1,721,023 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Predicting diarrhoea outbreak with climate change
Full text linkClimate change is expected to exacerbate diarrhoea outbreak in South Africa, a leading cause of morbidity and mortality in the region. In this study, we modelled the impacts of climate change on diarrhoea with machine learning methods. We applied two deep learning techniques, convolutional neural networks (CNNs) and long-short term memory networks (LSTMs); and a support vector machine to predict daily diarrhoea cases over the different South African provinces by incorporating climate information. Generative Adversarial Networks (GANs) was used to generate synthetic data which was used to augment the available dataset. Furthermore, relevance estimation and value calibration (REVAC) was used to tune the parameters of the machine learning algorithms to optimize the accuracy of their predictions. Sensitivity analysis was also performed to investigate the contribution of the different climate factors to the diarrhoea prediction model. The results of the study showed that all three ML methods were appropriate for predicting daily diarrhoea cases with respect to the selected climate variables in each South African province. The ML methods were all able to yield low and similar RMSE. However, the level of accuracy for each model varied across different experiments, with the deep learning models outperforming the SVM model. Among the deep learning techniques, the CNN model performed best when only real-world dataset was used, while the LSTM model outperformed the other models when the real dataset was augmented with synthetic data. Across the provinces, the accuracy of all three ML algorithms improved by at least 30% when data augmentation was implemented. In addition, REVAC improved the accuracy of the CNN model by more than 12% in KwaZulu Natal province. However, the percentage increase in accuracy of the LSTM model was less than 4% in Western Cape province when REVAC was used. Our sensitivity analysis revealed that the most influential climate variables to be considered when predicting outbreak of diarrhoea in South Africa are precipitation, humidity, evaporation and temperature conditions. The result of this study is important for the development of an early warning system for diarrhoea outbreak over South Africa
Neuro-evolution search methodologies for collective self-driving vehicles
Full text linkRecently there has been an increasing amount of research into autonomous vehicles for real-world driving. Much progress has been made in the past decade with many automotive manufacturers demonstrating real-world prototypes. Current predictions indicate that roads designed exclusively for autonomous vehicles will be constructed and thus this thesis explores the use of methods to automatically produce controllers for autonomous vehicles that must navigate with each other on these roads. Neuro-Evolution, a method that combines evolutionary algorithms with neural networks, has shown to be effective in reinforcement-learning, multi-agent tasks such as maze navigation, biped locomotion, autonomous racing vehicles and fin-less rocket control. Hence, a neuro-evolution method is selected and investigated for the controller evolution of collective autonomous vehicles in homogeneous teams. The impact of objective and non-objective search (and a combination of both, a hybrid method) for controller evolution is comparatively evaluated for robustness on a range of driving tasks and collection sizes. Results indicate that the objective search was able to generalise the best on unseen task environments compared to all other methods and the hybrid approach was able to yield desired task performance on evolution far earlier than both approaches but was unable to generalise as effectively over new environments
Multi-objective evolutionary algorithms for product design
Full text linkIdentifying chemical compounds with optimal properties for specific applications presents a fundamental challenge in materials science. Traditional methods, based on trialand-error, are inefficient and costly. This thesis introduces an innovative integration of Computational Chemistry and Machine Learning (ML) with Evolutionary MultiObjective Optimisation (EMOO) techniques to streamline compound design. This approach automates the design process by leveraging ML to accurately predict compound properties and using EMOO to select compounds that meet various criteria. The significance of this work lies in its potential to transform the traditional development process, facilitating the creation of chemical products that fulfill multiple objectives more efficiently. This study not only demonstrates the synergy between advanced ML and optimisation techniques but also presents a comprehensive comparison of the MultiObjective Covariance Matrix Adaptation Evolution Strategy (MO-CMA-ES) and Nondominated Sorting Genetic Algorithm II (NSGA-II), including two novel meta-heuristics for enhanced molecular exploration. Our findings reveal that MO-CMA-ES, especially when combined with an extended search meta-heuristic, excels in exploring molecular spaces, establishing it as a preferred method for compound synthesis. This research promises to accelerate compound development specifically for detergent compounds, offering significant implications for product design across various industries
Distributed autonomous intersection management with neuro-evolution
Full text linkThe sudden surge in computational power available to computer research in industry and academia has led to developments in AI automation. More and more tasks are able to be automated and replaced with machine learning systems. One such task that promises to be highly beneficial is that of driving, clearly indicated by the amount of resources being spent by companies such as Uber, Google and Tesla. Neuro-Evolution has shown promise in the field of controller development, due to its ability to develop complex behaviour without a need for any labelled training data. It has been applied previously in car controller generation, across many fields. This thesis aims to apply Neuro-Evolution specifically to the field of intersection management, in order to study which methods are the most effective for this particular task. In particular we investigate three key hyper-parameters: Neuro-Evolution algorithm, task difficulty and problem exposure. A traffic simulator was developed and the hyper-parameters were used to evolve car controllers, which where then tested on unseen tasks. We show that certain key combinations of hyper-parameters yield exceptional results, but that direct correlations between individual parameters and performance are unclear, indicating that these methods are highly sensitive to hyper-parameter selection. We further identify some areas in which to optimize the evolution method, by looking at hyper-parameters which have a computational cost but which did not produce better performance
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Scalable hierarchical evolution strategies
Full text linkHierarchical reinforcement learning (HRL) has been steadily growing in popularity for solving the hardest reinforcement learning problems. However, current HRL algorithms are relatively slow and brittle to hyperparameter changes. This paper offers a solution to these slow and brittle HRL algorithms, by investigating a novel method combining Scalable Evolution Strategies (SES) and HRL. S-ES, named for its excellent scalability, was popularised by Open AI when they showed its performance to be comparable to state-of-the art policy gradient methods. However, S-ES has not been tested in conjunction with HRL methods, which empower temporal abstraction thus allowing agents to tackle more challenging problems. We introduce a novel method merging S-ES and HRL, which creates a highly scalable and fast (wall-clock time) algorithm. We demonstrate that S-ES needs no hyper-parameter tuning for the HRL tasks tested and is indifferent to delayed rewards. This results in a method that is significantly faster than gradient-based HRL methods while having competitive task performance. We extend this method using transfer learning with the aim of increasing task performance and novelty search with the goal of improving its exploration characteristics. The paper's main contribution is thus a novel evolutionary HRL method, namely Scalable Hierarchical Evolution Strategies, which yields greater learning speed and competitive task-performance compared to state-of-the-art gradient-based methods, across a range of tasks
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Self-adapting simulated artificial societies
Full text linkAgent-Based Models (ABM) are computational models that utilize autonomous agents to interact and adapt to the environments in which they occupy. They are used in fields ranging from Economics to Ecology. More recently, ABM are being used in Computational Archaeology to aid in explaining the complex social phenomena that gave rise to ancient societies all over the world. Despite their potential, ABM are limited by the fact their agents are rarely adaptive despite adaptability often touted as one of Agent-Based Modelling's greatest strengths. In this work we remedy this by investigating whether Machine Learning (ML) algorithms can be used as adaptive mechanisms for Agent-based Models simulating complex social phenomena. We aim to do this by comparing ML agents, developed using Reinforcement Learning and two Evolutionary Algorithms as adaptive-mechanisms, to rule-based agents typically found in contemporary literature. To achieve this, we create NeoCOOP, an Agent-Based Model designed to simulate the complex social phenomena that arise from resource sharing agents in ancient societies. By conducting scenario experimentation, we examined the adaptive capacity of our four agent-types by measuring their ability to maintain both population and resources levels in a virtual re-creation of Ancient Egypt during the Predynastic Period. Our results indicate that our ML agents (Utility and IE) perform better or on par with even complex rule-based agents (Traditional and RBAdaptive). The IE agent-type ranked first and was the most adaptive agent-type. The Utility and RBAdaptive agents jointly ranked second and the Traditional agent ranked last. Overall, the findings of this work clearly show that adaptive-agents are more suited to modelling the dynamics of complex environments than their rule-based counterparts. More specifically, our results demonstrate that ML algorithms are particularly well suited as these adaptive mechanisms given that they not only allowed our agents to maintain high population and resource levels, they facilitated the emergence of additional emergent phenomena such as resource acquisition strategy specialization. It is our hope that the findings presented in this work pushes the state of the art such that future research endeavours seek to use truly adaptive-agents in their complex Archaeological AB
- …
