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Adventures of Huckleberry Finn, by Mark Twain (Samuel Clemens) is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book . here CHAPTER XXXVI. As soon as we reckoned everybody was asleep that night we went down the lightning-rod, and shut ourselves up in the lean-to, and got out our pile of fox-fire, and went to work. We cleared everything out of the way, about four or five foot along the middle of the bottom log. Tom said he was right behind Jim’s bed now, and we’d dig in under it, and when we got through there couldn’t nobody in the cabin ever know there was any hole there, because Jim’s counter-pin hung down most to the ground, and you’d have to raise it up and look under to see the hole. So we dug and dug with the case-knives till most midnight; and then we was dog-tired, and our hands was blistered, and yet you couldn’t see we’d done anything hardly. At last I says: “This ain’t no thirty-seven year job; this is a thirty-eight year job, Tom Sawyer.” He never said nothing. But he sighed, and pretty soon he stopped digging, and then for a good little while I knowed that he was thinking. Then he says: “It ain’t no use, Huck, it ain’t a-going to work. If we was prisoners it would, because then we’d have as many years as we wanted, and no hurry; and we wouldn’t get but a few minutes to dig, every day, while they was changing watches, and so our hands wouldn’t get blistered, and we could keep it up right along, year in and year out, and do it right, and the way it ought to be done. But we can’t fool along; we got to rush; we ain’t got no time to spare. If we was to put in another night this way we’d have to knock off for a week to let our hands get well—couldn’t touch a case-knife with them sooner.” “Well, then, what we going to do, Tom?” “I’ll tell you. It ain’t right, and it ain’t moral, and I wouldn’t like it to get out; but there ain’t only just the one way: we got to dig him out with the picks, and let on it’s case-knives.” “Now you’re talking!” I says; “your head gets leveler and leveler all the time, Tom Sawyer,” I says. “Picks is the thing, moral or no moral; and as for me, I don’t care shucks for the morality of it, nohow. When I start in to steal a nigger, or a watermelon, or a Sunday-school book, I ain’t no ways particular how it’s done so it’s done. What I want is my nigger; or what I want is my watermelon; or what I want is my Sunday-school book; and if a pick’s the handiest thing, that’s the thing I’m a-going to dig that nigger or that watermelon or that Sunday-school book out with; and I don’t give a dead rat what the authorities thinks about it nuther.” “Well,” he says, “there’s excuse for picks and letting-on in a case like this; if it warn’t so, I wouldn’t approve of it, nor I wouldn’t stand by and see the rules broke—because right is right, and wrong is wrong, and a body ain’t got no business doing wrong when he ain’t ignorant and knows better. It might answer for you to dig Jim out with a pick, without any letting on, because you don’t know no better; but it wouldn’t for me, because I do know better. Gimme a case-knife.” He had his own by him, but I handed him mine. He flung it down, and says: “Gimme a case-knife.” I didn’t know just what to do—but then I thought. I scratched around amongst the old tools, and got a pickaxe and give it to him, and he took it and went to work, and never said a word. He was always just that particular. Full of principle. So then I got a shovel, and then we picked and shoveled, turn about, and made the fur fly. We stuck to it about a half an hour, which was as long as we could stand up; but we had a good deal of a hole to show for it. When I got up stairs I looked out at the window and see Tom doing his level best with the lightning-rod, but he couldn’t come it, his hands was so sore. At last he says: “It ain’t no use, it can’t be done. What you reckon I better do? Can’t you think of no way?” “Yes,” I says, “but I reckon it ain’t regular. Come up the stairs, and let on it’s a lightning-rod.” So he done it. Next day Tom stole a pewter spoon and a brass candlestick in the house, for to make some pens for Jim out of, and six tallow candles; and I hung around the nigger cabins and laid for a chance, and stole three tin plates. Tom says it wasn’t enough; but I said nobody wouldn’t ever see the plates that Jim throwed out, because they’d fall in the dog-fennel and jimpson weeds under the window-hole—then we could tote them back and he could use them over again. So Tom was satisfied. Then he says: “Now, the thing to study out is, how to get the things to Jim.” “Take them in through the hole,” I says, “when we get it done.” He only just looked scornful, and said something about nobody ever heard of such an idiotic idea, and then he went to studying. By and by he said he had ciphered out two or three ways, but there warn’t no need to decide on any of them yet. Said we’d got to post Jim first. That night we went down the lightning-rod a little after ten, and took one of the candles along, and listened under the window-hole, and heard Jim snoring; so we pitched it in, and it didn’t wake him. Then we whirled in with the pick and shovel, and in about two hours and a half the job was done. We crept in under Jim’s bed and into the cabin, and pawed around and found the candle and lit it, and stood over Jim awhile, and found him looking hearty and healthy, and then we woke him up gentle and gradual. He was so glad to see us he most cried; and called us honey, and all the pet names he could think of; and was for having us hunt up a cold-chisel to cut the chain off of his leg with right away, and clearing out without losing any time. But Tom he showed him how unregular it would be, and set down and told him all about our plans, and how we could alter them in a minute any time there was an alarm; and not to be the least afraid, because we would see he got away, sure. So Jim he said it was all right, and we set there and talked over old times awhile, and then Tom asked a lot of questions, and when Jim told him Uncle Silas come in every day or two to pray with him, and Aunt Sally come in to see if he was comfortable and had plenty to eat, and both of them was kind as they could be, Tom says: “Now I know how to fix it. We’ll send you some things by them.” I said, “Don’t do nothing of the kind; it’s one of the most jackass ideas I ever struck;” but he never paid no attention to me; went right on. It was his way when he’d got his plans set. So he told Jim how we’d have to smuggle in the rope-ladder pie and other large things by Nat, the nigger that fed him, and he must be on the lookout, and not be surprised, and not let Nat see him open them; and we would put small things in uncle’s coat-pockets and he must steal them out; and we would tie things to aunt’s apron-strings or put them in her apron-pocket, if we got a chance; and told him what they would be and what they was for. And told him how to keep a journal on the shirt with his blood, and all that. He told him everything. Jim he couldn’t see no sense in the most of it, but he allowed we was white folks and knowed better than him; so he was satisfied, and said he would do it all just as Tom said. Jim had plenty corn-cob pipes and tobacco; so we had a right down good sociable time; then we crawled out through the hole, and so home to bed, with hands that looked like they’d been chawed. Tom was in high spirits. He said it was the best fun he ever had in his life, and the most intellectural; and said if he only could see his way to it we would keep it up all the rest of our lives and leave Jim to our children to get out; for he believed Jim would come to like it better and better the more he got used to it. He said that in that way it could be strung out to as much as eighty year, and would be the best time on record. And he said it would make us all celebrated that had a hand in it. In the morning we went out to the woodpile and chopped up the brass candlestick into handy sizes, and Tom put them and the pewter spoon in his pocket. Then we went to the nigger cabins, and while I got Nat’s notice off, Tom shoved a piece of candlestick into the middle of a corn-pone that was in Jim’s pan, and we went along with Nat to see how it would work, and it just worked noble; when Jim bit into it it most mashed all his teeth out; and there warn’t ever anything could a worked better. Tom said so himself. Jim he never let on but what it was only just a piece of rock or something like that that’s always getting into bread, you know; but after that he never bit into nothing but what he jabbed his fork into it in three or four places first. And whilst we was a-standing there in the dimmish light, here comes a couple of the hounds bulging in from under Jim’s bed; and they kept on piling in till there was eleven of them, and there warn’t hardly room in there to get your breath. By jings, we forgot to fasten that lean-to door! The nigger Nat he only just hollered “Witches” once, and keeled over on to the floor amongst the dogs, and begun to groan like he was dying. Tom jerked the door open and flung out a slab of Jim’s meat, and the dogs went for it, and in two seconds he was out himself and back again and shut the door, and I knowed he’d fixed the other door too. Then he went to work on the nigger, coaxing him and petting him, and asking him if he’d been imagining he saw something again. He raised up, and blinked his eyes around, and says: “Mars Sid, you’ll say I’s a fool, but if I didn’t b’lieve I see most a million dogs, er devils, er some’n, I wisht I may die right heah in dese tracks. I did, mos’ sholy. Mars Sid, I felt um—I felt um, sah; dey was all over me. Dad fetch it, I jis’ wisht I could git my han’s on one er dem witches jis’ wunst—on’y jis’ wunst—it’s all I’d ast. But mos’ly I wisht dey’d lemme ’lone, I does.” Tom says: “Well, I tell you what I think. What makes them come here just at this runaway nigger’s breakfast-time? It’s because they’re hungry; that’s the reason. You make them a witch pie; that’s the thing for you to do.” “But my lan’, Mars Sid, how’s I gwyne to make ’m a witch pie? I doan’ know how to make it. I hain’t ever hearn er sich a thing b’fo’.” “Well, then, I’ll have to make it myself.” “Will you do it, honey?—will you? I’ll wusshup de groun’ und’ yo’ foot, I will!” “All right, I’ll do it, seeing it’s you, and you’ve been good to us and showed us the runaway nigger. But you got to be mighty careful. When we come around, you turn your back; and then whatever we’ve put in the pan, don’t you let on you see it at all. And don’t you look when Jim unloads the pan—something might happen, I don’t know what. And above all, don’t you handle the witch-things.” “Hannel ‘M, Mars Sid? What is you a-talkin’ ’bout? I wouldn’ lay de weight er my finger on um, not f’r ten hund’d thous’n billion dollars, I wouldn’t.” About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from Twain, Mark,1993. Adventures of Huckleberry Finn. https://www.gutenberg.org/files/76/76-h/76-h.htm#c36 This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at , located at . www.gutenberg.org https://www.gutenberg.org/policy/license.html Photo by on Tim Foster Unsplash

Adventures of Huckleberry Finn: Chapter XXXVI

Happiness Is Based on a Flawed Equation — Here's How to Fix It

সুখ একটি ত্রুটিপূর্ণ সমীকরণের উপর ভিত্তি করে - এটি কীভাবে ঠিক করা যায় তা এখানে

Glück basiert auf einer fehlerhaften Gleichung – so lässt sie sich beheben

La felicidad se basa en una ecuación defectuosa: aquí se explica cómo solucionarla

Le bonheur est basé sur une équation erronée : voici comment y remédier

खुशी एक दोषपूर्ण समीकरण पर आधारित है - इसे ठीक करने का तरीका यहां बताया गया है

幸福は間違った方程式に基づいている — これを修正する方法はこれだ

행복은 잘못된 방정식에 기초합니다. 문제를 해결하는 방법은 다음과 같습니다.

A felicidade é baseada em uma equação falha – veja como consertar

Счастье основано на ошибочном уравнении — вот как это исправить

Mutluluk Kusurlu Bir Denkleme Dayalı - İşte Nasıl Düzeltileceği

Hạnh phúc dựa trên một phương trình sai lầm - Đây là cách khắc phục

幸福建立在一个有缺陷的等式上——下面是如何修复它

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

Multi-EuP: Analysis of Bias in Information Retrieval - Conclusion, Limitations, and Ethics Statement

This is paper is available on arxiv under CC 4.0 DEED license. Authors: (1) Leah Chrestien, Czech Technical University in Prague; (2) Tomá˘s Pevný, Czech Technical University in Prague, and Gen Digital, Inc.; (3) Stefan Edelkamp, Czech Technical University in Prague; (4) Antonín Komenda, Czech Technical University in Prague. Table of Links Abstract & Introduction Preliminaries Conditions on strictly optimally efficient heuristic Loss functions Related Work Experiments Conclusion and References Appendix 7 Conclusion The motivation of this paper stemmed from the observation that even the cost-to-goal, considered to be an optimal heuristic, fails to guarantee a strictly optimally efficient search. Since a large body of existing research optimizes this quantity, we are effectively lost with respect to what should be optimized. To fill this gap, we have stated the necessary and sufficient conditions guaranteeing the forward search to be strictly optimally efficient. These conditions show that the absolute value of the heuristic is not important, but that the ranking of states in the Open list is what controls the efficiency. Ranking can be optimized by minimizing the ranking loss functions, but its concrete implementation needs to correspond to a variant of the forward search. In case of mismatch, the resulting heuristic can perform poorly, which has been demonstrated when the heuristic optimized for BGFS search was used with A* search. The other benefit of ranking losses is that from the point of view of statistical learning theory, they solve a simpler problem than ubiquitous regression in estimating the cost-to-goal. We do not question the existence of a strictly optimally efficient heuristic. Given our experiments, we believe that if the space of heuristic functions over which the loss is optimized is sufficiently rich, the result will be sufficiently close to the optimal for the needs of the search. 7.1 Acknowledgements This work has been supported by project numbers 22-32620S and 22-30043S from Czech Science Foundation and OP VVV project CZ.02.1.01/0.0/0.0/16_019/0000765 "Research Center for Informatics". This work has also received funding from the European Union’s Horizon Europe Research and Innovation program under the grant agreement TUPLES No 101070149. References [1] Forest Agostinelli, Stephen McAleer, Alexander Shmakov, and Pierre Baldi. Solving the rubik’s cube with deep reinforcement learning and search. Nature Machine Intelligence, 1(8):356–363, 2019. [2] Thomas Anthony, Zheng Tian, and David Barber. Thinking fast and slow with deep learning and tree search. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. 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Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal: Conclusion and References

This paper is under CC 4.0 license. available on arxiv Authors: (1) Joel Jang, CarperAI,University of Washington & Allen Institute for AI; (2) Seungone Kim, KAIST AI; (3) Yizhong Wang, University of Washington; (4) Jack Hessel, University of Washington; (5) Luke Zettlemoyer, Aleph Alpha; (6) Hannaneh Hajishirzi, University of Washington & Allen Institute for AI; (7) Yejin Choi, UC San Diego. 5 CONCLUSION Previous work has shown that adapting LLMs with RLHF helps them generate outputs that are preferred by humans over the supervised fine-tuned counterpart. However, recent work has also pointed out that simply training LLMs to abide by the preference of the general may result in ignoring individual preferences and values. In this work, we provide the first steps to tackle this issue by proposing Reinforcement Learning from Personalized Human Feedback as a multi-objective problem so that LLMs can be aligned to follow conflicting preferences. We propose a promising method called P-SOUPS that is able to composite models trained on single objectives on the fly during inference. We also highlight the scalability of P-SOUPS by showing that it scales linearly, instead of exponentially like the MORL baseline, with regards to the number of new preferences, which is required to provide true personalization to individual users. ACKNOWLEDGMENTS Thanks to Minyoung Hwang, Sungdong Kim, Tim Dettmers, Yoonjoo Lee, and Margaret Li for helpful feedback and discussion. REFERENCES Xiang Ao, Xiting Wang, Ling Luo, Ying Qiao, Qing He, and Xing Xie. PENS: A dataset and generic framework for personalized news headline generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pp. 82–92, Online, August 2021. Association for Computational Linguistics. doi: 10.18653/v1/2021.acl-long.7. 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We also provide 3 demonstrations: one scenario where there is a tie because both responses do not contain any notion of the preference (e.g. both responses do not show any signs of friendliness), one scenario where there is a clear winner, and one scenario where they are both good, but one is better than the other. Simulated pairwise evaluation We recruited 24 crowd workers for our human evaluation. Figure 5 shows the interface used for human evaluation. We consider both the ‘Tie’ and ‘Both are bad’ options to be Ties. B CRITERIA-WISE EVALUATION The criteria-wise win rate (%) across all the methods are shown in Figure 6. The criteria-wise win rate was calculated by getting the average win-rate of the preference combinations that contained the specific preference dimension. For example, when calculating the criteria-wise win rate of ‘Elementary’, we got the average win-rate of the preference combinations that contained the ‘Elementary’ preference, which includes AAA, AAB, ABA, and ABB. C THE FULL LIST OF EVALUATION PROMPTS The full list of evaluation prompts used in our experiments are provided in Table 5 D DETAILED RESULTS FOR HUMAN EVALUATION AND GPT-4 EVALUATION We provide detailed results (win / loss / tie for each of the preference combinations for our main experimental results. Table 6 shows the GPT4 evaluation and Table 7 shows the human evaluation results. E EXAMPLES OF P-SOUPS TEXT GENERATIONS Table 8 shows empirical examples of the text generated from each preference combination of the 16 preference combination experiments for the same prompt.

Personalized Soups: LLM Alignment Via Parameter Merging - Conclusion & References

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Akash Chikhalikar, Graduate School of Engineering, Department of Robotics, Tohoku University; (2) Ankit A. Ravankar, Graduate School of Engineering, Department of Robotics, Tohoku University; (3)  Jose Victorio Salazar Luces, Graduate School of Engineering, Department of Robotics, Tohoku University; (4) Yasuhisa Hirata, Graduate School of Engineering, Department of Robotics, Tohoku University. Table of Links Abstract & Introduction Related Work Semantic Mapping Framework Multi-Target Search Experimental Studies Conclusions and Discussions, Acknowledgment and References VI. CONCLUSIONS AND DISCUSSIONS In this study, we have successfully demonstrated the feasibility of target search in indoor environments by effectively locating multiple targets using a novel heuristic. Our proposed approach offers a reliable region-to-region navigation strategy that can accommodate user preferences during the search. The region-to-region navigation is efficient in terms of time as well as energy. It is inherently more robust to obstacles and occlusions as compared to point-topoint navigation. Furthermore, our system can perform these tasks in real-time and is suitable for small to medium sized indoor spaces such as homes and offices. The system can be improved using decision making strategies that optimize long horizon navigation planners. Another non-trivial extension of this work is to add a manipulator to the system that can perform high-level object pickup tasks and extend the current system to accommodate more complex and challenging environments. ACKNOWLEDGMENT This work was partially supported by JST Moonshot R&D [Grant Number JPMJMS2034], JSPS Kakenhi [Grant Number JP21K14115], and JST SPRING [Grant Number JPMJSP2114]. REFERENCES [1] D. Belanche, L. V. Casalo, C. Flavi ´ an, and J. 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Conclusions and Discussions, Acknowledgment and References

Software Engineering for OpenHarmony—A Research Roadmap: Conclusion & References

Hello! I’m Vladimir Popov, Client Developer on the War Robots Project. At the time of writing, War Robots have been around for several years, and dozens of new mechs have appeared in the game during this time. Naturally, the varied abilities of the robots are important, because, without them, the robots lose their uniqueness which makes the game more interesting. In this post, I’ll share how the works in our game – and how it has evolved. And, to make things more accessible, I’ll explain things in simple terms and without too much technical detail. gameplay ability system How abilities used to be implemented First, let's dive into the project history and look at an older implementation that’s no longer being used. Previously, abilities were designed in a very trivial way: they had one component that was attached to the robot. This was a construction where the programmer fully described how the ability works: both its flow and how it interacts with other abilities. All the logic is described inside one component, and the game designer could simply attach this to the robot and configure the parameters as needed. It’s worth mentioning that it was not possible to change the flow of abilities – the game designers could only change parameters and timings. An old ability could only exist in two states: active and inactive, and each state could have its action assigned to it. Let's look at an example of the “Jammer” ability, which the robot “Stalker” previously possessed; it worked like this: If the ability is active, an animation is displayed and the robot enters the Jammer state; in this state, others cannot aim at the robot activating the ability. If the ability is inactive, nothing happens. When trying to activate an ability, we checked whether more than seconds had passed since the last activation. n Deactivation automatically occurred after seconds. m For a long while, this functionality was enough for us, but over time both the game designers and the programmers were no longer satisfied with this approach: it was difficult for the programmers to support these abilities because the code had become monstrous; this involved a very long legacy chain, where every situation had to be described. Additionally, game designers lacked flexibility – to make any change to an ability, they had to order modifications from the programmers, even if an adjacent ability had the same functionality. Enter the new ability system So, we realized that something needed to change. Therefore, we developed a new system, where each ability was represented as a set of several related objects. The functionality was divided into state abilities and state components. How does it work? Every ability has a main object. This central object connects other ability objects with the outside world, and vice versa; it also makes all the main decisions. There can be any number of states. Essentially, the state in this iteration isn’t much different from the active/inactive states in the old version, but now there can be any number of them, and their purpose has become more abstract. We’ll note that an ability can only have one state active at a time. The main innovation compared to the old system was the components: a component describes some action, and each state can have any number of components. How do the new abilities work? An ability can only be in one of the states; the main object is responsible for switching them. The components that link to a state react to the activation/deactivation of the state and, depending on this, can either begin to perform some action or stop performing it. All objects have become customizable; a game designer can mix states and components as they’d like, thus composing a new ability from pre-installed blocks. Now, programmers only need to enter the picture to create a new component or state, which makes writing code much easier: they work with small entities, describe some simple elements, and no longer build the ability themselves – the game designers do this now. The flow has become like this: The main object activates the first state The state activates all its components The state determines the moment the ability is switched to another state The main object deactivates the previous state The previous state deactivates its components The main object activates a new state The new state activates its components Subsequently, this procedure is repeated again and again. For ease of use, a state not only serves as a component container, it also determines when to switch to another state and requests the main object to make the switch. Over time, this still turned out not to be enough for us, and the ability diagram was transformed into the following: The main object, state, and components remained in their places, but new elements were also added. The first thing that catches your eye is that we added conditions to each state and component: for states, these define additional requirements for leaving the state; for components, they determine whether the component can perform its action. The charge container contains charges, recharges them, stops recharging if necessary, and provides charges for states to use. A timer is used when several states must have a common execution time, but their own execution time is not defined. It is important to note that all ability objects are optional. Technically, for an ability to work, only a main object and one state are needed. Now, while there aren’t actually that many abilities entirely built without the involvement of programmers, development in general has become noticeably cheaper, because programmers now only need to write very small things: for example, one new state or two components – the rest is reused. Let's summarize the components of our abilities: The performs the functions of a state machine. It provides states and components with information about the world and provides the world with information about abilities. The main object serves as a link between states, components, and service parts of the ability: charges and external timers. main object The listens to activation and deactivation commands from the main object and, accordingly, activates and deactivates components, and also requests the main object to switch to another state. State determines when it needs to switch to the next one; to do this, it uses its internal condition: whether the player clicked on the ability button, whether a certain time has passed since the activation of the state, and so on, and external conditions linked to the state. state The listens to activation and deactivation commands from the state and performs some action: discrete or long-term. Actions can be completely different: they might cause damage, heal an ally, turn on an animation, and so forth. component The checks what state the desired element is in and reports this to the state or component. Conditions can be complex. A state doesn’t request a transition to another state if the condition is not met. The component also doesn’t perform an action if the condition isn’t met. Conditions are an optional entity; not every ability has them. condition The holds charges, recharges them, stops recharging when necessary, and provides charges to states. It’s used in multi-charge abilities, when you need to allow the player to use it several times, but no more than times in a row. charge container n The is used when several states have a common duration, but it’s not known how long each of them will last. Any state can start a timer for seconds. All relevant states subscribe to the timer end event and do something when it ends. timer n Now let's return to the ability diagram. How did its functionality change? At the start of the game, the main object selects the first state and activates it The state activates all its components The component checks whether the condition has been met and only then performs an action The state begins to check the condition for a transition to another state If the condition is met and the additional condition linked to it is satisfied, the state requests the main object to move to another state The main object deactivates this state and activates another The entire procedure is repeated States can use charges as an additional transition condition. If such a transition occurs, the number of charges decreases. States can also use a common timer. In this case, the total time for their execution will be determined by a timer, and each state individually can last any time. Ability UIs We didn't completely reinvent the wheel for the new ability UIs. The main object has its own UI. It defines some elements that should always be in the UI and which do not depend on the currently active state. Each state has its own pair in the UI, and the state UI is displayed only when its state is active. It receives data about its state and can display it in one way or another. For example, duration states usually have a bar and text in their UI that displays the remaining time. In a case where the state is waiting for an external command to continue an ability, its UI displays a button, and pressing it sends the command to the state. Examples of abilities We’ll look at how the abilities work using specific examples; first, let's look at a robot called “Inquisitor”. We have four states that follow each other – above the states you can see their display in the UI. For two of them, we also see the components that belong to them; the other two states simply don’t have components. Here’s the flow of the ability: It all starts with the “WaitForClick” state. At this time, the ability does nothing; it just waits for commands. As soon as such a command is received, the main object switches states. The next active state is “WaitForGrounded”. This state has some components, and therefore, when activated, the robot jumps and plays a sound and animation. Among other things, while the state is active, the robot is affected by the Jammer effect, which prohibits aiming at the robot. When the robot lands, its ability moves to the next state. This state has three components: the already familiar Sound and Jammer, as well as Shake, which causes the camera to shake for all players within a radius of . n Since this state has , it works for seconds, then the ability moves to the next state. Duration n The last state also comes with a Duration, but it doesn’t have any components: it’s on a regular cooldown. Upon completion, the ability returns to the first state. Another example is “Phantom”. It’s a lot like Inquisitor, but there are some nuances: We start with WaitForClick. Then, Duration, in which the teleport is installed, the stats of the mech are changed, and sound and animation are played. After this: DurationOrClick, in which the mech's stats are changed, animation and FX are played. If a click was made, we go to another Duration, in which the mech teleports, stats change, and animation, FX, and sounds are played. After this state (or after the time expires for DurationOrClick), we move to Duration. The main difference here is that we see states with branching: DurationOrClick goes to state A if the specified time has passed, or to state B if the player has previously pressed the ability button. Conclusions While it would seem that our system has evolved from something simple to something quite complex, this change has simplified the lives of both programmers and game designers. Assistance from the programmers is now needed mostly when adding small components, while the latter group of team members have gained greater autonomy and can now independently assemble new abilities from existing states and components. As another bonus, at the same time, the players also received a profit in the form of more diverse and complex abilities of the mechs.

A More Complex Ability System, but a Smoother Gamedev Workflow – How?

একটি আরো জটিল ক্ষমতা সিস্টেম, কিন্তু একটি মসৃণ Gamedev ওয়ার্কফ্লো - কিভাবে?

Ein komplexeres Fähigkeitssystem, aber ein reibungsloserer Gamedev-Workflow – wie?

Un sistema de habilidades más complejo, pero un flujo de trabajo de desarrollo de juegos más fluido: ¿cómo?

Un système de capacités plus complexe, mais un flux de travail Gamedev plus fluide – Comment ?

एक अधिक जटिल क्षमता प्रणाली, लेकिन एक आसान गेमडेव वर्कफ़्लो - कैसे?

より複雑な能力システム、しかしよりスムーズなゲーム開発ワークフロー - どのようにして?

더 복잡한 능력 시스템이지만 더 원활한 Gamedev 작업 흐름 – 어떻게?

Um sistema de habilidades mais complexo, mas um fluxo de trabalho de desenvolvimento de jogo mais suave – como?

Более сложная система способностей, но более плавный рабочий процесс разработки игр – как?

Daha Karmaşık Bir Yetenek Sistemi, Ama Daha Sorunsuz Bir Gamedev İş Akışı – Nasıl?

Hệ thống khả năng phức tạp hơn nhưng quy trình làm việc của Gamedev mượt mà hơn – Bằng cách nào?

更复杂的能力系统，但更流畅的游戏开发工作流程——如何实现？

Authors: (1) Yunshan Ma, National University of Singapore; (2) Xiaohao Liu, University of Chinese Academy of Sciences; (3) Yinwei Wei, Monash University; (4) Zhulin Tao, Communication University of China and a Corresponding author; (5) Xiang Wang, University of Science and Technology of China and affiliated with Institute of Artificial Intelligence, Institute of Dataspace, Hefei Comprehensive National Science Center; (6) Tat-Seng Chua, National University of Singapore. Table of Links Abstract & Introduction Methodology Experiments Related Work Conclusion and Future Work, Acknowledgment and References 5 CONCLUSION AND FUTURE WORK In this work, we systematically study the problem of bundle construction and define a more comprehensive formulation by considering all the three types of data, i.e., multimodal features, item-level user feedback data, and existing bundles. Based on this formulation, we highlight two challenges: 1) how to learn expressive bundle representations given multiple features; and 2) how to counter the modality missing, noise, and sparity problem. To tackle these challenges, we propose a novel method of Contrastive Learningenhanced Hierarchical Encoder (CLHE) for bundle construction. Our method beats a list of leading methods on four datasets of two application domains. Extensive ablation and model studies justify the effectiveness of the key modules. Despite the great performance that has been achieved by this work, there is still large space to be explored for bundle construction. First, the current evaluation setting is a little bit rigid and inflexible, it is interesting to extend it to more flexible setting to align with real applications. For example, given arbitrary number of seed items, the model is asked to construct the bundle. 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Strategy-aware Bundle Recommender System. In SIGIR. ACM, 1198– 1207. [47] Yinwei Wei, Xiang Wang, Liqiang Nie, Xiangnan He, Richang Hong, and Tat-Seng Chua. 2019. MMGCN: Multi-modal Graph Convolution Network for Personalized Recommendation of Micro-video. In ACM MM. ACM, 1437–1445. [48] Yinwei Wei, Xiang Wang, Liqiang Nie, Shaoyu Li, Dingxian Wang, and Tat-Seng Chua. 2022. Causal inference for knowledge graph based recommendation. IEEE Transactions on Knowledge and Data Engineering (2022). [49] Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised Graph Learning for Recommendation. In SIGIR. ACM, 726–735. [50] Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. 2022. Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation. CoRR abs/2211.06687 (2022). [51] Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative Denoising Auto-Encoders for Top-N Recommender Systems. In WSDM. ACM, 153–162. [52] Xun Yang, Yunshan Ma, Lizi Liao, Meng Wang, and Tat-Seng Chua. 2019. TransNFCM: Translation-Based Neural Fashion Compatibility Modeling. In AAAI. AAAI Press, 403–410. [53] Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2022. XSimGCL: Towards extremely simple graph contrastive learning for recommendation. arXiv preprint arXiv:2209.02544 (2022). [54] Zhouxin Yu, Jintang Li, Liang Chen, and Zibin Zheng. 2022. Unifying multiassociations through hypergraph for bundle recommendation. Knowl. Based Syst. 255 (2022), 109755. [55] Sen Zhao, Wei Wei, Ding Zou, and Xianling Mao. 2022. Multi-View Intent Disentangle Graph Networks for Bundle Recommendation. In AAAI. AAAI Press, 4379–4387. This paper is under CC 4.0 license. available on arxiv

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Let's learn about via these 64 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Noonies Nominees /Learn Repo 1. Noonies Nominee Bader is Obsessed with Web3 and Blockchain I’m a web3 developer, and content creator, and generally love creating ideas from nothing - even if they don’t make too much sense. 2. Noonies Interview: Meet Juni Nguyen, a Self-taught Web Developer 2021 Noonies Nominee General Interview with Juni Nguyen. 3. Noonie Nominee Luiz Rosa Can't Live Without PyCharm and Git Luiz Guilherme Fonseca Rosa from Brazil has been nominated for a 2020 Noonie as Hacker Noon Contributor of the Year - ALGORITHMS. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Luiz had to share. 4. Noonies Nominee Mike Shine on Career Pivot, Technical Writing, and the Influence of Big Tech Mike Shine, a technical writer based in Seattle, WA, was nominated for a Noonies award for his writing contributions in 2021. Please vote for Mike! 5. On Memes, Bots, Humor, Tech Writing, and Life - Noonies Nominee Tomás Sirio Noonies Interview with Tomas Sirio, nominee for the HackerNoon Contributor of the Year - BOTS Award. 6. Internet Heroes Nominee Lisa Shares Why Climate-Focused Technology is Exciting Lisa Gibbons nominated for a noonies hackernoon award as contributor to Climate Change. 7. From Sustainable Agriculture to Programming - with Noonies Nominee Akis Loumpourdis I am Akis Loumpourdis, a 2021 Noonies Nominee. This is a  small interview to get to know me a bit better. 8. Our Most Significant Enhancements are Non-Technical: Interview with Noonies Nominee German Tebiev An Interview with 2022 HackerNoon Noonies nominee - German Tebiev. 9. In Love with Coding Puzzles: Interview with Noonies Nominee Abbey As a writer in the tech industry, I believe in documenting my growth. Learn more about me and my journey so far. 10. Hacking Synthetic Biology with Noonies Nominee Kahlil Corazo 2021 Noonies Nominee General Interview with Kahlil Corazo. Read for more on synthetic biology and genomics. 11. 2022 Noonies Programming Nominee Roy Derks on GraphQL, React, and More I’m Roy Derks! A tech speaker, writer and software developer from the Netherlands! I’ve been nominated for the categories below and if you've enjoyed anything 12. UX and Product Design Notes with Noonies Nominee,  Jordan Bowman An interview with  Jordan Bowman, Product Designer and Co-Creator of UX Tools; and 6x 2021 Noonies Nominee for Design Thinking, Product Design, UI; UX and more. 13. "Complaining only helps if you're prepared to do something about it" -Dawood Khan Masood Dawood Khan Masood from Pakistan has been nominated for a 2020 Noonie in the Back to the Internet category. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Dawood had to share. 14. 2022 Noonies Nominee Veronika Vasileva on Programming, Python, and More Learn more about my views on my journey in the tech industry via the interview below. 15. 'I am Passionate About Implantable Brain-machine Interfaces': Noonies Nominee Altynbek Altynbek is nominated for Noonies award HackerNoon Contributor of the Year - Swift. 16. "Investors Expect a Company to Own Its IP," Josh Ephraim, Attorney (AMA) Josh Ephraim is startup attorney and associate at Gunderson Dettmer, where he specializes in the representation of emerging growth companies throughout the corporate lifecycle. In Ask Me Anything session with Hacker Noon community, Josh shared his expertise on startups scene, startups legal issues and more! 17. How To Review A Game Bryan Applegate, a nominee for several Noonies awards explains how he reviewed Darksiders III for HackerNoon and his thoughts on THQ Nordic's game in general. 18. Being 'Chief Geek' and Running 15 Websites with Noonies Nominee Mathias Hellquist So who TF is Mathias Hellquist and what is a "Chief Geek"? Read this interview to find out. 19. On the Love of Problem Solving & Programming: Noonies Nominee Sukhpinder Singh Noonies interview with Sukhpinder Singh, Senior Engineer at SourceFuse. 20. On Food Waste and Relevance of Privacy: Noonies Nominee Paran Sonthalia Noonies 2021 nominee, Paran Sonthalia, is still a Berkley student. But that didn't stop him on the mission of reducing food waste. Hear more from him here. 21. Think Big and Keep Growing: Interview with Noonies Nominee Vlad Gukasov First of all, a huge thanks to the HackerNoon community and staff for considering me worthy of this opportunity. 22. AMA with Lina Survila, Women in Tech awards nominee Lina Survila is Founder and Editor in Chief of an online magazine "Abstract Stylist" and Noonies nominee in Women in Tech award. 23. Breaking the Stigma of Mental Health with Amy Pravin Shah, Noonies Nominee 2021 Amy Pravin Shah has been nominated for Noonies 2021: HackerNoon Contributor of the Year - Mental Health. Here is her story. 24. Noonie Nominee Lane Wagner Wants to Gamify Education In these unprecedented times, people build unprecedented products, and contribute to the internet in unprecedented ways. One such impressive Hacker Noon contributor is Lane Wagner from the United States. 25. Meet Mark Helfman: #Noonie2021 Nominee, Crypto Writer, Owner of "Crypto is Easy" Newsletter From a career in politics to book publishing and crypto writing, Noonie nominee Mark Helfman shares his insights on crypto, finance, business, & more. 26. Noonies Interview: Khamisi Hamisi on Decentralization of Social Media Khamisi Hamisi is the Founder of Khamisi Digital and nominee of 3 HackerNoon Noonies awards. 27. Ax Sharma's Top 5 AMA Answers: from Black Mirror to Governmental Attacks Ax Sharma is Senior Security Researcher at Sonatype and engineer, who is passionate about perpetual learning. He is an expert in vulnerability research, software development, and web app security. 28. Hackernoon Editors Gave A Voice To My Writing - Noonies Interview with Samiran Mondal Hackernoon editors gave importance to my writing skills and today, I am doing this Interview 29. “I’m Tigerchops and I’m the CEO of me.” Contributor of the Year - GAME DEVELOPMENT. An Interview with Tigerchops 30. Carlo Occhiena, 2020 Noonie Nominee, is Interested in No-Code Solutions Carlo Occhiena from Italy has been nominated for a 2020 Noonie in the Future Heroes and Technology categories. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Carlo had to share. 31. Noonies Nominee Vivek Tank on the Need of Creativity for Effective SEO 2021 Noonies Nominee General Interview with Vivek Tank. 32. Full Stack and True Black: Interview with 2022 Noonies Nominee Alexey Shepelev An interview with 2022 HackerNoon noonies nominee Alexey Shepelev 33. Teamwork, Humor and Fintech: Interview with 2022 Noonies Nominee Valerie Ejegi An interview with 2022 HackerNoon Noonies nominee, Valerie Ejegi. 34. "It's not necessary to be No. 1 in everything" - Nibu P Rajoo, 2020 Noonie Nominee You know that feeling when you work really hard on something for really long and it feels like nobody really notices? Hacker Noon’s Annual Tech Industry Awards, the 2020 Noonies, are here to help with that. 35. "Coffee is overrated" - William Kwan, 2020 Youtuber Noonie Nominee William Kwan from Canada has been nominated for a 2020 Noonie in the Technology category. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what William had to share. 36. "We will mostly live in virtual worlds in the future." - Timmu Tõke, 2020 Noonie Nominee Timmu Tõke from the United States has been nominated for a 2020 Noonie in the Future Heroes and Technology categories. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. 37. HackerNoon Contributor Celine “Oibiee” Aju on Tech, HackerNoon, and the Arts As a tech writer, I believe that the most exciting technology of the present is ED-TECH because it gives us the opportunity to move from the assembly line appro 38. Eko Simanjuntak, 2020 Noonie Nominee, Loves to Explore Cloud Native Technologies Go on, make a fellow human’s day and nominate the best YOUR best of 2020’s tech industry for a 2020 #Noonie, the tech industry’s most independent and community-driven awards: NOONIES.TECH. 39. "We are repeating history faster and at a greater scale" - Stacey Schneider, 2020 Noonie Nominee Stacey Schneider from the United States has been nominated for a 2020 Noonie in the Future Heroes and Technology categories. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Stacey had to share. 40. 2021 Noonies Nominee Interview with Sarrah Pitaliya Who is she and what's behind her passion for technical writing? 41. Matt Hartley, 2020 Noonie Nominee, is Learning About React Hooks The 2020 Noonies are here and they are both much greener and much bigger than last year. Among the 2,000+ deserving humans nominated across 5 categories for over 200 award titles, we discovered Matt Hartley from the United Kingdom, who’s has been nominated for a Noonie in the Future Heroes and Technology categories. Without further ado, we present to you, our big techy world, from the perspective of Matt. 42. Brian Wallace AMA on Infographics, Startups and LinkedIn Optimization Brian Wallace is the Founder and President of NowSourcing , an industry leading infographic design agency based in Louisville, KY and Cincinnati, OH which works with companies that range from startups to Fortune 500s. Brian has been named a Google Small Business Advisor for 2016-present and joined the SXSW Advisory Board in 2019. 43. Liyas Thomas, 2020 Noonie Nominee, is the Creator of Postwoman.io Liyas Thomas from India has been nominated for a 2020 Noonie Award in the Software Development and Technology categories. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Liyas had to share. 44. Being Told I Could Not Write Inspired Me to Publish on HackerNoon - Noonies Nominee no-name-c3po I want to thank the HackerNoon community and staff for their nomination in the Web 3 category. 45. DevPool, #Noonies2021 Nominee: “I would invest in the next generation of new developers.” Learn more about Artem- the developer behind DevPool via this Noonies interview 46. "There is no Failure, only Feedback"  - 5x Noonies Winner Christian Kameir Interview with Noonies Nominee Christian Kameir 47. "I aggressively applied to hundreds of different startups" #2021Noonies Nominee Lomit Patel has been nominated in the 2021 Noonies Awards: The most #democra-tech awards system! The first step to choosing the right career is self-awareness. 48. Meet Derek Lim - #Noonie2021 Nominee, Founder of Crypto Research Community Fincade "Blockchain, Blockchain, Blockchain" - CEO of Verse. 49. Aremu Adebisi on Tech Writing, the Future of Finance, and 2022 Noonies Nomination 2022 Noonies Nominee Interview with Aremu Adams Adebisi on Tech Writing & the Future of Finance 50. Racial Discrimination in Facial Recognition is a Challenge - With Noonies Nominee Alesia Traichuk 2021 Noonies Nominee General Interview with Alesia Traichuk 51. Our Ancestors Could Have Solved Food Scarcity With AI - Noonies Nominee TRex 52. Koushik Pal: 'Knowledge Should Be Free' An interview of a great cybersecurity leader. 53. CodeBucks, #Noonies2021 Nominee: I would not invest in cryptocurrency Self taught developer CodeBucks is most excited about Jam Stack anc Web3.0 54. Noonie Nominee Jimmy Song is Excited About The Prospect of Bitcoin Changing Civilization Jimmy Song  from the United States has been nominated for a 2020 Noonie in the Decentralization and Back to the Internet categories. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Joseph had to share. 55. Web3, Blockchain, and DAO: An Interview With 2022 Noonies Nominee Olayimika Oyebanji A web3 tech writer who loves keeping it S & S. 56. "Buy Bitcoin" is the Best Advice #Noonies2021 Nominee Sergei Khitrov Has Ever Received Hey Hackers! I’ve been nominated in these two following categories :  HackerNoon's DeFi Degen of 2021 and HackerNoon's NFT Wizard of 2021. 57. Adadot Founder Alex on Building a Analytics Tool and being Nominated For #Noonies 2022 I’m Alex and I’m the Founder at Adadot. 58. Maritime Service and Full Stack JS: Interview with 2022 Noonies Nominee, Serhii Rubets This short story is about me, and how I become a software engineer. Why I'm sharing my knowledge and why I create educational content. 59. Blue is Happiness: Interview with 2022 Noonies Nominee Anastasios 60. Technology Keeps Changing with Noonies Nominee Aswin Barath 61. Vasily Sumanov, 2020 Noonie Nominee, is a Crypto Enthusiast and Material Sciences Academic Vasily Sumanov from Russia has been nominated for a 2020 Noonie in the Future Heroes category. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. 62. "I would rather feel machine judging me, than people judging me" - Jeremy Morgan during AMA Jeremy Morgan is 5x Noonies nominee and an author for Pluralsight focused on Software Development and Integration. 63. Vasily Sumanov, 2020 Noonie Nominee, is a Crypto Enthusiast and Material Sciences Academic Vasily Sumanov from Russia has been nominated for a 2020 Noonie in the Future Heroes and Technology categories. The Noonies are Hacker Noon’s way of getting to know — from a community perspective —  what matters in tech today. So, we asked our Noonie Nominees to tell us. Here’s what Vasily had to share. 64. Hacker Noon Awards: #Noonies 2019—Full Nominees List The world’s most independent tech publication, Hacker Noon—where hackers start their afternoons, just partnered with Stream.io to present the inaugural Noonies 2019: The Tech Industry's Greenest Awards. Thank you for checking out the 64 most read stories about Noonies Nominees on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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In this series we’ll discuss the role of both operational and information technology in critical infrastructure from the point of view of an electric utility. We’ll then explore the role that Enterprise Resource Planning plays in this context. In doing so, we’ll demonstrate that, in addition to being an integral component of business operations, ERP systems also contain mission critical applications, crucial to maintaining safe and reliable critical infrastructure. We’ll expose the issues IT and Cybersecurity teams face in maintaining and protecting ERP systems, including a lack of transparency with business units, complexity as a result of high customization, and, most importantly, a persistent lack of adequate resources to maintain healthy and performant systems. Finally, we’ll demonstrate how a dedicated team of risk professionals can right these wrongs, gain a windfall of additional resources from federal and state governments, and build better, more transparent relationships with regulators. In the process, they’ll form lasting, collaborative relationships between IT and the rest of business, building a steadfast coalition dedicated to the highest purpose of all, greater public safety. ERP and Critical Infrastructure: A Series Electricity is the lifeblood of modern civilization, powering our homes, industries, and essential services. While electric, gas, and water utilities stand at the forefront, other forms of critical infrastructure include telecommunications, transportation systems, and healthcare facilities. Critical infrastructure refers to assets, systems, and networks foundational for our society to function. Electric utilities run on both Operational Technology (OT) and Information Technology (IT) networks. OT networks in electric utilities prioritize real-time operational functions, such as supervisory control and data acquisition (SCADA) systems and process control systems. These specialized networks facilitate the monitoring and control of physical devices and processes. IT networks, on the other hand, are the conventional user networks that the rest of the business uses to run its day-to-day operations and communicate with the outside world. Enterprise Resource Planning (ERP) systems are part of the IT network. They integrate various business processes, including finance, human resources, procurement, and inventory management. ERP systems streamline operations by providing a centralized platform for data management and decision-making, enhancing efficiency and resource utilization. … Can we safely and reliably provide electricity without it? That’s the question an electric utility asks itself when determining which of its assets are mission critical. In the context of an electric utility, and other critical infrastructure, mission critical assets are thought to live on the OT network. And they do. A failure or compromise of Operational Technology could result in the flooding of an entire city. This is because OT systems control the operation(s?) of dams. OT hydroelectric power generation systems exercise precise control over the flow of water, a process fundamental to achieving optimal power generation while maintaining safety standards and preserving environmental integrity. If compromised, such systems hold the potential to trigger a catastrophic event. The is provided by The Federal Energy Regulatory Commission (FERC) for utilities to determine the criticality of their tech-enabled dams based on the number of people at risk if compromised. flowchart below A failure or compromise of OT systems could also impact grid reliability, disrupting essential services and endangering public safety. OT bulk power systems control the power supply across North America. Due to the interconnected nature of the North American power grid, even a handful of utilities experiencing OT failures could have cascading effects throughout the entire region, or even across regions, causing widespread blackouts (region map below). This happened on November 9th, 1965, when a power failure in Ontario, Canada cut off power to over 30 million people in parts of Canada, New York, New Jersey and most of New England. And on August 14 and 15, 2003, the northeastern U.S. and southern Canada suffered the worst power blackout in history. Areas affected extended from New York, Massachusetts, and New Jersey west to Michigan, and from Ohio north to Toronto and Ottawa, Ontario. Approximately 50 million customers were impacted. So who regulates this? The Federal Energy Regulatory Commission (FERC) was chartered as a result of the Department of Energy Organization Act of 1977, signed by President Jimmy Carter. FERC serves as an independent agency within the US Department of Energy (DOE) that regulates the interstate transmission of electricity, natural gas, and oil. To maintain the reliability of the bulk power system, FERC reviews, approves, and enforces mandatory reliability standards developed by an organization called the North American Electric Reliability Corporation (NERC). FERC’s reliability jurisdiction primarily focuses on reliable operation of the grid. The aim is to ensure the real-time and day-to-day operations of the grid by developing and enforcing operational and business standards ( ). see “grid” as defined by FERC below According to FERC, grid reliability is based on two key elements: – A reliable power grid has the ability to withstand sudden electric system disturbances that can lead to blackouts. Reliable operation - Generally speaking, resource adequacy is the ability of the electric system to meet the energy needs of electricity consumers. This means having sufficient generation to meet projected electric demand. Resource adequacy OT systems are used to ensure “reliable operation” by isolating failures so they do not spread across the interconnected power grid. They are used to ensure “resource adequacy” by optimizing the distribution of electricity based on real-time demand and using automated load balancing to manage the flow of power to match consumption patterns, preventing overloading. OT systems also track the condition of equipment, analyzing performance data to predict potential failures, allowing proactive maintenance and protecting the lives of crew members in the field. They employ sensors and monitoring devices to detect faults, such as short circuits or equipment malfunctions that could cause disastrous and potentially deadly fires. And, In the event of emergencies like natural disasters, OT systems facilitate coordinated responses. They enable quick isolation of affected areas, rerouting power to minimize the impact of these life threatening events. A stable, protected, and defended electric utility is imperative to prevent disruptions that can have cascading effects on various sectors, including hospitals, elderly care homes, emergency response systems, and other critical infrastructure. When we think about IT technology in this context, we worry about adequate network segmentation to avoid cyber threats traversing from IT networks where they’re disruptive to OT networks where they’re potentially catastrophic (see illustration below). AT&T’s network segmentation But what if I told you that an attack on the IT network alone can also be catastrophic? That you could not safely and reliably provide electricity without ERP systems? And that these systems are far too often fragile, underprotected, under-resourced sitting ducks? More on that in part two

Enterprise Resource Planning & Critical Infrastructure

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