205 research outputs found
Review on the book “Historical Essays about Life on the Islands of the Northern Dvina Delta” by M.A. Lukina
No full textThe book is the result of a great work of M. A. Lukina on the history of villages in the Delta of the Northern Dvina River. The book is the first study of the island colonization, establishment, and centuries-old development of settlements in the mouth of the Northern Dvina River. The author combined the traditional historical approach to the data presentation and the method associated with the study of the value change over the centuries and historical memory
Interpretability and performance comparisons of decision tree surrogate models produced by AGGREVATE
No full textImitation learning algorithms, such as AggreVaTe, have proven successful in solving many challenging tasks accurately and efficiently. In practice, however, they have not been applied quite as much. Black box policies produced by imitation learning algorithms can not ensure the safety needed for real-world applications. This paper extends this field by outputting a decision tree surrogate model from AggreVaTe and comparing it to other imitation learning algorithms (Behavioral cloning, GAIL, DAgger, Viper) in terms of interpretability as well as performance. A modification to AggreVaTe is proposed to train decision tree policies that can be used to explain individual decision-making of the model. Three simple environments of open AI Gym have been used to compare the multiple different imitation learning algorithms. The experiments reveal that on performance, AggreVaTe overall performs better than the baseline behavioral cloning but slightly worse than GAIL, DAgger and Viper. AggreVaTe performs slightly better in terms of interpretability on these simple environments. Both of these conclusions could be explained by the fewer data points used by AggreVaTe. Further study can be done into the subjective interpretability of AggreVaTe as well as more difficult environments where the extra exploring of AggreVaTe should help with finding the best solution.CSE3000 Research ProjectComputer Science and Engineerin
Evaluation of complete efficiency of power systems on the basis of generalized net power characteristics
No full textThe author has formed a system of characteristics, has proposed the use of the concept of complete exergic efficiency and net exergic factor as a characteristic of the fuel-and-power complex and its branches, the application of the exergic balance to relieve the possibilities of power saving. The author has recommended to use the net power as a basis for analysing home power system including gas inductry and fuel-and-power complex of the country; has determined the scope of possible fuel saving in heat supply. The investigation results have been used in the development of the idea of prospective gas production by the GAZPROM ConcernAvailable from VNTIC / VNTIC - Scientific & Technical Information Centre of RussiaSIGLERURussian Federatio
Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models
No full textMachine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning, specifically Generative Adversarial ImitationLearning (GAIL), can be used to give insights into the black-box models by extracting decision trees. To achieve this, an extension of GAIL was made allowing it to extract decision trees. The decision trees were then measured in terms of performance, fidelity, behavior, and interpretability in three different environments. We find that GAIL is able to extract decision trees with high fidelity and can give insightful information into the expert models. Moreover, further research can be done on more complex environments and black-box models, other surrogate models, and possibilities for more specific local insights.CSE3000 Research ProjectComputer Science and Engineerin
Interpretability and performance of surrogate decision trees produced by Viper
No full textMachine learning models are being used extensively in many high impact scenarios. Many of these models are ‘black boxes’, which are almost impossible to interpret. Successful implementations have been limited by this lack of interpretability. One approach to increasing interpretability is to use imitation learning to extract a more interpretable surrogate model from a black box model. Our aim is to evaluate Viper, an imitation learning algorithm, in terms of performance and interpretability. To achieve this, we evaluate surrogate decision tree models produced by Viper on three different environments and attempt to interpret these models. We find that Viper generally produces high performance interpretable decision trees, and that performance and interpretability are highly dependent on context and oracle quality. We compare Viper performance to similarimitation learning approaches, and find that it performs as good as or better than these approaches, though our comparison is limited by the differences in oracle quality.CSE3000 Research ProjectComputer Science and Engineerin
The problem of detaching metatext in artistic text
No full textThe article is devoted to one of the discussion problem of modern linguistic. This article dives basic for necessity of detaching metatext in artistic text and for its full analysis. Besides the aggregate of language units is outline in this article. This aggregate is representant of metatext in artistic text, the author cites bright examples of metameans
Clustering faces of comic characters: An experimental investigation
No full textFace clustering is a subfield of computer vision and pattern recognition with many applications such as face recognition and surveillance. Accurate clustering of faces can also help us to create labeled datasets. However, in the domain of comics, face clustering is not well studied. Therefore, it is uncertain which methods of feature extraction and clustering perform well on faces of comic characters. In this paper, we investigate the effectiveness of comic face clustering. To conduct our investigation, we implement two pipelines: one that automatically extracts character faces from comic strips, and another that clusters the extracted faces. Using Dilbert Comics for our experiments, we examine the performance of various feature extraction and clustering methods. Additionally, we experiment with combining feature extraction methods and removing noisy samples to increase the clustering accuracy. We show that using color information is crucial for accurate clustering, and combining color with shape features further improves accuracy. However, our experiments indicate that accuracy improvement is not guaranteed for every combination of feature extraction methods. We also demonstrate that removing noisy samples using hierarchical clustering can increase clustering precision. Using our findings, we achieve an F1 score of 0.752 based on our Dilbert Comics dataset of 77,768 face images. We obtain this result by clustering 20,988 non-noisy face images into 35 clusters with a precision of 0.886.CSE3000 Research ProjectComputer Science and Engineerin
Active Monitoring of Neural Networks
No full textNeural-network classifiers are trained to achieve high prediction accuracy. However, their performance still suffers from frequently appearing inputs of unknown classes. As a component of a cyber-physical system, the classifier in this case can no longer be reliable and is typically retrained. We propose an algorithmic framework for monitoring reliability of a neural network. In contrast to static detection, a monitor wrapped in our framework operates in parallel with the classifier, communicates interpretable labeling queries to the human user, and incrementally adapts to their feedback.Algorithmic
Combining Multi-Objective Planning with Reinforcement Learning to Solve Complex Tasks in Environments with Sparse Rewards
No full textSequential decision-making problems are problems where the goal is to find a sequence of actions that complete a task in an environment. A particularly difficult type of sequential decision-making problem to solve is one in which the environment has sparse rewards, a large state space, and where the goal is to complete a complex task. In this research we create a controller that can be used to solve these types of environments in cases where the task needs to be optimized for multiple objectives. We create MOPRL, an approach that combines techniques from planning, formal methods, and reinforcement learning to synthesize such a controller. WElectrical Engineering | Embedded SystemsComputer Scienc
Comics Illustration Synthesizer using the Stable Diffusion Model: Fine-tuning for text-to-image Dilbert Comics Generation
No full textSynthetic art is the end result of artificial intelligence models that have been trained to generate images from text prompts. "Comic synthesis" is one such use case, where comic illustrations are produced from textual descriptions. Previous attempts at comic synthesis have utilized conditional Generative Adversarial Networks (cGANs), but this approach has encountered challenges in generating consistent and visually appealing comic panels. Strict data requirements and quality limitations have left room for improvement. We propose a novel approach to comic synthesis using Stable Diffusion, a powerful generative modelling technique. The study investigates the fine-tuning of the stable diffusion model specifically for the generation of Dilbert Comics from textual prompts. We explore different techniques to fine-tune the stable diffusion model for comic synthesis including Dreambooth and LoRA. Through extensive experimentation and analysis, with an FID score of 123, results produced using the Lora technique outperformed Dreambooth, excelling in understanding the Dilbert style, while Dreambooth struggled with multiple-subject training. Results are also compared with previous approaches based on conditional GANs. While the quality and detail greatly improved, the transition from conditionals to text descriptions meant the results were less accurate. The results show the potential of stable diffusion in generating appealing Dilbert Comic panels while highlighting the need for further exploration to enhance the alignment between textual descriptions and the generated images.CSE3000 Research ProjectComputer Science and Engineerin
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