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Authors: (1) Alexander E.I. Brownlee, University of Stirling, UK; (2) James Callan, University College London, UK; (3) Karine Even-Mendoza, King’s College London, UK; (4) Alina Geiger, Johannes Gutenberg University Mainz, Germany; (5) Justyna Petke, University College London, UK; (6) Federica Sarro, University College London, UK; (7) Carol Hanna, University College London, UK; (8) Dominik Sobania, Johannes Gutenberg University Mainz, Germany. Table of Links Abstract & Introduction Experimental Setup Results Conclusions and Future Work Acknowledgements & References Acknowledgments UKRI EPSRC EP/P023991/1 and ERC 741278. References Artifact of Enhancing Genetic Improvement Mutations Using Large Language
Models. Zenodo (Sep 2023). https://doi.org/10.5281/zenodo.8304433 B¨ohme, M., Soremekun, E.O., Chattopadhyay, S., Ugherughe, E., Zeller, A.: Where
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H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models
trained on code. arXiv preprint arXiv:2107.03374 (2021) Fan, A., Gokkaya, B., Harman, M., Lyubarskiy, M., Sengupta, S., Yoo, S., Zhang,
J.M.: Large language models for software engineering: Survey and open problems
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and large language models. Cognitive Systems Research p. 101155 (2023) Hou, X., Liu, Y., Yang, Z., Grundy, J., Zhao, Y., Li, L., Wang, K., Luo, X., Lo, D.,
Wang, H.: Large language models for software engineering: A systematic literature
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Program transformation landscapes for automated program modification using
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J.R.: Genetic improvement of software: A comprehensive survey. IEEE Transactions on Evolutionary Computation 22, 415–432 (2018) Siddiq, M.L., Santos, J., Tanvir, R.H., Ulfat, N., Rifat, F.A., Lopes, V.C.: Exploring
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Automated Program Repair (APR). pp. 23–30. IEEE Computer Society (2023) Xia, C.S., Paltenghi, M., Tian, J.L., Pradel, M., Zhang, L.: Universal fuzzing via
large language models. arXiv preprint arXiv:2308.04748 (2023) Xia, C.S., Zhang, L.: Keep the conversation going: Fixing 162 out of 337 bugs for $0.42 each using chatgpt. arXiv preprint arXiv:2304.00385 (2023) This paper is under CC 4.0 license. available on arxiv

Enhancing Genetic Improvement Mutations: Acknowledgements & References

Authors: (1) Alexander E.I. Brownlee, University of Stirling, UK; (2) James Callan, University College London, UK; (3) Karine Even-Mendoza, King’s College London, UK; (4) Alina Geiger, Johannes Gutenberg University Mainz, Germany; (5) Justyna Petke, University College London, UK; (6) Federica Sarro, University College London, UK; (7) Carol Hanna, University College London, UK; (8) Dominik Sobania, Johannes Gutenberg University Mainz, Germany. Table of Links Abstract & Introduction Experimental Setup Results Conclusions and Future Work Acknowledgements & References 4 Conclusions and Future Work Genetic improvement of software is highly dependent on the mutation operators it utilizes in the search process. To diversify the operators and enrich the search space further, we incorporated a Large Language Model (LLM) as an operator. To generalise, future work should consider projects besides our target, jCodec. Our experiments used an API giving us no control over the responses generated by the LLM or any way of modifying or optimizing them. Though we did not observe changes in behaviour during our experiments, OpenAI may change the model at any time, so future work should consider local models. We experimented with only three prompt types for LLM requests and within this limited number of prompts found a variation in the results. Finally, our implementation for parsing the responses from the LLMs was relatively simplistic. However, this would only mean that our reported results are pessimistic and an even larger improvement might be achieved by the LLM-based operator. Limitations. We found that, although more valid and diverse patches were found with standard edits using Random Sampling, more patches passing the unit tests were found with LLM-based edits. For example, with the LLM edit using the Medium prompt, we found over 75% more patches passing the unit tests than with the classic Insert edit. In our Local Search experiment, we found the best improvement with the Statement edit (508 ms). The best LLMbased improvement was found with the Medium prompt (395 ms). Thus there is potential in exploring approaches combining both LLM and ‘classic’ GI edits. Summary. Our experiments revealed that the prompts used for LLM requests greatly affect the results. Thus, in future work, we hope to experiment more with prompt engineering. It might also be helpful to mix prompts: e.g., starting with medium then switching to detailed to make larger edits that break out of local minima. Further, the possibility of combining LLM edits with others such as standard copy/delete/replace/swap or PAR templates [11] could be interesting. Finally, we hope to conduct more extensive experimentation on additional test programs. The code, LLMs prompt and experimental infrastructure, data from the evaluation, and results are available as open source at [1]. The code is also under the ‘llm’ branch of github.com/gintool/gin (commit 9fe9bdf; branched from master commit 2359f57 pending full integration with Gin). Data Availability. This paper is under CC 4.0 license. available on arxiv

Enhancing Genetic Improvement Mutations: Conclusions and Future Work

Authors: (1) Alexander E.I. Brownlee, University of Stirling, UK; (2) James Callan, University College London, UK; (3) Karine Even-Mendoza, King’s College London, UK; (4) Alina Geiger, Johannes Gutenberg University Mainz, Germany; (5) Justyna Petke, University College London, UK; (6) Federica Sarro, University College London, UK; (7) Carol Hanna, University College London, UK; (8) Dominik Sobania, Johannes Gutenberg University Mainz, Germany. Table of Links Abstract & Introduction Experimental Setup Results Conclusions and Future Work Acknowledgements & References 3 Results The first experiment compares standard GI mutations, namely Insert and Statement edits, with LLM edits using differently detailed prompts (Simple, Medium, and Detailed) using Random Sampling. Table 1 shows results for all patches as well as for unique patches only. We report how many patches were successfully parsed by JavaParser (named as Valid), how many compiled, and how many passed all unit tests (named as Passed). We excluded patches syntactically equivalent to the original software. Best results are in bold. We see that although substantially more valid patches were found with the standard Insert and Statement edits, more passing patches could be found by using the LLM-generated edits. In particular, for the Medium, and Detailed prompts 292 and 230 patches passed the unit tests, respectively. For the Insert and Statement edits only 166 and 91 passed the unit tests, respectively. Anecdotally, the hot methods with lowest/highest patch pass rates differed for each operator: understanding this variation will be interesting for future investigation. It is also notable that LLM patches are less diverse: over 50% more unique patches were found by standard mutation operators than the LLM using Medium, Table 1. Results of our Random Sampling experiment. We exclude patches syntactically equivalent to the original software in this table. For all and unique patches we report: how many patches passed JavaParser, compiled, and passed all unit tests. Table 2. Local Search results. We exclude all empty patches. We report how many patches compiled, passed all unit tests, and how many led to improvements in runtime. We report best improvement found and median improvement among improving patches. and Detailed prompts. With the Simple prompt, however, not a single patch passed the unit tests, since the suggested edits often could not be parsed. Thus detailed prompts are necessary to force LLM to generate usable outputs. We investigated further the differences between Medium and Detailed prompts to understand the reduction in performance with Detailed (in the unique patches sets) as Medium had a higher number of compiled and passed patches. In both prompt levels, the generated response was the same for 42 cases (out of the total unique valid cases). However, Detailed tended to generate longer responses with an average of 363 characters, whereas Medium had an average of 304 characters. We manually examined several Detailed prompt responses, in which we identified some including variables from other files, potentially offering a significant expansion of the set of code variants GI can explore. The second experiment expands our analysis, comparing the performance of the standard and LLM edits with Local Search. Table 2 shows the results of the Local Search experiment. We report the number of compiling and passing patches as well as the number of patches were runtime improvements were found. Furthermore, we report the median and best improvement in milliseconds (ms). In the table, we excluded all empty patches. As before, best results are in bold. Again, we see that more patches passing the unit tests could be found with the LLM using the Medium, and Detailed prompts. In addition, more improvements could be found by using the LLM with these prompts. Specifically, with Medium and Detailed, we found 164 and 196 improvements, respectively, while we only found 136 with Insert and 71 with Statement. The best improvement could be found with 508 ms with the Statement edit. The best improvement found using LLMs (using the Medium prompt) was only able to improve the runtime by 395 ms. We also examined a series of edits in Local Search results to gain insights into the distinctions between Medium and Detailed prompts due to the low compilation rate of Detailed prompt’s responses. In the example, a sequence of edits aimed to inline a call to function clip. The Detailed prompt tried to incorporate the call almost immediately within a few edits, likely leading to invalid code. On the other hand, the Medium prompt made less radical changes, gradually refining the code. It began by replacing the ternary operator expression with an if-then-else statement and system function calls before eventually attempting to inline the clip function call. This paper is under CC 4.0 license. available on arxiv

Standard GI Mutations vs. LLM Edits in Random Sampling and Local Search

This paper is available on arxiv under CC 4.0 license. Authors: (1) Alexander E.I. Brownlee, University of Stirling, UK; (2) James Callan, University College London, UK; (3) Karine Even-Mendoza, King’s College London, UK; (4) Alina Geiger, Johannes Gutenberg University Mainz, Germany; (5) Justyna Petke, University College London, UK; (6) Federica Sarro, University College London, UK; (7) Carol Hanna, University College London, UK; (8) Dominik Sobania, Johannes Gutenberg University Mainz, Germany. Table of Links Abstract & Introduction Experimental Setup Results Conclusions and Future Work Acknowledgements & References 2 Experimental Setup To analyze the use of LLMs as a mutation operator in GI, we used the GPT 3.5 Turbo model by OpenAI and the GI toolbox Gin [3]. We experimented with two types of search implemented within Gin: random search and local search. Requests to the LLM using the OpenAI API were via the Langchain4J library, with a temperature of 0.7. The target project for improvement in our experiments was the popular JCodec [7] project which is written in Java. ‘Hot’ methods to be targeted by the edits were identified using Gin’s profiler tool by repeating the profiling 20 times and taking the union of the resulting set. For the random sampling experiments, we set up the runs with statementlevel edits (copy/delete/replace/swap from [14] and insert break/continue/return from [4]) and LLM edits, generating 1000 of each type at random. A timeout of 10000 milliseconds was used for each unit test to catch infinite loops introduced by edits; exceeding the timeout counts as a test failure. For local search, experiments were set up similarly. There were 10 repeat runs (one for each of the top 10 hot methods) but the runs were limited to 100 evaluations resulting in 1000 evaluations in total, matching the random search. In practice this was 99 edits per run as the first was used to time the original unpatched code. We experimented with three different prompts for sending requests to the LLM for both types of search: a simple prompt, a medium prompt, and a detailed prompt. With all three prompts, our implementation requests five different variations of the code at hand. The simple prompt only requests the code without any additional information. The medium prompt provides more information about the code provided and the requirements, as shown in Figure 1. Specifically, we provide the LLM with the programming language used, the project that the code belongs to, as well as formatting instructions. The detailed prompt extends the medium prompt with an example of a useful change. This example was taken from results obtained by Brownlee et al. [4]. The patch is a successful instance of the insert edit applied to the jCodec project (i.e., an edit that compiled, passed the unit tests and offered a speedup over the original code). We use the same example for all the detailed prompt requests used in our experiments; this is because LLMs are capable of inductive reasoning where the user presents specific information, and the LLM can use that input to generate more general statements, further improved in GPT-4 [8]. The medium prompt for LLM requests, with line breaks added for readability. Fig. 1. LLM edits are applied by selecting a block statement at random in a target
‘hot’ method. This block’s content is <code> in the prompt. The first code block
in the LLM response is identified. Gin uses JavaParser (https://javaparser.org)
internally to represent target source files, so we attempt to parse the LLM suggestion with JavaParser, and replace the original block with the LLM suggestion. This paper is under CC 4.0 license. available on arxiv

Optimizing Genetic Improvement with GPT 3.5 Turbo

This paper is available on arxiv under CC 4.0 license. Authors: (1) Alexander E.I. Brownlee, University of Stirling, UK; (2) James Callan, University College London, UK; (3) Karine Even-Mendoza, King’s College London, UK; (4) Alina Geiger, Johannes Gutenberg University Mainz, Germany; (5) Justyna Petke, University College London, UK; (6) Federica Sarro, University College London, UK; (7) Carol Hanna, University College London, UK; (8) Dominik Sobania, Johannes Gutenberg University Mainz, Germany. Table of Links Abstract & Introduction Experimental Setup Results Conclusions and Future Work Acknowledgements & References Abstract Large language models (LLMs) have been successfully applied to software engineering tasks, including program repair. However, their application in search-based techniques such as Genetic Improvement (GI) is still largely unexplored. In this paper, we evaluate the use of LLMs as mutation operators for GI to improve the search process. We expand the Gin Java GI toolkit to call OpenAI’s API to generate edits for the JCodec tool. We randomly sample the space of edits using 5 different edit types. We find that the number of patches passing unit tests is up to 75% higher with LLM-based edits than with standard Insert edits. Further, we observe that the patches found with LLMs are generally less diverse compared to standard edits. We ran GI with local search to find runtime improvements. Although many improving patches are found by LLM-enhanced GI, the best improving patch was found by standard GI. Large language models · Genetic Improvement Keywords: 1 Introduction As software systems grow larger and more complex, significant manual effort is required to maintain them [2]. To reduce developer effort in software maintenance and optimization tasks, automated paradigms are essential. Genetic Improvement (GI) [15] applies search-based techniques to improve non-functional properties of existing software such as execution time as well as functional properties like repairing bugs. Although GI has had success in industry [12,13], it remains limited by the set of mutation operators it employs in the search [14]. Able to process textual queries without additional training for the particular task at hand. LLMs have been pre-trained on millions of code repositories spanning many different programming languages [5]. Their use for software engineering tasks has had great success [9,6], showing promise also for program repair [17,19]. Kang and Yoo [10] have suggested that there is untapped potential in using LLMs to enhance GI. GI uses the same mutation operators for different optimization tasks. These operators are hand-crafted before starting the search and thus result in a limited search space. We hypothesize that augmenting LLM patch suggestions as an additional mutation operator will enrich the search space and result in more successful variants. In this paper, we conduct several experiments to explore whether using LLMs as a mutation operator in GI can improve the efficiency and efficacy of the search. Our results show that the LLM-generated patches have compilation rates of 51.32% and 53.54% for random search and local search, respectively (with the Medium prompt category). Previously LLMs (using an LLM model as-is) were shown to produce code that compiled roughly 40% of the time [16,18]. We find that randomly sampled LLM-based edits compiled and passed unit tests more often compared to standard GI edits. We observe that the number of patches passing unit tests is up to 75% higher for LLM-based edits than GI Insert edits. However, we observe that patches found with LLMs are less diverse. For local search, the best improvement is achieved using standard GI Statement edits, followed by LLM-based edits. These findings demonstrate the potential of LLMs as mutation operators and highlight the need for further research in this area. This paper is under CC 4.0 license. available on arxiv

Enhancing Genetic Improvement Mutations Using Large Language Models

First and Last Things by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . ON FORGETTING, AND THE NEED OF PRAYER, READING, DISCUSSION AND WORSHIP ON FORGETTING, AND THE NEED OF PRAYER, READING, DISCUSSION AND WORSHIP. One aspect of life I had very much in mind when I planned those Samurai disciplines of mine. It was forgetting. We forget. Even after we have found Salvation, we have to keep hold of Salvation; believing, we must continue to believe. We cannot always be at a high level of noble emotion. We have clambered on the ship of Faith and found our place and work aboard, and even while we are busied upon it, behold we are back and drowning in the sea of chaotic things. Every religious body, every religious teacher, has appreciated this difficulty and the need there is of reminders and renewals. Faith needs restatement and revival as the body needs food. And since the Believer is to seek much experience and be a judge of less or more in many things, it is particularly necessary that he should keep hold upon a living Faith. How may he best do this? I think we may state it as a general duty that he must do whatever he can to keep his faith constantly alive. But beyond that, what a man must do depends almost entirely upon his own intellectual character. Many people of a regular type of mind can refresh themselves by some recurrent duty, by repeating a daily prayer, by daily reading or re-reading some devotional book. With others constant repetition leads to a mental and spiritual deadening, until beautiful phrases become unmeaning, eloquent statements inane and ridiculous,—matter for parody. All who can, I think, should pray and should read and re-read what they have found spiritually helpful, and if they know of others of kindred dispositions and can organize these exercises, they should do so. Collective worship again is a necessity for many Believers. For many, the public religious services of this or that form of Christianity supply an atmosphere rich in the essential quality of religion and abounding in phrases about the religious life, mellow from the use of centuries and almost immediately applicable. It seems to me that if one can do so, one should participate in such public worship and habituate oneself to read back into it that collective purpose and conscience it once embodied. Very much is to be said for the ceremony of Holy Communion or the Mass, for those whom accident or scruples do not debar. I do not think your modern liberal thinkers quite appreciate the finer aspects of this, the one universal service of the Christian Church. Some of them are set forth very finely by a man who has been something of a martyr for conscience’ sake, and is for me a hero as well as a friend, in a world not rich in heroes, the Rev. Stewart Headlam, in his book, “The Meaning of the Mass.” With others again, Faith can be most animated by writing, by confession, by discussion, by talk with friends or antagonists. One or other or all of these things the Believer must do, for the mind is a living and moving process, and the thing that lies inert in it is presently covered up by new interests and lost. If you make a sort of King Log of your faith, presently something else will be sitting upon it, pride or self-interest, or some rebel craving, King de facto of your soul, directing it back to anarchy. For many types that, however, is exactly what happens with public worship. They DO get a King Log in ceremony. And if you deliberately overcome and suppress your perception of and repugnance to the perfunctoriness of religion in nine-tenths of the worshippers about you, you may be destroying at the same time your own intellectual and moral sensitiveness. But I am not suggesting that you should force yourself to take part in public worship against your perceptions, but only that if it helps you to worship you should not hesitate to do so. We deal here with a real need that is not to be fettered by any general prescription. I have one Cambridge friend who finds nothing so uplifting in the world as the atmosphere of the afternoon service in the choir of King’s College Chapel, and another, a very great and distinguished and theologically sceptical woman, who accustomed herself for some time to hear from a distant corner the evening service in St. Paul’s Cathedral and who would go great distances to do that. Many people find an exaltation and broadening of the mind in mountain scenery and the starry heavens and the wide arc of the sea; and as I have already said, it was part of the disciplines of these Samurai of mine that yearly they should go apart for at least a week of solitary wandering and meditation in lonely and desolate places. Music again is a frequent means of release from the narrow life as it closes about us. One man I know makes an anthology into which he copies to re-read any passage that stirs and revives in him the sense of broad issues. Others again seem able to refresh their nobility of outlook in the atmosphere of an intense personal love. Some of us seem to forget almost as if it were an essential part of ourselves. Such a man as myself, irritable, easily fatigued and bored, versatile, sensuous, curious, and a little greedy for experience, is perpetually losing touch with his faith, so that indeed I sometimes turn over these pages that I have written and come upon my declarations and confessions with a sense of alien surprise. It may be, I say, that for some of us forgetting is the normal process, that one has to believe and forget and blunder and learn something and regret and suffer and so come again to belief much as we have to eat and grow hungry and eat again. What these others can get in their temples we, after our own manner, must distil through sleepless and lonely nights, from unavoidable humiliations, from the smarting of bruised shins. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2009). First and Last Things: A Confession of Faith and Rule of Life. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, https://www.gutenberg.org/files/4225/4225-h/4225-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

ON FORGETTING, AND THE NEED OF PRAYER, READING, DISCUSSION AND WORSHIP

Yele Bademosi is a Tech entrepreneur and investor who is passionate about helping his home continent of Africa. Yele studied Medicine at King’s College London before opting out to focus on technology entrepreneurship. He is the co-founder of Nestcoin, Founder of Bundle and Founding Partner of Microtraction. He was also a director at the venture capital arm of the world’s largest cryptocurrency exchange Binance. In this video, Yele speaks to me about his journey into entrepreneurship, his passion for investment and cryptocurrency. He also sheds light on the solutions Nestcoin will be providing to expose Africans to the benefits and possibilities of Blockchain. Watch the video https://www.youtube.com/watch?v=QyViPx6bt6Y Video timestamps 0:09 Introduction
2:19 Beyond crypto and VC, how would you describe yourself?
3:56 Can you give us context on your career and educational background?
8:55 Why did you go that route as against simple starting what you wanted?
14:39 How did Microtraction start?
23:00 How have you built your team at Microtraction?
27:14 What’s the biggest lesson you’ve learnt as a VC?
28:13 What do you look for in a Founder to earn your believe and trust?
39:19 How did you find Crypto?
42:46 How did the Bundle Idea come in?
45:10 What was the biggest challenge in building Bundle and actions that led to your successes?
49:00 When did you know it was the right time to leave Bundle?
51:10 You’ve done a lot of things right, what are the things you did wrong
53:20 Of all of these things you’ve done in the space of 5-6 years, which would you say is one of the most daring and why?
56:17 Which of your successes is your personal and favorite win?
58:29 Tell me about the new thing you’re building?
1:02:47 When you are picking the people closest to you? What do you look out for?
1:04:39 What’s one city you haven’t visited but would love to visit soon?
1:06:00 What are two things on your bucket list that you would like to tick off?
1:07:30 There are so many ways to get into tech, for people who are looking to join the Tech Industry, what would be your one or two primary advice to them?

Yele Bademosi, Founder of Bundle Africa, Talks About Tech Entrepreneurship

The blockchain technology has come a long way since the time it was introduced back in 2009 by Satoshi Nakamoto. Today, the latest statistics show that global is expected to grow from 1.5 billion in 2018 to an estimated 11.7 billion by 2022. spending on blockchain solutions Blockchain’s growing popularity prompts global public awareness of the importance of this technology. In fact, authorities and educational organizations are already pushing forward some fundamental changes in the system. Just like that, many well-known and innovative universities, including , are already offering programs focused on Blockchain technology. Cornell and Georgetown Universities However, aside from disrupting the usual education programs across global organizations, there is another way Blockchain changes the global educational system. Blockchain Disrupts Education System Some of the greatest features of blockchain include decentralization, transparency, and immutability. Thanks to these functionalities, the technology is able to improve global industries in many different ways. Today, we will talk about how blockchain could make the education system better. As mentioned before, the blockchain technology has a ton of applications, and the same is true for the education sector.  In the course of the past several years, blockchain companies and enthusiasts were working on solutions that can allow us to improve the higher education sector. Below are some of the ways blockchain can start a fundamental change in the global education system, according to Gartner. Record Keeping One of the greatest applications of blockchain in the education sector involves the digitization of educational credentials, including the degrees, certificates, diplomas, and the like. This kind of record-keeping can significantly enhance the security of the credentials and make sure that there is no need for an intermediary to verify them. Furthermore, blockchain technology can be applied in the case of accreditation of educational organizations. At the moment, this process involves a number of complex steps in some of the countries. This time-consuming and cost-inefficient procedure can be easily substituted by using digital records. Boosted Efficiency There is no doubt that blockchain-stored credentials for university are already a great improvement over the traditional education sector’s approach. Yet, a step-up from this method of application would be the establishment of the virtual transcripts. These transcripts would include all the information about the educational activities and achievements of a person’s history. This application would diminish resume fraud and speed up student transfers. Another great advantage of this application is the diminished need for staff related to credential verification. Overall, the blockchain-fueled virtual transcripts would streamline a lot of internal processes and increase efficiency for the educational organizations. Streamline the Payments Today, paying for your higher education with cryptocurrencies might sound like crazy talk. Yet, a couple of years from now on, it might very well become the reality. In fact, back in 2014, became a pioneer in this field by accepting bitcoin as payment, and thus marking its place as the first accredited US institution to do so. King’s College in New York City Processing payments for the studies requires a lot of labor and time. It also involves a lot of parties, including parents, financial institutions, scholarship-granting companies, educational institutions, and governments. By simply paying the student fee with a cryptocurrency, people would eliminate the need for intermediaries and cut down on processing fees as well as save a ton of time. New Business Models Aside from the use-cases, which focus on improving record storage, security, and efficiency, there is still a lot of potential in the blockchain technology that can be utilized in the educational sector. One of the ground-breaking applications of blockchain is the creation of new business models. Following this strategy, aims to become the very first blockchain-powered borderless university. The university was founded by a group of academics from Cambridge and Oxford. Woolf University This non-profit organization will be based on blockchain and smart contacts. These will act as the foundation of the relationship between teachers and students. How Far Are We From This Future? While the majority of countries have acknowledged the potential of blockchain, there is still a long way to go before we will use blockchain on a daily basis in our lives. One of the main obstacles on the way of wider blockchain adoption is the lack of awareness among the general public. When it comes to , people are usually divided into two groups: those who are not interested in understanding the ways this technology works, and those who are willing to learn but have limited resources to do so. learning about this technology The latter group usually comprises of younger individuals, and they are eager to learn and apply their knowledge in the industry. Yet, due to the limits of the educational system, not many universities offer blockchain-focused degrees. So, many ambitious students are choosing to learn about blockchain online, via guides and video courses. Nevertheless, as blockchain becomes more popular, many global organizations move to help educate the growing generation about this technology. In addition, the help also comes from within the industry, with a number of blockchain projects organizing events and seminars to teach students around the globe about the ins and outs of blockchain. In addition, many blockchain projects are heavily investing in developing the education sector with the help of technology. One such blockchain company, APPII from London, has collaborated with The Open University to launch a qualification and accreditation platform. The platform would be helping to manage incoming students and create tamper-proof academic records. Another ambitious project, Tokoin, has come forward with its initiative that is dubbed “Campus Meeting Events”. As a part of these events, the Indonesia-based blockchain project is visiting a number of universities across the country. In the same fashion, another blockchain startup from Estonia called Disciplina, helps students and universities get acquainted with the technology. The project’s Student App enables students to monitor their educational records. The Educator App from Disciplina helps professors build their profiles. Conclusion It is crystal clear that blockchain application is a game-changer in the education sector: it can increase productivity and security, boost efficiency and help cut costs. In addition, blockchain technology gives life to many new beginnings and projects that inevitably improve the quality of life. Hopefully, thanks to the efforts of the global authorities, educational organizations, and blockchain projects across the planet, people will soon be more comfortable with using blockchain technology in their everyday life.

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KING'S COLLEGE

kings-college.org

-- employees

Since n.d.

COMPANY RANKING

KING'S COLLEGE

EVERGREEN INDEX #5560

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STORIES ABOUT KING'S COLLEGE

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King's College WIKI

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