Palenca is a Payroll API for Latin America

Palenca

Postwoman is celebrating the milestone of reaching 10,000 GitHub stars 🎉 This is an achievement of our whole community! Thank you for each pull request, implementation, love and any other commit that you made to improve Postwoman! We are lucky to build this open-source PWA for API testing with such an amazing and engaged bunch of people around 🙌 To honor our 10,000th star on GitHub, we invite you to join our celebration feature: i18n 🌎 beta , , , , - thank you for showing us the real power of our community. John Harker Nick Palenchar Andrew Bastin James George Hossein Nedaee Let’s do a quick time-travel through our journey, 👀 gaze at our milestones, and wrap it up with a glance through the exciting things in the pipeline. The Beginnings 🌱 Aug 21, 2019 - 6.30 PM It's the 2nd week at my first ever job in the role of a full-stack dev. New job, new location, I was pretty excited 😄. I was assigned to do the API integration of an old project. That's when I came across Postman API testing tool. I installed it, but it crashed my PC when I opened it for the very first time. I use a low-end PC and can't possibly afford to run another Electron app. I was depressed 😞 7.30 PM After desperately trying few online cURL tools, I literally said 😡 "f*ck it!". So I started with an HTML page: one `<select>`, one `<input>` and one `<button>` tag. That's it. 11.30 PM MVP is done. Working, far better than any other online cURL tools. I was so happy 😊 because I could use this from tomorrow onward at workplace. Before shutting down, I pushed my small project to (as always). GitHub 🐙 Aug 22, 2019 - 4.00 AM I woke up from bed to find out 100+ notifications on my phone. It's all from GitHub. Stars, forks, and few issue tickets. I was super excited to get that much fame (~100 stars, >20 forks) on GitHub. I ran to my PC, wrote on my favorite platform - here on Hackernoon. Later I spend that day at workplace. an article Fast Forward to Oct 1 Project now have 2,000+ stars and 100+ forks. John Harker, Nick Palenchar, Andrew Bastin, Hossein Nedaee and izerozlu joined the team ⚽ We added more HTTP request methods, more customizations, WebSocket support, authentication, headers, parameters and history 🔥 Fast Forward to Nov 1 Many bugs were fixed, more features were added. Added Proxy, Collections, Pre-request scripts + env variables. At that time, We thought 5,000 stars was already incredible, but the support from the community has kept steadily growing at a faster pace 🚀 Nov 25 Launched . Postwoman v1.0 Fast Forward to Present Postwoman just crossed 12K GitHub stars, with a total of 41 contributors, and over 860 commits. What’s next? We are preparing a new release of Postwoman for version v2.0, which will come with enhanced performance as well as a myriad of features and issue fixes contributed by the PW team but also many of our contributors. Thanks to our contributors we are able not only to find defects on our projects but also to fix them faster and provide a direction to the whole project. A good example of this is our commitment to building support for the GraphQL playground. The community was an essential factor that led GraphQL to make it to the top of our roadmap. We are continuously listening to your feedbacks, so please make sure you join us and share your ideas on: the Postwoman GitHub repository the Discord server the Telegram group or get in touch directly with me via email at or on Twitter liyascthomas@gmail.com @liysathomas And if you really want to help us make Postwoman better, contribute to our [GitHub repo](https://github.com/liyasthomas/postwoman)! Postwoman is free, and always will be. Support us on: PayPal Open Collective Patreon Remember, we are not stopping here. Each day we change Postwoman to make it the best free, fast and beautiful testing solution. Star the , , share among your friends, teams and contacts! to get the latest changes, improvements, and community updates! repo on GitHub Tweet Follow me on Twitter If you want a heads up on my next projects, or just want to chat about the web, make sure to follow me 💙. If any of my projects helped you please consider or to help me pay my server bills and rent. @liyasthomas Buying me a coffee making a donation on PayPal

Celebrating 10,000+ stars on GitHub repository⭐ ❤️️

Graphical illustration the two stages which comprise the Qtum benchmarking tool… #1: Prepare: Generate enough UTXOs for benchmarking — #2: Send: Send prepared UTXOs to the node - Sourced via Github In the fight for attention among the crypto media and community, it is exceptionally common for projects to boast competitive achievements pertaining to quantitative metrics in a bid to make headline news. Transactions-per-second (or TPS) is just one of these and sits at the centre of an ongoing technological arms race to create the fastest engine for blockchain transaction speeds. Whilst many bogus and otherwise unbelievable claims have been made by rivals, the transactions-per-second achieved by the QtumX blockchain (if the benchmark is to be believed) puts it far ahead of the ‘top tier’ chains. (For the record: Bitcoin is estimated to be capable of processing just seven TPS, whilst Ripple can currently process approximately 1,000 TPS.) The Need For Qtum Speed “Three nodes are launched to form a blockchain network. Two of them are miners for generating new blocks. The other is to accept transactions from RPC and relay them to miners.” — Sourced via QtumX Medium One of the latest companies to pitch its stake in the ‘TPS race’ is an enterprise-level blockchain called which, in a and press-release distributed yesterday, provided substantial evidence that collectively boasts how the team (eponymously named ‘Qtum’) has created a blockchain that can handle 10,000 TPS. QtumX Medium post For the Qtum team, what their recorded result of 10,000 TPS represents is “that QtumX has a high performance” with functional results including immediate confirmation of transactions, upon being sent to the network “with low storage and network consumption.” The blockchain will additionally implement a novel consensus mechanism, Proof of Authority, along with a proprietary ‘Scalable Consensus Algorithm’ (abbreviated as ‘ ’). The primary use case suggested by the team is DApps (blockchain compatible decentralized applications): SCAR “ Especially in business scenarios, where it will have great advantages.” “Generated blocks and transactions are shown as follows. You can see that there are 5 blocks containing more than 10,000 transactions: height 1461, 1459, 1445, 1439, and 1433.” — Sourced via QtumX Medium Why TPS Can Be Important To put it bluntly: transaction speeds matter for the majority of blockchain based projects, especially those intending to operate on a global / enterprise level. The ‘TPS race’ stemmed from the scalability debate spurred by low TPS blockchains like . When SegWit and subsequently the were deployed however, many of its problems disappeared. It threw into question of the potential that a blockchain has to fulfill its duties no matter how large and demanding its audience is. Bitcoin Lightning Network just how indicative is TPS For payment providers the importance is paramount to the business, with the ideal service-model relying on immediacy of both inter and intra-banking transactions. E-commerce possesses similar demands, and Logistics requires immediacy immensely: be it warehouse management, or the storage and transport of vital, sterile materials in the healthcare and chemicals sectors. Regardless, ‘transactions-per-second’ is a significant metric when it comes to comparing blockchains to non-blockchain and centralized enterprise giants, such as major global payments processor Visa (which still blows all blockchain solutions out of the water with a reported 24,000 TPS). Parallel Lines ‘Decentralized Governance Protocol’ is another unique feature. Captured from the . Qtum DGP demo video We reached out to the Qtum team for comment, and received the following statement from Chief Information Officer (CIO) regarding the testing procedures and how they accomplished the speeds recorded: Mike Palencia “The method was designed to simulate a real use case scenario. Three nodes were set up with QtumX nodes, one of those nodes was the “sender”. This node sent a large number of transactions into the blockchain, these transactions were sent in parallel to optimize resources. In a real use case scenario, you’d be looking at a similar implementation as one or more nodes can act as senders and these are accessible via an API which is linked to a mobile or web app. The benchmark tool is open source and can be downloaded from our GitHub . Anyone can test and compare results.” QtumX is to be with both the Bitcoin UTXO structure as well as Ethereum Solidity smart contracts. compatible

New Benchmark Supports Claims of ‘10,000 TPS’ Blockchain

Loss of Amazon Rainforest Resilience: Author Contributions

This paper is under CC 4.0 license. available on arxiv Authors: (1) Jinrui Yang, School of Computing & Information Systems, The University of Melbourne (Email: jinruiy@student.unimelb.edu.au); (2) Timothy Baldwin, School of Computing & Information Systems, The University of Melbourne and Mohamed bin Zayed University of Artificial Intelligence, UAE (Email: (tbaldwin,trevor.cohn)@unimelb.edu.au); (3) Trevor Cohn, School of Computing & Information Systems, The University of Melbourne. Table of Links Abstract and Intro Background and Related Work Multi-EuP Experiments and Findings Language Bias Discussion Conclusion, Limitations, Ethics Statement, Acknowledgements, References, and Appendix 6 Conclusion In this paper, we introduce Multi-EuP, a novel dataset for multilingual information retrieval across 24 languages, collected from European Parliament debates. The demographic information provided by the Multi-EuP dataset serves a dual purpose: not only does it contribute to multilingual retrieval tasks, but it also holds significant potential for advancing research in the realm of fairness and bias. This dataset can play a pivotal role in investigating issues of equitable representations and mitigation of biases within document ranking settings. Multi-EuP facilitates diverse information retrieval (IR) scenarios, encompassing one-vs-one, one-vs-many, and many-vs-many settings. We demonstrated the utility of Multi-EuP as a benchmark for evaluating both monolingual and multilingual IR. Our study reveals the presence of language bias in multilingual IR when employing BM25. We further validate the effectiveness of mitigating this bias through the strategic implementation of whitespace as a language tokenizer. We propose to conduct future work in three main areas. First, we intend to expand our investigation of language bias to encompass a broader range of ranking methods, including neural methods such as mDPR (Zhang et al., 2021), mColBERT (Lawrie et al., 2023) and PLAID-X(Santhanam et al., 2022). Second, we will expand the dataset by developing an automated API to retrieve data published by the European Parliament (EP), thereby ensuring realtime synchronization of our dataset. Lastly, our current experiments have explored language bias only, but we plan to further investigate gender bias, age bias, and nationality bias. Limitations The limitations of the Multi-EuP dataset are notable but navigable. Primarily, the temporal coverage of the dataset is confined to the past three years. This temporal constraint arises due to the fact that, preceding 2020, documents released by the EU were predominantly available in mono-lingual versions only. However, a potential remedy lies in the amalgamation of the Europarl (Koehn, 2005) collection, enabling a more comprehensive and holistic MultiEuP dataset. Furthermore, it is worth noting the domain skew of the dataset, in that Multi-EuP inevitably centers on political matters. While this presents challenges, particularly in terms of the intricate nuances of political language, it inherently serves as an excellent foundational stepping stone for delving into the intricacies of multilingual retrieval. We believe, however, that this dataset can serve as a launching pad for broader explorations encompassing crossdomain and open-domain transfer learning scenarios, thus contributing to the broader landscape of language understanding and retrieval. Ethics Statement The dataset contains publicly-available EP data that does not include personal or sensitive information, with the exception of information relating to public officeholders, e.g., the names of the active members of the European Parliament, European Council, or other official administration bodies. The collected data is licensed under the Creative Commons Attribution 4.0 International licence. [8] Acknowledgements This research was funded by Melbourne Research Scholarship and undertaken using the LIEF HPCGPGPU Facility hosted at the University of Melbourne. This facility was established with the assistance of LIEF Grant LE170100200. We would like to thank George Buchanan for providing valuable feedback. References Luiz Henrique Bonifacio, Israel Campiotti, Roberto de Alencar Lotufo, and Rodrigo Frassetto Nogueira. 2021. mMARCO: A multilingual version of MS MARCO passage ranking dataset. CoRR, abs/2108.13897. Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos. 2021. MultiEURLEX - a multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6974–6996, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. 8 https://eur-lex.europa.eu/cont Jonathan H. Clark, Eunsol Choi, Michael Collins, Dan Garrette, Tom Kwiatkowski, Vitaly Nikolaev, and Jennimaria Palomaki. 2020. TyDi QA: A benchmark for information-seeking question answering in typologically diverse languages. Transactions of the Association for Computational Linguistics, 8:454–470. Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense passage retrieval for opendomain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Online. Association for Computational Linguistics. Omar Khattab and Matei Zaharia. 2020. Colbert: Efficient and effective passage search via contextualized late interaction over BERT. CoRR, abs/2004.12832. Philipp Koehn. 2005. Europarl: A parallel corpus for statistical machine translation. In Proceedings of Machine Translation Summit X: Papers, pages 79–86, Phuket, Thailand. Dawn Lawrie, Eugene Yang, Douglas W. Oard, and James Mayfield. 2023. Neural approaches to multilingual information retrieval. arXiv cs.IR 2209.01335. Jimmy Lin, Xueguang Ma, Sheng-Chieh Lin, JhengHong Yang, Ronak Pradeep, and Rodrigo Nogueira. 2021. Pyserini: A Python toolkit for reproducible information retrieval research with sparse and dense representations. https://github.com/ castorini/pyserini. Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016. MS MARCO: A human generated machine reading comprehension dataset. CoRR, abs/1611.09268. Ella Rabinovich, Raj Nath Patel, Shachar Mirkin, Lucia Specia, and Shuly Wintner. 2017. Personalized machine translation: Preserving original author traits. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 1074–1084, Valencia, Spain. Association for Computational Linguistics. Razieh Rahimi, Azadeh Shakery, and Irwin King. 2015. Multilingual information retrieval in the language modeling framework. Information Retrieval Journal, 18:246–281. Keshav Santhanam, Omar Khattab, Christopher Potts, and Matei Zaharia. 2022. PLAID: An efficient engine for late interaction retrieval. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management, CIKM ’22, page 1747–1756, New York, NY, USA. Association for Computing Machinery. Jörg Tiedemann and Santhosh Thottingal. 2020. OPUSMT – building open translation services for the world. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 479–480, Lisboa, Portugal. European Association for Machine Translation. Eva Vanmassenhove and Christian Hardmeier. 2018. Europarl datasets with demographic speaker information. In Proceedings of the 21st Annual Conference of the European Association for Machine Translation, page 391, Alicante, Spain. Denny Vrandeciˇ c and Markus Krötzsch. 2014. ´ Wikidata: A free collaborative knowledge base. Communications of the ACM, 57:78–85. Konrad Wojtasik, Vadim Shishkin, Kacper Wołowiec, Arkadiusz Janz, and Maciej Piasecki. 2023. BEIRPL: Zero shot information retrieval benchmark for the Polish language. arXiv cs.IR 2305.19840. Peilin Yang, Hui Fang, and Jimmy Lin. 2017. Anserini: Enabling the use of lucene for information retrieval research. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1253–1256. Xinyu Zhang, Xueguang Ma, Peng Shi, and Jimmy Lin. 2021. Mr. TyDi: A multi-lingual benchmark for dense retrieval. arXiv cs.CL 2108.08787. A. Appendix [8] https://eur-lex.europa.eu/content/ legal-notice/legal-notice.html

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