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We are currently at the early stage of the bull run, and many new people will be exploring the industry and looking for ways to profit from the bull run. So, I have decided to write this article. crypto I will advise you to read this article till the end, as it will help you profit from the bull run. This is not your regular article, I have outlined a very specific and easy-to-follow strategy that most traders don’t talk about in the crypto trading space. Please Note: Nothing in this post is financial advice, I will encourage you to also do your own research before investing your money into any crypto project. Crypto is high risk and, high reward In crypto trading, the market can easily give you $100,000. But the question is, can you keep it? To answer this question, it is important to have a profitable and working strategy, a good risk management strategy, and a good mindset to be able to keep the free money that the market will give you, or else you will lose it. In this article, I will be focusing on the top three crypto trading strategy that is perfect for beginners. You don’t need to have months or years of experience to profit from these strategies. Three Best Crypto Trading Strategy For Beginners New Coin/Token Listing Trading Strategy This is one of the easiest ways to profit from the market. Whenever a new coin is listed in a popular or top exchange, the coin/token will rise. For instance, ORDI, a BRC-20 token that was listed on Binance, increased by over 50% after it was listed on Binance. And since then, it has increased by over 1,700%. If you had invested just $100 before the listing, by now, you would have made $1700 just by buying ORDI before it was listed on Binance. Another example is the JITO Token - JTO. Since JTO was listed on Binance, the token increased by over 90% in a few days. There are too many examples to show; you can also do more research about new coin listings. How to know when a coin will be listed If you like this trading strategy, you might be asking yourself, how would you know when a new one is going to be listed? Well, depending on the exchanger you are using, they will announce the coin/token a few hours before it will be listed. For instance, four days ago announced that they would be listing the MARSH/USDT trading pair. BYDFI Binance announced that they would be listing 1000SATS/USDT trading pairs a few hours before this post. To profit from this strategy, you need to get in early. So, you should follow your exchanger on all social media platforms so you can be able to get in early once the announcement once is made. Swing Trading with Spot To profit from this strategy in this next bull run, you need to think long-term. If you don’t have patience, you will lose your money instead of profiting from it. This is how the strategy works: you will need to hold the coin/token for some days or weeks and sell when you are in profit. We are currently at the early stage of the bull run. All you need to do is go to the spot trading section on your exchanger account (You can use BYDFI), choose a trading pair, buy, and hold. If your tolerance is low, I will advise you to buy tokens or coins in the top 200 of , though the profit may be small. But, if you have a high-risk tolerance, you can look for tokens or coins above 300 in . Coinmarketcap Coinmarketcap For instance, if you had bought ETC when it was $18 and held for five days, you would have made an extra $5 on each token before the price went down. Again, this is the beginning of the bull market, so the token will rise again. Launchpad Listing Trading In crypto, when a project is launched, and the company wants to raise funds, they either use ICO, IDO, or Launchpad. Launchpad listing allows blockchain-based projects to generate revenue by selling tokens at a discounted price. For instance, Pyth Network - A crypto project in partnership with Bitmat (an exchanger) offered PYTH token flash sales at a 30% discount. This means that once the coin is listed on the platform you will be in 30% profit because the coin was sold or you bought the coin at a 30% discount. So, you may choose to hold the coin if you believe it will increase in price or sell it off at a 30% profit. I could go on and list more crypto trading strategies, but these are the once I think are easy for beginners to follow. Please, avoid trading: Future trading Arbitrage trading In the beginning, you may lose all your money if you don’t have the right knowledge, so stick with the three crypto trading tips I have outlined. Again, nothing in this article is financial advice. Take them as educational content, and please do your research. Tips to Help You Stay Profitable If the market gives you money and you can’t keep it, it will take it back. Here are some tips to help you keep what the market gives you so you don’t lose it; Set Profit Target In crypto, what goes up goes down. To stay profitable, you need to set clear targets for you to take profits because the market will reverse. Nothing goes up forever in crypto. Avoid FOMO If the market leaves you, don’t jump in because you feel that you will lose money if you don’t get in. If you jump in when the market is already like 40% up, I will advise you to look for another pair to trade because you may be used as liquidity; those who bought earlier may be taking profit when you are entering. Patience Many times, you may enter a swing trade and the market will start going against you and immediately you will be in a drawdown, if you lack patience you may close the trade at a loss. You need to have a strong mindset to be able to hold your tokens even when they are in drawdown. Know The Token or Coin is Holding There are too many scams in the crypto industry; if you want to invest in any token, please check the token very well. Check the official website, all the social media platforms, , and more. Make sure that the token is genuine before investing in it. Coinmarketcap If you  enjoyed this post, please share. Thanks, and happy trading.

3 Best Crypto Trading Strategy for Beginners

Part 3 of The where I interview my mentors series I have very recently started making some progress with my . But to be honest, it wouldn’t be possible at all without the amazing community online and the great people that have helped me. In this Series of Blog Posts, I talk with People that have really inspired me. Self-Taught Machine Learning Journey People that I look up to and that I learn and work with. The motivation behind doing this is, you might see some patterns and hopefully you’d be able to learn from the amazing people that I have had the chance of learning from. In this interview, I’m talking to Aakash Kumar Nain, A kernels expert, Data Scientist, Deep Engineer. (Also an underground CS:Source Grand Master) kaggle Learning **Sanyam:**​ Hey Aakash! Thank you for doing this interview. Hey Sanyam, thanks for having me. Aakash: You’re one of the coolest college senior I know from India. Could you tell the readers more about yourself? Sanyam: Thanks for the compliment. Well, as a professional, I am a data scientist. I love solving complex problems with Machine Learning. Playing with data is my another hobby and computer vision is my favorite field. Python is the only true love of my life. Apart from that, I am an athlete, a footballer, a part-time traveler, and an avid reader. Philosophy is my second favorite after Computer Science. Aakash: Awesome. Sanyam: You’re also addicted to the Legal Data Science Drug — Kaggle. Could you tell us a little about your kaggle journey? What got you interested in AI to an extent that you decided to take it up as a career? Ha! Yes, you are right. I am completely addicted to it. My journey to Kaggle is a bit different than most of the people. When I was in my pre-final year in college, one of my seniors showed me the self-playing Mario game which was trained using Reinforcement learning. That thing literally blew my mind at that time. I started taking online courses on Machine Learning. The problem is that none of the courses give you a hands-on experience on complex problems. That is when I heard about Kaggle. I joined Kaggle in Jan 2016. In the beginning, I felt completely overwhelmed and I didn’t participate in any competition for a while. After practising a few small and simple problems, I entered in competitions in July 2016. I joined the KaggleNoobs community and started asking people very noob questions to clarify and get a solid understanding of the underlying concepts. Since then, the journey has been quite interesting. Aakash: I’ve really enjoy learning from your kernels. Could you tell us more about the motivation for writing them? How much effort goes into one behind the scenes? Sanyam: Data Science and Machine Learning are vast fields. To a beginner, the path is almost always overwhelming and that’s my personal experience. Sharing knowledge is an important part of self-learning. Also, it’s our duty to give back to the community who has always been there for you. This is exactly why I started writing kernels. Every time I write a kernel, I make sure that it contains something new that others haven’t tried yet Aakash: Regarding the efforts, I would say that writing a quality kernel requires a huge effort. For example, I can write a simple kernel in few hours but in order to make sure that the code is clean, optimized and is along with the lines of best practises, it can take up to days. I understand being from the same country that AI Hiring is sparse. Still, what made you decide to take this up as your career choice? Sanyam: When you are looking for a job, there are two things that you should always consider: Aakash: Your personal interest The scope of your work and the growth rate You should do the work you love. When I was in college, I learned programming because I wanted to be a core developer at that time, then I tried web programming but nothing excited me more than Machine Learning. This was the point when I decided that I am going to work in the field of Data Science and Machine Learning only. I think we both can agree that Machine Learning opportunities in India are currently very sparse for a fresher’s position. What advice would you give to our juniors who want to take up a job in the field? Sanyam: I would correct the first statement to “Machine Learning opportunities in India are currently very sparse”. This doesn’t mean openings aren’t there. The problem is everyone wants to be a Machine Learning engineer and every company wants to apply Machine Learning in their work because they think it is “cool”. On the top of this, even when the company itself doesn’t know what it is doing with Machine Learning, the job requirements include “5+ years of experience in Machine Learning”. So, the problem lies on the other end. Aakash: I already stated that Machine Learning is a vast field. Within Machine Learning there are multiple disciplines like Computer Vision, Natural Language Processing, Speech, etc. Each of these subfields is very diverse and there are too many things to learn within one subfield. You should choose one that interests you the most but at the same time you should know the basic concepts used in other subfields as well. Don’t try to be an expert in every discipline. From our earlier conversation, we both agreed on the viewpoint of Online learning being an equally good if not better alternative to a Masters. Can you share your thoughts on this? Sanyam: Do you think Kaggle can prepare you for a Data Science position better than a Masters? There are two types of online courses available on the Internet today. The first ones are those that cover the “width” while the second one are those that cover the “depth”. For example, Udacity courses cover the width. They will teach you almost all the aspects but none of them will be covered in depth. On the other hand, coursera courses are great if you are looking to deep dive into some aspects but they won’t cover every element required. This the major bottleneck today. Finding all the things that are required to learn in a single place is a bit difficult when it comes to an online course. Plus nothing is cheap. Online courses are not that expensive as Masters but they are expensive. Aakash: Competing on Kaggle and opting for Masters are two totally different things. The former one makes you better at trying different things and being recognized to a wider audience while the latter one makes you good, if not better, at research. Some people love to be in college again while other prefer the hands-on experience and solving problems on a daily basis. Before we conclude, Anything else you would like to say to the readers? Sanyam: We are living in an incredible time considering the progress we have made in Machine learning in the past few years, especially in deep learning. When you are self-taught, things can be very very overwhelming. The path to becoming a Machine learning engineer or a Data Scientist is easy but the path to becoming a very good Machine Learning Engineer/Data Scientist is not easy at all. At some point in time, you would also feel low seeing the amount of research going on. It is okay if you feel that way. There are only two things that we should always remember: Aakash: Your success isn’t dependent on others and vice-versa. Never be shy/afraid to ask even the most basic things. If you don’t ask, you are the one who is hindering your progress. is the best place to get in touch. Although, I am active on also but I don’t use twitter for communication at all. Aakash: KaggleNoobs slack Twitter Thank you so much for talking doing this interview. Sanyam: is the best community for kaggle where you can find , Kaggle Grandmasters, Masters, Experts and it’s a community where even noobs like me are welcome. Kaggle Noobs Aakash Come join if you aspire to become an Expert like Aakash. If you found this interesting and would like to be a part of My Learning Path, you can find me on Twitter . here

Interview with Kaggle Kernels Expert: Aakash Nain

Adaptive Graph Neural Networks for Cosmological Data Generalization: Acknowledgements and Disclosure

Authors: (1) Jonathan H. Rystrøm. Table of Links Abstract and Introduction Previous Literature Methods and Data Results Discussions Conclusions and References A. Validation of Assumptions B. Other Models C. Pre-processing steps 6 Conclusion Understanding how recommender systems shape our behaviour is essential to avoid manipulation. In this paper, we investigated the Amazon recommender system concerning whether it has made it harder to change preferences. By analysing the Barrier-to-Exit (Rakova & Chowdhury, 2019) of more than 50,000 users, we found a highly significant growth in Barrier-to-Exit over time, which indicates that it has indeed become harder to change preferences for the analysed users. However, sampling bias induced by the calculation of Barrier-to-Exit makes it difficult to draw conclusions about the general population of Amazon customers. This highlights the dilemma of portability in measuring socio-technical systems (Selbst et al., 2019): accurately evaluating a concept like ”changing preferences” requires adapting to the context of the system, which makes it more difficult to generalise (and compare) to other systems. Comparing recommender systems is necessary for ensuring that these respect human autonomy (Varshney, 2020) and live up to new regulations such as the EU AI Act (Kop, 2021). Further work, should aim to create auditing procedures and metrics that allow third parties to measure potential preference manipulation in a way that fits within the context of the industry and allows for comparisons between different systems. This will help assess the pressures of Surveillance Capitalism (Zuboff, 2019) on human autonomy. References Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of memory and language, 59 (4), 390–412. 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Ensuring Ethical AI: Lessons from Amazon's Barrier-to-Exit Analysis

Sentiment Analysis through LLM Negotiations: Conclusion and References

Loss of Amazon Rainforest Resilience: Author Contributions

This paper is under CC 4.0 license. available on arxiv Authors: (1) Zhe Liu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (2) Chunyang Chen, Monash University, Melbourne, Australia; (3) Junjie Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author; (4) Mengzhuo Chen, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (5) Boyu Wu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (6) Zhilin Tian, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (7) Yuekai Huang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (8) Jun Hu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (9) Qing Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author. Table of Links Abstract and Introduction Motivational Study and Background Approach Experiment Design Results and Analysis Discussion and Threats to Validity Related Work Conclusion and References 8 CONCLUSION Automated testing is crucial for helping improve app quality. Despite the dozens of mobile app GUI testing techniques, how to automatically generate the diversified unusual text inputs for fully testing mobile apps remains a challenge. This paper proposes InputBlaster which leverages the LLM to produce the unusual inputs together with the mutation rules which serve as the reasoning chains. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. The evaluation is conducted for both effectiveness and usefulness, with 136% higher bug detection rate than the best baselines, and uncovering 37 new crashes. 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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Conclusion and References

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