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A data representation method using distance correlation

Frontiers of Computer science 2025年第1期19卷 1-14页

作者： Xinyan LIANG Yuhua QIAN Qian GUO Keyin ZHENG Institute of Big Data Science and Industry Shanxi UniversityTaiyuan 030006China Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi UniversityTaiyuan 030006China School of Computer Science and Technology Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and TechnologyTaiyuan 030024China

Association in-between features has been demonstrated to improve the representation ability of data. However, the original association data reconstruction method may face two issues: the dimension of reconstructed data is undoubtedly higher than that of original data, and adopted association measure method does not well balance effectiveness and efficiency. To address above two issues, this paper proposes a novel association-based representation improvement method, named as AssoRep. AssoRep first obtains the association between features via distance correlation method that has some advantages than Pearson’s correlation coefficient. Then an improved matrix is formed via stacking the association value of any two features. Next, an improved feature representation is obtained by aggregating the original feature with the enhancement matrix. Finally, the improved feature representation is mapped to a low-dimensional space via principal component analysis. The effectiveness of AssoRep is validated on 120 datasets and the fruits further prefect our previous work on the association data reconstruction.

关键词： association representation distance correlation classification

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Tourist Destination Recommendation System based on Machine Learning 24

Tourist Destination Recommendation System based on Machine L...

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9th International Conference on Big data and Computing, ICBDC 2024

作者： Kongpeng, Sumitra Hanskunatai, Anantaporn Data Science and Computational Intelligence Lab Department of Computer Science School of Science King Mongkut's Institute of Technology Ladkrabang Thailand

ISBN: (纸本)9798400718205

Thailand has a wide variety of tourist attractions, making it difficult for tourist to choose where to go on vacation. The tourist destination recommendation system is a challenge for creating a system to help recommend tourist destinations that are appropriate for personal. Therefore, the principal aims of this research encompass two distinct objectives: firstly, to create a recommendation system for tourist destinations in Thailand by applying machine learning algorithms;and secondly, to analyze factors influencing tourists' choices of destinations. The dataset was gathered from an online survey conducted via Google Forms, comprising responses from 429 tourists in Thailand. In the experiments, three different types of feature selection methods were applied in a data preprocessing step. In the modeling process, four machine learning algorithms, namely Decision Tree, Random Forest, k-Nearest Neighbors (k-NN), and Multi-Layer Perceptron (MLP), were used to construct the model and compare the predictive performance of the recommendation system based on hit rate and NDCG. The experimental results showed that suggesting tourist destinations in the Central region was the most effective, with the highest hit rate and NDCG compared to other regions. The average hit rate and NDCG for the five regions were 0.8 and 0.59, respectively. In addition, there has been an analysis of key factors influencing destination selection, such as activity, travel month, travel budget, and the age of tourists, to understand their impact on travel choices in each region of Thailand. © 2024 Owner/Author.

关键词： Adversarial machine learning

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G3DC:A Gene-Graph-Guided Selective Deep Clustering Method for Single Cell RNA-seq data

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Big data Mining and Analytics 2024年第3期7卷 809-827页

作者： Shuqing He Jicong Fan Tianwei Yu Department of Statistics University of MichiganAnn ArborMI 48109USA School of Data Science The Chinese University of Hong KongShenzhen(CUHK-Shenzhen)Shenzhen 518172China Shenzhen Research Institute of Big Data Shenzhen 518172China Warshel Institute for Computational Biology Shenzhen 518172China

Single-cell RNA sequencing(scRNA-seq)technology measures the expression of thousands of genes at the cellular *** single-cell transcriptome allows the identification of heterogeneous cell groups,cellular-level regulations,and the trajectory of cell *** important aspect in the analyses of scRNA-seq data is the clustering of cells,which is hampered by issues,such as high dimensionality,cell type imbalance,redundancy,and *** cells of each type are functionally consistent,incorporating biological relations among genes may improve the clustering *** light of this,we have developed a deep-embedded clustering method,*** method combines a graph regularization based on the pre-existing gene network and a feature selector based on the$l$2,1-norm regularization,along with a reconstruction loss,to generate a discriminatory and informative *** the gene interaction network bolsters the clustering performance and aids in selecting functionally coherent genes,consequently enriching the clustering *** experiments have shown that G3DC offers high clustering accuracy with regard to agreement with true cell types,outperforming other leading single-cell clustering *** addition,G3DC selects biologically relevant genes that contribute to the clustering,providing insight into biological functionality that differentiates cell groups.

关键词： gene graphs feature selection deep learning

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Minimal Counters, Maximum Insight: Simplifying System Performance With HPC Clusters for Optimized Monitoring

IEEE Computer Architecture Letters

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IEEE Computer Architecture Letters 2025年第1期24卷 177-180页

作者： Shukla, Shubhi Singh, Abhijeet Chakraborty, Rajdeep Chakraborty, Anirban Rathod, Tejas Mumbaikar, Harshal Munigala, Manoj Kumar Madhusudhan, K.N. Mitra, Pabitra Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Centre for Computational Data Sciences India Indian Institute of Technology Kharagpur Computer Science and Engineering Department India Intel Corporation Bangalore India

As computer systems become more complex, evaluating performance requires tracking various hardware performance counters that capture the system's internal activities. While these counters provide valuable insights, their growing number makes it challenging to identify the most relevant ones for performance analysis. In this paper, we investigate the correlation between performance counter values and overall system performance, while also exploring the inter-correlation between different counters. Our findings demonstrate that specific counters are strongly correlated with key performance metrics and that significant redundancy exists among counters. By leveraging these relationships, we propose a method for selecting a small, representative set of performance counters. This streamlined set can further be used to accurately predict performance score across various workloads and system configurations. © 2002-2011 IEEE.

关键词： Complex networks

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Sequence to Graph Alignment Using Gap-Sensitive Co-linear Chaining 27th

Sequence to Graph Alignment Using Gap-Sensitive Co-linear C...

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27th International Conference on Research in computational Molecular Biology, RECOMB 2023

作者： Chandra, Ghanshyam Jain, Chirag Department of Computational and Data Sciences Indian Institute of Science KA Bangalore560012 India

ISBN: (纸本)9783031291180

Co-linear chaining is a widely used technique in sequence alignment tools that follow seed-filter-extend methodology. It is a mathematically rigorous approach to combine short exact matches. For co-linear chaining between two sequences, efficient subquadratic-time chaining algorithms are well-known for linear, concave and convex gap cost functions [Eppstein et al. JACM’92]. However, developing extensions of chaining algorithms for directed acyclic graphs (DAGs) has been challenging. Recently, a new sparse dynamic programming framework was introduced that exploits small path cover of pangenome reference DAGs, and enables efficient chaining [Makinen et al. TALG’19, RECOMB’18]. However, the underlying problem formulation did not consider gap cost which makes chaining less effective in practice. To address this, we develop novel problem formulations and optimal chaining algorithms that support a variety of gap cost functions. We demonstrate empirically the ability of our provably-good chaining implementation to align long reads more precisely in comparison to existing aligners. For mapping simulated long reads from human genome to a pangenome DAG of 95 human haplotypes, we achieve precision while leaving reads unmapped. Implementation: https://***/at-cg/minichain. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cost functions

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A Machine Learning Model for Active-Break Spell Forecasting of Indian Summer Monsoon from Outgoing Longwave Radiation data

A Machine Learning Model for Active-Break Spell Forecasting ...

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2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024

作者： Singh, Apoorva Dinesh Jha, Ritesh Nanjundiah, Ravi Subramani, Deepak Indian Institute of Science Department of Computational and Data Sciences Bengaluru560094 India Indian Institute of Science Center for Atmospheric and Oceanic Sciences Bengaluru560094 India

ISBN: (纸本)9798350360325

Forecasting the active/break spell of the Indian Summer Monsoon is important for planning flood management and irrigation activities. In the present work, we develop a simple machine learning model to forecast the active/break spells at multiple lead times. The input to the model is a sequence of 15 days of daily 1°× 1° gridded Outgoing Longwave Radiation (OLR) spatial maps. The spatial extents are from 30°S to 30°N and 80°W to 150°E. The output from the model is whether the next 5-day, 10-day and 15-day periods have active, neutral or break spells. The active/neutral spells are defined as a 5-day period with cumulative rainfall more/less than one standard deviation of the long term 5-day mean. This simple model consists of a linear dimensionality reduction in space and multiple classical classification models that use the principal components in time as input. This simple model achieves an F1 score of 0.46 for the first, second and third pentad forecasts. These results will serve as a common sense baseline for the development of any model based on complex neural networks. The code can be found at https://***/Shikamaru-Nara1729/OLR-PCA-Baseline. © 2024 IEEE.

关键词： Artificial Intelligence Indian Summer Monsoon OLR data

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Asynchrony-Tolerant Schemes to Enhance Scalability of High-Order Compressible Flow Solver 31

Asynchrony-Tolerant Schemes to Enhance Scalability of High-O...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Aswin Kumar, A. Vadlamani, Nagabhushana Rao Aditya, Konduri Department of Aerospace Engineering Indian Institute of Technology Madras Chennai India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

High-order asynchrony-tolerant (AT) schemes are used to solve the compressible Navier-Stokes equations. The AT schemes are validated for accuracy using canonical flow problems and are found to be in excellent agreement with the standard schemes. Strong and weak scaling analyses are performed, and significant speedups (up to 3.5×) are observed for the asynchronous algorithms compared to the standard synchronous algorithm. © 2024 IEEE

关键词： Navier Stokes equations

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey 17

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chaudhuri, Arunava Shukla, Shubhi Bhattacharya, Sarani Mukhopadhyay, Debdeep Indian Institute of Technology Kharagpur Department of Computer Science and Engineering Kharagpur India Indian Institute of Technology Kharagpur Centre for Computational and Data Sciences Kharagpur India

ISBN: (纸本)9798331531195

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges. © 2025 IEEE.

关键词： Adversarial machine learning

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Toward Responsible Artificial Intelligence Systems: Safety and Trustworthiness 8th

Toward Responsible Artificial Intelligence Systems: Safety a...

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8th International Conference on Engineering of Computer-Based Systems, ECBS 2023

作者： Herrera, Francisco Department Computer Sciences and Artificial Intelligence Andalusian Research Institute on Data Science and Computational Intelligence University of Granada Granada Spain

ISBN: (纸本)9783031492518

This short paper associated to the invited lectures introduces two key concepts essential to artificial intelligence (AI), the area of trustworthy AI and the concept of responsible AI systems, fundamental to understand the technological, ethical and legal context of the current framework of debate and regulation of AI. The aim is to understand their dimension and their interrelation with the rest of the elements involved in the regulation and auditability of AI algorithms in order to achieve safe and trusted AI. We highlight concepts in bold in order to fix the moment when they are described in context. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Artificial intelligence

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Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device 31

Evaluating Multi-Instance DNN Inferencing on Multiple Accele...

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31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Tayal, Mumuksh Simmhan, Yogesh Robert Bosch Center for Cyber Physical Systems Indian Institute of Science Bangalore560012 India Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India

ISBN: (纸本)9798331509118

Edge devices like Nvidia Jetson have started to have multiple on-board accelerators such as GPU CUDA cores, Tensor Cores and Deep Learning Accelerators (DLA). Maximizing the DNN inferencing performance of such devices requires us to concurrently use these col-located hardware components, but this has not yet been studied. We analyze the performance of accelerators present in Jetson AGX Orin, both independently and concurrently, using multiple instances of ResNet50 model. We assess the effects of using different combinations of the components and varying batch sizes on the inference throughput and latency. Our results indicate that using CUDA Core with Tensor Cores offers a higher throughput, while using them in conjunction with DLAs reduces the benefits. This paves the way to explore more intelligent configurations to maximize the performance of edge platforms for AI workloads. © 2024 IEEE

关键词： Tensors

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