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Jiji is a leading classified platform in the largest markets of Pan-African countries with a combined population of 450+ mn expected by 2025. It provides buyers and sellers with an avenue to meet and exchange goods and services.

Jiji strives to establish itself as a long-term investor in Africa. We're excited by the exceptional opportunities this young and dynamic continent has to offer.

Our goal is to enter the TOP-5 classifieds in the world by traffic by 2025.

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How To Register, Sell And Buy On Jiji Right in this article, I will be guiding you on how to perform the major activities on jiji such as account free registration, selling and buying on jiji. We are going to be looking at jiji as …

How To Register, Sell And Buy On Jiji

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JiJi is a 1 year old female Cross-Breed. She has travelled from N.Macedonia for a new and safe life. She was hit by a car at just six months, she suffered from a fractured pelvis. This is now healed but there are signs of historic injury in her gait. JiJi ...

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ChatGPT in Classrooms: Opportunities, Challenges, and Ethical Considerations

Earlier this year, investors warned startups to "prepare for the worst." Due to the global financial crisis, the looming recession, and the war in Ukraine, no one expected to see large rounds. And yet, startups continue to innovate and even manage to support their Ukrainian friends, who found themselves in the worst of the crisis. With support from the international community, Ukrainian-founded startups keep raising money, keep winning grants, and bring their ideas to all corners of the world. Among Ukraine supporters, there’s New York-based firm . It launched a $50 million fund to specifically support startups founded in Ukraine. Amazon Web Services has recently provided 52 Ukrainian startups with nearly $1 million worth of credits for using its cloud-computing services. More than 30 Ukraine-founded startups have received up to $75,000 from Google’s $5 million fund created in March to support Ukraine. ffVC The war was supposed to sever ties between Ukrainian entrepreneurs and the international startup community — many Ukrainians cannot even leave the country because of martial law. However, Ukrainian allies took care of this: they provide online consultations for Ukrainian techies and give free tickets to tech conferences to those who can travel abroad. Thanks to all this, dozens of startups with Ukrainian founders managed to raise funding and continue working on their Next Big Thing. Here’s my pick of 10 of them. Unstoppable Domains — $65 million Blockchain startup raised $65 million in a Series A round in July. The startup was valued at $1 billion. This round brought its external funding to a total of $72 million. Unstoppable Domains In total, 17 investors participated in the round led by Pantera Capital. Among them, there are Polygon, Rainfall Capital, Mayfield, Alchemy Ventures, and OKG Investments. Unstoppable Domains allows its users to register a crypto domain name on the Ethereum blockchain and turn them into NFTs. This gives the owners broader control and ownership and allows them to shorten their long crypto-wallet addresses. The startup has already registered over 2.5 million domains, generating over $80 million in sales. The most popular domains include .crypto, .nft, .wallet, and .bitcoin. The company employs 80 people and has offices in San Francisco and Kyiv. It was founded in 2018 by Matthew Gould, Braden Pezeshki, Bradley Kam, and Ukrainian . Bogdan Gusiev AirSlate — $51.5 million Workflow automation startup raised $51.5 million in a Series B round in June. The startup was valued at $1.25 billion. This round brought its external funding to a total of $181.5 million. AirSlate Chicago-based G Squared led the round with participation from UiPath Ventures. AirSlate provides its users with no-code tools for automating business processes, including PDF editing and e-signature workflow. The startup boasts more than 900,000 customers and 100 million users worldwide. Founded in 2008 and formerly named PDFfiller, AirSlate employs over 900 people. It has offices in Boston, Wroclaw and Kyiv. The company used to have an office in Saint Petersburg but, in July, it would be shutting it down. announced Its founders are and Ukrainian-born . Borya Shakhnovich Vadim Yasinovsky Preply — $50 million Tutoring platform raised $50 million in a Series C round in July, not disclosing its valuation. This round brought its external funding to $101.1 million. Preply In total, eight investors participated in the round led by Owl Ventures, which focuses on education. Among other investors, there are Educapital, Evli Growth Partners, Hoxton Ventures, and Diligent Capital. Preply develops a platform that allows tutors to teach 50 languages, including English, German, Arabic, French, Polish, and Ukrainian. It uses AI to find teachers depending on students’ schedules and learning goals. The startup says it has from 190 countries. 32,000 tutors Preply was founded in 2012 by three Ukrainian entrepreneurs — , , and . The startup employs over 400 people and has offices in Barcelona, New York and Kyiv. Dmytro Voloshyn Kirill Bigai Sergey Lukyanov Spin Technology — $16 million Data protection software developer raised $16 million in a Series A round in August, bringing its funding to $18 million. The company was valued at $55 million. Spin Technology The money came from Santa Barbara Venture Partners, Blu Venture Investors, and Blueprint Equity (lead investor). Spin Technology is a SaaS data protection company defending enterprises against ransomware, human error, and other cloud threats. Its software acts like an antivirus program, detecting ransomware across platforms like Google Workspace and Microsoft Office 365. It can automatically assess risks and make backups if needed. The startup caters to enterprise clients. It has partnered with over 1,600 companies, including Outbrain, Autodoc, Miller and Toyota. Founded in 2016 by Odesa-born , the company now has four offices: in Los Angeles, Palo Alto, Odesa, and Kyiv. It employs about 80 people. Dmitry Dontov ATLANT 3D — $15 million Nanotechnology startup raised $15 million in a Series A round in September, bringing its total funding to $17.2 million. It didn't disclose the valuation at which it closed the round. ATLANT 3D British VC West Hill Capital led the round, but other investors haven’t been disclosed. ATLANT 3D is a deep-tech startup that works on a tech that enables the manufacturing of materials and electronics at the atom level. “(We are) revolutionizing electronics of tomorrow atom by atom,” reads its motto. According to its website, it works — among other organizations — with STMicroelectronics, Sony, and NASA. Although the startup is based in Copenhagen, it was co-founded in 2018 by Ukrainian , who now leads the company as CEO. The ATLANT 3D’s website lists 10 employees, half of them PhDs Maksym Plakhotnyuk Fintech Farm — $7.4 million In the company’s first Seed round in January, raised $7.4 million, not disclosing its valuation. Fintech Farm Flyer One Ventures and Solid led the round; four other investors are AVentures Capital, TA Ventures, u.ventures, and Jiji. Dmytro Dubilet, the co-founder of U.K.-based Fintech Farm, was one of the founders of Ukraine’s neobank Monobank, which now has about 4.5 million users. Now, Fintech Farm is going to use the raised money to launch a similar product for the Nigerian market. Its name is . Fibo The company was co-founded by , , and serial entrepreneur . The company employs around 200 people and has two offices — in Dnipro and Kyiv. Alexander Vityaz Mykola Bezkrovnyy Dmytro Dubilet Salto X — €5.2 million Web3 startup raised €5.2 million in a Seed round in July, not disclosing its valuation. Salto X The round was led by Copenhagen-based venture fund byFounders and Frankfurt-based Blockwall. Box Group, crypto fintech 3Commas, and angels such as Liu Jiang and Alex Gluchkowski also participated in it (in total, six investors). Salto X uses blockchain to develop a remote-friendly alternative to stock options for European companies. Contributors receive NFT certificates for the cliff period. According to the startup, “it gives transparent access to equity compensation to contributors regardless of geography.” The startup was founded in 2021 by Estonians and , Latvian , and Ukrainian . Registered in Tallinn, the company doesn’t have a physical presence, with its 16 employees working remotely from Latvia, Estonia, and Ukraine. Ragnar Sass Elise Sass Krists Avots Tanya Chaikovska PromoRepublic — €2 million SaaS startup raised €2 million in a Pre-Series A round in June, not disclosing its valuation. This round brought its external funding to $3.9 million. PromoRepublic Ukraine-based fund NetSolid Investments put €200,000 into the startup in this round. The names of other investors have not been disclosed. The Ukraine-Finnish-U.S. startup develops a social media marketing platform, catering to enterprises, marketing agencies, and SMBs. According to its website, the company has over 50,000 clients, including Curves International, doTERRA, Expedia Group Mannatech, and EZCORP. Founded back in 2014 by trio , , and , the company now employs over 50 people (contractors included). It has offices in Helsinki, London, New York, and Kyiv. Max Pecherskyi Mike Baranovskyi Val Grabko Cargofy — $2 million Freight-tech startup raised $2 million in a Seed round in February. The startup did not disclose its valuation, but according to , the company was valued at $10-15 million. Cargofy Forbes JKR Investment Group led the round, which also saw participation from Flyer One Ventures, Fair East Labs, and two angel investors — Murat Abdrakhmanov and Hannu Turunen. Dubbed Uber for truck drivers, Cargofy develops an AI-driven digital logistics platform that helps link up truck owners with people seeking to ship goods in Eastern Europe, India, and the U.S. Over the last three years up to February, the company had organized about 46,000 deliveries. Founded in 2014 by , , and , Cargofy now employs about 60 people. It’s based in Cedar Rapids in eastern Iowa. Overall, it has three offices in two countries, but the company’s website doesn’t specify where. Dimitri Alexiou Alex Kovalchuk Stakh Vozniak Choice QR — €1.5 million FoodTech startup raised €1.5 million in a Seed round in July, bringing its external funding to €2.4 million. The company did not disclose its valuation in the latest round. Choice QR Five investors participated in the round: Seedcamp, Reflex Capital, Purple Ventures, J&T Ventures, and Presto Ventures (lead investor). Choice QR develops a restaurant payment app. Using it, hospitality businesses can create a website for pick up and delivery, as well as digital menus, which allow their guests to order food and pay through QR codes. Over 7,000 businesses use the service, according to the Choice website. The company was founded in Kyiv by two Ukrainians — and — in 2021. Now it is based in the Czech Republic but still has an office in Ukraine. Alex Ilyash Vladimir Olyanitsky Originally published . here

Startups With Ukrainian Roots Raise $200+ Million in 2022

Startups con raíces ucranianas recaudan más de $200 millones en 2022

Les startups aux racines ukrainiennes lèvent plus de 200 millions de dollars en 2022

यूक्रेनी जड़ों के साथ स्टार्टअप ने 2022 में $200+ मिलियन जुटाए

ウクライナにルーツを持つスタートアップが 2022 年に 2 億ドル以上を調達

Startups com raízes ucranianas arrecadam mais de US$ 200 milhões em 2022

Các công ty khởi nghiệp có nguồn gốc từ Ukraine Huy động được 200 USD + Triệu vào năm 2022

乌克兰血统的初创公司在 2022 年筹集 200 多万美元

My name is Oleksandr Yaroshenko. I am Ukrainian, Kyiv-borne, and I work on investor relations and strategy development for tech product startups with Ukrainian roots. Like many of my Ukrainian colleagues, I woke up on February 24, 2022, not to the alarm clock but to the sounds of bombing and air raid sirens. Russia had started a full-fledged war against my country, destroying our civilian districts, entire villages, and children and maternity hospitals. Though my life and the lives of all Ukrainians have drastically changed, we all stay strong and fight for our country on various fronts — from military to informational and cybersecurity. And what is more, the majority of Ukrainian tech businesses keep on with their work. In this article — I’m writing it between my apartment and a bomb shelter in Kyiv — . I will tell you how my life and the lives of my Ukrainian colleagues have changed and how we manage to carry on business during these turbulent times The day that changed everything For the last three years, I have been working at the largest startup ecosystem of Ukraine called Genesis. And for about a year, as a Head of Investor Relations, I have been fully focused on , a global EdTech startup with Ukrainian roots, with the R&D department in Kyiv, and the recently opened office in London. 2021 was remarkably fruitful for our startup — Headway had x2 year-over-year growth, the number of paid subscribers of our app has increased three times, and our team grew more than twofold. We have been on a steep growth trajectory, starting a non-deal roadshow for investors and entering new markets while maintaining profitability. Headway On February 24, I was supposed to go to work, finalize our usual update on the financial model, and transfer the data into the pitch deck. After work, I planned to get a ride on the bicycle to prepare for the IronMan 70.3 this summer. But that day went totally different. Many people were talking about how Russia was ramping up its forces near the Ukrainian borders over the past several weeks, but nobody believed they would start bombing Ukraine. Therefore, the beginning of a full-scale invasion caught us all by surprise. We kept on working and joined the fight Headway developed a contingency plan prior to the Russian invasion, so the company helped our team with relocation to safer places. As for me, I stayed in Kyiv, a decision I made primarily because of my parents, who were against leaving their house in these uncertain times. My family bond is strong, and I realized that I wouldn’t be able to focus on work if I left them behind. The feeling of safety is quite relative in Kyiv, but I believe people can adapt and overcome nearly everything. Just take a look at how my house is constantly transforming into a light fortress. On the first day of the war, I managed to grab my work set up, including my laptop, two screens, and the mount for them. So now I can keep working comfortably, except that work is accompanied by the "soundtrack" of air raid sirens and explosions outside my window. My colleagues were lucky to escape the great danger just in time, especially those who lived in small villages in the suburbs of Kyiv, where thousands of young families have settled in recent years. The Russians demolished nearly all the houses there, left people without food, water, heat, and connection, and killed some civilians on the streets. Fortunately, none of our team was there, and nobody was hurt. Finally safe, our Ukrainian unit was divided and settled in different parts in the west of Ukraine and abroad. Some people were relocated in organized groups, some went to their relatives in locations without active hostilities, and others went abroad. We are now physically apart but act as one. As soon as the company secured our safety, we all teamed up to resume our work to ensure business continuity and development. Readily-available emergency plans and established operations enabled Headway to conduct business as usual and fulfill previously set KPIs. From the first days, we created a collection of content about the war in a new format, which we were to launch later in 2022. This new type of content on hot topics generated a boost in users’ engagement in our app. So, it not only spreads the truth about current events but also has a positive impact on business and product metrics. Also, as a business, we have donated UAH 1M to support Ukraine. Ukrainian tech companies don’t stop The startup scene of Ukraine is underrepresented in the global arena, but Ukrainian IT talents are already well known and hunted by the world’s dominant tech players like Meta and Google. The IT industry is the third-largest category of foreign exchange earnings in Ukraine and has generated more than $6 billion in export revenue in 2021. There are some success stories, like the unicorn with Ukrainian roots — , the viral face-wrap app — , which has been famously tweeted by Elon Musk and Hollywood superstars, the security hardware company — formerly known as purchased by Amazon for $1 billion, — a leading pan-African classified that conquered markets from OLX and my personal source of pride — an EdTech company providing fun and easy educational content suiting busy adults’ lives. Grammarly Reface Ring Jiji Headway Many of these startups became global IT companies with teams distributed across several countries but leveraging the access to tech talent and maintaining development R&D centers in cost-efficient cities of Ukraine. Having an R&D center in Ukraine has already become an “efficiency rule” for global tech companies. with headquarters in Tel Aviv, with their head office in San Francisco, from Montreuil, and from Paris are among them. Wix JustAnswer Ubisoft Gameloft CEOs of these companies condemn the war, focusing on the security of their employees over the past few weeks. Many IT businesses in Ukraine established temporary offices in the west of Ukraine or started relocating their employees to EU countries. For example, , a company developing Mac utility apps, ensured its infrastructure, user data, and cloud operation were outside Ukraine, their team was safe, and the business would operate uninterrupted. Ukrainian security systems company is relocating not only its team but the whole factory to continue fulfilling its obligations to customers and partners. Even those companies whose employees stayed in Kyiv continue working, like , turning from a fun face-swapping app into a tool of information resistance. MacPaw Ajax Systems Reface Now all Ukrainian IT companies are among the leading domestic financial guardians of Ukraine. They have already to support the Ukrainian Armed forces and the whole country. donated almost $25M When even the war does not interfere with business results, the situation shows that Ukrainian businesses focused on international markets can be lucrative in terms of risk/return investment and, after the breakout of the war, can be considered even with the ESG-angle. I believe that in the upcoming weeks and months, more and more investors will try to approach our “tech gems” — Ukrainian startups. Moreover, a range of world-known investors has already expressed their stance supporting Ukraine. The world stands united against the Russian aggression Ukraine has clearly won the information side of the war. The world is watching a real representation of the ongoing events in my home state. , this victory became possible primarily due to the vast presence of reporters from the leading world media who have been in Ukrainian cities starting from late 2021, allowing the reality to be truthfully depicted in news outlets free from “pro-Russian narratives.” According to Anders Åslund The international community reaffirms its support for democratic values and peace and continues giving . generous donations to humanitarian, military, and infrastructure-rebuilding charity funds supporting Ukraine On the other hand, Russian affiliation is becoming a tremendous reputational risk for businesses. Therefore, more and more companies are cutting their ties with the aggressor state. Leaving Russia is a part of the necessary Environmental, Social and Corporate Governance (ESG) factors compliance and is simply a matter of ethics. Corporations are taking care of ESG, and investors are increasingly applying these non-financial factors as part of their analysis process to identify material risks and growth opportunities. And in this case, I believe that the weights have significantly shifted to the ban of Russian ties sending this country ages back in its development. “As an investor, you have to consider not just the company, but the environment in which they operate” — said , managing partner at research firm Sustainable Market Strategies in Montreal. Felix Boudreault The balance should be restored in terms of investment for the sustainable future of Ukrainian companies. It's not only about donations but also about that can be considered part of the ESG mandate from the perspective of the investment funds. taking a closer look at the Ukrainian gems Ukrainian unity and the world’s support will help Ukraine thrive Though the life of each Ukrainian was separated into “before” and “after” the Russian invasion, the “after” doesn’t mean the devastation, decline, and stagnation of Ukraine. The unity and power of Ukrainians fighting against the enemy will indeed transform into unity and power for rebuilding the country and its rapid and steady development. We’ve already seen such a tendency after the Revolution of Dignity in 2013-2014. It became a robust push for the flourishing of Ukrainian business in various sectors — from tech to hospitality — providing a lucrative risk/return ratio for investments. So, after the war, Ukraine will expectedly experience an even stronger boost for the future development of businesses and the economy. As Ukraine has been brought to the spotlight, funds are being gathered to rebuild its cities and combat the humanitarian catastrophe taking place in a range of Ukrainian cities. , , , , , , and have also joined to support Ukrainian startups and IT specialists. Index Ventures BlackRock Mirova Insight Partners Flyer One Ventures Atmos Ventures s16vc Donations from all over the world are flooding into Ukraine and already total hundreds of millions of dollars, ensuring everything will be restored after the war, and Ukrainian cities will return to everyday life with even more energy to overcome new daily challenges. You can also support Ukraine via the following initiatives National Bank of Ukraine Humanitarian aid to Ukraine: https://bank.gov.ua/en/about/humanitarian-aid-to-ukraine You can find more international initiatives to help Ukraine and our people in tough conditions via this link: https://how-to-help-ukraine-now.super.site

Ukrainian Tech Startups Carry On: How We Are Enduring The Invasion

Las nuevas empresas tecnológicas ucranianas continúan: cómo estamos soportando la invasión

Market Cap: $109,672,007,61724h Vol: $16,543,133,132BTC Dominance: 54.8% From Monday, December 3rd when the evaluation of the cryptocurrency market capitalization was around $135 billion, the market has depreciated in evaluation by approximately 25,4 billion dollars measured to the current levels. The evaluation has gone even lower and was $104B on yesterday’s low. Looking at the global chart you can see that the evaluation is in a clear downtrend has been established and the evaluation is again back to its resistance line. As the evaluation looks like is going to make a lower high a lower low would be expected from here. Zooming out further on the global chart we can see that the evaluation previously broke out from a symmetrical triangle from the downside on December 3rd when this current downtrend has begun. After the evaluation decreased by over 25 billion dollars at one go, a lower leg has started developing which is what was happening every step of the way down, over and over again. Inversely the evaluation of the Bitcoin’s market dominance has been steadily increasing and since the 3rd of December when it was 53.67% it has increased by 1.13% as its currently sitting around 54.8%. Zooming out further onto the Bitcoin’s percentage of total market capitalization you can see that this increase meant a breakout from the triangle on the upside. Now that the evaluation has ended its consolidation and is in an upward trajectory I would expect it to go up to 68% before the bear market ends or potentially even higher than that. Bitcoin’s market dominance evaluation serves as a sentiment indicator as in times of confidence, people tend to risk more and they diversify more over the crypto market with altcoins, but in times of fear uncertainty and doubt, people sell their alts for Bitcoin and ultimately sell their Bitcoin for cash in an attempt to preserve their capital. News From the start of the week, major news headlines have come out with many of them adding to the bearish sentiment and could impact or already have impacted the market negatively. The headlines significance is measured by its impact on the market so among those who are of utmost significance are regarding regulation and formal frameworks for the crypto industry. The most notable news about the overall regulative future outlook for the crypto market and the industry, in general, can be hinted by what has recently been said at the G20 summit in Argentina. Cryptocurrencies have been addressed by the international government and central bank representatives in the declaration on sustainable development for the global economy titled “Building Consensus for Fair and Sustainable Development” among other important issues. The summary of what has been stated there is that more regulation is coming and with it the taxation of the entire space and the overview of the transactional frameworks and exchanges, in the attempt to prevent money laundering and tax avoidance. “We will regulate crypto-assets for anti-money laundering and countering the financing of terrorism in line with FATF standards and we will consider other responses as needed.” On the other hand, G20 counties will develop an international taxation system which views the whole cryptocurrency market as one big IT company, making it possible to tax international transactions and other cross-border payments. As reported by the Japanese news outlet Jiji, under current laws that is impossible so the declaration further states: “We will seek solutions for the international taxation issue accompanying the digitization of the economy and will continue to collaborate”. These measures should be implemented by 2020 but the process has already started since members of the G20 counties are to submit their reports on the crypto taxation system during the next G20 summit scheduled for 2019 in Osaka, Japan. The second by its significance on the market is the adoption and the most important headline from this week from that category would be news regarding SEC delaying the decision for a Bitcoin ETF for late February. It is considered that an ETF would bring some institutional money into the space and help bring some legitimacy to the cryptocurrency trading, thus bringing new investors. This new inflow of capital would potentially spark the end of the bear market which is why this would be considered as a significant event, however, the Securities and Exchange Commission has however postponed the decision and the new deadline is set for 27. February next year. This was done in order for the commission to buy some time and review the rule change proposals to approve the request for an ETF by investment firm VanEck and blockchain company SolidX “The Commission finds it appropriate to designate a longer period within which to issue an order approving or disapproving the proposed rule change so that it has sufficient time to consider this proposed rule change.” This means that the adoption of the cryptocurrencies among trader at least would have to wait and the new inflow of capital isn’t going to be injected any time soon. The other headline regarding the SEC is the cease and desist order they had fined to a cryptocurrency digital asset fund CoinAlpha Advisors LLC for violating the law that “prohibits the sale of securities through interstate commerce or the mails unless a registration statement is in effect.” While we are on lawsuits, the “hash wars” have escalated with a lawsuit from a Florida-based United American Corp. (UnitedCorp) against Bitmain, Bitcoin.com, Roger Ver, and the cryptocurrency exchange Kraken for they have “planned a scheme to take control of the Bitcoin Cash (BCH) network.” “UnitedCorp believes that the defendants colluded to effectively hijack the Bitcoin Cash network after the November 15, 2018 scheduled software update with the intent of centralizing the network — all in violation of the accepted standards and protocols associated with Bitcoin since its inception.” For those who maybe don’t know, this situation escalated after Bitcoin Cash was scheduled for a hard fork which divided into two chains Bitcoin ABC and Bitcoin SV, so the war between two sides for hash power took place in order to take control to be established as the main chain. The situation in the crypto industry resembles the situation with the current state of the cryptocurrency prices — crash. Both miners and companies around blockchain tech are starting to fell the pressure of these low prices and are starting to cut off some of the unnecessary expenses and some quitting altogether. Among those who are quitting altogether are miners. Many news media outlets have reported that miners who are outcompeted and cannot keep up the with the current cost to profit ratios are selling their equipment as the prices of cryptos have fallen below the profitable benchmark for some. Meanwhile, some of the large names from the space are starting to lay off their excess staff like ConsenSys who confirmed 13% layoffs as reported by Coindesk: “Excited as we are about ConsenSys 2.0, our first step in this direction has been a difficult one: we are streamlining several parts of the business including ConsenSys Solutions, spokes, and hub services, leading to a 13% reduction of mesh members.” On the other hand, the blockchain based social media platform Steemit is laying off even 70% of their current staff. Steemit CEO and founder Ned Scott said: “While we were building out our team over the last many months we have been relying on projections of basically a higher bottom for the market and since that’s no longer there, we’ve been forced to lay off more than 70 percent of our organization and begin a restructuring.” Conclusion All of these headlines paint a grim picture for the cryptocurrency market. Tracking the media and analyzing the market sentiment in that way could serve as a good indicator of where we are in the market cycle stages. According to current news analysis, we are currently at the start of the capitulation stage We were in denial until around $6500 for the price of Bitcoin and since the price crashed down by 50% there isn’t any doubt that the market is heading for a further decline with the despair stage not even in sight. For those who are prepared and can objectively look at the market, this serves as a good opportunity to make some profits while the market participants are acting on fear-based decision making.

Cryptocurrency Market Overview [December 2018]

Authors:
(1) Ken Hasselmann, ECAM Brussels School of engineering, Brussels, Belgium and Universite libre de Bruxelles, Brussels, Belgium;
(2) Quentin Lurkin, ECAM Brussels School of engineering, Brussels, Belgium. Table of Links Abstract and 1. Introduction Course design
Course assessment
Conclusion, Software and Data, Acknowledgements, and References 4. Conclusion We have presented our design for an introductory course to AI and software engineering. The course involves group project-based learning and gamification through competition-based learning. The results of the course suggest that both project-based learning and gamification help motivate students and create a better learning experience through peer-based learning and the intrinsic motivation created by friendly competition. We wish to conduct a formal teaching assessment campaign before next year’s edition of the course to confirm what our informal assessment has suggested about the students’ highly positive sentiment towards this course. Software and Data The tournament system software is available online (Lurkin, 2021) (documentation in french). Acknowledgements We thank the reviewers for their careful reading of our manuscript and their insightful comments and suggestions. We acknowledge the support of ECAM Brussels School of engineering. References Boss, S. and Krauss, J. Reinventing Project-Based Learning: Your Field Guide to Real-World Projects in the Digital Age / Suzie Boss, Jane Krauss. International Society for Technology in Education, Eugene, Or, 1st ed edition, 2007. Burguillo, J. C. Using game theory and Competitionbased Learning to stimulate student motivation and performance. Computers & Education, 55(2):566–575, 2010. doi: 10.1016/j.compedu.2010.02.018. Ebner, M. and Holzinger, A. Successful implementation of user-centered game based learning in higher education: An example from civil engineering. Computers & Education, 49(3):873–890, 2007. doi: 10.1016/j.compedu. 2005.11.026. Ho, J. C.-S., Hung, Y.-S., and Kwan, L. Y.-Y. The impact of peer competition and collaboration on gamified learning performance in educational settings: A Meta-analytical study. Education and Information Technologies, 27(3): 3833–3866, 2022. doi: 10.1007/s10639-021-10770-2. Jawad, H. M. and Tout, S. Gamifying Computer Science Education for Z Generation. Information, 12(11):453, 2021. doi: 10.3390/info12110453. Johnson, D., Murayama, G., R.T., J., Nelson, D., and Skon, L. Effects of cooperative, competitive, and individualistic goal structures on achievement: A metaanalysis. Psychological Bulletin, 89:47–62, 1981. doi: 10.1037/0033-2909.89.1.47. Jung, S. and Shim, K. K. A case study as a class project of master-slave line tracer competition for undergraduate student education. In ICCAS 2010, pp. 2485–2489, Gyeonggi-do, 2010. IEEE. doi: 10.1109/ICCAS.2010. 5670275. Lawrence, R. Teaching Data Structures Using Competitive Games. IEEE Transactions on Education, 47(4):459–466, 2004. doi: 10.1109/TE.2004.825053. Lurkin, Q. Tournament software, PI2CChampionshipRunner, 2021. URL https://github.com/qlurkin/ PI2CChampionshipRunner. Ribeiro, P., Simoes, H., and Ferreira, M. Teaching Artificial ˜ Intelligence and Logic Programming in a Competitive Environment. Informatics in Education, 8(1):85–100, 2009. doi: 10.15388/infedu.2009.06. Russell, S. J. and Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Series in Artificial Intelligence. Pearson, Hoboken, fourth edition, 2021. Saeed, S. and Zyngier, D. How Motivation Influences Student Engagement: A Qualitative Case Study. Journal of Education and Learning, 1(2):p252, 2012. doi: 10.5539/jel.v1n2p252. Seaborn, K. and Fels, D. I. Gamification in theory and action: A survey. International Journal of Human-Computer Studies, 74:14–31, 2015. doi: 10.1016/j.ijhcs. 2014.09.006. Shahid, M., Wajid, A., Haq, K. U., Saleem, I., and Shujja, A. H. A Review of Gamification for Learning Programming Fundamental. In 2019 International Conference on Innovative Computing (ICIC), pp. 1–8, Lahore, Pakistan, 2019. IEEE. doi: 10.1109/ICIC48496.2019.8966685. Wollowski, M., Selkowitz, R., Brown, L., Goel, A., Luger, G., Marshall, J., Neel, A., Neller, T., and Norvig, P. A Survey of Current Practice and Teaching of AI. Proceedings of the AAAI Conference on Artificial Intelligence, 30 (1), 2016. doi: 10.1609/aaai.v30i1.9857. Ye, R., Sun, F., and Li, J. Artificial Intelligence in Education: Origin, Development and Rise. In Liu, X.-J., Nie, Z., Yu, J., Xie, F., and Song, R. (eds.), Intelligent Robotics and Applications, Lecture Notes in Computer Science, pp. 545–553, Cham, 2021. Springer International Publishing. doi: 10.1007/978-3-030-89092-6 49. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Ken Hasselmann, ECAM Brussels School of engineering, Brussels, Belgium and Universite libre de Bruxelles, Brussels, Belgium; (2) Quentin Lurkin, ECAM Brussels School of engineering, Brussels, Belgium. Authors: Authors: (1) Ken Hasselmann, ECAM Brussels School of engineering, Brussels, Belgium and Universite libre de Bruxelles, Brussels, Belgium; (2) Quentin Lurkin, ECAM Brussels School of engineering, Brussels, Belgium. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction Course design Course assessment Conclusion, Software and Data, Acknowledgements, and References Course design Course design Course assessment Course assessment Conclusion, Software and Data, Acknowledgements, and References Conclusion, Software and Data, Acknowledgements, and References 4. Conclusion We have presented our design for an introductory course to AI and software engineering. The course involves group project-based learning and gamification through competition-based learning. The results of the course suggest that both project-based learning and gamification help motivate students and create a better learning experience through peer-based learning and the intrinsic motivation created by friendly competition. We wish to conduct a formal teaching assessment campaign before next year’s edition of the course to confirm what our informal assessment has suggested about the students’ highly positive sentiment towards this course. Software and Data The tournament system software is available online (Lurkin, 2021) (documentation in french). Acknowledgements We thank the reviewers for their careful reading of our manuscript and their insightful comments and suggestions. We acknowledge the support of ECAM Brussels School of engineering. References Boss, S. and Krauss, J. Reinventing Project-Based Learning: Your Field Guide to Real-World Projects in the Digital Age / Suzie Boss, Jane Krauss. International Society for Technology in Education, Eugene, Or, 1st ed edition, 2007. Burguillo, J. C. Using game theory and Competitionbased Learning to stimulate student motivation and performance. Computers & Education, 55(2):566–575, 2010. doi: 10.1016/j.compedu.2010.02.018. Ebner, M. and Holzinger, A. Successful implementation of user-centered game based learning in higher education: An example from civil engineering. Computers & Education, 49(3):873–890, 2007. doi: 10.1016/j.compedu. 2005.11.026. Ho, J. C.-S., Hung, Y.-S., and Kwan, L. Y.-Y. The impact of peer competition and collaboration on gamified learning performance in educational settings: A Meta-analytical study. Education and Information Technologies, 27(3): 3833–3866, 2022. doi: 10.1007/s10639-021-10770-2. Jawad, H. M. and Tout, S. Gamifying Computer Science Education for Z Generation. Information, 12(11):453, 2021. doi: 10.3390/info12110453. Johnson, D., Murayama, G., R.T., J., Nelson, D., and Skon, L. Effects of cooperative, competitive, and individualistic goal structures on achievement: A metaanalysis. Psychological Bulletin, 89:47–62, 1981. doi: 10.1037/0033-2909.89.1.47. Jung, S. and Shim, K. K. A case study as a class project of master-slave line tracer competition for undergraduate student education. In ICCAS 2010, pp. 2485–2489, Gyeonggi-do, 2010. IEEE. doi: 10.1109/ICCAS.2010. 5670275. Lawrence, R. Teaching Data Structures Using Competitive Games. IEEE Transactions on Education, 47(4):459–466, 2004. doi: 10.1109/TE.2004.825053. Lurkin, Q. Tournament software, PI2CChampionshipRunner, 2021. URL https://github.com/qlurkin/ PI2CChampionshipRunner. Ribeiro, P., Simoes, H., and Ferreira, M. Teaching Artificial ˜ Intelligence and Logic Programming in a Competitive Environment. Informatics in Education, 8(1):85–100, 2009. doi: 10.15388/infedu.2009.06. Russell, S. J. and Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Series in Artificial Intelligence. Pearson, Hoboken, fourth edition, 2021. Saeed, S. and Zyngier, D. How Motivation Influences Student Engagement: A Qualitative Case Study. Journal of Education and Learning, 1(2):p252, 2012. doi: 10.5539/jel.v1n2p252. Seaborn, K. and Fels, D. I. Gamification in theory and action: A survey. International Journal of Human-Computer Studies, 74:14–31, 2015. doi: 10.1016/j.ijhcs. 2014.09.006. Shahid, M., Wajid, A., Haq, K. U., Saleem, I., and Shujja, A. H. A Review of Gamification for Learning Programming Fundamental. In 2019 International Conference on Innovative Computing (ICIC), pp. 1–8, Lahore, Pakistan, 2019. IEEE. doi: 10.1109/ICIC48496.2019.8966685. Wollowski, M., Selkowitz, R., Brown, L., Goel, A., Luger, G., Marshall, J., Neel, A., Neller, T., and Norvig, P. A Survey of Current Practice and Teaching of AI. Proceedings of the AAAI Conference on Artificial Intelligence, 30 (1), 2016. doi: 10.1609/aaai.v30i1.9857. Ye, R., Sun, F., and Li, J. Artificial Intelligence in Education: Origin, Development and Rise. In Liu, X.-J., Nie, Z., Yu, J., Xie, F., and Song, R. (eds.), Intelligent Robotics and Applications, Lecture Notes in Computer Science, pp. 545–553, Cham, 2021. Springer International Publishing. doi: 10.1007/978-3-030-89092-6 49. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Gamified Learning With An AI Board Game Tournament: Conclusion, Software and Data

Authors:
(1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;
(4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction ANN-Based Scene Classification Methodology Experiments Conclusion and Future Research, Acknowledgments and References 5 Conclusion and Future Research In this paper was provided an insight into the possibility of using artificial neural networks for scene recognition location from a video sequence with a small set of repeated shooting locations (such as in television series) was provided. Our idea was to select more than one frame from each scene and classify the scene using that sequence of frames. We used a pretrained VGG19 network without two last layers. This results were used as an input to the trainable part our neural network architecture. We have designed six neural network models with different layer types. We have investigated different neural network layers to combine video frames, in particular average-pooling, max-pooling, product, flatten, LSTM, and bidirectional LSTM layers. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. The model with max-pooling layer was not successful, its accuracy was the lowest of all models. The models with a flatten or product layer were very unstable, their standard deviation was very large. The most stable among all models was the one with an average-pooling layer. The models with unidirectional LSTM and bidirectional LSTM had similar standard deviation of the accuracy. The model with a bidirectional LSTM had the highest accuracy among all considered models. In our opinion, this is because its internal memory cells preserve information in both directions. Those results shows, that models with internal memory are able to classify with a higher accuracy than models without internal memory. Our method may have limitations due to the chosen pretrained ANN and the low dimension of some neural layer parts. In future research, it is desirable to achieve higher accuracy in scene location recognition. This task may also need modifying model parameters or using other architectures. It also may need other pretrained models or combining several pretrained models. It is also desirable that, if the ANN detects an unknown scene, it will remember it and next time it will recognize a scene from the same location properly. Acknowledgments The research reported in this paper has been supported by the Czech Science Foundation (GACR) grant 18-18080S. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Computational resources were provided by the ELIXIRCZ project (LM2018131), part of the international ELIXIR infrastructure. References [1] Zhong, W., Kjellström, H.: Movie scene recognition with Convolutional Neural Networks. https://www.diva-portal.org/smash/get/diva2 :859486/FULLTEXT01.pdf KTH ROYAL INSTITUTE OF TECHNOLOGY (2015) 5–36 [2] Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/pdf/1409.1556v6.pdf Visual Geometry Group, Department of Engineering Science, University of Oxford (2015) [3] Russakovsky O., Deng J., Hao S., Krause J., Satheesh S., Ma Sean, Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (2015), pp. 211–252 [4] Li-jia L., Hao S., Fei-fei L., Xing E.: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. https://cs.stanford.edu/groups/vision/pdf/ LiSuXingFeiFeiNIPS2010.pdf NIPS (2010) [5] Felix A. Gers, Schmidhuber J., and Cummins F., Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN. ENNS (1999), pp. 850-–855 [6] Benavoli A., Corani G., Mangili F.: Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research 17 (2016), pp. 1–10 [7] García S., Herrera F.: An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons Journal of Machine Learning Research 9 (2008), pp. 2677–2694 [8] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012) [9] Kaiming H., Xiangyu Z., Shaoqing R., Jian S.: Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [10] Sudha V., Ganeshbabu T. R.: A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning. http://www.techscience.com/cmc/v66n1/40483 Computers, Materials & Continua (2021), pp. 827–842 [11] Zhou B., Lapedriza A., Khosla A., Oliva A., Torralba A.: Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), pp. 1452–1464 This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Authors: Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction Abstract and Introduction ANN-Based Scene Classification ANN-Based Scene Classification Methodology Methodology Experiments Experiments Conclusion and Future Research, Acknowledgments and References Conclusion and Future Research, Acknowledgments and References 5 Conclusion and Future Research In this paper was provided an insight into the possibility of using artificial neural networks for scene recognition location from a video sequence with a small set of repeated shooting locations (such as in television series) was provided. Our idea was to select more than one frame from each scene and classify the scene using that sequence of frames. We used a pretrained VGG19 network without two last layers. This results were used as an input to the trainable part our neural network architecture. We have designed six neural network models with different layer types. We have investigated different neural network layers to combine video frames, in particular average-pooling, max-pooling, product, flatten, LSTM, and bidirectional LSTM layers. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. The model with max-pooling layer was not successful, its accuracy was the lowest of all models. The models with a flatten or product layer were very unstable, their standard deviation was very large. The most stable among all models was the one with an average-pooling layer. The models with unidirectional LSTM and bidirectional LSTM had similar standard deviation of the accuracy. The model with a bidirectional LSTM had the highest accuracy among all considered models. In our opinion, this is because its internal memory cells preserve information in both directions. Those results shows, that models with internal memory are able to classify with a higher accuracy than models without internal memory. Our method may have limitations due to the chosen pretrained ANN and the low dimension of some neural layer parts. In future research, it is desirable to achieve higher accuracy in scene location recognition. This task may also need modifying model parameters or using other architectures. It also may need other pretrained models or combining several pretrained models. It is also desirable that, if the ANN detects an unknown scene, it will remember it and next time it will recognize a scene from the same location properly. Acknowledgments The research reported in this paper has been supported by the Czech Science Foundation (GACR) grant 18-18080S. Computational resources were supplied by the project "e-Infrastruktura CZ" (e-INFRA LM2018140) provided within the program Projects of Large Research, Development and Innovations Infrastructures. Computational resources were provided by the ELIXIRCZ project (LM2018131), part of the international ELIXIR infrastructure. References [1] Zhong, W., Kjellström, H.: Movie scene recognition with Convolutional Neural Networks. https://www.diva-portal.org/smash/get/diva2 :859486/FULLTEXT01.pdf KTH ROYAL INSTITUTE OF TECHNOLOGY (2015) 5–36 [2] Simonyan, K., Zisserman, A.: Very Deep Convolutional Networks for Large-Scale Image Recognition. https://arxiv.org/pdf/1409.1556v6.pdf Visual Geometry Group, Department of Engineering Science, University of Oxford (2015) [3] Russakovsky O., Deng J., Hao S., Krause J., Satheesh S., Ma Sean, Huang Z., Karpathy A., Khosla A., Bernstein M., Berg A. C., Fei-Fei L.: ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision 115 (2015), pp. 211–252 [4] Li-jia L., Hao S., Fei-fei L., Xing E.: A High-Level Image Representation for Scene Classification & Semantic Feature Sparsification. https://cs.stanford.edu/groups/vision/pdf/ LiSuXingFeiFeiNIPS2010.pdf NIPS (2010) [5] Felix A. Gers, Schmidhuber J., and Cummins F., Learning to forget: Continual prediction with LSTM, in Proceedings of ICANN. ENNS (1999), pp. 850-–855 [6] Benavoli A., Corani G., Mangili F.: Should We Really Use Post-Hoc Tests Based on Mean-Ranks? Journal of Machine Learning Research 17 (2016), pp. 1–10 [7] García S., Herrera F.: An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons Journal of Machine Learning Research 9 (2008), pp. 2677–2694 [8] Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks. Springer (2012) [9] Kaiming H., Xiangyu Z., Shaoqing R., Jian S.: Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778 [10] Sudha V., Ganeshbabu T. R.: A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning. http://www.techscience.com/cmc/v66n1/40483 Computers, Materials & Continua (2021), pp. 827–842 [11] Zhou B., Lapedriza A., Khosla A., Oliva A., Torralba A.: Places: A 10 Million Image Database for Scene Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence (2018), pp. 1452–1464 This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Video Scene Location Recognition Using AI: Conclusion and Future Research

Authors:
(1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;
(4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction ANN-Based Scene Classification Methodology Experiments Conclusion and Future Research, Acknowledgments and References 4 Experiments 4.1 Experimental Setup The ANNs for scene location classification were implemented in the libraries Python language using TensorFlow and Keras. Neural network training was accelerated using a NVIDIA GPU. The versions of the employed hardware and software are listed in Table 1. For image preparation, OpenCV and Numpy were used. The routine for preparing frames is a generator. It has lower capacity requirements, because data are loaded just in time when they are needed and memory is released after the data have been used for ANN. All non-image information about inputs (video location, scenes information, etc.) are processed in text format by Pandas. We have 17 independent datasets prepared by ourselves from proprietary videos of the The Big Bang Theory series, thus the datasets can’t be public. Each dataset originates from one episode of the series. Each experiment was trained with one dataset, so results are independent as well. So we can compare behavior of the models with different datasets. Our algorithm to select data in training routine is based on oversampling. It randomly selects target class and from the whole training dataset is randomly select source scene with replacement. This algorithm is applied due to an unbalanced proportion of different target classes. Thanks to this method, all targets are distributed equally and the network does not overfit a highly represented class. 4.2 Results Summary statistics of the predictive accuracy of classification all 17 episode datasets are in Table 3. Every experiment was performed on every dataset at least 7 times. The table is complemented with results for individual episodes, depicted in box plots. The model with a max-pooling layer had the worst results (Figure 12) of all experiments. Its overall mean accuracy was around 10 %. This is only slighty higher than random choice which is 1/12. The model was not able to achieve better accuracy than 20 %. Its results were stable and standard deviation was very low. Slightly better results (Figure 10) had the model with the a flatten layer, it was sometimes able to achieve a high accuracy, but its standard deviation was very high. On the other hand, results for some other episodes were not better than those of the max-pooling model. A better solution is the product model, whose predictive accuracy (Figure 9) was for several episodes higher than 80 %. On the other hand, other episodes had only slightly better results than the flatten model. And it had the highest standard deviation among all considered models. The most stable results (Figure 11) with good accuracy had the model based on average-pooling layer. Its mean accuracy was 32 % and for no episode, the accuracy was substantially different. The model with unidirectional LSTM layer had the second mean accuracy of considered our models (Figure 13). Its internal memory brings advantage in compare over the previous approaches, over 40 %, though also a comparatively high standard deviation. The highest mean accuracy had the model with a bidirectional LSTM layer (Figure 14). It had a similar standard deviation as the one with a unidirectional LSTM, but an accuracy mean nearly 50 %. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Authors: Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction Abstract and Introduction ANN-Based Scene Classification ANN-Based Scene Classification Methodology Methodology Experiments Experiments Conclusion and Future Research, Acknowledgments and References Conclusion and Future Research, Acknowledgments and References 4 Experiments 4.1 Experimental Setup The ANNs for scene location classification were implemented in the libraries Python language using TensorFlow and Keras. Neural network training was accelerated using a NVIDIA GPU. The versions of the employed hardware and software are listed in Table 1. For image preparation, OpenCV and Numpy were used. The routine for preparing frames is a generator. It has lower capacity requirements, because data are loaded just in time when they are needed and memory is released after the data have been used for ANN. All non-image information about inputs (video location, scenes information, etc.) are processed in text format by Pandas. We have 17 independent datasets prepared by ourselves from proprietary videos of the The Big Bang Theory series, thus the datasets can’t be public. Each dataset originates from one episode of the series. Each experiment was trained with one dataset, so results are independent as well. So we can compare behavior of the models with different datasets. Our algorithm to select data in training routine is based on oversampling. It randomly selects target class and from the whole training dataset is randomly select source scene with replacement. This algorithm is applied due to an unbalanced proportion of different target classes. Thanks to this method, all targets are distributed equally and the network does not overfit a highly represented class. 4.2 Results Summary statistics of the predictive accuracy of classification all 17 episode datasets are in Table 3. Every experiment was performed on every dataset at least 7 times. The table is complemented with results for individual episodes, depicted in box plots. The model with a max-pooling layer had the worst results (Figure 12) of all experiments. Its overall mean accuracy was around 10 %. This is only slighty higher than random choice which is 1/12. The model was not able to achieve better accuracy than 20 %. Its results were stable and standard deviation was very low. Slightly better results (Figure 10) had the model with the a flatten layer, it was sometimes able to achieve a high accuracy, but its standard deviation was very high. On the other hand, results for some other episodes were not better than those of the max-pooling model. A better solution is the product model, whose predictive accuracy (Figure 9) was for several episodes higher than 80 %. On the other hand, other episodes had only slightly better results than the flatten model. And it had the highest standard deviation among all considered models. The most stable results (Figure 11) with good accuracy had the model based on average-pooling layer. Its mean accuracy was 32 % and for no episode, the accuracy was substantially different. The model with unidirectional LSTM layer had the second mean accuracy of considered our models (Figure 13). Its internal memory brings advantage in compare over the previous approaches, over 40 %, though also a comparatively high standard deviation. The highest mean accuracy had the model with a bidirectional LSTM layer (Figure 14). It had a similar standard deviation as the one with a unidirectional LSTM, but an accuracy mean nearly 50 %. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Video Scene Location Recognition Using AI: Experiments

Video Scene Location Recognition Using AI: Methodology

Authors:
(1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;
(4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction ANN-Based Scene Classification Methodology Experiments Conclusion and Future Research, Acknowledgments and References 2 ANN-Based Scene Classification The problem of scene classification has been studied for many years. There are many approaches based on neural networks, where an ANN using huge amount of images learned to recognize the type of given scene (for example, a kitchen, a bedroom, etc.). For this case several datasets are available. One example is [11], but it does not specify locations, so this and similar datasets are not usable for our task. However, our classification problem is different. We want to train an ANN able to recognize a particular location (for example “Springfield-EverGreenTerrace-742- floor2-bathroom”), which can me recorded by camera from many angles (typically, some object can be occluded by other objects from some angles). One approach using ANN to solve this task is described in [1], there convolutional networks were used. The difference to our approach is on the one hand in the extraction and usage of video images, on the other hand in types of ANN layers. Another approach is described in [4]. The authors propose a high-level image representation, called Object Bank, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors. Leveraging on the Object Bank representation, good performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Authors: Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction Abstract and Introduction ANN-Based Scene Classification ANN-Based Scene Classification Methodology Methodology Experiments Experiments Conclusion and Future Research, Acknowledgments and References Conclusion and Future Research, Acknowledgments and References 2 ANN-Based Scene Classification The problem of scene classification has been studied for many years. There are many approaches based on neural networks, where an ANN using huge amount of images learned to recognize the type of given scene (for example, a kitchen, a bedroom, etc.). For this case several datasets are available. One example is [11], but it does not specify locations, so this and similar datasets are not usable for our task. However, our classification problem is different. We want to train an ANN able to recognize a particular location (for example “Springfield-EverGreenTerrace-742- floor2-bathroom”), which can me recorded by camera from many angles (typically, some object can be occluded by other objects from some angles). One approach using ANN to solve this task is described in [1], there convolutional networks were used. The difference to our approach is on the one hand in the extraction and usage of video images, on the other hand in types of ANN layers. Another approach is described in [4]. The authors propose a high-level image representation, called Object Bank, where an image is represented as a scale-invariant response map of a large number of pre-trained generic object detectors. Leveraging on the Object Bank representation, good performances on high level visual recognition tasks can be achieved with simple off-the-shelf classifiers such as logistic regression and linear SVM. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Video Scene Location Recognition Using AI: ANN-Based Scene Classification

Authors:
(1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic;
(3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic;
(4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction ANN-Based Scene Classification Methodology Experiments Conclusion and Future Research, Acknowledgments and References Abstract: This paper provides an insight into the possibility of scene recognition from a video sequence with a small set of repeated shooting locations (such as in television series) using artificial neural networks. The basic idea of the presented approach is to select a set of frames from each scene, transform them by a pre-trained singleimage pre-processing convolutional network, and classify the scene location with subsequent layers of the neural network. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. We have investigated different neural network layers to combine individual frames, particularly AveragePooling, MaxPooling, Product, Flatten, LSTM, and Bidirectional LSTM layers. We have observed that only some of the approaches are suitable for the task at hand. 1 Introduction People watching videos are able to recognize where the current scene is located. When watching some film or serial, they are able to recognize that a new scene is on the same place they have already seen. Finally, people are able to understand scenes hierarchy. All this supports human comprehensibility of videos. The role of location identification in scene recognition by humans motivated our research into scene location classification by artificial neural networks (ANNs). A more ambitious goal would be a make system able to remember unknown video locations and using this data identify video scene that is located in that location and mark it with the same label. This paper reports a work in progress in that direction. It describes the employed methodology and presents first experimental results obtained with six kinds of neural networks. The rest of the paper is organized as follows. The next section is about existing approaches to solve this problem. Section 3 is divided to two parts. The first one is about data preparation before their usage in ANNs. The second one is about design of the ANNs in our experiments. Finally, Section 4 – the last section before the conclusion shows our results of experiments with these ANNs. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Authors: Authors: (1) Lukáš Korel, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (2) Petr Pulc, Faculty of Information Technology, Czech Technical University, Prague, Czech Republic; (3) Jirí Tumpach, Faculty of Mathematics and Physics, Charles University, Prague, Czech Republic; (4) Martin Holena, Institute of Computer Science, Academy of Sciences of the Czech Republic, Prague, Czech Republic. Table of Links Abstract and Introduction Abstract and Introduction ANN-Based Scene Classification ANN-Based Scene Classification Methodology Methodology Experiments Experiments Conclusion and Future Research, Acknowledgments and References Conclusion and Future Research, Acknowledgments and References Abstract : This paper provides an insight into the possibility of scene recognition from a video sequence with a small set of repeated shooting locations (such as in television series) using artificial neural networks. The basic idea of the presented approach is to select a set of frames from each scene, transform them by a pre-trained singleimage pre-processing convolutional network, and classify the scene location with subsequent layers of the neural network. The considered networks have been tested and compared on a dataset obtained from The Big Bang Theory television series. We have investigated different neural network layers to combine individual frames, particularly AveragePooling, MaxPooling, Product, Flatten, LSTM, and Bidirectional LSTM layers. We have observed that only some of the approaches are suitable for the task at hand. Abstract 1 Introduction People watching videos are able to recognize where the current scene is located. When watching some film or serial, they are able to recognize that a new scene is on the same place they have already seen. Finally, people are able to understand scenes hierarchy. All this supports human comprehensibility of videos. The role of location identification in scene recognition by humans motivated our research into scene location classification by artificial neural networks (ANNs). A more ambitious goal would be a make system able to remember unknown video locations and using this data identify video scene that is located in that location and mark it with the same label. This paper reports a work in progress in that direction. It describes the employed methodology and presents first experimental results obtained with six kinds of neural networks. The rest of the paper is organized as follows. The next section is about existing approaches to solve this problem. Section 3 is divided to two parts. The first one is about data preparation before their usage in ANNs. The second one is about design of the ANNs in our experiments. Finally, Section 4 – the last section before the conclusion shows our results of experiments with these ANNs. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Video Scene Location Recognition Using AI: Abstract and Introduction

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Bitcoin UTXO’s Model, Powering A Unique Ecosystem

বিটকয়েন UTXO এর মডেল, একটি অনন্য ইকোসিস্টেমকে শক্তিশালী করে

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This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk;
(2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com;
(3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk;
(4) Jener Rasmussen, jener@jener.dk;
(5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com;
(6) Asger G. Weirsøe, asger@weircon.dk;
(7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk;
(8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Definitions Model Checking Tool Overview Evaluation Conclusion and References 6 Conclusion In this paper, we present CGAAL, our model checker of alternating-time temporal logic properties in concurrent games. CGAAL checks such properties by encoding the problem as an extended dependency graph and then computes the satisfaction relation using the distributed local on-the-fly CERTAINZERO algorithm by Dalsgaard et al. [4]. We provide multiple novel search strategies for the algorithm and allow concurrent games to be expressed in our language LCGS. Our experiments show that the local on the-fly algorithm outperforms the global algorithm in the majority of cases. We also find that CGAAL outperforms the state-of-the-art tool PRISM-games by being up to two orders of magnitude faster, especially in models where synchronisations affect the internal state of modules and whenever we are not required to compute the entire fixed point. However, this comparison is unfair, since our test only uses a fraction of PRISM-games feature set. CGAAL is still in early development and much work is needed before it competes with PRISM in terms of capabilities. However, dependency graphs have been used for encoding various model checking problems, and we intend to incorporate these techniques into CGAAL. References [1] Rajeev Alur, Thomas A. Henzinger & Orna Kupferman (2002): Alternating-Time Temporal Logic. J. ACM 49(5), p. 672–713, doi:10.1145/585265.585270. [2] Edmund M. Clarke & E. Allen Emerson (1982): Design and synthesis of synchronization skeletons using branching time temporal logic. In Dexter Kozen, editor: Logics of Programs, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52–71, doi:10.1007/BFb0025774. [3] Edmund M Clarke, Thomas A Henzinger, Helmut Veith, Roderick Bloem et al. (2018): Handbook of model checking. 10, Springer, doi:10.1007/978-3-319-10575-8. [4] Dalsgaard, Andreas E. and Enevoldsen, Søren and Fogh, Peter and Jensen, Lasse S. and Jepsen, Tobias S. and Kaufmann, Isabella and Larsen, Kim G. and Nielsen, Søren M. and Olesen, Mads Chr. and Pastva, Samuel and Srba, Jiˇrí (2017): Extended Dependency Graphs and Efficient Distributed Fixed-Point Computation. In Wil van der Aalst & Eike Best, editors: Application and Theory of Petri Nets and Concurrency, Springer International Publishing, Cham, pp. 139–158, doi:10.1007/978-3-319-57861-3_10. [5] Søren Enevoldsen, Mathias Claus Jensen, Kim Guldstrand Larsen, Anders Mariegaard & Jiˇrí Srba (2020): Verification of Multiplayer Stochastic Games via Abstract Dependency Graphs. LOPSTR2020, doi:10.1007/978-3-030-68446-4_13. [6] Søren Enevoldsen, Kim Guldstrand Larsen & Jirí Srba (2022): Extended abstract dependency graphs. Int. J. Softw. Tools Technol. Transf. 24(1), pp. 49–65, doi:10.1007/s10009-021-00638-8. [7] Glen Jeh & Jennifer Widom (2002): SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada, ACM, pp. 538–543, doi:10.1145/775047.775126. [8] Jonas F. Jensen, Thomas Nielsen, Lars K. Oestergaard & Jiˇrí Srba (2016): TAPAAL and Reachability Analysis of P/T Nets, pp. 307–318. Springer Berlin Heidelberg, Berlin, Heidelberg, doi:10.1007/978-3-662-53401-4_16. [9] Jonas Finnemann Jensen, Kim Guldstrand Larsen, Jiri Srba & Lars Kaerlund Østergaard (2016): Efficient model-checking of weighted CTL with upper-bound constraints. International Journal on Software Tools for Technology Transfer 18(4), pp. 409–426, doi:10.1007/s10009-014-0359-5. [10] Mathias Claus Jensen, Anders Mariegaard & Kim Guldstrand Larsen (2019): Symbolic model checking of weighted PCTL using dependency graphs. NASA Formal Methods Symposium, pp. 298–315, doi:10.1007/978-3-030-20652-9_20. [11] M. Kwiatkowska, G. Norman & D. Parker (2011): PRISM 4.0: Verification of Probabilistic Real-time Systems. In G. Gopalakrishnan & S. Qadeer, editors: Proc. 23rd International Conference on Computer Aided Verification (CAV’11), LNCS 6806, Springer, pp. 585–591, doi:10.1007/978-3-642-22110-1_47. [12] M. Kwiatkowska, G. Norman, D. Parker & G. Santos (2020): PRISM-games 3.0: Stochastic Game Verification with Concurrency, Equilibria and Time. In: Proc. 32nd International Conference on Computer Aided Verification (CAV’20), LNCS 12225, Springer, pp. 475–487, doi:10.1007/978-3-030-53291-8_25. [13] Sangkeun Lee, Minsuk Kahng & Sang goo Lee (2015): Constructing compact and effective graphs for recommender systems via node and edge aggregations. Expert Systems with Applications 42(7), pp. 3396–3409, doi:10.1016/j.eswa.2014.11.062. [14] Xinxin Liu & Scott A. Smolka (1998): Simple Linear-Time Algorithms for Minimal Fixed Points (Extended Abstract). In Kim Guldstrand Larsen, Sven Skyum & Glynn Winskel, editors: Automata, Languages and Programming, 25th International Colloquium, ICALP’98, Aalborg, Denmark, July 13-17, 1998, Proceedings, Lecture Notes in Computer Science 1443, Springer, pp. 53–66, doi:10.1007/BFb0055040. [15] Lawrence Page, Sergey Brin, Rajeev Motwani & Terry Winograd (1998): The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford Digital Library Technologies Project. Available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1768. This paper is available on Arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Falke B. Ø. Carlsen, Department of Computer Science, Aalborg University, Denmark & falkeboc@cs.aau.dk; (2) Lars Bo P. Frydenskov, Department of Computer Science, Aalborg University, Denmark & larsbopark@gmail.com; (3) Nicolaj Ø. Jensen, Department of Computer Science, Aalborg University, Denmark & noje@cs.aau.dk; (4) Jener Rasmussen, jener@jener.dk; (5) Mathias M. Sørensen, mathiasmehlsoerensen@gmail.com; (6) Asger G. Weirsøe, asger@weircon.dk; (7) Mathias C. Jensen, Department of Computer Science, Aalborg University, Denmark & mcje@cs.aau.dk; (8) Kim G. Larsen, Department of Computer Science, Aalborg University, Denmark & kgl@cs.aau.dk. Table of Links Introduction Introduction Definitions Definitions Model Checking Model Checking Tool Overview Tool Overview Evaluation Evaluation Conclusion and References Conclusion and References 6 Conclusion In this paper, we present CGAAL, our model checker of alternating-time temporal logic properties in concurrent games. CGAAL checks such properties by encoding the problem as an extended dependency graph and then computes the satisfaction relation using the distributed local on-the-fly CERTAINZERO algorithm by Dalsgaard et al. [4]. We provide multiple novel search strategies for the algorithm and allow concurrent games to be expressed in our language LCGS. Our experiments show that the local on the-fly algorithm outperforms the global algorithm in the majority of cases. We also find that CGAAL outperforms the state-of-the-art tool PRISM-games by being up to two orders of magnitude faster, especially in models where synchronisations affect the internal state of modules and whenever we are not required to compute the entire fixed point. However, this comparison is unfair, since our test only uses a fraction of PRISM-games feature set. CGAAL is still in early development and much work is needed before it competes with PRISM in terms of capabilities. However, dependency graphs have been used for encoding various model checking problems, and we intend to incorporate these techniques into CGAAL. References [1] Rajeev Alur, Thomas A. Henzinger & Orna Kupferman (2002): Alternating-Time Temporal Logic. J. ACM 49(5), p. 672–713, doi:10.1145/585265.585270. [2] Edmund M. Clarke & E. Allen Emerson (1982): Design and synthesis of synchronization skeletons using branching time temporal logic. In Dexter Kozen, editor: Logics of Programs, Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 52–71, doi:10.1007/BFb0025774. [3] Edmund M Clarke, Thomas A Henzinger, Helmut Veith, Roderick Bloem et al. (2018): Handbook of model checking. 10, Springer, doi:10.1007/978-3-319-10575-8. [4] Dalsgaard, Andreas E. and Enevoldsen, Søren and Fogh, Peter and Jensen, Lasse S. and Jepsen, Tobias S. and Kaufmann, Isabella and Larsen, Kim G. and Nielsen, Søren M. and Olesen, Mads Chr. and Pastva, Samuel and Srba, Jiˇrí (2017): Extended Dependency Graphs and Efficient Distributed Fixed-Point Computation. In Wil van der Aalst & Eike Best, editors: Application and Theory of Petri Nets and Concurrency, Springer International Publishing, Cham, pp. 139–158, doi:10.1007/978-3-319-57861-3_10. [5] Søren Enevoldsen, Mathias Claus Jensen, Kim Guldstrand Larsen, Anders Mariegaard & Jiˇrí Srba (2020): Verification of Multiplayer Stochastic Games via Abstract Dependency Graphs. LOPSTR2020, doi:10.1007/978-3-030-68446-4_13. [6] Søren Enevoldsen, Kim Guldstrand Larsen & Jirí Srba (2022): Extended abstract dependency graphs. Int. J. Softw. Tools Technol. Transf. 24(1), pp. 49–65, doi:10.1007/s10009-021-00638-8. [7] Glen Jeh & Jennifer Widom (2002): SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada, ACM, pp. 538–543, doi:10.1145/775047.775126. [8] Jonas F. Jensen, Thomas Nielsen, Lars K. Oestergaard & Jiˇrí Srba (2016): TAPAAL and Reachability Analysis of P/T Nets, pp. 307–318. Springer Berlin Heidelberg, Berlin, Heidelberg, doi:10.1007/978-3-662-53401-4_16. [9] Jonas Finnemann Jensen, Kim Guldstrand Larsen, Jiri Srba & Lars Kaerlund Østergaard (2016): Efficient model-checking of weighted CTL with upper-bound constraints. International Journal on Software Tools for Technology Transfer 18(4), pp. 409–426, doi:10.1007/s10009-014-0359-5. [10] Mathias Claus Jensen, Anders Mariegaard & Kim Guldstrand Larsen (2019): Symbolic model checking of weighted PCTL using dependency graphs. NASA Formal Methods Symposium, pp. 298–315, doi:10.1007/978-3-030-20652-9_20. [11] M. Kwiatkowska, G. Norman & D. Parker (2011): PRISM 4.0: Verification of Probabilistic Real-time Systems. In G. Gopalakrishnan & S. Qadeer, editors: Proc. 23rd International Conference on Computer Aided Verification (CAV’11), LNCS 6806, Springer, pp. 585–591, doi:10.1007/978-3-642-22110-1_47. [12] M. Kwiatkowska, G. Norman, D. Parker & G. Santos (2020): PRISM-games 3.0: Stochastic Game Verification with Concurrency, Equilibria and Time. In: Proc. 32nd International Conference on Computer Aided Verification (CAV’20), LNCS 12225, Springer, pp. 475–487, doi:10.1007/978-3-030-53291-8_25. [13] Sangkeun Lee, Minsuk Kahng & Sang goo Lee (2015): Constructing compact and effective graphs for recommender systems via node and edge aggregations. Expert Systems with Applications 42(7), pp. 3396–3409, doi:10.1016/j.eswa.2014.11.062. [14] Xinxin Liu & Scott A. Smolka (1998): Simple Linear-Time Algorithms for Minimal Fixed Points (Extended Abstract). In Kim Guldstrand Larsen, Sven Skyum & Glynn Winskel, editors: Automata, Languages and Programming, 25th International Colloquium, ICALP’98, Aalborg, Denmark, July 13-17, 1998, Proceedings, Lecture Notes in Computer Science 1443, Springer, pp. 53–66, doi:10.1007/BFb0055040. [15] Lawrence Page, Sergey Brin, Rajeev Motwani & Terry Winograd (1998): The PageRank Citation Ranking: Bringing Order to the Web. Technical Report, Stanford Digital Library Technologies Project. Available at http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.1768. This paper is available on Arxiv under CC 4.0 license. Arxiv

CGAAL: Wrapping It Up and What Comes Next

The featured image can be confusing and elitist for newcomers, so I wrote an overview of various sources to provide a better orientation and share my experience. Disclaimer I disapprove of using modified hardware and software for copyright infringement. This tutorial deals with hardware and software modification purely for educational reasons. Modifications to change the style, operating system, ability to backup your own files or similar are not considered copyright infringement. Modifying hardware you own in the Czech Republic, where I am located, is legal I do not cause any loss to nintendo, I purchased all the games I play. See the results of two courts in European countries that have dealt with this: [1]: https://web.archive.org/web/20060324123541/http://management.silicon.com/government/0%2C39024677%2C39117891%2C00.htm [2]: https://www.theregister.com/2004/04/27/spanish_xbox_modding_ruling/ Needed components - modchip Nintendo Switch Modchip (4$); I used this , but it was overpriced. one Soldering iron (15$), something like this SD card, at least 16GB, recommended 64GB and more. (10$) USB C to USB 2.0 cable. Soldering flux is recommended but not required. Electrical tape Heat sink paste Hack - hardest part I don't provide a detailed tutorial, there are enough of them. This post is to improve the orientation in this scene, at the same time provide a collection of various guides. How to start If you have a Nintendo Switch V1 and belong to the lucky 12% (see hackable serial numbers[1]) who have an unpatched switch, you can get by with tinfoil and an SD card. There are plenty of jailbreak tutorials[2]. The hack is more complicated if you have a patched Switch, Switch Lite, or OLED version. What to do after determining version of my switch? You have to solder a modchip, often or , which are very cheap on Aliexpress, to a Nintendo motherboard. They work on the principle of voltage changes on the processor, which skips the integrity check mechanism to check unauthorized software. PICOFLY HWFLY The easiest is the V1 version, then Switch Lite, then OLED. The USB C connector is often included with the modchip, through which the chip's firmware must be uploaded. This consists of dragging only a few files to the modchip. The firmware can be found on . GitHub My experience In my situation, I used the PICOFLY chip purchased from AliExpress to modify my Nintendo V1 Switch. Without a microscope or any experience in micro-soldering. I used a budget soldering iron. Although I suggested using flux, I neglected to utilize it myself, leading to an dirty solder. While the installation eventually worked, the process was tedious and stresfull. What to do after the hack You must load the software that starts after boot and must be on the SD card. Easy route You can use , which is a package of different tools. The community will tell you that you must install individual tools to understand how they work. The reality is that copy-pasting different tools to the SD card will not help you understand the tools, so don't be afraid to take the easy way out. This is very common in the community, where people claim the only correct way is without using the script. Of course, this is wrong. HATS linux arch archinstall Where do I find Hats? The HATS source code has been taken down but can be found in various mirrors; use Google. https://www.youtube.com/watch?v=jfJIVzeJSyE&embedable=true The following section describes some tools used in HATS, and you would probably set up these tools even if you went the manual route. The recommended starting point is this guide: https://nh-server.github.io/switch-guide/ Other options - Ubuntu https://www.s31bz.com/linux-on-my-nintendo-switch - Android 10 https://wiki.switchroot.org/wiki/android/10-q-setup-guide Also, you can dualboot between multiple operating systems on your SD card. Toolset - The most used bootloader that allows you to boot custom firmware (CFW). Hekate - Tinfoil allows you to access the SD card via USB C and install software. Tinfoil - Menu for launching homebrew applications. This menu will be available via the album icon on Switch. nx-hbmenu - This is an industry-standard tool for managing various emulators. RetroArch - OS for the switch. You will probably be using this one. Otherwise, you can use multiple linux distributions. also works on Switch. Atmosphere RetroPie - Install software via USB C. This is helpful because some games are bigger than 4GB, which is the maximum file size for FAT32. DBI - Tool for managing game save files. JKSV - Switch sysmodule allowing you to set cpu/gpu/mem clocks according to the running application and docked state. sys-clk - Use controllers from other consoles natively on your Nintendo Switch via Bluetooth. No dongles or other external hardware neccessary. MissionControl How to restore game save files from Yuzu/Ryujinx on Nintendo Switch This process apply if you want to dump Switch saves to other location. Go to the emulator and click on “open save files“ Copy files to the JKSV folder on the SD card path will be something like: SD:/JKSV/<game>/<save_name>/ In JKSV, click restore save and hold the button. A JKSV has to be correctly set up. See the JKSV . Note: guide Also, run the game at least once. Otherwise, it won’t show up in JKSV, What is X? - This is a memory chip with the original firmware on the Switch motherboard. Switch NAND - emulated NAND saved on SD card. emuMMC - custom firmware cfw - custom console software not provided with original homebrew - a program that gets executed after exploiting a vulnerability payload - a format of programs that can be run with CFW .nro Links [1]: https://suchmememanyskill.github.io/guides/switchserials/ [2]: https://www.youtube.com/watch?v=zX7eBg2gIzI Featured image source . here

Hacking a Nintendo Switch for 30$

273 Stories To Learn About Venture Capital

Software engineering soft skills are non-technical skills used to facilitate collaboration, communication, problem-solving, and decision-making in software development. These skills are important because they enable to work effectively with diverse teams, manage projects, and understand the needs of users and stakeholders. software engineers Improving one’s soft skills can significantly enhance career success by improving the ability to lead, communicate effectively, and deliver valuable solutions. This article will provide practical guidance on how to improve your software engineering soft skills, including techniques for building collaboration, communication, and teamwork skills. By the end, you will be better equipped to navigate complex software high-functioning development teams. projects and contribute to building Why Soft Skills Matter in Software Engineering In software engineering, it’s easy to focus solely on technical skills and ignore the importance of soft skills. Social media certainly doesn’t help us any with this. However, soft skills play a crucial role in software development. Soft skills refer to interpersonal skills that help individuals communicate, collaborate, and work well with others. There are different kinds of soft skills in software engineering, and I often just like to think of these as people skills. As funny as it might sound, these are just the skills that make working with someone enjoyable and that you can trust them fully in the work you’re doing with them. I’ll share some examples, but they’re certainly not limited to these. You can follow along with : this video for some thoughts on non-technical software engineering skills https://www.youtube.com/watch?v=9NIhzWDAmzE&embedable=true Communication Effective communication is essential for software members and stakeholders. This includes communicating technical information to non-technical stakeholders, such as product managers or customers. And to be clear, that’s not to say that these individuals aren’t ever technical. It’s just that they likely won’t have the same technical depth as you. developers since they must often interact with other team To be successful in software development, it’s crucial to be able to communicate effectively, both verbally and in writing. We spend so much of our time: Reading and writing code Exchanging messages on internal chat Reading and writing emails Reviewing code and leaving comments Discussing designs and writing design documents The list goes on! These are all regularly occurring things for software engineers. Teamwork & Collaboration Teamwork is another critical soft skill in software engineering. Many software projects require the collaboration of teams with different areas of expertise. In fact, I think it’s extremely rare from my own professional experience that you’re ever working on stuff completely in isolation. Even if you’re the only software engineer on a project, you still need to collaborate with stakeholders at some point! Effective teamwork requires clear communication, trust, and the ability to handle conflicts constructively. If you don’t have experience working in teams, then it’s a skill I’d suggest building up. You can consider collaborating online with other developers, team sports, school projects, etc… Find opportunities where you can consciously put effort into getting better at teamwork. Adaptability Adaptability is also a vital soft skill in software development. This refers to the ability to embrace changes and adapt to new situations. Software development projects are rarely stagnant, so developers need to be ready to change direction when necessary. Being adaptable allows developers to handle changing requirements or unforeseen roadblocks effectively. The only thing certain is that the plans will change. Ways to Improve Your Software Engineering Soft Skills Improving soft skills is an ongoing process that requires intentionality and dedication. Here are six ways to improve your soft skills: 1. Building Self-Awareness Through Feedback and Assessment Types of feedback and assessments can be useful tools to build self-awareness. Feedback from mentors, peers, and even co-workers can help you understand your strengths and weaknesses. Assessments such as or can also provide insight into your personality type and how to communicate effectively with others. Once you understand your soft skill strengths and weaknesses, you can create an actionable plan to improve them. DISC Myers-Briggs 2. Communicating Effectively with Team Members and Stakeholders Clear and effective communication is crucial for success in software development. Effective communication requires active listening and a clear understanding of the message being conveyed. Tips for clear and concise communication include: Breaking down complex information into small, digestible pieces Ensuring the message is appropriate for the recipient’s level of technical expertise Choosing the appropriate method of communication. 3. Developing Active Listening Skills Active listening is an essential part of effective communication. It involves hearing and understanding the message being conveyed. Active listening involves making eye contact, clarifying the message, summarizing what was said, and providing feedback. Do you find yourself just waiting for an opening to share your thoughts? If so, you’re likely not actively listening. You’ve probably encountered other people doing this when you interact with them. Sometimes it’s very obvious, and other times it’s subtle. But when you catch on, you really feel like there’s no point in sharing your perspective because nobody is listening. Practicing active listening skills can lead to better communication and a clearer understanding of software development challenges and solutions. Try it in your next one-on-one or when you’re meeting with a colleague next. If you find it difficult, try making a mental note to pause for a full second or two before replying. 4. Building Empathy with Users and Customers Empathy is the ability to understand and feel the emotions of others. Building empathy with users and customers can help software developers understand their needs better, which can lead to more effective solutions. Ways to build empathy include user research, customer feedback, and observing customer behavior. It’s important to balance empathy with business needs to ensure the software development solution meets the business requirements. 5. Building Effective Collaboration Through Teamwork Effective teamwork is vital to the success of software development projects. Creating a collaborative culture through open communication, sharing ideas, and constructive feedback can lead to better outcomes. Tips for effective collaboration include setting clear expectations, embracing diversity, and providing opportunities for trust and respect for each other. team members to develop You can lean into this to put a more positive spin on a lot of your collaborative efforts with other software engineers: “yes and” strategy that I discuss in this video https://www.youtube.com/watch?v=WBLTQQMc3c8&embedable=true 6. Demonstrating Flexibility and Adaptability Flexibility and adaptability are critical in fast-paced software development projects. Being open to changing priorities and adjusting to new requirements can lead to improved outcomes. Tips for developing flexibility and adaptability include being open to feedback and constructive criticism and accepting that change is inevitable. Balancing flexibility and adaptability with deadlines and business needs is essential to achieving success in software development projects. Common Challenges Improving Software Engineering Soft Skills Improving software engineering soft skills can be a challenge, but some common barriers can make it challenging. Recognizing these barriers and implementing strategies to address them can help you overcome them and improve your soft skills. even more Lack of Self Awareness One common barrier is the lack of self-awareness. Without recognizing your soft skill strengths and weaknesses, it can be challenging to know where to focus your efforts. You might think you’re communicating clearly when you’re not, or the opposite — you think you’re doing a very poor job, but your messaging comes across very clearly. I often find it’s the former though, which can create friction with others. Seek out feedback from mentors, peers, and colleagues to gain a greater understanding of your strengths and weaknesses. Utilize assessments to gain additional insights into personality type and communication style. Once you have a clear understanding of your strengths and weaknesses, you can develop and implement an actionable plan to improve them. You might even be someone in a more senior position being asked for feedback! Speaking of self-awareness, : consider the topics covered in this video to improve feedback opportunities https://www.youtube.com/watch?v=JJi_GnFnUHM&embedable=true Lack of Support and Resources Another common barrier is the lack of support and resources. It can be challenging to gain access to resources or support systems to help you improve soft skills such as communication, teamwork, or empathy. However, there are many resources available if you go looking! They just might not feel like they’re in plain sight: Online, such as blogs, tutorials, and webinars. Seek out a mentor or a peer who can provide guidance and support on improving specific soft skills. Finding a colleague you’ve built trust with and getting peer feedback Asking your manager to help go through your software engineering soft skill set with you Finding something to start with is certainly better than inaction! Improper Time Management Time management can be another challenge when improving your soft skills. Juggling work, personal life, and improving your soft skills can be daunting. Consider scheduling specific times each week to focus on improving your soft skills. This could be done through online classes, reading articles, or working with a mentor. The ability to prioritize and manage the development of soft skills in software engineering can lead to lasting improvements. Fear and Self-Doubt Finally, overcoming self-doubt and fear of failure can often be a significant barrier to improving your soft skills. Remember that much of your growth will happen through trial and error, and it’s okay to make mistakes. Set goals for yourself and celebrate your successes. Don’t shy away from new experiences that force you out of your comfort zone. Embracing challenges can help you grow and develop new soft skills. In summary, recognizing the common barriers to improving soft skills and implementing strategies to overcome them can help you become a more well-rounded software developer with more effective communication, better teamwork, and greater adaptability. Improve your soft skills to excel in the software development industry. Wrapping Up Software Engineering Soft Skills As software engineering grows in complexity, innovative solutions can only be achieved through collaboration, creativity, and vision. All of which are unreachable without effective communication. In addition, software developers are always interacting with various team members, including end-users, QA teams, product owners, and fellow engineers. Developing project deliverables from highly technical code to user stories and communicating what is being done to both technical and non-technical members of the project teams requires software engineering soft skills. Improving these skills could help budding or experienced engineers generate better solutions for a broader range of issues. Improving soft skills requires ongoing self-examination and continuous effort, akin to technical skill growth. As a software engineer, you should contemplate and reflect on how you can better improve your soft skills with users, customers, and team members. Consequently, your growth in these skills can not only lead to a successful career but also help you advance your skills further! 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