrowing, and portfolio management. Engineered by a team with unmatched experience building institutional trading systems, the Talos platform connects institutions to key participants in today’s digital asset ecosystem – exchanges, OTC desks, prime brokers, lenders, custodians, and more – through a single point of entry. By streamlining the entire trading process, Talos helps mitigate intermediary risk and facilitate best execution. For additional information, visit www.talos.com . www.talos.com www.talos.com Talos Disclaimer: Talos offers software-as-a-service products that provide connectivity tools for institutional clients. Talos does not provide clients with any pre-negotiated arrangements with liquidity providers or other parties. Clients are required to negotiate bilaterally and independently with liquidity providers and other parties. Talos is not a party to any of these arrangements. Services and venues may not be available in all jurisdictions. About One Trading One Trading is a leading European digital asset trading platform with a VASP registration in Italy with Organismo Agenti e Mediatori (OAM). The One Trading platform has various offerings: Exchange, Instant Trade, and an OTC desk. The Exchange is where registered customers can access the fastest trading venue in the world with zero fees — also boasting a transparent order book with deep liquidity, and charting tools for technical analysis. The team behind One Trading has strong ex-TradFi expertise and is focused on providing an unparalleled product experience. One Trading One Trading Exchange Exchange Instant Trade offers a simplified UI for trading a wide range of fiat, stablecoin, and altcoin pairs at 0% additional commission. One Trading retrieves the best prices for customers by plugging into a number of major liquidity providers with access to deep liquidity. These relationships have been established through the over-the-counter (OTC) offering and are typically only reserved for HNWs or institutional customers. Through Instant Trade, all trader types can access this unique trading mechanism through a simple UI. Instant Trade Instant Trade The OTC desk offers a high-touch trading team to work with clients, typically institutional partners, to facilitate trades of any size and digital asset, with access to deep pools of liquidity, rapid settlement, and large asset coverage. OTC OTC One Trading Disclaimer: This material is for informational purposes only and is not intended to provide legal, tax, financial, or investment advice. Past performance is not necessarily indicative of the future nor a reliable indicator of the likely performance of any investment. Recipients should consult their own advisors before making these types of decisions. One Trading has no responsibility or liability for any decision made or any other acts or omissions in connection with the Recipient’s use of this material. Contact Andy Keelaghan One Trading info@3webdigital.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

One Trading Partners With Talos To Extend Institutional Trading Services Across Europe

Form manipulation is a typical task for a Front-End engineer. They are used everywhere - from simple feedback forms to a complex ones for creating something big. While you are using any of the UI frameworks or libraries, they offer us a convenient way to create forms of any complexity easily and quickly. With Angular, we have two options for form building: template-driven and reactive forms. Both of them can be applied in different scenarios. For example, if we need to create some simple form with primitive (or even without) validation, we can consider a template-driven form as a good candidate. Another case is when we need to build something more complex than a login form, with tons of validation, field dependencies from another field, etc. In this way, reactive forms come on the stage. But even with this powerful tool, we sometimes need to build some functionality that is not available from the box. In this article, I will show one of the possible cases, and you can apply this approach to your project. Let's cut to the chase and move to the problem: we need to update a form field based on a value from another form field. Moreover, to enhance the application experience and not confuse a user, we want to highlight (put some message and color the field) the updated field. The fields can be any kind of element (input, dropdown, date picker, etc.), and any combination of field values can lead to the updation of another field. How can we solve this problem? There are some possible ways: We can write a directive for it. It will be used in any control you want despite your choice of form type. But it's quite a complicated task to support this directive in the future, and possibly, it will go for an awkward legacy code that nobody would like to extend or fix some issue related to this; Use EventEmitter. We can observe the expected condition for updating a target field and emit a value. When we do it - update the target field with the emitted value (or you can use this like a trigger and set any value you want). This option is better than the previous one, but it still requires a lot of manual work, and it won't be easy to reuse it somewhere - we will have to implement this mechanism for each case by hand; Extend the FormControl class. In functionality, which is available from the box, we won't find any methods or fields (or even a combination of them) to check that the form control was updated by another one. Let's create it! First things first, we need to create a class for our new control, which extends the FormControl class: import { FormControl } from '@angular/forms';

export class AutoUpdateFormControl<TValue> extends FormControl {
  constructor() {
    super();
  }
} Then, to add a new functionality, we need to write our method for the control updating and mark the inner state as auto-updated: import { FormControl } from '@angular/forms';

export class AutoUpdateFormControl<TValue> extends FormControl {
    public isAutoUpdated: boolean = false;

    constructor() {
        super();
    }

    public autoUpdateValue(value: any, options?: { emitEvent: boolean }): void {
        super.setValue(value, options);
        this.markAsAutoUpdated();
    }

    public setValue(
        value: TValue,
        options?: {
            onlySelf?: boolean;
            emitEvent?: boolean;
            emitModelToViewChange?: boolean;
            emitViewToModelChange?: boolean;
        },
    ): void {
        super.setValue(value, options);
        this.markAsNotAutoUpdated();
    }

    public markAsAutoUpdated(): void {
        this.isAutoUpdated = true;
    }

    public markAsNotAutoUpdated(): void {
        this.isAutoUpdated = false;
    }
} An example of usage: Create a form: interface IForm {
  sourceField: AbstractControl<string>;
  targetField: AutoUpdateFormControl<string>;
}

const form = new FormGroup<IForm>({
    sourceField: new FormControl(''),
    targetField: new AutoUpdateFormControl(),
}); Subscribe to the control and check the required value. If matches - call the autoUpdateValue method. It sets the control value and updates the control field: sourceField form.controls.sourceField.valueChanges.subscribe((value) => {
    if (value === 'test') {
        this.form.controls.testField.autoUpdateValue('updated field');
    }
}); We can add mark-up for handling field highlighting and showing the text depending on the control field: <span *ngIf="form.controls.targetField.isAutoUpdated">Auto-updated</span> If you use some custom components or the wrappers around the third-party ones, you can directly add this kind of template into the component to not repeat it again and again. I found this solution perfect for my needs and decided to share it. Perhaps, it will help someone. But if you want to go further, remember: Your custom form control should remain compatible with Angular's forms API. Avoid altering the existing behavior of FormControl unless necessary; Test your custom form control thoroughly, especially if you're also overriding any of the existing methods; Document your custom methods and any changes to standard behavior to help other developers in your team understand your custom control. With these bits of advice, you can create your custom controls safely and with pleasure.

How to Extend the FormControl Functionality in Angular

USA v. eBay Court Filing, retrieved on January 11, 2024, is part of . You can jump to any part in this filing . This is part 11 of 12. HackerNoon’s Legal PDF Series here COUNT FIVE - Witness Tampering (18 U.S.C. § 1512(b)(3) and 2) The United States Attorney further charges: 58. The United States Attorney re-alleges and incorporates by reference paragraphs 1 through 53 of this Information. 59. From on or about August 20, 2019 through on or about August 30, 2019, in the District of Massachusetts and elsewhere, the defendant, eBay Inc., did knowingly engage in misleading conduct toward the persons described below, with intent to hinder, delay, and prevent the communication to a law enforcement officer of the United States of information relating to the commission and possible commission of a Federal offense, that is, the harassment and intimidation campaign charged in this Information. All in violation of 18 U.S.C. § 1512(b)(3). Continue Reading . Here About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on January 11, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. ecommercebytes.com

eBay Inc.'s Legal Woes Extend to Witness Tampering

Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples Abstract Learning from free-text human feedback is essential for dialog systems, but annotated data is scarce and usually covers only a small fraction of error types known in conversational AI. Instead of collecting and annotating new datasets from scratch, recent advances in synthetic dialog generation could be used to augment existing dialog datasets with the necessary annotations. However, to assess the feasibility of such an effort, it is important to know the types and frequency of free-text human feedback included in these datasets. In this work, we investigate this question for a variety of commonly used dialog datasets, including MultiWoZ, SGD, BABI, PersonaChat, Wizardsof-Wikipedia, and the human-bot split of the Self-Feeding Chatbot. Using our observations, we derive new taxonomies for the annotation of free-text human feedback in dialogs and investigate the impact of including such data in response generation for three SOTA language generation models, including GPT-2, LLAMA, and Flan-T5. Our findings provide new insights into the composition of the datasets examined, including error types, user response types, and the relations between them. 1 Introduction If a dialog system generates a dissatisfying or erroneous response, e.g., a response with factually incorrect information, users tend to provide a textual descriptions of what went wrong or what they would have expected (See and Manning, 2021; Xu et al., 2023; Ung et al., 2022). This textual description is usually referred to as free-text human feedback, and it is an important source to improve dialog systems, to keep them engaging and socially acceptable (Shuster et al., 2022; Christiano et al., 2017; Ouyang et al., 2022; Xu et al., 2023). Learning from such data requires annotations for errors in system utterances and subsequent free-text human feedback, but available datasets are scarce and typically cover only a small subset of the error types known in conversational AI, such as in the cases of SaFeRDialogues (Ung et al., 2022) and FITS (Xu et al., 2023). As a result, data collection is usually a first step for research on learning from free-text human feedback (Hancock et al., 2019; Veron et al., 2021; Park et al., 2021). To avoid this in future research, recent advances in synthetic dialog generation (Kim et al., 2022; Zheng et al., 2022) could be used to augment existing dialog datasets with annotations for errors and free-text human feedback. However, to assess the feasibility of such an effort, it is important to know the types and frequency of such data included in these datasets. In this work, we investigate this question for a variety of dialog datasets, including MultiWoZ (Budzianowski et al., 2018), PersonaChat (Zhang et al., 2018), Wizards-ofWikipedia (Dinan et al., 2019), SGD (Rastogi et al., 2020), BABI (Bordes et al., 2017), and the humanbot split from the Self-Feeding Chatbot (Hancock et al., 2019). For this, we follow a two-step approach in which we first use the Integrated Error Taxonomy proposed by Higashinaka et al. (2021) to study the types of errors in system utterances and subsequent user responses in subsets of these datasets. We use the insights gained during this process to derive a new user response type taxonomy and a modified Integrated Error Taxonomy for the annotation of free-text human feedback in dialog data. In the second step, we use SentenceTransformer (Reimers and Gurevych, 2019) to identify similar situations in the remaining dialogs of the datasets. For a subsequent statistical analysis, we manually annotate subsets of the identified dialogs with error and user response types. We also use this manually annotated data to investigate the impact of including this data in response generation, using three SOTA language generation models, i.e., GPT-2 (Radford et al., 2019), LLAMA (Touvron et al., 2023), and FlanT5 (Chung et al., 2022). We find that types and frequency of errors and user responses that include free-text human feedback largely depend on (1) whether the dialogs are human-human or human-bot, and (2) whether the dialogs are task-oriented, open-domain, or knowledge-grounded. Our analysis and experiments show that using our taxonomies is beneficial for identifying free-text human feedback, and that including such data has a positive impact in response generation. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Learning From Free-Text Human Feedback—Collect New Datasets Or Extend Existing Ones?

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