1,721,060 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
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
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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Mastering DeepRTS with Transformers
Full text linkThe Transformer deep learning model has recently proven its superiority in tasks like natural language processing and computer vision, as tools like ChatGPT and DALL-E have become widespread and helps humans complete tasks faster with high accuracy. This comes from the ability of Transformer models to comprehend sequential data by weighing the importance of each token in sequences through an attention mechanism and being trained on massive amounts of data. As researchers seek to apply Transformer models to other disciplines, the sequential nature of reinforcement learning tasks becomes an interesting study area. Despite the demonstrated superiority of transformer-based models in various domains, their adoption within reinforcement learning paradigms, particularly within game-based learning environments, has yet to become widespread. In particular, reinforcement learning problems where an intelligent agent learns how to act in a video game are interesting, as they can help simulate real-life scenarios and therefore make autonomous systems less expensive and safer to train. Real-Time Strategy games are complex video games where players must develop a strategy in realtime to gain an advantage over other players, and reaching game objectives often involve performing a specific sequence of actions, making them an excellent area of study for reinforcement learning combined with Transformers.
This thesis explores, evaluates and improves Transformer models applied in Real-Time Strategy Game environments with a particular focus on limited data and computational power resources. To this end, DeepRTS is chosen as the reinforcement learning environment for its high performance and simplified game mechanics, but also to enrich its relatively small research domain. This work implements several sub-environments in DeepRTS with various objectives and levels of complexity to give agents a diverse range of tasks and to compare deep learning algorithms. The authors of this thesis also contributed to the DeepRTS project by fixing source code issues to improve performance. As there is no publicly available dataset for DeepRTS to train a Transformer model on, this thesis proposes a novel model, namely the Genetic Algorithm Decision Transformer, a new implementation for data generation in reinforcement learning environments by leveraging the autoregressive Decision Transformer model for action prediction. The novelty lies in using the genetic algorithm to select the best data samples from a pool to train a Decision Transformer agent. Results are compared against a Double Deep Q-learning agent and a standard Decision Transformer agent, the latter being trained using different datasets, and results show its dependency on high-quality data. Genetic Algorithm Decision Transformer improves the aforementioned algorithms by generating its own dataset with high-quality data samples while using the same underlying Decision Transformer model. Results show that Genetic Algorithm Decision Transformer outperforms its counterpart Decision Transformer algorithm by a magnitude of up to 3.3 times the reward. However, improvements to data collection could improve the model further
Combating class imbalances in image classification - a deep neural network-based method for skin disease classification
Full text linkSkin cancer is the most common type of cancer globally, and the current estimates say that as many as one in five Americans will develop one form of skin cancer in their lifetime. While there are many types of skin cancer, some of the more severe types can lead to life expectancies of less than five years if the cancer is left untreated. While these statistics are very severe, early detection and treatment are very prominent, reaching survival rates as high as 99\% in cases where the cancer was spotted and removed early on. Unfortunately, traditional methods of diagnosis are very time-consuming and not always accurate, causing skin cancer to be one of the cancer types with the highest misdiagnosis rate.
Due to early diagnosis and treatment having such a critical role in patients' survival rate and life expectancy, together with the time-consuming and high error rate of classical diagnosis methods, a better method of diagnosing skin cancer is needed. Deep learning and convolutional neural networks have shown very promising results in image analysis in the past few years due to the ability to extract multiple features out of an image that are not recognizable to humans. Tasks that predominantly contain visual symptoms, such as skin lesions in this case, are perfect use cases where convolutional neural networks shine.
In this thesis, I have taken a deep dive into how convolutional neural networks can be used on dermoscopic images of skin cancer. To see how they performed, multiple state-of-the-art CNN models were tested, such as ResNet34, VGG16, and EfficientNet\_B4. Experimentation with methods such as utilizing different types of image augmentation, oversampling, class grouping, different train/valid/test splits, and using multiple models that vote and act as a jury were conducted to combat the severe class imbalances in the dataset. The best models achieved a classification accuracy of 84.62\% between the seven classes and 85.81\% classification accuracy when working as an anomaly detector.
In cases where a hospital wants to reduce the workload by making the model perform all of the easy classifications, a model using confidence thresholds was made. The threshold can be changed based on the accuracy requirements of the hospital. For example, If a 90\% accuracy is required, the model will achieve it while diagnosing 90\% of the patients. If a 95\% accuracy is required, it will achieve the results by diagnosing 70\% of the patients. In cases where the network is not allowed to make any mistakes, the model managed 99.5\% accuracy while still diagnosing 40\% of the patients, almost cutting the hospital's workload in half.
All of my models outperformed human experts (60-76\%) by a large margin. Because there is such a significant increase in performance, utilizing these new artificial intelligence models in the real world can save countless lives
Mastering DeepRTS with Transformers
Full text linkThe Transformer deep learning model has recently proven its superiority in tasks like natural language processing and computer vision, as tools like ChatGPT and DALL-E have become widespread and helps humans complete tasks faster with high accuracy. This comes from the ability of Transformer models to comprehend sequential data by weighing the importance of each token in sequences through an attention mechanism and being trained on massive amounts of data. As researchers seek to apply Transformer models to other disciplines, the sequential nature of reinforcement learning tasks becomes an interesting study area. Despite the demonstrated superiority of transformer-based models in various domains, their adoption within reinforcement learning paradigms, particularly within game-based learning environments, has yet to become widespread. In particular, reinforcement learning problems where an intelligent agent learns how to act in a video game are interesting, as they can help simulate real-life scenarios and therefore make autonomous systems less expensive and safer to train. Real-Time Strategy games are complex video games where players must develop a strategy in real-time to gain an advantage over other players, and reaching game objectives often involve performing a specific sequence of actions, making them an excellent area of study for reinforcement learning combined with Transformers.
This thesis explores, evaluates and improves Transformer models applied in Real-Time Strategy Game environments with a particular focus on limited data and computational power resources. To this end, DeepRTS is chosen as the reinforcement learning environment for its high performance and simplified game mechanics, but also to enrich its relatively small research domain. This work implements several sub-environments in DeepRTS with various objectives and levels of complexity to give agents a diverse range of tasks and to compare deep learning algorithms. The authors of this thesis also contributed to the DeepRTS project by fixing source code issues to improve performance. As there is no publicly available dataset for DeepRTS to train a Transformer model on, this thesis proposes a novel model, namely the Genetic Algorithm Decision Transformer, a new implementation for data generation in reinforcement learning environments by leveraging the autoregressive Decision Transformer model for action prediction. The novelty lies in using the genetic algorithm to select the best data samples from a pool to train a Decision Transformer agent. Results are compared against a Double Deep Q-learning agent and a standard Decision Transformer agent, the latter being trained using different datasets, and results show its dependency on high-quality data. Genetic Algorithm Decision Transformer improves the aforementioned algorithms by generating its own dataset with high-quality data samples while using the same underlying Decision Transformer model. Results show that Genetic Algorithm Decision Transformer outperforms its counterpart Decision Transformer algorithm by a magnitude of up to 3.3 times the reward. However, improvements to data collection could improve the model further
Generation and Evaluation of Coherent Game Terrain using Conditional Diffusion Models
Full text linkThe manual creation of expansive game terrains can be a significant bottleneck in video game
development. Procedural Content Generation via Machine Learning (PCGML), particularly
using Denoising Diffusion Probabilistic Models (DDPMs), offers a promising method for
automation. This thesis investigates the effectiveness of conditional DDPMs in generating coherent 2D heightmaps for three distinct 3D game terrain archetypes, including ’Flat’,
’Mountain Ridges’, and ’Mountain Rivers’, from a custom procedural dataset. A key objective was to develop and apply a comprehensive quantitative framework, including statistical
analysis, Power Spectral Density (PSD) comparison, a Convolutional Neural Network (CNN)
classifier, and a novel aggregate Terrain Similarity Score (TSS), to evaluate the generated
terrains’ fidelity.
The methodology involved creating a 15,000-image dataset using Perlin and ridged noise,
training a conditional U-Net based DDPM, and evaluating its outputs. Results show the
DDPM learned to generate visually recognizable and structurally distinct archetypes, achieving an overall TSS of 75.02/100, significantly outperforming a random noise baseline (29.83/100).
While generated terrains replicated relative spectral distributions and general visual characteristics, they consistently exhibited smoother surfaces and lower mean pixel intensity than
the dataset. The evaluation framework highlighted these nuances, with the classifier showing
high accuracy for ’Flat’ (98%) and ’Mountain Rivers’ (86%) but lower for ’Mountain Ridges’
(50%).
This study concludes that conditional DDPMs are a strong tool for generating specific game
terrain archetypes, offering good sample quality and training stability. The proposed TSS
serves as a valuable metric for assessing such generative models. Future work should aim to
enhance fine-detail replication and improve conditioning for complex features
- …
