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B.H.M. Gerritsen
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Despite the ever-growing advances in artificial intelligence (AI), common sense acquisition and reasoning is still comparingly in their early stages to other fields in AI. To further advance this field, it is necessary to collect large amounts of common sense facts or tacit knowledge to train such AI models. One effective way is to use crowdsourcing and games to make the collection process both widely available and fun at the same time. Currently, there are no tools to collect discriminative tacit knowledge efficiently and accurately. In this work, we propose FindItOut: a crowdsourcing tacit knowledge elicitation solution with multiplayer gamification and images to collect general-purpose and discriminative tacit knowledge. We show that using FindItOut, we can both efficiently and accurately collect discriminative tacit knowledge while also being fun and engaging.
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Despite the ever-growing advances in artificial intelligence (AI), common sense acquisition and reasoning is still comparingly in their early stages to other fields in AI. To further advance this field, it is necessary to collect large amounts of common sense facts or tacit knowledge to train such AI models. One effective way is to use crowdsourcing and games to make the collection process both widely available and fun at the same time. Currently, there are no tools to collect discriminative tacit knowledge efficiently and accurately. In this work, we propose FindItOut: a crowdsourcing tacit knowledge elicitation solution with multiplayer gamification and images to collect general-purpose and discriminative tacit knowledge. We show that using FindItOut, we can both efficiently and accurately collect discriminative tacit knowledge while also being fun and engaging.
The manual process of collecting and labelling data required for machine learning tasks is labour-intensive, expensive, and time consuming. In the past, efforts have been made to crowdsource this data by either offering people monetary incentives, or by using a gamified approach where users contribute to databases as a side-effect of playing an enjoyable game. However, most of these efforts focus on using a competitive setting to incentivize players. This sometimes results in users spamming the dataset for personal gains. Research is lacking in how a collaborative setup, where players work together to make decisions by consensus, can be used to source knowledge that is more accurate and reliable. This paper describes the design and evaluation of SceneFinder, a game that aims to crowdsource reliable and diverse textual data about scenes (such as rooms, parks, monuments, etc) and the tacit knowledge relevant to them, such as information about their contents, their purpose and their surroundings. SceneFinder makes use of a collaborative setup that elicits a relevance based ranking of facts about these scenes, that distinguishes it from existing games in the field.
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The manual process of collecting and labelling data required for machine learning tasks is labour-intensive, expensive, and time consuming. In the past, efforts have been made to crowdsource this data by either offering people monetary incentives, or by using a gamified approach where users contribute to databases as a side-effect of playing an enjoyable game. However, most of these efforts focus on using a competitive setting to incentivize players. This sometimes results in users spamming the dataset for personal gains. Research is lacking in how a collaborative setup, where players work together to make decisions by consensus, can be used to source knowledge that is more accurate and reliable. This paper describes the design and evaluation of SceneFinder, a game that aims to crowdsource reliable and diverse textual data about scenes (such as rooms, parks, monuments, etc) and the tacit knowledge relevant to them, such as information about their contents, their purpose and their surroundings. SceneFinder makes use of a collaborative setup that elicits a relevance based ranking of facts about these scenes, that distinguishes it from existing games in the field.
We introduce Swipe for Science, a single-player mobile game designed for collecting discriminative evidence from crowds. When people play this game, we collect data about how certain concepts are associated with different contexts, which is valuable knowledge for machine learning systems. The main gameplay loop consists of swiping concepts up, down, left, or right based on how these is associated with the given contexts. The game is simplistic and intuitive to control, and collects data of overall good quality, but it is not engaging enough to retain its players. Further development and research is required to unlock the full potential of this game.
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We introduce Swipe for Science, a single-player mobile game designed for collecting discriminative evidence from crowds. When people play this game, we collect data about how certain concepts are associated with different contexts, which is valuable knowledge for machine learning systems. The main gameplay loop consists of swiping concepts up, down, left, or right based on how these is associated with the given contexts. The game is simplistic and intuitive to control, and collects data of overall good quality, but it is not engaging enough to retain its players. Further development and research is required to unlock the full potential of this game.
Tacit knowledge, unlike explicit knowledge, is not easily codifiable, yet important for machine learning models. This research explores a method to gather tacit knowledge about humor using a simple text-based party game, building on the existing idea of using games to gather tacit knowledge from crowds of people. Players propose prompts, which will then be answered by other players. They will then vote to determine which of the two answers to each prompt is the funniest. The engagement of the players with the game is measured and tacit knowledge is obtained from the jokes. With a large and diverse enough group of participants across games, a variety of tacit knowledge can be extracted.
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Tacit knowledge, unlike explicit knowledge, is not easily codifiable, yet important for machine learning models. This research explores a method to gather tacit knowledge about humor using a simple text-based party game, building on the existing idea of using games to gather tacit knowledge from crowds of people. Players propose prompts, which will then be answered by other players. They will then vote to determine which of the two answers to each prompt is the funniest. The engagement of the players with the game is measured and tacit knowledge is obtained from the jokes. With a large and diverse enough group of participants across games, a variety of tacit knowledge can be extracted.
Machine learning can still make harmful mistakes. A solution would be tacit knowledge. Machine learning needs this type of knowledge to improve. An example of such knowledge that can help make the system draw better logical conclusions would be: if presented with an open fridge, then it could deduct that the food will go bad. Tacit knowledge or common-sense knowledge refers to the type of knowledge which is acquired through experience, the kind only humans can create. GWAPs (game with a purpose) have shown quite promising results for acquiring such knowledge. Unfortunately, it could still contain errors due to users who only want to harm the game data, etc. and there is no method for validating such knowledge without involving humans somehow. Therefore, from the previously stated problem, our goal has emerged - develop a method for validating an existing data set and for later training machine learning models using a GWAP. There has been work done before using GWAPs to elicit such information, yet they are limited in the sense that their main focus is set on data collection, not validation. Since very few projects looked into it, we decided to investigate a new GWAP which has as main purpose tacit knowledge validation. The main question which we aim to answer is "How can we elicit and validate tacit knowledge using a game with the following settings: single-player, textual concepts, goal: associate words with their concepts." The game presents hints to the users and they have to guess, as fast as possible and with the least amount of tries as possible, which answer is correct from the 6 options that are provided. The evaluation of the game will be made using standard metrics such as games played, time spent playing, number of users, etc. The conclusion is that the GWAP, even with the lack of data, was quite capable of analyzing the quality of the data set and reached a conclusion that is easily confirmed by a mere look over the initial data set.
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Machine learning can still make harmful mistakes. A solution would be tacit knowledge. Machine learning needs this type of knowledge to improve. An example of such knowledge that can help make the system draw better logical conclusions would be: if presented with an open fridge, then it could deduct that the food will go bad. Tacit knowledge or common-sense knowledge refers to the type of knowledge which is acquired through experience, the kind only humans can create. GWAPs (game with a purpose) have shown quite promising results for acquiring such knowledge. Unfortunately, it could still contain errors due to users who only want to harm the game data, etc. and there is no method for validating such knowledge without involving humans somehow. Therefore, from the previously stated problem, our goal has emerged - develop a method for validating an existing data set and for later training machine learning models using a GWAP. There has been work done before using GWAPs to elicit such information, yet they are limited in the sense that their main focus is set on data collection, not validation. Since very few projects looked into it, we decided to investigate a new GWAP which has as main purpose tacit knowledge validation. The main question which we aim to answer is "How can we elicit and validate tacit knowledge using a game with the following settings: single-player, textual concepts, goal: associate words with their concepts." The game presents hints to the users and they have to guess, as fast as possible and with the least amount of tries as possible, which answer is correct from the 6 options that are provided. The evaluation of the game will be made using standard metrics such as games played, time spent playing, number of users, etc. The conclusion is that the GWAP, even with the lack of data, was quite capable of analyzing the quality of the data set and reached a conclusion that is easily confirmed by a mere look over the initial data set.
The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes are able to directly access memory on adjacent nodes and can therefore overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced ThymesisFlow memory disaggregation system. The framework extends the functionality of the existing Apache Arrow Plasma object store framework to distributed systems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development cost. In addition, the paper includes latency and throughput measurements to evaluate the framework’s performance and to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework.
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The concept of memory disaggregation has recently been gaining traction in research. With memory disaggregation, data center compute nodes are able to directly access memory on adjacent nodes and can therefore overcome local memory restrictions, introducing a new data management paradigm for distributed computing. This paper proposes and demonstrates a memory disaggregated in-memory object store framework for big data applications by leveraging the newly introduced ThymesisFlow memory disaggregation system. The framework extends the functionality of the existing Apache Arrow Plasma object store framework to distributed systems by enabling clients to easily and efficiently produce and consume data objects across multiple compute nodes. This allows big data applications to increasingly leverage parallel processing at reduced development cost. In addition, the paper includes latency and throughput measurements to evaluate the framework’s performance and to guide the design of future systems that leverage memory disaggregation as well as the newly presented framework.
Recent times once more informed us on the relevance of capable online collaborative tools. For our online collaborative XML editor, we have looked into technologies for constrained block editing which, obeying schemas such as with XML, permit on- and offline users or agents to add, delete, copy, move, split and merge blocks of text. To that end, we studied the current state of Operational Transformations (OT) and Conflict-free Replicated Data Types (CRDT). Furthermore, after selection of the best-ranking enabling technology, we studied existing CRDT implementations for unstructured texts, and extended a Logoot-based CRDT to implement on-and offline split and merge block support. We designed a proof of concept and created a scientific prototype to deliver a proof of concept stability, reproducibility and convergence. For now, we excluded undo and redo operations. Given these conditions, we deliver emperical evidence of our implementation to converge under all circumstances, including split and acyclic block mergers. Finally, we give an outlook and design recommendations for production implementations, and suggestions for tackling the problem of cyclic references in block mergers.
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Recent times once more informed us on the relevance of capable online collaborative tools. For our online collaborative XML editor, we have looked into technologies for constrained block editing which, obeying schemas such as with XML, permit on- and offline users or agents to add, delete, copy, move, split and merge blocks of text. To that end, we studied the current state of Operational Transformations (OT) and Conflict-free Replicated Data Types (CRDT). Furthermore, after selection of the best-ranking enabling technology, we studied existing CRDT implementations for unstructured texts, and extended a Logoot-based CRDT to implement on-and offline split and merge block support. We designed a proof of concept and created a scientific prototype to deliver a proof of concept stability, reproducibility and convergence. For now, we excluded undo and redo operations. Given these conditions, we deliver emperical evidence of our implementation to converge under all circumstances, including split and acyclic block mergers. Finally, we give an outlook and design recommendations for production implementations, and suggestions for tackling the problem of cyclic references in block mergers.
Bachelor thesis
(2020)
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J.L. Buijnsters, D. Hofman, J.G.P. Klein Kranenbarg, C. El Moussaoui, K. Zheng, B.H.M. Gerritsen, K.F. Chan, H. Wang, O.W. Visser
ScenWise is an innovative company that specializes in data science revolving around traffic management. ScenWise strives to use the newest and best technologies and practices when it comes to web applications, data science and traffic management. The reason for this is that they provide tools to analyse and visualise a variety of situations that occur in traffic management. One such tool is SmartRoads 1.0, which allows users to analyse traffic data and situations via a web application. Unfortunately SmartRoads 1.0 does not perform as desired. Additionally, ScenWise itself has the problem of not being able to integrate previously made products by student groups into their own existing products. During the research aimed to resolve these problems another issue arose; the software development life cycle of ScenWise is very lacking. Research on the SmartRoads 1.0 performance problem showed that the bottleneck of its performance is due to the front-end. The outdated SmartRoads 1.0 front-end was thus replaced with a new and better SmartRoads 2.0 front-end. The integration problem and development life cycle problem are both addressed in the Longterm evolution (LTE) design found in appendix I. This LTE design contains the architecture migration plan. This plan will transform the current software architecture to a Service-oriented architecture (SOA) providing a solution for the current integration problems. A result of the first steps of this architecture migration plan is the Application Programming Interface (API) Gateway, which has been implemented in the aforementioned SmartRoads 2.0. Next to the migration plan, guidelines for ScenWise to improve their software development life cycle are elaborated in the LTE design. In this report the identified problems, their solutions and executions are explained, discussed and evaluated.
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ScenWise is an innovative company that specializes in data science revolving around traffic management. ScenWise strives to use the newest and best technologies and practices when it comes to web applications, data science and traffic management. The reason for this is that they provide tools to analyse and visualise a variety of situations that occur in traffic management. One such tool is SmartRoads 1.0, which allows users to analyse traffic data and situations via a web application. Unfortunately SmartRoads 1.0 does not perform as desired. Additionally, ScenWise itself has the problem of not being able to integrate previously made products by student groups into their own existing products. During the research aimed to resolve these problems another issue arose; the software development life cycle of ScenWise is very lacking. Research on the SmartRoads 1.0 performance problem showed that the bottleneck of its performance is due to the front-end. The outdated SmartRoads 1.0 front-end was thus replaced with a new and better SmartRoads 2.0 front-end. The integration problem and development life cycle problem are both addressed in the Longterm evolution (LTE) design found in appendix I. This LTE design contains the architecture migration plan. This plan will transform the current software architecture to a Service-oriented architecture (SOA) providing a solution for the current integration problems. A result of the first steps of this architecture migration plan is the Application Programming Interface (API) Gateway, which has been implemented in the aforementioned SmartRoads 2.0. Next to the migration plan, guidelines for ScenWise to improve their software development life cycle are elaborated in the LTE design. In this report the identified problems, their solutions and executions are explained, discussed and evaluated.