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Balanced And Discriminative Contrastive Learning For Class-Imbalanced Medical Images

Balanced And Discriminative Contrastive Learning For Class-I...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xuewei Li Yilong Fan Hao Zheng Jie Gao Xi Wei Mei Yu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of Feature Technology Tianjin University Tianjin China Tianjin Medical University Cancer Institute and Hospital Tianjin China

The class imbalance problem, which is prevalent in medical image datasets, seriously affects the diagnostic effectiveness of deep learning-based network models. Recently, the method based on two-stage learning has produced promising results in solving class imbalance. In two-stage learning, the learning of unbiased classifiers has been well studied, but the representation of imbalanced data is still being explored. In this paper, we focus on the representation learning stage of class-imbalanced and propose a novel balanced and discriminative contrastive learning (BDCL) method. Compared with supervised contrastive learning, BDCL has two improvements: temperature dynamic learning, which balances the gradient contribution of negative samples from different classes; hard example prototypes, which can better learn the feature differences between tail classes. A variety of experimental results on the imbalanced medical image datasets show that BDCL enables the network models to learn representations with desirable balancedness and discriminativeness, effectively solving the class imbalance problem.

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Apptainer-Based Containers for Legacy and Platform Dependent Machine Learning Applications on HPC Systems

Apptainer-Based Containers for Legacy and Platform Dependent...

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ieee international conference on Big data

作者： Rajesh Pasupuleti Ravi Vadapalli Christopher Mader Victor J. Milenkovic Frost Institute for Data Science and Computing(IDSC) University of Miami Coral Gables Miami FL USA Department of Computer Science University of Miami Coral Gables Miami FL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Apptainer (Formerly known as Singularity) is a secure, portable, and easy-to-use container system that provides absolute trust and security. It is widely used across industry and academia and suitable for filling the gaps in integration between running applications on new software technologies and legacy hardware using the optimized resource utilization of CPU and memory. It runs complex applications on HPC clusters in a simple, reproducible way. In this paper we are discussing about various implementations of Artificial Intelligence and Machine learning container-based applications running on Pegasus Supercomputing Nodes using Singularity, Nextflow. It reduces configuration setup work manually by singularity applications and it increases current workflows of High-Performance computing (HPC), High Throughput computing (HTC) and run time performance by 3X. we also incorporated comparative based evaluation analytical results of running an application through normal LSF job with singularity container CPU, GPU utilization and its tradeoffs.

关键词： Industries High performance computing Graphics processing units Machine learning Containers Throughput Software Hardware Security Resource management

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data-Driven Vulnerable Community Identification During Compound Disasters

Data-Driven Vulnerable Community Identification During Compo...

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ieee international conference on cognitive Machine Intelligence (CogMI)

作者： Jainil Anilkumar Patel Mohammadreza Akbari Lor Shu-Ching Chen Mei-Ling Shyu Steven Luis School of Science and Engineering University of Missouri-Kansas City Kansas City MO USA Data Science and Analytics Innovation Center (dSAIC) University of Missouri-Kansas City Kansas City MO USA Knight Foundation School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

This paper aims to introduce research directions to identify vulnerable communities during compound disasters, such as the intersection of hurricanes and pandemics like COVID-19. We suggest integrating mobility, socioeconomic, and geographic factors to develop a comprehensive vulnerability index. By analyzing mobility data, U.S. Census socioeconomic data, disaster data, pandemic data, and geographic data, we extract information like hospital visits, business closures, increased travel distances, decreased inbound movement to commercial census block groups (CBGs), decreased outbound from home CBGs, and changes in mobility trends to various categories of stores, to develop a vulnerability index that integrates all of these factors. This index aims to capture and analyze the unique impacts of natural disasters on communities, especially those exacerbated by simultaneous disaster scenarios. We also propose a model design that can capture the spatial and temporal nature of the data and can be trained to perform the dual task of vulnerability index calculation in a disaster scenario along with predicting pandemic growth features in case of a compound disaster.

关键词： Pandemics Hospitals Disasters Predictive models Market research data models Real-time systems Indexes Compounds Socioeconomics

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Quantum PC Algorithm: data-Efficient and Nonlinear Causal Discovery

Quantum PC Algorithm: Data-Efficient and Nonlinear Causal Di...

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Quantum computing and Engineering (QCE), ieee international conference on

作者： Yota Maeda Ken Arai Yu Tanaka Yu Terada Hiroshi Ueno Hiroyuki Tezuka Advanced Research Laboratory Research Platform Sony Group Corporation Tokyo Japan

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Causal discovery is the task of finding causal relationships between random variables from observed data. Typically, one assumes that the causal relationships can be represented by a directed acyclic graph (DAG), and makes additional assumptions to ensure that the DAG can be recovered from the observed data. In this study, we propose the quantum Peter-Clark (qPC) algorithm for nonlinear causal discovery based on quantum kernel methods. The qPC algorithm takes advantage of the quantum kernel-based conditional independent test. Through the synthetic data experiment, we show that the qPC algorithm outperforms the classical method in the regime of a small number of samples. This suggests that the kernel-based causal discovery can significantly improve performance under such conditions. Our experiments highlight that the proposed algorithm can accurately support classical algorithms in causal discovery, paving the way for future advances with the utilization of quantum computation.

关键词： Directed acyclic graph Quantum advantage Costs Statistical analysis Computational modeling Random variables Kernel complex systems Synthetic data

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A Multi-granularity Graph-based Representation for Image Recognition

A Multi-granularity Graph-based Representation for Image Rec...

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Power Electronics, Computer Applications (ICPECA), ieee international conference on

作者： Danfeng Lan Dawei Dai Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China

Graph neural networks (GNNs) are specially designed to process graph data because of their ability to effectively capture complex structures and relationships within graphs. However, due to the pixel-based nature of images, representing them as graphs and then using graph networks poses challenges. Inspired by granular computing, we propose a method to compute multi-granularity structured features from image tiles, which facilitates efficient and direct graph-based representation learning. Specifically: (1) We design a method to reorganize all regional features into multiple tiles and construct a multi-granularity graph representation, which includes nodes and edges. (2) We use gray-level co-occurrence matrices to extract the image texture features to compensate for the information lost during graph representation. (3) We design a graph neural network classifier that combines the above graph features and image texture features. Experimental evaluations on two benchmark image classification datasets demonstrate the efficiency of our proposed method and its potential to establish a new paradigm for image processing. Codes are available at https://***/ddw2AIGROUP2CQUPT/MGRIR.

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PhD Forum: MalFormer001- Multimodal Transformer Fused Attention based Malware Detector

PhD Forum: MalFormer001- Multimodal Transformer Fused Attent...

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international conference on Smart computing (SMARTCOMP)

作者： Pradip Kunwar Department of Computer Science Tennessee Tech University Cookeville USA

ISBN: (数字)9798350349948

ISBN: (纸本)9798350349955

Current systems, relying heavily on single-modality analysis, often fail to identify sophisticated malware such as zero-day attacks. Our proposed Malformer model offers a novel approach by integrating multiple data modalities-text, image, and graph, using advanced transformer models and cross-attention fusion mechanisms. This multimodal approach aims to capture a comprehensive spectrum of malware characteristics that might have been missed under single-modality analysis. The model's integration of cross-modal attention and modality fusion techniques is expected to garner a deeper understanding and identification of complex malware characteristics, potentially establishing a new frontier in cybersecurity defense mechanisms.

关键词： Computational modeling Detectors Transformers Malware data models Computer security

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Demystifying Cyberattacks: Potential for Securing Energy Systems With Explainable AI

Demystifying Cyberattacks: Potential for Securing Energy Sys...

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international conference on computing, Networking and Communications (ICNC)

作者： Shuva Paul Sanjana Vijayshankar Richard Macwan Energy Security and Resilience National Renewable Energy Laboratory Golden Colorado USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

Modernization of energy systems has led to in-creased interactions among multiple critical infrastructures and diverse stakeholders making the challenge of operational decision making more complex and at times beyond cognitive capabilities of human operators. The state-of-the-art machine learning and deep learning approaches show promise of supporting users with complex decision-making challenges, such as those occur-ring in our rapidly transforming cyber-physical energy systems. However, successful adoption of data-driven decision support technology for critical infrastructure will be dependent on the ability of these technologies to be trustworthy and contextu-ally interpretable. In this paper, we investigate the feasibility of implementing explainable artificial intelligence (XAI) for interpretable detection of cyberattacks in the energy system. Leveraging a proof-of-concept simulation use case of detection of a data falsification attack on a photovoltaic system using XGBoost algorithm, we demonstrate how Local Interpretable Model-Agnostic Explanations (LIME), a flavor XAI approach, can help provide contextual and actionable interpretation of cyberattack detection.

关键词： Photovoltaic systems Deep learning Explainable AI Computational modeling Decision making data models Critical infrastructure

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Leveraging In-Network computing and Programmable Switches for Streaming Analysis of Scientific data 7

Leveraging In-Network Computing and Programmable Switches fo...

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ieee 7th international conference on Network Softwarization (NetSoft) - Accelerating Network Softwarization in the cognitive Age

作者： Sankaran, Ganesh C. Chung, Joaquin Kettimuthu, Raj Univ Calif Davis Davis CA 95616 USA Argonne Natl Lab Lemont IL USA

ISBN: (纸本)9781665405225

With the emergence of programmable network devices that match the performance of fixed function devices, several recent projects have explored in-network computing, where the processing that is traditionally done outside the network is offloaded to the network devices. In-network computing has typically been applied to network functions (e.g., load balancing, NAT, and DNS), caching, data reduction/aggregation, and coordination/consensus functions. In some cases it has been used to accelerate stream-processing tasks that involve small payloads and simple operations. In this work we focus on leveraging in-network computing for stream processing of scientific datasets with large payloads that require complex operations such as floating-point computations and logarithmic functions. We demonstrate in-network computing for a real-world scientific application performing streaming normalization of a 2-D image from a light source experiment. We discuss the challenges we encountered and potential approaches to address them.

关键词： in-network computing programmable switches scientific streaming analysis

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StreamDP: Continual Observation of Real-world data Streams with Differential Privacy

StreamDP: Continual Observation of Real-world Data Streams w...

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ieee international conference on Performance, computing and Communications (IPCCC)

作者： Shuailou Li Yu Wen Zhaoyang Wang Wenbo Wang Lisong Zhang Dan Meng Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

The real-time collection and query analysis of dynamic data streams have become increasingly common and important, yet the protection of sensitive private information remains a pressing challenge. Differential privacy, as the gold standard for protecting personal data privacy, has been widely studied and applied. However, existing mechanisms mostly focus on static datasets and specific simple stream queries. This paper presents StreamDP, a novel framework designed to achieve differential privacy for complex real-world stream queries. We introduce the observation-prediction mechanism that predicts statistics such as join attribute frequency using observations and truncates the data stream based on the predicted threshold. Then we design operation-oriented recursive sensitivity calculation rules and employ a hierarchy algorithm for noise perturbation. Extensive experimental evaluations on multiple real-world datasets and distributed stream processing benchmarks show that StreamDP can support various complex real-world data stream queries/applications with high utility and low-performance overhead.

关键词： Differential privacy Sensitivity Perturbation methods Noise Distributed databases Pressing Prediction algorithms Real-time systems Protection Streams

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PowerHeat: A non-intrusive approach for estimating the power consumption of bare metal water-cooled servers

PowerHeat: A non-intrusive approach for estimating the power...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Maxime Agusti Eddy Caron Benjamin Fichel Laurent Lefèvre Olivier Nicol Anne-Cécile Orgerie OVHcloud France Inria Université de Lyon (UCBL Lyon1) CNRS ENS de Lyon LIP France Inria CNRS IRISA Univ. Rennes France

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

Numerous cloud providers offer physical servers for rental in bare metal paradigm. This mode gives customers total control over hardware resources, but limits cloud providers’ visibility of their usage. Accurately measuring server energy consumption in this context represents a major challenge, as installing physical energy meters is both costly and complex. Existing energy models are generally based on system usage data, which is incompatible with the general privacy policies of bare-metal server contracts. To deal with these problems, it is imperative to develop new approaches for estimating the energy consumption of these servers. This paper presents an original non-intrusive method for estimating the energy consumption of a server cooled by direct-chip liquid-cooling, based on the coolant temperature and the processor temperature obtained via IPMI. Our approach is evaluated on an experiment carried out on 19 bare metal servers of a production infrastructure equipped with physical wattmeters.

关键词： Wattmeters Temperature sensors Energy consumption Cloud computing Power demand Computational modeling Metals Production data models Servers

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