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International Conference on VLSI Systems, Architecture, technology and Applications (VLSI-SATA)

作者： Divarshana Saxena Biswajit Bhowmik Department of Computer Science and Engineering Swami Vivekananda NoC Lab BRICS Lab National Institute of Technology Karnataka Surathkal Mangalore Bharat

ISBN: (数字)9798350362268

ISBN: (纸本)9798350362275

Microservice architecture has become a widely accepted solution to address the challenges, particularly scala-bility, deployment, and flexibility associated with monolithic architecture. A vital attribute of the microservices architecture is its capability to handle load balancing on a large scale. The load balancer collaborates with a scaler to distribute the workload efficiently across multiple instances. In the literature, different studies employ load-balancing algorithms for efficient microservice load balancing. These works overlook cloud-based microservice applications or focus solely on virtual machines, neglecting containers. This paper addresses these limitations by comparatively assessing selected load-balancing algorithms. The three most used algorithms, random, round-robin, and least connection, are studied on a microservice application. The extensive experiments are conducted using Elastic Container Service (ECS) of Amazon Web Service (AWS) for containerized cloud setup where each service resides in a cluster and traffic is generated through Locust. Experimental results show that throughput and response time range of 6.2-288.7 and 312.2-3375.8 ms, respectively.

关键词： Cloud computing Heuristic algorithms Software algorithms Microservice architectures Clustering algorithms computer architecture Very large scale integration

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FusionPortableV2: A Unified Multi-Sensor Dataset for Generalized SLAM Across Diverse Platforms and Scalable Environments

arXiv

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arXiv 2024年

作者： Wei, Hexiang Jiao, Jianhao Hu, Xiangcheng Yu, Jingwen Xie, Xupeng Wu, Jin Zhu, Yilong Liu, Yuxuan Wang, Lujia Liu, Ming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Robot Perception and Learning Lab Department of Computer Science University College London United Kingdom Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology China Guangzhou China

Simultaneous Localization and Mapping (SLAM) technology has been widely applied in various robotic scenarios, from rescue operations to autonomous driving. However, the generalization of SLAM algorithms remains a significant challenge, as current datasets often lack scalability in terms of platforms and environments. To address this limitation, we present FusionPortableV2, a multi-sensor SLAM dataset featuring sensor diversity, varied motion patterns, and a wide range of environmental scenarios. Our dataset comprises 27 sequences, spanning over 2.5 hours and collected from four distinct platforms: a handheld suite, a legged robot, a unmanned ground vehicle (UGV), and a vehicle. These sequences cover diverse settings, including buildings, campuses, and urban areas, with a total length of 38.7km. Additionally, the dataset includes ground-truth (GT) trajectories and RGB point cloud maps covering approximately 0.3km2. To validate the utility of our dataset in advancing SLAM research, we assess several state-of-the-art (SOTA) SLAM algorithms. Furthermore, we demonstrate the dataset's broad application beyond traditional SLAM tasks by investigating its potential for monocular depth estimation. The completae dataset, including sensor data, GT, and calibration details, is accessible at https://***/dataset/fusionportable v2. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

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Optimizing Split Algorithm Performance: A Heuristic Method for Enhanced Tensor Product Matrix Computations

Optimizing Split Algorithm Performance: A Heuristic Method f...

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IEEE India Conference (INDICON)

作者： Biswajit Bhowmik Sunil Kumar Sanjay Ramapogu Raju Anupam Prakash Omkar Mense Department of Computer Science and Engineering Maharshi Sushrut CAS Lab BRICS Lab National Institute of Technology Surathkal Mangalore Karnataka India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks.

关键词： Wireless sensor networks Tensors High dimensional data Scalability Heuristic algorithms High performance computing Robustness Computational efficiency Distributed computing Optimization

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A Case Study on Context-Aware Neural Machine Translation with Multi-Task Learning

arXiv

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arXiv 2024年

作者： Appicharla, Ramakrishna Gain, Baban Pal, Santanu Ekbal, Asif Bhattacharyya, Pushpak Department of Computer Science and Engineering Indian Institute of Technology Patna India Wipro AI Lab45 London United Kingdom School of AI and Data Science Indian Institute of Technology Jodhpur India Department of Computer Science and Engineering Indian Institute of Technology Bombay India

In document-level neural machine translation (DocNMT), multi-encoder approaches are common in encoding context and source sentences. Recent studies (Li et al., 2020) have shown that the context encoder generates noise and makes the model robust to the choice of context. This paper further investigates this observation by explicitly modelling context encoding through multi-task learning (MTL) to make the model sensitive to the choice of context. We conduct experiments on cascade MTL architecture, which consists of one encoder and two decoders. Generation of the source from the context is considered an auxiliary task, and generation of the target from the source is the main task. We experimented with German–English language pairs on News, TED, and Europarl corpora. Evaluation results show that the proposed MTL approach performs better than concatenation-based and multi-encoder DocNMT models in low-resource settings and is sensitive to the choice of context. However, we observe that the MTL models are failing to generate the source from the context. These observations align with the previous studies, and this might suggest that the available document-level parallel corpora are not context-aware, and a robust sentence-level model can outperform the context-aware models. © 2024, CC0.

关键词： Neural machine translation

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Online Ensemble of Ensemble OVA Framework for Class Evolution with Dominant Emerging Classes

Online Ensemble of Ensemble OVA Framework for Class Evolutio...

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IEEE International Conference on Data Mining (ICDM)

作者： Zhi Cao Shuyi Zhang Chin-Teng Lin Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China CIBCI Lab School of Computer Science University of Technology Sydney Australia CERCIA School of Computer Science University of Birmingham Birmingham UK

In real-world data stream mining, the composition of classes undergoes unpredictable changes, giving rise to the challenge of class evolution, encompassing class emergence, disappearance, and reoccurrence. However, most existing approaches require the storage of past data to adapt their model. While some studies have focused on online learning approaches, they are built on an underlying assumption that the number of instances in any single class is consistently less than the sum of other classes. This assumption becomes invalid when a class emerges with a dominant amount, e.g., news about a pandemic outbreak, harming the performance of existing methods. In this paper, we thoroughly investigate this scenario and propose a novel online ensemble of ensemble one-versus-all framework (EEOF) to handle class evolution adaptively. The novel ensemble of ensemble architecture boosts diversity in each one-versus-all classifier. A novel adaptive model adaptation method is also designed to balance the error feedback between the emerging class and the other classes. A confidence-triggered fallback mode is integrated to prevent performance drop due to a wrong decision regarding class disappearance. Experimental studies are conducted on both synthetic and real-world data streams to show that our method achieves higher accuracy in diverse class evolution scenarios compared with the state-of-the-art method, particularly when classes emerge with dominant amounts.

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Non-Uniform Illumination Attack for Fooling Convolutional Neural Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Jain, Akshay Dubey, Shiv Ram Singh, Satish Kumar Santosh, K.C. Chaudhuri, Bidyut Baran Indian Institute of Information Technology Allahabad Computer Vision and Biometrics Lab Department of Information Technology Uttar Pradesh Prayagraj211015 India University of South Dakota AI Research Lab Department of Computer Science VermillionSD57069 United States Indian Statistical Institute Computer Vision and Pattern Recognition Unit Kolkata700108 India

Convolutional Neural Networks (CNNs) have made remarkable strides;however, they remain susceptible to vulnerabilities, particularly to image perturbations that humans can easily recognize. This weakness, often termed as 'attacks,' underscores the limited robustness of CNNs and the need for research into fortifying their resistance against such manipulations. This study introduces a novel Non-Uniform Illumination (NUI) attack technique, where images are subtly altered using varying NUI masks. Extensive experiments are conducted on widely-accepted datasets including CIFAR10, TinyImageNet, CalTech256 and NWPU-RESISC45 focusing on image classification with 12 different NUI masks. The resilience of VGG, ResNet, MobilenetV3-small, InceptionV3 and efficientNet b0 models against NUI attacks are evaluated. Our results show a substantial decline in the CNN models' classification accuracy when subjected to NUI attacks, due to changes in the image pixel value distribution, indicating their vulnerability under non-uniform illumination. To mitigate this, a defense strategy is proposed, including NUI-attacked images, generated through the new NUI transformation, into the training set. The results demonstrate a significant enhancement in CNN model performance when confronted with perturbed images affected by NUI attacks. This strategy seeks to bolster CNN models' resilience against NUI attacks. A comparative study with other attack techniques shows the effectiveness of the NUI attack and defense technique. © 2020 IEEE.

关键词： Convolutional neural networks

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Intrusion Detection System for IoHT Devices using Federated Learning

Intrusion Detection System for IoHT Devices using Federated ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Mosaiyebzadeh, Fatemeh Pouriyeh, Seyedamin Parizi, Reza M. Han, Meng Batista, Daniel Macêdo University of São Paulo Department of Computer Science Brazil Kennesaw State University Department of Information and Technology MariettaGA United States Kennesaw State University Decentralized Science Lab MariettaGA United States Binjiang Institute Zhejiang University Zhejiang Hangzhou China

ISBN: (纸本)9781665494274

With the growing number of sensitive data transmit-ted in IT infrastructures, healthcare organizations and compa-nies that generate users' wearable data have become a target for attackers. To protect electronic healthcare data, Internet of Healthcare Things (IoHT) devices must be protected by robust Intrusion Detection Systems (IDS) to provide a secure environment. Since it is undesirable to collect this data and perform machine learning tasks directly, recently, to preserve users' privacy, federated learning has obtained attention from the government and healthcare organizations. Unlike the cen-tralized paradigm, federated learning is a privacy-aware machine learning framework designed to analyze data without sharing it. This paper proposes a deep neural network in federated learning (DNN-FL) to detect anomalies in the IoHT traffic that may result in security threats. We evaluate the detection performance of our proposal using metrics such as accuracy and precision. The proposed DNN-FL is validated using the wustl-ehms-2020 and ECU- IoHT datasets. It reached 91.40% of accuracy in detecting attacks in the first dataset and 98.47% in the second. All the developed source code in this work is being made publicly available to ensure reproducibility. © 2023 IEEE.

关键词： Sensitive data

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PEAC: unsupervised pre-training for cross-embodiment reinforcement learning 24

PEAC: unsupervised pre-training for cross-embodiment reinfor...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Chengyang Ying Zhongkai Hao Xinning Zhou Xuezhou Xu Hang Su Xingxing Zhang Jun Zhu Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University and Pazhou Lab (Huangpu) Guangzhou China

ISBN: (纸本)9798331314385

Designing generalizable agents capable of adapting to diverse embodiments has achieved significant attention in Reinforcement Learning (RL), which is critical for deploying RL agents in various real-world applications. Previous Cross-Embodiment RL approaches have focused on transferring knowledge across embodiments within specific tasks. These methods often result in knowledge tightly coupled with those tasks and fail to adequately capture the distinct characteristics of different embodiments. To address this limitation, we introduce the notion of Cross-Embodiment Unsupervised RL (CEURL), which leverages unsupervised learning to enable agents to acquire embodiment-aware and task-agnostic knowledge through online interactions within reward-free environments. We formulate CEURL as a novel Controlled Embodiment Markov Decision Process (CE-MDP) and systematically analyze CEURL's pre-training objectives under CE-MDP. Based on these analyses, we develop a novel algorithm Pre-trained Embodiment-Aware Control (PEAC) for handling CEURL, incorporating an intrinsic reward function specifically designed for cross-embodiment pre-training. PEAC not only provides an intuitive optimization strategy for cross-embodiment pre-training but also can integrate flexibly with existing unsupervised RL methods, facilitating cross-embodiment exploration and skill discovery. Extensive experiments in both simulated (e.g., DMC and Robosuite) and real-world environments (e.g., legged locomotion) demonstrate that PEAC significantly improves adaptation performance and cross-embodiment generalization, demonstrating its effectiveness in overcoming the unique challenges of CEURL. The project page and code are in https://***/ceurl.

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Pay Self-Attention to Audio-Visual Navigation 33

Pay Self-Attention to Audio-Visual Navigation

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33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Yu, Yinfeng Cao, Lele Sun, Fuchun Liu, Xiaohong Wang, Liejun Department of Computer Science and Technology State Key Lab on Intelligent Technology and Systems Tsinghua University Beijing China Motherbrain EQT Stockholm Sweden College of Information Science and Engineering Xinjiang University Urumqi China

Audio-visual embodied navigation, as a hot research topic, aims training a robot to reach an audio target using egocentric visual (from the sensors mounted on the robot) and audio (emitted from the target) input. The audio-visual information fusion strategy is naturally important to the navigation performance, but the state-of-the-art methods still simply concatenate the visual and audio features, potentially ignoring the direct impact of context. Moreover, the existing approaches requires either phase-wise training or additional aid (e.g. topology graph and sound semantics). Up till this date, the work that deals with the more challenging setup with moving target(s) is still rare. As a result, we propose an end-to-end framework FSAAVN (feature self-attention audio-visual navigation) to learn chasing after a moving audio target using a context-aware audio-visual fusion strategy implemented as a self-attention module. Our thorough experiments validate the superior performance (both quantitatively and qualitatively) of FSAAVN in comparison with the state-of-the-arts, and also provide unique insights about the choice of visual modalities, visual/audio encoder backbones and fusion patterns. © 2022. The copyright of this document resides with its authors.

关键词： Robots

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Abstract: A Spatiotemporal Model for Precise and Efficient Fully-automatic 3D Motion Correction in OCT

Abstract: A Spatiotemporal Model for Precise and Efficient F...

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Bildverarbeitung für die Medizin Workshop, BVM 2023

作者： Ploner, Stefan B. Chen, Siyu Won, Jungeun Husvogt, Lennart Breininger, Katharina Schottenhamml, Julia Fujimoto, James G. Maier, Andreas K. Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany Department of Electrical Engineering and Computer Science Research Laboratory for Electronics Massachusetts Institute of Technology CambridgeMA United States

ISBN: (纸本)9783658416560

Optical coherence tomography (OCT) is a micrometer-scale, volumetric imaging modality that has become a clinical standard in ophthalmology. OCT instruments image by raster-scanning a focused light spot across the retina, acquiring sequential cross-sectional images to generate volumetric data. Patient eye motion during the acquisition poses unique challenges: Non-rigid, discontinuous distortions can occur, leading to gaps in data and distorted topographic measurements. We present a new distortion model and a corresponding fully-automatic, reference-free optimization strategy for computational motion correction in orthogonally raster-scanned, retinal OCT volumes. Using a novel, domain-specific spatiotemporal parametrization of forward-warping displacements, eye motion can be corrected continuously for the first time. Parameter estimation with temporal regularization improves robustness and accuracy over previous spatial approaches. We correct each A-scan individually in 3D in a single mapping, including repeated acquisitions used in OCT angiography protocols. Specialized 3D forward image warping reduces median runtime to © 2023 Der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature.

关键词： Optical tomography

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