15,364 research outputs found

    Analysis and Mitigation of Shared Resource Contention on Heterogeneous Multicore: An Industrial Case Study

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    In this paper, we address the industrial challenge put forth by ARM in ECRTS 2022. We systematically analyze the effect of shared resource contention to an augmented reality head-up display (AR-HUD) case-study application of the industrial challenge on a heterogeneous multicore platform, NVIDIA Jetson Nano. We configure the AR-HUD application such that it can process incoming image frames in real-time at 20Hz on the platform. We use micro-architectural denial-of-service (DoS) attacks as aggressor tasks of the challenge and show that they can dramatically impact the latency and accuracy of the AR-HUD application, which results in significant deviations of the estimated trajectories from the ground truth, despite our best effort to mitigate their influence by using cache partitioning and real-time scheduling of the AR-HUD application. We show that dynamic LLC (or DRAM depending on the aggressor) bandwidth throttling of the aggressor tasks is an effective mean to ensure real-time performance of the AR-HUD application without resorting to over-provisioning the system

    GlobalMind: Global Multi-head Interactive Self-attention Network for Hyperspectral Change Detection

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    High spectral resolution imagery of the Earth's surface enables users to monitor changes over time in fine-grained scale, playing an increasingly important role in agriculture, defense, and emergency response. However, most current algorithms are still confined to describing local features and fail to incorporate a global perspective, which limits their ability to capture interactions between global features, thus usually resulting in incomplete change regions. In this paper, we propose a Global Multi-head INteractive self-attention change Detection network (GlobalMind) to explore the implicit correlation between different surface objects and variant land cover transformations, acquiring a comprehensive understanding of the data and accurate change detection result. Firstly, a simple but effective Global Axial Segmentation (GAS) strategy is designed to expand the self-attention computation along the row space or column space of hyperspectral images, allowing the global connection with high efficiency. Secondly, with GAS, the global spatial multi-head interactive self-attention (Global-M) module is crafted to mine the abundant spatial-spectral feature involving potential correlations between the ground objects from the entire rich and complex hyperspectral space. Moreover, to acquire the accurate and complete cross-temporal changes, we devise a global temporal interactive multi-head self-attention (GlobalD) module which incorporates the relevance and variation of bi-temporal spatial-spectral features, deriving the integrate potential same kind of changes in the local and global range with the combination of GAS. We perform extensive experiments on five mostly used hyperspectral datasets, and our method outperforms the state-of-the-art algorithms with high accuracy and efficiency.Comment: 14 page, 18 figure

    Graph-based Algorithm Unfolding for Energy-aware Power Allocation in Wireless Networks

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    We develop a novel graph-based trainable framework to maximize the weighted sum energy efficiency (WSEE) for power allocation in wireless communication networks. To address the non-convex nature of the problem, the proposed method consists of modular structures inspired by a classical iterative suboptimal approach and enhanced with learnable components. More precisely, we propose a deep unfolding of the successive concave approximation (SCA) method. In our unfolded SCA (USCA) framework, the originally preset parameters are now learnable via graph convolutional neural networks (GCNs) that directly exploit multi-user channel state information as the underlying graph adjacency matrix. We show the permutation equivariance of the proposed architecture, which is a desirable property for models applied to wireless network data. The USCA framework is trained through a stochastic gradient descent approach using a progressive training strategy. The unsupervised loss is carefully devised to feature the monotonic property of the objective under maximum power constraints. Comprehensive numerical results demonstrate its generalizability across different network topologies of varying size, density, and channel distribution. Thorough comparisons illustrate the improved performance and robustness of USCA over state-of-the-art benchmarks.Comment: Published in IEEE Transactions on Wireless Communication

    Data Balancing Techniques for Predicting Student Dropout Using Machine Learning

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    This research article was published MDPI, 2023Predicting student dropout is a challenging problem in the education sector. This is due to an imbalance in student dropout data, mainly because the number of registered students is always higher than the number of dropout students. Developing a model without taking the data imbalance issue into account may lead to an ungeneralized model. In this study, different data balancing techniques were applied to improve prediction accuracy in the minority class while maintaining a satisfactory overall classification performance. Random Over Sampling, Random Under Sampling, Synthetic Minority Over Sampling, SMOTE with Edited Nearest Neighbor and SMOTE with Tomek links were tested, along with three popular classification models: Logistic Regression, Random Forest, and Multi-Layer Perceptron. Publicly accessible datasets from Tanzania and India were used to evaluate the effectiveness of balancing techniques and prediction models. The results indicate that SMOTE with Edited Nearest Neighbor achieved the best classification performance on the 10-fold holdout sample. Furthermore, Logistic Regression correctly classified the largest number of dropout students (57348 for the Uwezo dataset and 13430 for the India dataset) using the confusion matrix as the evaluation matrix. The applications of these models allow for the precise prediction of at-risk students and the reduction of dropout rates

    HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning

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    Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly-labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a view generation method for representation learning using joint embedding architectures that enhances representation learning for histology images. HistoPerm permutes augmented views of patches extracted from whole-slide histology images to improve classification performance. We evaluated the effectiveness of HistoPerm on two histology image datasets for Celiac disease and Renal Cell Carcinoma, using three widely used joint embedding architecture-based representation learning methods: BYOL, SimCLR, and VICReg. Our results show that HistoPerm consistently improves patch- and slide-level classification performance in terms of accuracy, F1-score, and AUC. Specifically, for patch-level classification accuracy on the Celiac disease dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal Cell Carcinoma dataset, patch-level classification accuracy is increased by 2% for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease dataset, models with HistoPerm outperform the fully-supervised baseline model by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for the models up to 10% relative to the fully-supervised baseline. These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully-supervised methods

    FairGen: Towards Fair Graph Generation

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    There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite the remarkable success, the vast majority of these works are unsupervised in nature and are typically trained to minimize the expected graph reconstruction loss, which would result in the representation disparity issue in the generated graphs, i.e., the protected groups (often minorities) contribute less to the objective and thus suffer from systematically higher errors. In this paper, we aim to tailor graph generation to downstream mining tasks by leveraging label information and user-preferred parity constraint. In particular, we start from the investigation of representation disparity in the context of graph generative models. To mitigate the disparity, we propose a fairness-aware graph generative model named FairGen. Our model jointly trains a label-informed graph generation module and a fair representation learning module by progressively learning the behaviors of the protected and unprotected groups, from the `easy' concepts to the `hard' ones. In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability. Experimental results on seven real-world data sets, including web-based graphs, demonstrate that FairGen (1) obtains performance on par with state-of-the-art graph generative models across six network properties, (2) mitigates the representation disparity issues in the generated graphs, and (3) substantially boosts the model performance by up to 17% in downstream tasks via data augmentation

    Evolving Decision Rules with Geometric Semantic Genetic Programming

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceDue to the ever increasing amount of data available in today’s world, a variety of methods to harness this information are continuously being created, refined and utilized, drawing inspiration from a multitude of sources. Relevant to this work are Supervised Learning techniques, that attempt to discover the relationship between the characteristics of data and a certain feature, to uncover the function that maps input to output. Among these, Genetic Programming (GP) attempts to replicate the concept of evolution as defined by Charles Darwin, mimicking natural selection and genetic operators to generate and improve a population of solutions for a given prediction problem. Among the possible variants of GP, Geometric Semantic Genetic Programming (GSGP) stands out, due to its focus on the meaning of each individual it creates, rather than their structure. It achieves by imagining an hypothetical and perfect model, and evaluating the performance of others by measuring how much their behaviour differ from it, and uses a set of genetic operators that have a specific effect on the individual’s semantics (i.e., its predictions for training data), with the goal of reaching ever closer to the so called perfect specimen. This thesis conceptualizes and evaluates the performance of aGSGPimplementation made specifically to deal with multi-class classification problems, using tree-based individuals that are composed by a set of rules to allow the categorization of data. This is achieved through the careful translation of GSGP’s theoretical foundation, first into algorithms and then into an actual code library, able to tackle problems of this domain. The results demonstrate that the implementation works successfully and respects the properties of the the original technique, allowing us to obtain excellent results on training data, although performance on unseen data is a slightly worse than that of other state-of-the-art algorithms.Devido à crescente quantidade de dados do mundo de hoje, uma variedade de métodos para utilizar esta informação é continuamente criada, melhorada e utilizado, com inspiração de diversas fontes. Com particular relevância para este trabalho são técnicas de Supervised Learning, que visam descobrir a relação entre as características dos dados e um traço específico destes, de modo a encontrar uma função que consiga mapear os inputs aos outputs. Entre estas, Programação Genética (PG) tenta recriar o conceito de evolução como definido por Charles Darwin, imitando a seleção natural e operadores genéticos para gerar e melhorar uma população de soluções para um dado problema preditivo. Entre as possíveis variantes de PG, Programação Genética em Geometria Semântica (PGGS) é notável, pois coloca o seu foco no significado de cada indivíduo que cria, em vez da sua estrutura. Realiza isto ao imaginar um modelo hipotético e perfeito, e avaliar as capacidades dos outros medindo o quão diferente o seu comportamento difere deste, e utiliza um conjunto de operadores genéticos com um efeito específico na semântica de um indíviduo (i.e., as suas previsões para dados de treino), visando chegar cada vez mais perto ao tão chamado espécime perfeito. Esta tese conceptualiza e avalia o desempenho de uma implementação de PGGS feita especificamente para lidar com problemas de classificação multi-classe, utilizando indivíduos baseados em árvores compostos por uma série de regras que permitem a categorização de dados. Isto é feito através de uma tradução cuidadosa da base teórica de PGGS, primeiro para algoritmos e depois para uma biblioteca de código, capaz de enfrentar problemas deste domínio. Os resultados demonstram que a implementação funciona corretamente e respeita as propriedades da técnica original, permitindo que obtivéssemos resultados excelentes nos dados de treino, embora o desempenho em dados não vistos seja ligeiramente abaixo de outros algoritmos de última geração

    Intelligent architecture to support second generation general accounting

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThis study aimed to innovate the world of accounting software. After so many years, accountants are faced with an unbelievable amount of work, which is not always productive, effective and efficient for both the accountant and the company that provided him with the data required to carry out the accounting. There is already accounting software with various automation processes, from ornamentation to profitability analysis and management reporting. There is also software that is updated in accordance with the accounting laws, i.e., the platform changes its mechanisms according to the changes in the law. Despite the existence of this software, manual work remains, and the amount of information accountants are faced with is still very large. It is difficult for accountants to do a 100% reliable job with so much information and data they have. One of the most common situations in the accounting world is undoubtedly the miscalculation or forgetting of some financial or non-financial data found in accounting operations (income statements, balance sheets, etc.). To render accounting operations efficient, effective and productive, errorfree and 100% reliable, an intelligent architecture has been developed to support second generation general accounting. This architectural design was developed with a view to make the existing software smarter with the help of artificial intelligence. A study was carried out on accounting keys and concepts, on AI and main process automation techniques to build the model. With these studies it was intended to acquire all possible requirements for the creation of the architecture. Towards the end of the thesis the model was validated

    ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT

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    Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human natural language. However, LLMs currently have difficulty in bridging perception, language understanding and reasoning capabilities due to incompatibility of the underlying information flow among them, making it challenging to accomplish tasks autonomously. On the other hand, abductive learning (ABL) frameworks for integrating the two abilities of perception and reasoning has seen significant success in inverse decipherment of incomplete facts, but it is limited by the lack of semantic understanding of logical reasoning rules and the dependence on complicated domain knowledge representation. This paper presents a novel method (ChatABL) for integrating LLMs into the ABL framework, aiming at unifying the three abilities in a more user-friendly and understandable manner. The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format. Similarly, perceptual module provides necessary reasoning examples for LLMs in natural language format. The variable-length handwritten equation deciphering task, an abstract expression of the Mayan calendar decoding, is used as a testbed to demonstrate that ChatABL has reasoning ability beyond most existing state-of-the-art methods, which has been well supported by comparative studies. To our best knowledge, the proposed ChatABL is the first attempt to explore a new pattern for further approaching human-level cognitive ability via natural language interaction with ChatGPT
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