Chinese manufacturer Sany is launching its all terrain cranes in the United Kingdom. It has begun its first official foray with all terrain cranes into the UK and Irish markets.

Sany all terrain crane on show in UK

Heavy Construction Equipment Market 2023 Report highlights its rapid emergence as a key player in the [Machinery and Equipment] sector, segmented by

2031,Heavy Construction Equipment Market Size 2023 Booming Growth In Demand Comprehensive Analysis

Governmental officials, industry experts and enterprises called for efforts in digital transformation to promote the high-quality development of China's manufacturing sector. They made the remarks at the 2023 Guizhou Conference on High-Quality Development of Manufacturing Industry Driven by Digital Transformation in Guiyang last week.

Digital transformation highlighted for gaining manufacturing prowess

SANY has launched five all-new small excavators with elevated functionality and performance. The SY60C, SY75C, SY80U, SY95C and SY135C have been developed for European and North American markets.

SANY Launches Five Small Excavators

CHANGSHA, China, Oct. 30, 2023 /PRNewswire/ -- SANY Group (SANY), a leading global machinery manufacturer, has launched five all-new small excavator products with fully elevated functionality and performance, which will officially go into the market in 2024.

SANY Launches Five New Small Excavators, Anchoring Future Development of International Market

SANY Group (SANY), a leading global machinery manufacturer, has launched five all-new small excavator products with fully

Recent track technology and undercarriage technology innovations provide operators with more safety and comfort and extend equipment life and efficiency. These latest developments also help overcome longstanding challenges in the industry, such as ...

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Notably, import data highlights that approximately 40% of the total construction equipment brought into Argentina in 2022 originated from China. Consequently, Chinese firms like XCMG and SANY have gained a notable foothold in the Argentina construction ...

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The "Argentina Construction Equipment Market - Strategic Assessment & Forecast 2023-2029" report has been added to ResearchAndMarkets.com's offering.

These Regulations, often abbreviated to PUWER, place duties on people and companies who own, operate or have control over work equipment. PUWER also places responsibilities on businesses and organisations whose employees use work equipment, whether owned ...

Provision and Use of Work Equipment Regulations 1998 (PUWER)

SANY

sany

Have you ever been in a negotiation, a relationship, or a business partnership and realized that the other person had absolutely no consideration for your needs and lashed out at the slightest inconvenience? You may have been dealing with a narcissist. Narcissists are people who have an inflated sense of self-importance and a deep need for admiration. They believe that they’re superior to others and have little regard for other people’s feelings. They can also often be charming and charismatic at first glance, but their true colors reveal themselves over time. In September, I sat down to talk , one of the most successful family law attorneys in the country and the best-selling author of . with Rebecca Zung for the Success Story Podcast Negotiate Like You M.A.T.T.E.R: The Sure Fire Method to Step Up and Win She once found herself in two of these narcissistic relationships at the same time — one with a business partner and one with a family member — and had to figure out a way to come out ahead. Her S.L.A.Y. method is built to give regular people the power to stand up and win against narcissists. She walked me through the top-level overview of the method and gave me a little bit of insight into what it’s like to deal with someone that only cares about themselves. How to Spot a Narcissist Before we jump in, I think it’s important to provide a little context because we all know the narcissist but we may not know how to properly recognize them. If we’re extremely lucky, we may have not even come across one in the past, but just in case you have (or potentially will), here’s a guide that will give you some insight as to who these people are… and just how to manage them. Let’s start with a definition. A narcissist is someone who excessively admires and loves themselves. At the core, that seems relatively harmless… but wait… there’s more. They have an inflated sense of self-importance and a strong need for attention and admiration. Narcissists are often very charming, but they can also be manipulative, selfish, and demanding. If you want to spot a narcissist, look for someone who: Talks about themselves… a lot Brags and boasts about their achievements Has a sense of entitlement Is preoccupied with power and success Isn’t interested in other people’s feelings or needs Is easily insulted or offended It’s that last point that can be so telling. A narcissist is someone who is very sensitive to criticism and can’t handle even the slightest bit of negativity. It might surprise you to hear that the rate of Narcissistic Personality Disorder some experts estimate to be as high as 5%. That means of the 20 people you might deal with daily, one of them is likely a narcissist. That’s pretty scary… But fear not, here’s your guide on how to deal with these 1/20 problematic people. Narcissistic Supply There is a term in psychology called “narcissistic supply.” This refers to the attention and admiration that a narcissist craves. They are always seeking validation and will go to great lengths to get it. This can make them very difficult to deal with because they are never satisfied. Even if you provide them with what they want, they will still find something to complain about. If you’re dealing with a narcissist, it’s important to understand that to them, it is a survival-level need. It comes because of deep-seated insecurity, low self-esteem, and often a trauma or deficiency in early childhood that left them feeling empty and worthless. Understanding this kind of need can help you to be more patient with a narcissist and to see their behavior in a different light. It can also help you to set boundaries and not allow yourself to be used or manipulated. Even more, it is critical in any negotiation and the first step to implementing Rebecca’s S.L.A.Y. technique. Strategy Ok, so let’s unpack this S.L.A.Y technique. The first step is creating a strategy. You need to understand what you want to achieve in the negotiation and what your bottom line is. This is especially important when dealing with a narcissist because they will try to take advantage of you if they sense any weakness. They will also likely make completely unreasonable demands, so it’s important to know what you can and cannot compromise on. Once you have a clear understanding of your goals, you need to be prepared to stick to them and pursue them aggressively. Even if this is normally against your nature, it is essential when dealing with a narcissist. They will try to wear you down and make you feel like you are the one being unreasonable. It’s also important to remember that a narcissist is not interested in hearing your side of the story or understanding your point of view. They only want to be heard and they will quickly become impatient if you don’t let them have their say. So, be prepared for this by having a clear idea of what you want to say and how you want to say it. Leverage When we got to this point, I could really start to see how tough a negotiator Rebecca was. Her tactics even unsettled me, and we were just having a friendly conversation — I wouldn’t want to be across from her in a board room. She’s a total badass. Gaining leverage against a narcissist is critical, and Rebecca suggests targeting their “diamond source of supply” or in other words, the thing they value most. This could be their reputation, their power, or their money. Whatever it is, you need to find a way to use it against them. One way to do this is by threatening to expose them. For example, if you know they have been engaging in unethical behavior, you can threaten to go public with the information unless they agree to your terms. This is a very effective tactic because narcissists are very afraid of being exposed and losing their image. But the point is, you need to figure out what that particular thing is and double down on it. Does that make your spine tingle a little bit? I know mine sure did (remember how I told you she was a badass?) Outing someone’s personal life isn’t something I’m immediately ready to do but the way Rebecca put it was “ethically manipulate the manipulator.” These tactics are reserved for the worst of the worst, who are actively trying to cause you harm, so in Rebecca’s mind, you need to play by their rules (ONLY while dealing with them). Another way to gain leverage is by playing on their need for approval. For instance, you can agree to give them what they want but only if they meet your demands first. This will make them feel like they are in control and getting the upper hand, which is something they crave. The key here is to be creative and think outside the box. There is always a way to gain leverage against a narcissist, you just have to find it. Anticipation Third is anticipating the tactics a narcissist is likely to use against you and preparing for them in advance. Narcissists are known for being manipulative, so it’s important to be aware of the different ways they might try to take control of the situation. They will try to make you feel sorry for them or paint themselves as the victim to gain sympathy. Playing the victim: This is a technique whereby a narcissist will try to make you question your reality or memory. They will lie and distort the truth to confuse you. Gaslighting: This is when a narcissist projects their flaws onto you in an attempt to deflect responsibility. Projection: They may try to force you into agreeing with them by using intimidation or threats. Coercion: It’s important to be prepared for these tactics so that you can keep control of the situation. The best way to do this is by staying calm and not reacting emotionally to anything they say or do. (Focusing on) You The thing that Rebecca was most clear about was the importance of your mindset going into a negotiation, and believing that you can win it. If you aren’t confident, the narcissist will sense it and use it against you. So, the first step is to get into the right frame of mind. Rebecca also stressed the importance of focusing on your own needs and not getting caught up in what the narcissist wants. It can be easy to get wrapped up in their drama and forget what you’re trying to achieve. But if you keep your eye on the prize, you’re more likely to come out ahead. Final Thoughts If you find yourself in this kind of situation, it would likely do some good to check out Rebecca’s or check out her other appearances in places like Forbes, NPR, or Time magazine. negotiation worksheet There was a bunch more that she touched on in our talk, though, including her struggles of taking law school at night with three small children, teaching elementary school in the inner-city, and leaving a hugely successful law practice behind to focus on helping people learn the skill of negotiating. If you want to check out the rest of the interview, or any of the other hundreds of podcast episodes, head over to the and subscribe! Success Story YouTube channel I’ll be back next week with another incredible guest.

Dealing With Narcissists: How to S.L.A.Y. Any Dispute

Swarm Summit 2024 Recap

Nanotargeting Risks: The Urgent Need for Better Privacy Protections

Physical Properties of KIC 9851944: Conclusion, Data Availability, Acknowledgments, and References

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Sebastien Ferr ´e, Univ Rennes, CNRS, Inria, IRISA Campus de Beaulieu, 35042 Rennes, France and ferre@irisa.fr. Table of Links Abstract & Introduction Abstraction and Reasoning Corpus (ARC) Related Work Object-centric Models for ARC Grids MDL-based Model Learning Evaluation Conclusion and Perspectives, References & Supplementary Materials 7 Conclusion and Perspectives We have presented a novel and general approach to efficiently learn skills at tasks that consist in generating structured outputs as a function of structured inputs. Following Chollet’s measure of intelligence, efficiently learning here means limited knowledge prior for the target scope of tasks, only a few examples per task, and low computational resources. Our approach is based on descriptive task models that combine object-centric patterns and computations, and on the MDL principle for guiding the search for models. We have detailed an application to ARC tasks on colored grids, and sketched an application to FlashFill tasks on strings. We have shown promising results, especially in terms of efficiency, model complexity, and model naturalness. Going further on ARC will require a substantial design effort as our current models cover so far a small subset of the knowledge priors that are required by ARC tasks (e.g., goal-directedness). For FlashFill tasks, the addition of a counterpart for loops and common functions is expected to suffice to match the state-of-the-art. References 1. Acquaviva, S., Pu, Y., Kryven, M., Sechopoulos, T., Wong, C., Ecanow, G., Nye, M., Tessler, M., Tenenbaum, J.: Communicating natural programs to humans and machines. Advances in Neural Information Processing Systems 35, 3731–3743 (2022) 2. Ainooson, J., Sanyal, D., Michelson, J.P., Yang, Y., Kunda, M.: An approach for solving tasks on the abstract reasoning corpus. arXiv preprint arXiv:2302.09425 (2023) 3. Alford, S., Gandhi, A., Rangamani, A., Banburski, A., Wang, T., Dandekar, S., Chin, J., Poggio, T.A., Chin, S.P.: Neural-guided, bidirectional program search for abstraction and reasoning. CoRR abs/2110.11536 (2021), https://arxiv.org/abs/2110.11536 4. Chollet, F.: On the measure of intelligence. arXiv preprint arXiv:1911.01547 (2019) 5. Chollet, F.: A definition of intelligence for the real world. Journal of Artificial General Intelligence 11(2), 27–30 (2020) 6. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT. pp. 4171– 4186. Assoc. Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423 7. Elias, P.: Universal codeword sets and representations of the integers. IEEE Trans. Information Theory 21(2), 194–203 (1975) 8. Ellis, K., et al.: Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning. In: ACM Int. Conf. Programming Language Design and Implementation. pp. 835–850 (2021) 9. Faas, M., Leeuwen, M.v.: Vouw: geometric pattern mining using the MDL principle. In: Int. Symp. Intelligent Data Analysis. pp. 158–170. Springer (2020) 10. Fischer, R., Jakobs, M., Mucke, S., Morik, K.: Solving Abstract Reasoning Tasks with Gram- ¨ matical Evolution. In: LWDA. pp. 6–10. CEUR-WS 2738 (2020) 11. Goertzel, B.: Artificial general intelligence: concept, state of the art, and future prospects. Journal of Artificial General Intelligence 5(1), 1 (2014) 12. Grunwald, P., Roos, T.: Minimum description length revisited. International journal of math- ¨ ematics for industry 11(01) (2019) 13. Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: Symp. Principles of Programming Languages. pp. 317–330. ACM (2011) 14. Johnson, A., Vong, W.K., Lake, B., Gureckis, T.: Fast and flexible: Human program induction in abstract reasoning tasks. arXiv preprint arXiv:2103.05823 (2021) 15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25, 1097–1105 (2012) 16. Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015) 17. Lieberman, H.: Your Wish is My Command. The Morgan Kaufmann series in interactive technologies, Morgan Kaufmann / Elsevier (2001). https://doi.org/10.1016/b978-1-55860- 688-3.x5000-3 18. Menon, A., Tamuz, O., Gulwani, S., Lampson, B., Kalai, A.: A machine learning framework for programming by example. In: Int. Conf. Machine Learning. pp. 187–195. PMLR (2013) 19. Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19,20, 629–679 (1994) 20. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978) 21. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016) 22. Vreeken, J., Van Leeuwen, M., Siebes, A.: Krimp: mining itemsets that compress. Data Mining and Knowledge Discovery 23(1), 169–214 (2011) 23. Xu, Y., Khalil, E.B., Sanner, S.: Graphs, constraints, and search for the abstraction and reasoning corpus. arXiv preprint arXiv:2210.09880 (2022) Supplementary Materials We here describe the contents of the supplementary file accompanying the paper. A public repository of the source code is also available at https://github.com/sebferre/ ARC-MDL for ARC and at https://github.com/sebferre/ARC-MDL-strings for FlashFill. There are two main directories: one for ARC tasks and another for FlashFill tasks. Task Sets The public task sets of ARC are available online at https://github.com/fchollet/ARC. There are two task sets: training tasks and evaluation tasks, each containing 400 tasks. The task set of FlashFill is made of the 14 examples in [13]. We provide them as JSON files in FlashFill/taskset/, using the same format as ARC tasks, except that strings and arrays of strings are used instead of colored grids. For convenience, we also provide the file FlashFill/taskset/all examples.json to allow for browsing all examples in one file. Results We provide the learning and prediction logs for each task set: – ARC/training tasks.log – ARC/evaluation tasks.log – FlashFill/tasks.log Each log file starts with the hyperparameter values, and ends with global statistical measures. For each task, it gives: – the detailed DL (description length) of the initial model; – the learning trace (including the pruning phase) as a sequence of refinements, and showing the decrease of the normalized DL; – the learned models before and after pruning and their detailed DL; – the best joint description for each training example, except for ARC evaluation tasks so as not to leak their contents to the AI developer (a recommendation made by F. Chollet); – the prediction for each training and test example; – and finally a few measures for the task. The measures given for each task and at the end are the following: – runtime-learning: learning time in seconds (including the pruning phase); – bits-train-error: the remaining error commited on output training grids, in bits; – acc-train-micro: the proportion of training output grids that are correctly predicted; – acc-train-macro: 1 if all training output grids are correctly predicted, 0 otherwise; – acc-train-mrr: Mean Reciprocal Rank (MRR) of correct predictions for training output grids, 1 if all first predictions are correct; – acc-test-micro: the proportion of test output grids that are correctly predicted; – acc-test-macro: 1 if all test output grids are correctly predicted, 0 otherwise; – acc-test-mrr: Mean Reciprocal Rank (MRR) of correct predictions for test output grids, 1 if all first predictions are correct. The reference measure in ARC is acc-test-macro. The micro measures provide a more fine-grained and more optimistic measure of success. For convenience, we also provide in ARC/solved tasks a picture for each of the 96 training ARC tasks that are solved by our approach. We kindly invite the reader to browse them to get a quick idea of the diversity of the tasks that our approach can solve. The pictures are screenshots from the UI provided along with ARC tasks. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sebastien Ferr ´e, Univ Rennes, CNRS, Inria, IRISA Campus de Beaulieu, 35042 Rennes, France and ferre@irisa.fr. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Sebastien Ferr ´e, Univ Rennes, CNRS, Inria, IRISA Campus de Beaulieu, 35042 Rennes, France and ferre@irisa.fr. Table of Links Abstract & Introduction Abstract & Introduction Abstraction and Reasoning Corpus (ARC) Abstraction and Reasoning Corpus (ARC) Related Work Related Work Object-centric Models for ARC Grids Object-centric Models for ARC Grids MDL-based Model Learning MDL-based Model Learning Evaluation Evaluation Conclusion and Perspectives, References & Supplementary Materials Conclusion and Perspectives, References & Supplementary Materials 7 Conclusion and Perspectives We have presented a novel and general approach to efficiently learn skills at tasks that consist in generating structured outputs as a function of structured inputs. Following Chollet’s measure of intelligence, efficiently learning here means limited knowledge prior for the target scope of tasks, only a few examples per task, and low computational resources. Our approach is based on descriptive task models that combine object-centric patterns and computations, and on the MDL principle for guiding the search for models. We have detailed an application to ARC tasks on colored grids, and sketched an application to FlashFill tasks on strings. We have shown promising results, especially in terms of efficiency, model complexity, and model naturalness. Going further on ARC will require a substantial design effort as our current models cover so far a small subset of the knowledge priors that are required by ARC tasks (e.g., goal-directedness). For FlashFill tasks, the addition of a counterpart for loops and common functions is expected to suffice to match the state-of-the-art. References 1. Acquaviva, S., Pu, Y., Kryven, M., Sechopoulos, T., Wong, C., Ecanow, G., Nye, M., Tessler, M., Tenenbaum, J.: Communicating natural programs to humans and machines. Advances in Neural Information Processing Systems 35, 3731–3743 (2022) 2. Ainooson, J., Sanyal, D., Michelson, J.P., Yang, Y., Kunda, M.: An approach for solving tasks on the abstract reasoning corpus. arXiv preprint arXiv:2302.09425 (2023) 3. Alford, S., Gandhi, A., Rangamani, A., Banburski, A., Wang, T., Dandekar, S., Chin, J., Poggio, T.A., Chin, S.P.: Neural-guided, bidirectional program search for abstraction and reasoning. CoRR abs/2110.11536 (2021), https://arxiv.org/abs/2110.11536 4. Chollet, F.: On the measure of intelligence. arXiv preprint arXiv:1911.01547 (2019) 5. Chollet, F.: A definition of intelligence for the real world. Journal of Artificial General Intelligence 11(2), 27–30 (2020) 6. Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Conf. North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT. pp. 4171– 4186. Assoc. Computational Linguistics (2019). https://doi.org/10.18653/v1/n19-1423 7. Elias, P.: Universal codeword sets and representations of the integers. IEEE Trans. Information Theory 21(2), 194–203 (1975) 8. Ellis, K., et al.: Dreamcoder: Bootstrapping inductive program synthesis with wake-sleep library learning. In: ACM Int. Conf. Programming Language Design and Implementation. pp. 835–850 (2021) 9. Faas, M., Leeuwen, M.v.: Vouw: geometric pattern mining using the MDL principle. In: Int. Symp. Intelligent Data Analysis. pp. 158–170. Springer (2020) 10. Fischer, R., Jakobs, M., Mucke, S., Morik, K.: Solving Abstract Reasoning Tasks with Gram- ¨ matical Evolution. In: LWDA. pp. 6–10. CEUR-WS 2738 (2020) 11. Goertzel, B.: Artificial general intelligence: concept, state of the art, and future prospects. Journal of Artificial General Intelligence 5(1), 1 (2014) 12. Grunwald, P., Roos, T.: Minimum description length revisited. International journal of math- ¨ ematics for industry 11(01) (2019) 13. Gulwani, S.: Automating string processing in spreadsheets using input-output examples. In: Symp. Principles of Programming Languages. pp. 317–330. ACM (2011) 14. Johnson, A., Vong, W.K., Lake, B., Gureckis, T.: Fast and flexible: Human program induction in abstract reasoning tasks. arXiv preprint arXiv:2103.05823 (2021) 15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25, 1097–1105 (2012) 16. Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350(6266), 1332–1338 (2015) 17. Lieberman, H.: Your Wish is My Command. The Morgan Kaufmann series in interactive technologies, Morgan Kaufmann / Elsevier (2001). https://doi.org/10.1016/b978-1-55860- 688-3.x5000-3 18. Menon, A., Tamuz, O., Gulwani, S., Lampson, B., Kalai, A.: A machine learning framework for programming by example. In: Int. Conf. Machine Learning. pp. 187–195. PMLR (2013) 19. Muggleton, S., Raedt, L.D.: Inductive logic programming: Theory and methods. Journal of Logic Programming 19,20, 629–679 (1994) 20. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978) 21. Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016) 22. Vreeken, J., Van Leeuwen, M., Siebes, A.: Krimp: mining itemsets that compress. Data Mining and Knowledge Discovery 23(1), 169–214 (2011) 23. Xu, Y., Khalil, E.B., Sanner, S.: Graphs, constraints, and search for the abstraction and reasoning corpus. arXiv preprint arXiv:2210.09880 (2022) Supplementary Materials We here describe the contents of the supplementary file accompanying the paper. A public repository of the source code is also available at https://github.com/sebferre/ ARC-MDL for ARC and at https://github.com/sebferre/ARC-MDL-strings for FlashFill. There are two main directories: one for ARC tasks and another for FlashFill tasks. Task Sets The public task sets of ARC are available online at https://github.com/fchollet/ARC. There are two task sets: training tasks and evaluation tasks, each containing 400 tasks. The task set of FlashFill is made of the 14 examples in [13]. We provide them as JSON files in FlashFill/taskset/, using the same format as ARC tasks, except that strings and arrays of strings are used instead of colored grids. For convenience, we also provide the file FlashFill/taskset/all examples.json to allow for browsing all examples in one file. Results We provide the learning and prediction logs for each task set: – ARC/training tasks.log – ARC/evaluation tasks.log – FlashFill/tasks.log Each log file starts with the hyperparameter values, and ends with global statistical measures. For each task, it gives: – the detailed DL (description length) of the initial model; – the learning trace (including the pruning phase) as a sequence of refinements, and showing the decrease of the normalized DL; – the learned models before and after pruning and their detailed DL; – the best joint description for each training example, except for ARC evaluation tasks so as not to leak their contents to the AI developer (a recommendation made by F. Chollet); – the prediction for each training and test example; – and finally a few measures for the task. The measures given for each task and at the end are the following: – runtime-learning: learning time in seconds (including the pruning phase); – bits-train-error: the remaining error commited on output training grids, in bits; – acc-train-micro: the proportion of training output grids that are correctly predicted; – acc-train-macro: 1 if all training output grids are correctly predicted, 0 otherwise; – acc-train-mrr: Mean Reciprocal Rank (MRR) of correct predictions for training output grids, 1 if all first predictions are correct; – acc-test-micro: the proportion of test output grids that are correctly predicted; – acc-test-macro: 1 if all test output grids are correctly predicted, 0 otherwise; – acc-test-mrr: Mean Reciprocal Rank (MRR) of correct predictions for test output grids, 1 if all first predictions are correct. The reference measure in ARC is acc-test-macro. The micro measures provide a more fine-grained and more optimistic measure of success. For convenience, we also provide in ARC/solved tasks a picture for each of the 96 training ARC tasks that are solved by our approach. We kindly invite the reader to browse them to get a quick idea of the diversity of the tasks that our approach can solve. The pictures are screenshots from the UI provided along with ARC tasks. This paper is available on Arxiv under CC 4.0 license. Arxiv

Conclusion and Perspectives, References & Supplementary Materials: Our Journey With ARC

Authors: (1) Maria Rigaki, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and maria.rigaki@fel.cvut.cz; (2) Sebastian Garcia, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic and sebastian.garcia@agents.fel.cvut.cz. Table of Links Abstract & Introduction Threat Model Background and Related Work Methodology Experiments Setup Results Discussion Conclusion, Acknowledgments, and References Appendix 8 Conclusions By employing model-based reinforcement learning, MEME generates adversarial malware samples that successfully evade antivirus systems and train a surrogate model mimicking the target classifier accurately. Our experiments show that MEME surpasses existing methods in evasion rate, suggesting its potential for various applications, such as testing model robustness and enhancing cyber security against advanced persistent threats. Future work may involve exploring ensemble surrogates and other optimizations to enhance MEME’s performance further. Acknowledgments The authors acknowledge support from the Strategic Support for the Development of Security Research in the Czech Republic 2019–2025 (IMPAKT 1) program, by the Ministry of the Interior of the Czech Republic under No. VJ02010020 – AI-Dojo: Multi-agent testbed for the research and testing of AI-driven cyber security technologies. The authors acknowledge the support of NVIDIA Corporation with the donation of a Titan V GPU used for this research. References 1. Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: A Next-generation Hyperparameter Optimization Framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 2623–2631. KDD ’19, Association for Computing Machinery, New York, NY, USA (Jul 2019). https://doi.org/10.1145/3292500.3330701 2. Anderson, H.S., Kharkar, A., Filar, B., Evans, D., Roth, P.: Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning (Jan 2018). https://doi.org/10.48550/arXiv.1801.08917, arXiv:1801.08917 [cs] 3. Anderson, H.S., Roth, P.: EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models (Apr 2018). https://doi.org/10.48550/arXiv.1804.04637, arXiv:1804.04637 [cs] 4. Bergstra, J., Bardenet, R., Bengio, Y., K´egl, B.: Algorithms for Hyper-Parameter Optimization. In: Advances in Neural Information Processing Systems. vol. 24. Curran Associates, Inc. (2011) 5. Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: OpenAI Gym (Jun 2016). https://doi.org/10.48550/arXiv.1606.01540, arXiv:1606.01540 [cs] 6. Ceschin, F., Botacin, M., Gomes, H.M., Oliveira, L.S., Gr´egio, A.: Shallow Security: on the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors. 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Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017) 36. Security.org, T.: 2023 Antivirus Market Annual Report (Feb 2023), https://www.security.org/antivirus/antivirus-consumer-report-annual/ 37. Severi, G., Meyer, J., Coull, S., Oprea, A.: Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers. In: 30th USENIX Security Symposium (USENIX Security 21). pp. 1487–1504. USENIX Association (2021) 38. Song, W., Li, X., Afroz, S., Garg, D., Kuznetsov, D., Yin, H.: MAB-Malware: A Reinforcement Learning Framework for Blackbox Generation of Adversarial Malware. In: Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security. pp. 990–1003. ASIA CCS ’22, Association for Computing Machinery, New York, NY, USA (May 2022). https://doi.org/10.1145/3488932.3497768 39. Sussman, B.: New Malware Is Born Every Minute (May 2023), https://blogs.blackberry.com/en/2023/05/new-malware-born-every-minute 40. Sutton, R.S., Barto, A.G.: Reinforcement Learning, second edition: An Introduction. MIT Press (Nov 2018) 41. Total, V.: VirusTotal - Stats, https://www.virustotal.com/gui/stats 42. Uprety, A., Rawat, D.B.: Reinforcement Learning for IoT Security: A Comprehensive Survey. IEEE Internet of Things Journal 8(11), 8693–8706 (Jun 2021). https://doi.org/10.1109/JIOT.2020.3040957, conference Name: IEEE Internet of Things Journal 43. Wu, C., Shi, J., Yang, Y., Li, W.: Enhancing Machine Learning Based Malware Detection Model by Reinforcement Learning. In: Proceedings of the 8th International Conference on Communication and Network Security. pp. 74–78. ICCNS 2018, Association for Computing Machinery, New York, NY, USA (Nov 2018). https://doi.org/10.1145/3290480.3290494 44. 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Enhancing Cybersecurity with MEME: Reinforcement Learning for Adversarial Malware Evasion

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The ArbStore is a platform that revolutionizes trust and dispute resolution in international trade online. It offers a secure framework for decentralized escrow and arbitration, eliminating the need for traditional intermediaries. Parties involved in transactions can select expert arbiters from the available pool and create customized smart contracts within the platform —without coding or complex steps. . The fosters cost-effective, timely, and fair-trade interactions by providing a global, accessible, and customizable solution for resolving disputes without traditional courts or expensive middlemen. These contracts, powered by , are legal and based on predefined conditions, ensuring transparency and efficiency Obyte ArbStore In other words, anyone, everywhere, anytime, could access safe, decentralized, and easy international purchases and sales with strangers. We’ll explore here some of the advantages this will bring to international trade. Global Accessibility The platform is accessible to parties from around the world, promoting inclusive and borderless trade interactions. With the ability to select arbiters based on their expertise and language proficiency, parties can effectively communicate and resolve disputes across different jurisdictions, languages, and cultures. ArbStore This accessibility enhances trust and cooperation among international trading partners. Besides, one of the key factors contributing to the accessibility here is the utilization of cryptocurrencies. Unlike traditional financial systems that often involve complex currency conversions, cross-border transaction fees, and delays, cryptocurrencies offer a seamless and efficient means of value exchange on a global scale. via . ArbStore isn’t a financial entity, and it doesn’t hold or custody funds for the parties. By using digital assets, the ArbStore enables parties engaged in international trade to transact directly, bypassing the challenges associated with traditional fiat currencies. It’s possible to use GBYTEs, stablecoins, or any other token available for importing to Obyte Counterstake Bridge Flexibility and Customization allows parties to tailor their trade agreements and dispute resolution mechanisms to their specific needs. Users can select arbiters with expertise relevant to their industry, ensuring that disputes are resolved by professionals with a deep understanding of the subject matter. ArbStore Additionally, the ability to choose among different arbiters with their own terms and fees offers flexibility and adaptability to various trade scenarios. The creation of the contracts is fully customized as well. The parties can discuss their terms externally, or internally, by using the encrypted chat available in the . Obyte wallet The contract is a written text, like any other legal contract, and both parties must read it and select an arbiter before depositing the funds and sending them. However, the arbiter won’t be involved (or take any fees) unless one of the parties initiates a dispute. If the transaction goes as expected, the arbitration won’t be necessary. On the other hand, Obyte also offers the that require no coding skills. Its user-friendly interface facilitates the design of custom tokens to represent assets, services, or rights. These tokens can then be seamlessly integrated into contracts with arbitration via the . creation of personalized tokens ArbStore platform Speed and Efficiency nternational trade can be hampered by time-consuming legal processes and bureaucratic hurdles. ArbStore's decentralized escrow and arbitration system streamlines dispute resolution. Smart contracts automatically execute once predefined conditions are met, ensuring the release of funds in a timely manner. I This accelerates the overall trade process, allowing parties to focus on their core business activities. Of course, if one of the parties thinks that the agreed conditions weren’t met, they can involve the human factor in the form of an arbiter. It follows a process in which both parties must present their evidence and arguments to the arbiter. The latter will be the last responsible for releasing the funds to the winning party, likely in some days. It may not sound like a quick process in case of disputes, but traditional services or even months to reach a result and require recognition and enforcement in the fiat world. Not to mention that international transactions with fiat money are usually cumbersome, slow, and expensive. could take weeks In the case, once the arbiter has taken a decision based on their analysis of the facts and the documentation presented to them, they only need to click on a button to immediately release the funds from the smart contract. ArbStore Lower Costs Traditional methods of international trade often involve significant costs, including legal fees, court expenses, and fees for intermediaries such as escrow agents. At the same time, settlement times could take several days, and negative changes in the economy worldwide (like banking issues, supply chain turbulence, or political sanctions) could seriously limit international trade. By utilizing the ArbStore platform, parties can significantly reduce these costs. With transparent and competitive arbiter fees, ensuring that more resources are directed toward the actual trade transaction. users can choose the most cost-effective option for dispute resolution, According to , “For a relatively simple [international] arbitration requiring 1,500 hours of legal work, for instance, if billed at the rate of USD 300/hour, legal fees would equal USD 450,000 per arbitration.” Aceris Law LLC Some companies offer the service including a filing fee between $2,000 and $6,000, besides a high percentage to properly manage the case. , and making any contract with arbitration (decentralized escrow) from the Obyte wallet only requires some very small ledger transaction fees. That’s between 700 and 5,000 bytes (up to $0.000060) per operation. There’s no such thing as a “filing fee” in the . On the contrary, the ArbStore only takes 0.75% per case ArbStore Only in the event of a dispute, the plaintiff party has to pay the fee asked by the selected arbiter. That currently varies between 2% and 5%, with an optional minimum and maximum —e.g., “2%, min $50, max $1,000.” Decentralization and Privacy As we mentioned above, the Obyte wallet has an encrypted chat. This means no one but the involved parties could read or know about what’s in there. The messages (including the ones sent to a chatbot) and contracts are always stored in the user(s) device(s), and never in the cloud. It’s completely decentralized and private. Therefore, no one, not even the ArbStore, the arbiter, or Obyte, have access to your data since the beginning. Only if one of the parties decides to disclose the contract to other people or an arbiter is called to solve a dispute, the content of the agreement would be known to someone else. However, it’s important to note that the arbiters are bound by the . ArbStore Privacy Policy Going a bit further, after arbitration, the losing party could share the contract with the ArbStore moderators to appeal the arbiter’s decision. Nobody else learns about the terms of the contract unless, of course, required by law or any party discloses them, likely in violation of the contract’s own non-disclosure terms. For their part, the funds will always be safe inside a decentralized smart contract built on Obyte. That means no one but the involved parties has access to them, not even the or Obyte itself. That’s why it’s of the utmost importance to back up and protect the private keys from your wallet. ArbStore How to Start in the ArbStore? It’s easy, it’s useful, and it’s cheap. First of all, both parties need to download, back up, and pair their Obyte wallets. This is a available for Android, iOS, Windows, Mac, and Linux. simple and light app The security settings are in the “Global Preferences” section, while the pairing is done by adding a new device (Contact) in the Chat tab. Given that smart contracts only work with cryptocurrencies, one of the parties (the buyer) must fund their wallet with their preferred token. In the Receive tab, it’s possible to find a button to buy new coins. Independently of the contract currency, some funds in GBYTE will be necessary to pay transaction fees (usually less than $1). The parties can discuss and copy their agreement through the encrypted chat, and select their preferred arbiter from the ArbStore. Once done, the buyer shares with the seller their Obyte address, Then it’s just a matter of filling the spaces, paying for the contract, and shipping the goods. and the seller offers a Contract with Arbitration by clicking on it. The funds will be locked in the smart contract until the conditions are met. In case of dispute, the plaintiff contacts the previously selected arbiter, pays for their fees, and starts the arbitration process. After studying the evidence presented by the parties, the arbiter releases the funds to the winning party. And that’s it! Moreover, to register as an arbiter, it’s only necessary to follow the instructions of a chatbot in the Obyte wallet. You can read the too! Arbiter’s Guide Featured Vector Image by vector4stock / Freepik

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In a many to many relationship, it's just a table between the entities, but what is the purpose of this table to be between them? 24. Green Lights at the End of a Dock: The Secret to Happiness by Jay Gatsby and Richard Curtis My first experience with time travel was when I was 21 years old. Thank you for checking out the 24 most read stories about Relationships on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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