683 research outputs found

    Deep learning based approaches for imitation learning.

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    Imitation learning refers to an agent's ability to mimic a desired behaviour by learning from observations. The field is rapidly gaining attention due to recent advances in computational and communication capabilities as well as rising demand for intelligent applications. The goal of imitation learning is to describe the desired behaviour by providing demonstrations rather than instructions. This enables agents to learn complex behaviours with general learning methods that require minimal task specific information. However, imitation learning faces many challenges. The objective of this thesis is to advance the state of the art in imitation learning by adopting deep learning methods to address two major challenges of learning from demonstrations. Firstly, representing the demonstrations in a manner that is adequate for learning. We propose novel Convolutional Neural Networks (CNN) based methods to automatically extract feature representations from raw visual demonstrations and learn to replicate the demonstrated behaviour. This alleviates the need for task specific feature extraction and provides a general learning process that is adequate for multiple problems. The second challenge is generalizing a policy over unseen situations in the training demonstrations. This is a common problem because demonstrations typically show the best way to perform a task and don't offer any information about recovering from suboptimal actions. Several methods are investigated to improve the agent's generalization ability based on its initial performance. Our contributions in this area are three fold. Firstly, we propose an active data aggregation method that queries the demonstrator in situations of low confidence. Secondly, we investigate combining learning from demonstrations and reinforcement learning. A deep reward shaping method is proposed that learns a potential reward function from demonstrations. Finally, memory architectures in deep neural networks are investigated to provide context to the agent when taking actions. Using recurrent neural networks addresses the dependency between the state-action sequences taken by the agent. The experiments are conducted in simulated environments on 2D and 3D navigation tasks that are learned from raw visual data, as well as a 2D soccer simulator. The proposed methods are compared to state of the art deep reinforcement learning methods. The results show that deep learning architectures can learn suitable representations from raw visual data and effectively map them to atomic actions. The proposed methods for addressing generalization show improvements over using supervised learning and reinforcement learning alone. The results are thoroughly analysed to identify the benefits of each approach and situations in which it is most suitable

    Predicting a Protein's Stability under a Million Mutations

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    Stabilizing proteins is a foundational step in protein engineering. However, the evolutionary pressure of all extant proteins makes identifying the scarce number of mutations that will improve thermodynamic stability challenging. Deep learning has recently emerged as a powerful tool for identifying promising mutations. Existing approaches, however, are computationally expensive, as the number of model inferences scales with the number of mutations queried. Our main contribution is a simple, parallel decoding algorithm. Our Mutate Everything is capable of predicting the effect of all single and double mutations in one forward pass. It is even versatile enough to predict higher-order mutations with minimal computational overhead. We build Mutate Everything on top of ESM2 and AlphaFold, neither of which were trained to predict thermodynamic stability. We trained on the Mega-Scale cDNA proteolysis dataset and achieved state-of-the-art performance on single and higher-order mutations on S669, ProTherm, and ProteinGym datasets. Code is available at https://github.com/jozhang97/MutateEverythingComment: NeurIPS 2023. Code available at https://github.com/jozhang97/MutateEverythin

    Resource allocation technique for powerline network using a modified shuffled frog-leaping algorithm

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    Resource allocation (RA) techniques should be made efficient and optimized in order to enhance the QoS (power & bit, capacity, scalability) of high-speed networking data applications. This research attempts to further increase the efficiency towards near-optimal performance. RA’s problem involves assignment of subcarriers, power and bit amounts for each user efficiently. Several studies conducted by the Federal Communication Commission have proven that conventional RA approaches are becoming insufficient for rapid demand in networking resulted in spectrum underutilization, low capacity and convergence, also low performance of bit error rate, delay of channel feedback, weak scalability as well as computational complexity make real-time solutions intractable. Mainly due to sophisticated, restrictive constraints, multi-objectives, unfairness, channel noise, also unrealistic when assume perfect channel state is available. The main goal of this work is to develop a conceptual framework and mathematical model for resource allocation using Shuffled Frog-Leap Algorithm (SFLA). Thus, a modified SFLA is introduced and integrated in Orthogonal Frequency Division Multiplexing (OFDM) system. Then SFLA generated random population of solutions (power, bit), the fitness of each solution is calculated and improved for each subcarrier and user. The solution is numerically validated and verified by simulation-based powerline channel. The system performance was compared to similar research works in terms of the system’s capacity, scalability, allocated rate/power, and convergence. The resources allocated are constantly optimized and the capacity obtained is constantly higher as compared to Root-finding, Linear, and Hybrid evolutionary algorithms. The proposed algorithm managed to offer fastest convergence given that the number of iterations required to get to the 0.001% error of the global optimum is 75 compared to 92 in the conventional techniques. Finally, joint allocation models for selection of optima resource values are introduced; adaptive power and bit allocators in OFDM system-based Powerline and using modified SFLA-based TLBO and PSO are propose

    A Survey of Monte Carlo Tree Search Methods

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    Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work

    Bio-inspired optimization in integrated river basin management

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    Water resources worldwide are facing severe challenges in terms of quality and quantity. It is essential to conserve, manage, and optimize water resources and their quality through integrated water resources management (IWRM). IWRM is an interdisciplinary field that works on multiple levels to maximize the socio-economic and ecological benefits of water resources. Since this is directly influenced by the river’s ecological health, the point of interest should start at the basin-level. The main objective of this study is to evaluate the application of bio-inspired optimization techniques in integrated river basin management (IRBM). This study demonstrates the application of versatile, flexible and yet simple metaheuristic bio-inspired algorithms in IRBM. In a novel approach, bio-inspired optimization algorithms Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) are used to spatially distribute mitigation measures within a basin to reduce long-term annual mean total nitrogen (TN) concentration at the outlet of the basin. The Upper Fuhse river basin developed in the hydrological model, Hydrological Predictions for the Environment (HYPE), is used as a case study. ACO and PSO are coupled with the HYPE model to distribute a set of measures and compute the resulting TN reduction. The algorithms spatially distribute nine crop and subbasin-level mitigation measures under four categories. Both algorithms can successfully yield a discrete combination of measures to reduce long-term annual mean TN concentration. They achieved an 18.65% reduction, and their performance was on par with each other. This study has established the applicability of these bio-inspired optimization algorithms in successfully distributing the TN mitigation measures within the river basin. Stakeholder involvement is a crucial aspect of IRBM. It ensures that researchers and policymakers are aware of the ground reality through large amounts of information collected from the stakeholder. Including stakeholders in policy planning and decision-making legitimizes the decisions and eases their implementation. Therefore, a socio-hydrological framework is developed and tested in the Larqui river basin, Chile, based on a field survey to explore the conditions under which the farmers would implement or extend the width of vegetative filter strips (VFS) to prevent soil erosion. The framework consists of a behavioral, social model (extended Theory of Planned Behavior, TPB) and an agent-based model (developed in NetLogo) coupled with the results from the vegetative filter model (Vegetative Filter Strip Modeling System, VFSMOD-W). The results showed that the ABM corroborates with the survey results and the farmers are willing to extend the width of VFS as long as their utility stays positive. This framework can be used to develop tailor-made policies for river basins based on the conditions of the river basins and the stakeholders' requirements to motivate them to adopt sustainable practices. It is vital to assess whether the proposed management plans achieve the expected results for the river basin and if the stakeholders will accept and implement them. The assessment via simulation tools ensures effective implementation and realization of the target stipulated by the decision-makers. In this regard, this dissertation introduces the application of bio-inspired optimization techniques in the field of IRBM. The successful discrete combinatorial optimization in terms of the spatial distribution of mitigation measures by ACO and PSO and the novel socio-hydrological framework using ABM prove the forte and diverse applicability of bio-inspired optimization algorithms

    New Biocatalytic Approaches for Alcohol Oxidations and Ketone Reductions Using (Deaza)Flavoenzymes

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    Biocatalysis is increasing in popularity compared to chemical approaches to produce the most diverse chemical components. This popularity is often the consequence of the lower environmental impact and/or of the selectivity of biocatalysts. Despite this, to compete with chemical processes, biocatalysts must fulfill many requirements. Therefore, there is a strong demand for stable, fast, and easy to produce biocatalysts. The research described in this thesis focused on the exploration of new or engineered redox enzymes that can be used for selective alcohol oxidations or ketone reductions

    FuzzTheREST - Intelligent Automated Blackbox RESTful API Fuzzer

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    In recent years, the pervasive influence of technology has deeply intertwined with human life, impacting diverse fields. This relationship has evolved into a dependency, with software systems playing a pivotal role, necessitating a high level of trust. Today, a substantial portion of software is accessed through Application Programming Interfaces, particularly web APIs, which predominantly adhere to the Representational State Transfer architecture. However, this architectural choice introduces a wide range of potential vulnerabilities, which are available and accessible at a network level. The significance of Software testing becomes evident when considering the widespread use of software in various daily tasks that impact personal safety and security, making the identification and assessment of faulty software of paramount importance. In this thesis, FuzzTheREST, a black-box RESTful API fuzzy testing framework, is introduced with the primary aim of addressing the challenges associated with understanding the context of each system under test and conducting comprehensive automated testing using diverse inputs. Operating from a black-box perspective, this fuzzer leverages Reinforcement Learning to efficiently uncover vulnerabilities in RESTful APIs by optimizing input values and combinations, relying on mutation methods for input exploration. The system's value is further enhanced through the provision of a thoroughly documented vulnerability discovery process for the user. This proposal stands out for its emphasis on explainability and the application of RL to learn the context of each API, thus eliminating the necessity for source code knowledge and expediting the testing process. The developed solution adheres rigorously to software engineering best practices and incorporates a novel Reinforcement Learning algorithm, comprising a customized environment for API Fuzzy Testing and a Multi-table Q-Learning Agent. The quality and applicability of the tool developed are also assessed, relying on the results achieved on two case studies, involving the Petstore API and an Emotion Detection module which was part of the CyberFactory#1 European research project. The results demonstrate the tool's effectiveness in discovering vulnerabilities, having found 7 different vulnerabilities and the agents' ability to learn different API contexts relying on API responses while maintaining reasonable code coverage levels.Ultimamente, a influência da tecnologia espalhou-se pela vida humana de uma forma abrangente, afetando uma grande diversidade dos seus aspetos. Com a evolução tecnológica esta acabou por se tornar uma dependência. Os sistemas de software começam assim a desempenhar um papel crucial, o que em contrapartida obriga a um elevado grau de confiança. Atualmente, uma parte substancial do software é implementada em formato de Web APIs, que na sua maioria seguem a arquitetura de transferência de estado representacional. No entanto, esta introduz uma série vulnerabilidade. A importância dos testes de software torna-se evidente quando consideramos o amplo uso de software em várias tarefas diárias que afetam a segurança, elevando ainda mais a importância da identificação e mitigação de falhas de software. Nesta tese é apresentado o FuzzTheREST, uma framework de teste fuzzy de APIs RESTful num modelo caixa preta, com o objetivo principal de abordar os desafios relacionados com a compreensão do contexto de cada sistema sob teste e a realização de testes automatizados usando uma variedade de possíveis valores. Este fuzzer utiliza aprendizagem por reforço de forma a compreender o contexto da API que está sob teste de forma a guiar a geração de valores de teste, recorrendo a métodos de mutação, para descobrir vulnerabilidades nas mesmas. Todo o processo desempenhado pelo sistema é devidamente documentado para que o utilizador possa tomar ações mediante os resultados obtidos. Esta explicabilidade e aplicação de inteligência artificial para aprender o contexto de cada API, eliminando a necessidade de analisar código fonte e acelerando o processo de testagem, enaltece e distingue a solução proposta de outras. A solução desenvolvida adere estritamente às melhores práticas de engenharia de software e inclui um novo algoritmo de aprendizagem por reforço, que compreende um ambiente personalizado para testagem Fuzzy de APIs e um Agente de QLearning com múltiplas Q-tables. A qualidade e aplicabilidade da ferramenta desenvolvida também são avaliadas com base nos resultados obtidos em dois casos de estudo, que envolvem a conhecida API Petstore e um módulo de Deteção de Emoções que fez parte do projeto de investigação europeu CyberFactory#1. Os resultados demonstram a eficácia da ferramenta na descoberta de vulnerabilidades, tendo identificado 7 vulnerabilidades distintas, e a capacidade dos agentes em aprender diferentes contextos de API com base nas respostas da mesma, mantendo níveis de cobertura aceitáveis

    Examples Galleries Generated by Interactive Genetic Algorithms

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    Long paper - Session: Design esxplorationInternational audienceExamples browsing is a common designer practice in user interface design. Several design galleries can be found on Internet. However, those galleries are hand crafted and thus limited and cumbersome to build. In this paper, we claim for tools for supporting both the production and exploration of examples. We describe a running prototype based on Interactive Genetic Algorithms (IGA), and relate an early evaluation
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