Quantum computing could quickly be the game-changer. Imagine faster drug discoveries or unhackable passwords: That's the good

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This paper is available on arxiv under CC 4.0 license. Authors: (1) Muzhaffar Hazman, University of Galway, Ireland; (2) Susan McKeever, Technological University Dublin, Ireland; (3) Josephine Griffith, University of Galway, Ireland. Table of Links Abstract and Introduction Related Works Methodology Results Limitations and Future Works Conclusion, Acknowledgments, and References A Hyperparameters and Settings B Metric: Weighted F1-Score C Architectural Details D Performance Benchmarking E Contingency Table: Baseline vs. Text-STILT 6 Conclusion In this work, we addressed the challenge of training multimodal meme sentiment classifiers on a limited number of labelled memes by incorporating unimodal sentiment analysis data. We did so by proposing the first instance of STILT that applies unimodal intermediate tasks to a multimodal target task. Specifically, we tested image-only and text-only sentiment classification as intermediate tasks in training a meme sentiment classifier. We showed that this approach worked – unimodal text improved meme classification performance to a statistically significant degree. This novel approach allowed us to train a meme classifier that outperforms meme-only finetuning with only 60% as many labelled meme samples. As possible explanations for our observations, we discuss apparent similarities and differences in the roles of image and text modalities between unimodal and multimodal sentiment analysis tasks. Acknowledgments This work was conducted with the financial support of the Science Foundation Ireland Centre for Research Training in Digitally-Enhanced Reality (d-real) under Grant No. 18/CRT/6224; and the provision of computational facilities and support from the Irish Centre for High-End Computing (ICHEC). References Christian Bauckhage. 2011. Insights into internet memes. Proceedings of the International AAAI Conference on Web and Social Media, 5(1):42–49. Efrat Blaier, Itzik Malkiel, and Lior Wolf. 2021. Caption enriched samples for improving hateful memes detection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9350–9358, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Ana-Maria Bucur, Adrian Cosma, and Ioana Iordache. 2022. BLUE at memotion 2.0 2022: You have my image, my text and my transformer. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova. 2019. BoolQ: Exploring the surprising difficulty of natural yes/no questions. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2924–2936, Minneapolis, Minnesota. Association for Computational Linguistics. CrowdFlower. 2016. Image sentiment polarity - dataset. Available at https://data.world/ crowdflower/image-sentiment-polarity. Accessed: 2023-01-15. Baishan Duan and Yuesheng Zhu. 2022. Browallia at memotion 2.0 2022 : Multimodal memotion analysis with modified ogb strategies (short paper). In DeFactify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Noam Gal, Limor Shifman, and Zohar Kampf. 2016. “it gets better”: Internet memes and the construction of collective identity. New Media & Society, 18(8):1698–1714. Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, and Ivan Marsic. 2018. Hybrid Attention based Multimodal Network for Spoken Language Classification. In Proceedings of the 27th International Conference on Computational Linguistics, pages 2379–2390, Santa Fe, New Mexico, USA. Association for Computational Linguistics. Muzhaffar Hazman, Susan McKeever, and Josephine Griffith. 2023. Meme sentiment analysis enhanced with multimodal spatial encoding and face embedding. In Artificial Intelligence and Cognitive Science, pages 318–331, Cham. Springer Nature Switzerland. Junguang Jiang, Yang Shu, Jianmin Wang, and Mingsheng Long. 2022. Transferability in deep learning: A survey. arXiv preprint, arXiv:2201.05867. Vishal Keswani, Sakshi Singh, Suryansh Agarwal, and Ashutosh Modi. 2020. IITK at SemEval-2020 task 8: Unimodal and bimodal sentiment analysis of Internet memes. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 1135–1140, Barcelona (online). International Committee for Computational Linguistics. Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, et al. 2020. The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in Neural Information Processing Systems, 33:2611–2624. Liisi Laineste and Piret Voolaid. 2017. Laughing across borders: Intertextuality of internet memes. The European Journal of Humour Research, 4(4):26–49. Gwang Gook Lee and Mingwei Shen. 2022. Amazon pars at memotion 2.0 2022: Multi-modal multi-task learning for memotion 2.0 challenge. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Quinn McNemar. 1947. Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2):153–157. Shreyash Mishra, S Suryavardan, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, and Srijan Kumar. 2023. Memotion 3: Dataset on sentiment and emotion analysis of codemixed hindi-english memes. arXiv preprint, arXiv:2303.09892. Niklas Muennighoff. 2020. Vilio: state-of-the-art visiolinguistic models applied to hateful memes. arXiv preprint arXiv:2012.07788. Thanh Van Nguyen, Nhat Truong Pham, Ngoc Duy Nguyen, Hai Nguyen, Long H. Nguyen, and YongGuk Kim. 2022. Hcilab at memotion 2.0 2022: Analysis of sentiment, emotion and intensity of emotion classes from meme images using single and multi modalities (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Asaf Nissenbaum and Limor Shifman. 2017. Internet memes as contested cultural capital: The case of 4chan’s /b/ board. New Media & Society, 19(4):483– 501. Parth Patwa, Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra, S Suryavardan, Aishwarya Reganti, et al. 2022. Findings of memotion 2: Sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal FactChecking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Kim Ngan Phan, Gueesang Lee, Hyung-Jeong Yang, and Soo-Hyung Kim. 2022. Little flower at memotion 2.0 2022 : Ensemble of multi-modal model using attention mechanism in memotion analysis (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Jason Phang, Thibault Fevry, and Samuel R. Bowman. ´ 2019. Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks. arXiv preprint, arXiv:1811.01088. Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the word-in-context dataset for evaluating context-sensitive meaning representations. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 1267–1273, Minneapolis, Minnesota. Association for Computational Linguistics. Clifton Poth, Jonas Pfeiffer, Andreas Ruckl ¨ e, and Iryna ´ Gurevych. 2021. What to pre-train on? Efficient intermediate task selection. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 10585–10605, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. Christopher Potts, Zhengxuan Wu, Atticus Geiger, and Douwe Kiela. 2021. DynaSent: A dynamic benchmark for sentiment analysis. 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), pages 2388–2404, Online. Association for Computational Linguistics. Shraman Pramanick, Md Shad Akhtar, and Tanmoy Chakraborty. 2021a. Exercise? i thought you said ’extra fries’: Leveraging sentence demarcations and multi-hop attention for meme affect analysis. Proceedings of the International AAAI Conference on Web and Social Media, 15(1):513–524. Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, and Tanmoy Chakraborty. 2021b. MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4439–4455, Punta Cana, Dominican Republic. Association for Computational Linguistics. Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R. Bowman. 2020. Intermediate-task transfer learning with pretrained language models: When and why does it work? In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5231–5247, Online. Association for Computational Linguistics. Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, et al. 2021. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748–8763. PMLR. Sathyanarayanan Ramamoorthy, Nethra Gunti, Shreyash Mishra1, S Suryavardan, Aishwarya Reganti, Parth Patwa, Amitava Das, Tanmoy Chakraborty, Amit Sheth, Asif Ekbal, and Chaitanya Ahuja. 2022. Memotion 2: Dataset on sentiment and emotion analysis of memes. In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI. Elad Segev, Asaf Nissenbaum, Nathan Stolero, and Limor Shifman. 2015. Families and Networks of Internet Memes: The Relationship Between Cohesiveness, Uniqueness, and Quiddity Concreteness. Journal of Computer-Mediated Communication, 20(4):417–433. Lanyu Shang, Yang Zhang, Yuheng Zha, Yingxi Chen, Christina Youn, and Dong Wang. 2021. AOMD: An Analogy-Aware Approach to Offensive Meme Detection on Social Media. Inf. Process. Manage., 58(5). Chhavi Sharma, Deepesh Bhageria, William Scott, Srinivas PYKL, Amitava Das, Tanmoy Chakraborty, et al. 2020. SemEval-2020 task 8: Memotion analysisthe visuo-lingual metaphor! In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 759–773, Barcelona (online). International Committee for Computational Linguistics. Shivam Sharma, Mohd Khizir Siddiqui, Md. Shad Akhtar, and Tanmoy Chakraborty. 2022. Domainaware self-supervised pre-training for label-efficient meme analysis. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 792–805, Online only. Association for Computational Linguistics. Limor Shifman. 2014. The cultural logic of photo-based meme genres. Journal of Visual Culture, 13(3):340–358. Pranaydeep Singh, Aaron Maladry, and Els Lefever. 2022. Combining language models and linguistic information to label entities in memes. In Proceedings of the Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situations, pages 35–42, Dublin, Ireland. Association for Computational Linguistics. Shardul Suryawanshi, Bharathi Raja Chakravarthi, Pranav Verma, Mihael Arcan, John Philip McCrae, and Paul Buitelaar. 2020. A dataset for troll classification of TamilMemes. In Proceedings of the WILDRE5– 5th Workshop on Indian Language Data: Resources and Evaluation, pages 7–13, Marseille, France. European Language Resources Association (ELRA). Cagri Toraman, Furkan S¸ahinuc¸, and Eyup Yilmaz. 2022. Large-scale hate speech detection with crossdomain transfer. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2215–2225, Marseille, France. European Language Resources Association. Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R. Thomas McCoy, Roma Patel, Najoung Kim, Ian Tenney, Yinghui Huang, Katherin Yu, Shuning Jin, Berlin Chen, Benjamin Van Durme, Edouard Grave, Ellie Pavlick, and Samuel R. Bowman. 2019. Can you tell me how to get past sesame street? sentence-level pretraining beyond language modeling. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4465–4476, Florence, Italy. Association for Computational Linguistics. Ron Zhu. 2020. Enhance multimodal transformer with external label and in-domain pretrain: Hateful meme challenge winning solution. arXiv preprint, arXiv:2012.08290. Yan Zhuang and Yanru Zhang. 2022. Yet at memotion 2.0 2022 : Hate speech detection combining bilstm and fully connected layers (short paper). In De-Factify @ AAAI 2022. First Workshop on Multimodal Fact-Checking and Hate Speech Detection, CEUR Workshop Proceedings. AAAI.

Innovative Strategies in Meme Sentiment Analysis: The Impact of Unimodal Training

If you stop and ask the average person on the street whether they think that politicians, banksters, and CEOs have their best interests at heart, you would be hard-pressed to find someone who says yes. Even the most statist, war-promoting, tax-paying, flag-waving people out there can no longer miss the obvious fact that "society" as we know it is simply a planet-sized human cattle farm, run by a small contingent of socio-/psychopaths. They probably still go along with it, and they vote for a new president or prime minister every few years, and they listen to the propaganda on the tele-screens... Depending on which half of the Duopoly party they support, there are probably different issues that they most notice: Team Red can easily see human trafficking, child mutilation, Authoritarian Socialism, and the attempted destruction of morality as a whole... While Team Blue can easily see the corporate greed, the immorality of "War," and the push for one "chosen" supreme "race" to control the whole planet... But for some reason, both sides are only capable of seeing some of these issues, specifically, they each seem to only be capable of holding awareness for the "problems" that the team they root for calls out as problems. This isn't their fault though, because this is just how the human brain works. The human experience is a hyper-personalized one, wherein our sense organs feed our brains terabytes of information every second, and our conscious mind receives a fraction of a percent of that information after it's all been filtered through currently existing beliefs, expectations, and assumptions. The human experience is, simply put, confirmation bias. The story you tell yourself in your mind is the story you will live out in the physical world. The game(s) you are playing decide what the "rules" are in your world, what "success" is, and what your relationships with others will look like... Competitive? Cooperative? Are your relationships & connections open, loving, trusting & intimate? Or simply quid-pro-quo exchanges... The Games We Play... or Don't Play Imagine you are with a group of friends, and everyone is playing a game. You notice that the rules & goals of the game tend to lead to people lying to each other, taking advantage of each other, and maybe even dehumanizing each other. Now, this is an established game, with rules that m/billions of people know. A game that has been played in more or less the exact same way for thousands of years. Do you think it's going to be possible to re-define that game? To get people to play it in a completely different way? To change what "winning" is and what acceptable behavior is to get there? 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The Exit & Build game, the Agorism game, the Off-Grid game... there are so many other options. Just choosing not to play the "banks" game, and instead using non-custodial, non-governmental, not debt-based, private currencies like Monero, or physical metals/barter, removes you from most of the games the psychopaths have set up. https://youtu.be/MDnnQt8AKNo?si=ezLeAJ-X26cborfI&embedable=true How I Opt Out I haven't had a bank account since 2009/10. Never had a credit card. Never registered to vote, much less voted. Never paid taxes. Only filed for two years when I was a student. I live out of two backpacks, and have since 2015. I have my tent, sleeping bag, sleeping pad, laptop, herbal medicine, books, journals, survival gear, sewing kit, and everything else needed besides water & food... all coming in at less than 50 pounds of equipment... You may think that means I probably live like a hobo... which is almost true in a way, but I also rent fancy houses around the world to bring friends together, help produce festivals & conferences, work with a variety of projects in the Freedom Movement, etc. I've had no money since last spring - the last of my crypto is staked away in $CULT that I won't be unstaking anytime soon... but I threw a conference called "Be Empowered" in Colorado in September, rented a retreat center in Morelia for the month of January, did a little US tour, and am currently writing on the train from Chicago to Denver, where I'm heading to host an Ethereum Denver after-party. Now, the best part of my story is that how I live now, totally free (financially, spiritually, physically) is not because I grew up wealthy, or on a commune, or anything like that... My family moved every 6-12 months when I was a kid; on top of that, I was bounced between parents more than once and kicked out of a few schools. We lived in small apartments or tiny houses until I was in high school. I struggled with suicidal depression for more than a decade, became addicted to cocaine, alcohol, and other chemicals, worked corporate jobs & general labor gigs, and was a full-time drug dealer for a good while. 12 years ago, a psychedelic experience woke me up to the fact that I didn't like the game I was living in, AND I could change it if I wanted to. It took me years of disciplined eating & sleeping, of brain-reprogramming practices, retreats, and books, and releasing everything that I thought I knew about reality (I was a physicalist reductionist atheist back then - a big part of my depression.) In Summary Basically, my message is simple. You are the AUTHORity of your own story, of your life. The only way for someone else to have authority over you is if you give it to them. At any time, you can simply take that authority back. No paperwork, no court, no battles. Just a choice to reclaim your sovereignty & personal responsibility. If you'd like to dig deeper into these concepts, I highly recommend looking into the work of folks like: Abraham-Hicks Derrick Broze James Redfield Alan Watts and so many others! Thanks for tuning in beautiful humans! You Are Beautiful
You Are Powerful
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581 Stories To Learn About Non Fiction

In my previous article, I gave a very basic explanation of what , but in this article, I'll be talking about crypto wallets in the African space (I'm sure you read the title of this article😐). Without further ado, let's get into it. blockchain was and its types Although cryptocurrencies are not yet recognized as legal tender by a couple of countries, it has continued to gain massive adoption. People use it to store value, trade, and as investments. These activities are usually available on crypto exchanges. Each of these crypto exchange platforms(available as apps) has offices in Africa and, of course, allows African payment methods. Some of the platforms also provide learning materials for people who want to delve into the cryptocurrency space. Here's a list of some of the most popular crypto wallets in Africa: Binance. Luno. Quidax. Yellow card. BuyCoins. KuCoin. Huobi. Binance. Binance is one of the most popular blockchains and crypto-asset exchanges in Sub-Saharan Africa. The platform is simple, secure, fast, and beginner-friendly. Binance averages a daily trading volume of more than $38.31b.1,400,000+ transaction speed 24/7 support, Binance has a native asset called BNB. This platform supports 10 fiat currencies. Launched in 2017, the leading global digital wallet enables users from over 180 countries to buy, sell, and trade cryptocurrencies. A vital edge Binance has over other platforms is a large number of cryptocurrencies that can be traded on the platform -over 380 pairs. They currently have an office in Cape Town, South Africa. Read more about Binance. Luno. Luno is a simple crypto investment platform that is customized for everyone. It is an independent operating subsidiary of Digital Currency Group. It was launched in 2013 in Singapore. Luno has a very strong base in Nigeria, South Africa, Singapore, Malaysia, and over 40 more countries. It has a daily trading platform of over $18.62m daily. The platform supports cryptocurrencies like Bitcoin, Ethereum, Bitcoin Cash, USDC, XRP, Liverpool, Chainlink, and Uniswap. The platform was the first exchange to permit customers to buy and sell crypto at zero transaction fees. Read more about Luno. Quidax. Quidax is a Bitcoin and Altcoin, an African-focused cryptocurrency exchange platform based in Nigeria. It was founded by Buchi Okoro in 2018. The exchange platform enables users to send, receive, store, buy, and sell digital currencies such as Bitcoin (BTC), Ethereum (ETH), Tether (USDT), Cardano (ADA), Litecoin (LTC), and others that can be traded against the Naira or USDT The platform enables fintech companies to provide crypto-related services to their customers. The platform's interface is easy to navigate, and customers are not charged transaction fees. Quidax provides a native QDX token that can be used for transactions, transaction fees, on the Quidax Exchange, in-store payments, and several other services. Read more about Quidax. Yellow Card. Yellow Card is a secure and convenient cryptocurrency exchange launched in 2018. It allows its users to buy Bitcoin, Ethereum, and Tether(USDT) using bank transfers, mobile money, and cash. The platform is available in over 13 African countries, supporting local currencies like the South African Rand, Nigerian Naira, Botswana Pula, Ghanaian Cedi, Kenyan Shilling, Tanzanian Shilling, Ugandan Shilling, and so on. The yellow card is built on a mission, "Financial Inclusion For All.” The platform averages a daily trading volume of $165m. Read more about Yellow Card. BuyCoins. BuyCoins is a virtual marketplace where users can buy and sell cryptocurrencies and other digital assets. It launched as a crypto exchange platform in 2017 as Bitkoin Africa but later rebranded to BuyCoins Africa in 2018 and has since evolved into a digital marketplace. A unique proposition of this wallet is that not only do users trade digital currencies, but they can also buy and sell US shares and stocks such as Tesla, Amazon, etc. The platform is also known for its stablecoin called NGNT. The NGNT coin is pegged to the value of the Nigerian Naira and is backed by the world's leading digital dollar stablecoin - USDC. Buycoins offers its users a way to gain interest in their coins after storing them for a particular period of time using its Coinlock feature. Read more about BuyCoins. KuCoin. KuCoin is a secure crypto marketplace that makes it easy to buy, sell, and store digital assets such as cryptocurrencies and NFTs. It boasts one of the world's most sophisticated security technology and maintenance teams. KuCoin allows users to improve their practical experience through a variety of trading and financial products for as low as $1. Its headquarters is located in Seychelles, KuCoin offers basic functions such as buying and selling crypto, as well as deposit and withdrawal. The platform has an average of $5.3m USD daily transaction volume. KuCoin has no restrictions on the exchange between fiat and crypto and supports over 50 fiat currencies through our P2P market and credit/debit channel. Part of KuCoin’s marketplace is the Windvane platform. The Windvane platform allows KuCoin’s users to sell, swap, and mint NFTs. KuCoin also provides crypto lending, pooling, and cloud mining services. Read more about KuCoin. Huobi. Huobi, which was founded in 2013, is a world-leading Bitcoin & Ethereum trading platform that provides secure and convenient trading services for hundreds of digital assets. The Seychelles-based platform averages $3.55b daily transaction volume per day. Users can also give educational materials on the platform to gain knowledge in cryptocurrency, blockchain, DeFi, metaverse, etc. Liquidity is ranked No.1 around the world. Huobi has zero security incidents and has twenty thousand BTC security reserve funds. Read more about Huobi. The list above is my little list of crypto wallets in the African space, but rest assured that there are tons of other crypto wallets in the African space. Disclaimer: This list should not be considered investment advice. Also published . here

QUID

quid.com

201-500 employees

Since 2010

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QUID

quid.com

201-500 employees

Since 2010

COMPANY RANKING

QUID

EVERGREEN INDEX #8838

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