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5th International Conference on Intelligent Computing and Control systems, ICICCS 2021

作者： Arulkumar, V. Aruna, M. Lakshmi, M. Anantha Rao, B. Hanumantha Computer Science and Business Systems Sri Krishna College of Engineering and Technology Coimbatore India Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Ravindra College of Engineering for Women Department of Computer Science and Engineering Kurnool India PSCMR College of Engineering and Technology Vijayawada India

ISBN: (纸本)9781665412728

It is an effective technique that can be applied to visual diversity such as editing, visual criticism and the so-called fragmentation of painting images. Creativity, image-based optimization and high-resolution frame work were showcased based on the record-breaking success of a minority representation process. The proposed structure for reconstructing the image is learned from a complete dictionary and much of the image input used for two important problems. It was first used by creating a Gaussian noise figure and then blaming the rain. Special structures using conventional methods suggest that the production environment is grappling with artistic solutions and problems. The proposed method was implemented in rich color using ultra-resolution and demo techniques with removing sound and craftsmanship. © 2021 IEEE.

关键词： Image denoising

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Improving Zero-Shot Cross-Lingual Transfer via Progressive Code-Switching

arXiv

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arXiv 2024年

作者： Li, Zhuoran Hu, Chunming Chen, Junfan Chen, Zhijun Guo, Xiaohui Zhang, Richong SKLSDE School of Computer Science and Engineering Beihang University Beijing China School of Software Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

Code-switching is a data augmentation scheme mixing words from multiple languages into source lingual text. It has achieved considerable generalization performance of cross-lingual transfer tasks by aligning cross-lingual contextual word representations. However, uncontrolled and over-replaced code-switching would augment dirty samples to model training. In other words, the excessive code-switching text samples will negatively hurt the models' cross-lingual transferability. To this end, we propose a Progressive Code-Switching (PCS) method to gradually generate moderately difficult code-switching examples for the model to discriminate from easy to hard. The idea is to incorporate progressively the preceding learned multilingual knowledge using easier code-switching data to guide model optimization on succeeding harder code-switching data. Specifically, we first design a difficulty measurer to measure the impact of replacing each word in a sentence based on the word relevance score. Then a code-switcher generates the code-switching data of increasing difficulty via a controllable temperature variable. In addition, a training scheduler decides when to sample harder code-switching data for model training. Experiments show our model achieves state-of-the-art results on three different zero-shot cross-lingual transfer tasks across ten languages. Copyright © 2024, The Authors. All rights reserved.

关键词： Zero-shot learning

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FedVAE: Trajectory privacy preserving based on Federated Variational AutoEncoder

FedVAE: Trajectory privacy preserving based on Federated Var...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yuchen Jiang Ying Wu Shiyao Zhang James J.Q. Yu Department of Computer Science and Engineering Southern University of Science and Technology China Research Institute for Trustworthy Autonomous Systems Southern University of Science and Technology China Department of Computer Science University of York United Kingdom

The use of trajectory data with abundant spatial-temporal information is pivotal in Intelligent Transport systems (ITS) and various traffic system tasks. Location-Based Services (LBS) capitalize on this trajectory data to offer users personalized services tailored to their location information. However, this trajectory data contains sensitive information about users’ movement patterns and habits, necessitating confidentiality and protection from unknown collectors. To address this challenge, privacy-preserving methods like K-anonymity and Differential Privacy have been proposed to safeguard private information in the dataset. Despite their effectiveness, these methods can impact the original features by introducing perturbations or generating unrealistic trajectory data, leading to suboptimal performance in downstream tasks. To overcome these limitations, we propose a Federated Variational AutoEncoder (FedVAE) approach, which effectively generates a new trajectory dataset while preserving the confidentiality of private information and retaining the structure of the original features. In addition, FedVAE leverages Variational AutoEncoder (VAE) to maintain the original feature space and generate new trajectory data, and incorporates Federated Learning (FL) during the training stage, ensuring that users’ data remains locally stored to protect their personal information. The results demonstrate its superior performance compared to other existing methods, affirming FedVAE as a promising solution for enhancing data privacy and utility in location-based applications.

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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IEEE International Conference on systems, Man and Cybernetics

作者： Jiahui Zhai Jing Bi Haitao Yuan Jinhong Yang Jia Zhang MengChu Zhou College of Computer Beijing University of Technology Beijing China School of Automation Science and Electrical Engineering Beihang University Beijing China CSSC Systems Engineering Research Institute Beijing China Dept. of Computer Science Southern Methodist University Dallas TX USA Dept. of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions.

关键词： Energy consumption Quantum computing Multitasking Energy efficiency Internet Manufacturing Optimization Sorting Industrial Internet of Things Genetic algorithms

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Domain Adaptation Random Vector Functional Link for White Blood Cell Classification

Domain Adaptation Random Vector Functional Link for White Bl...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Qin Wang Xi-Zhao Wang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798331528041

ISBN: (纸本)9798331528058

There's growing recognition of how machine learning can revolutionize the precision and swiftness of clinical diagnoses by improving the classification of white blood cells. However, a machine learning model specialized in white blood cell classification demands access to a large trove of well-annotated data. This poses a challenge, as in the medical field, annotating data comprehensively comes with a high cost in terms of both money and time. To circumvent this, domain adaptation (DA) techniques are employed to leverage similar cell images from related domains, thereby boosting the model's performance in white blood cell classification in specific contexts. Among the myriad DA approaches available for this application, including TCA-based and deep learning methods, all typically suffer from lengthy training durations. Addressing this challenge, we introduce a novel DA method inspired by the random vector functional link (RVFL) network, a type of neural network characterized by its random weights. Our proposed method, named Domain Adaptation Random Vector Functional Link (DA-RVFL), capitalizes on the efficiency of RVFL networks to enhance white blood cell classification. This innovative approach shows some insights for future research in the efficient and effective classification of white blood cells.

关键词： White blood cells Training Deep learning Adaptation models Neural networks Focusing Vectors Data models Medical diagnosis Medical diagnostic imaging

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AdvDiffuser: Generating Adversarial Safety-Critical Driving Scenarios via Guided Diffusion

AdvDiffuser: Generating Adversarial Safety-Critical Driving ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yuting Xie Xianda Guo Cong Wang Kunhua Liu Long Chen School of Computer Science and Engineering Sun Yat-Sen University School of Computer Science Wuhan University State Key Laboratory for Management and Control of Complex Systems at the Institute of Automation Chinese Academy of Sciences School of Mechanical and Automotive Engineering Qingdao University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Safety-critical scenarios are infrequent in natural driving environments but hold significant importance for the training and testing of autonomous driving systems. The prevailing approach involves generating safety-critical scenarios automatically in simulation by introducing adversarial adjustments to natural environments. These adjustments are often tailored to specific tested systems, thereby disregarding their transferability across different systems. In this paper, we propose AdvDiffuser, an adversarial framework for generating safety-critical driving scenarios through guided diffusion. By incorporating a diffusion model to capture plausible collective behaviors of background vehicles and a lightweight guide model to effectively handle adversarial scenarios, AdvDiffuser facilitates transferability. Experimental results on the nuScenes dataset demonstrate that AdvDiffuser, trained on offline driving logs, can be applied to various tested systems with minimal warm-up episode data and outperform other existing methods in terms of realism, diversity, and adversarial performance.

关键词： Training Diffusion models Intelligent robots Autonomous vehicles Testing

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Context-Guided Spatio-Temporal Video Grounding

Context-Guided Spatio-Temporal Video Grounding

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Xin Gu Heng Fan Yan Huang Tiejian Luo Libo Zhang University of Chinese Academy of Sciences Beijing China Chinese Academy of Sciences Institute of Software Beijing China Department of Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Spatio-temporal video grounding (or STVG) task aims at locating a spatio-temporal tube for a specific instance given a text query. Despite advancements, current methods easily suffer the distractors or heavy object appearance variations in videos due to insufficient object information from the text, leading to degradation. Addressing this, we propose a novel framework, context-guided STVG (CG-STVG), which mines discriminative instance context for object in videos and applies it as a supplementary guidance for target localization. The key of CG-STVG lies in two specially designed modules, including instance context generation (ICG), which focuses on discovering visual context information (in both appearance and motion) of the instance, and instance context refinement (ICR), which aims to improve the instance context from ICG by eliminating irrelevant or even harmful information from the context. During grounding, ICG, together with ICR, are deployed at each decoding stage of a transformer architecture for instance context learning. Particularly, instance context learned from one decoding stage is fed to the next stage, and leveraged as a guidance containing rich and discriminative object feature to enhance the target-awareness in decoding feature, which conversely benefits generating better new instance context to improve localization finally. Compared to existing methods, CG-STVG enjoys object information in text query and guidance from mined instance visual context for more accurate target localization. In experiments on HCSTVG-v1/-v2 and VidSTG, CG-STVG sets new state-of-the-arts in m_tIoU and m_vIoU on all of them, showing efficacy. Code is released at https://***/HengLan/CGSTVG.

关键词： Location awareness Degradation Visualization computer vision Codes Grounding computer architecture

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Dynamic Curing and Network Design in SIS Epidemic Processes

Dynamic Curing and Network Design in SIS Epidemic Processes

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IEEE Conference on Decision and Control

作者： Yuhao Yi Liren Shan Philip E. Paré Karl H. Johansson Institute of Clinical Pathology West China Hospital and College of Computer Science Sichuan University Chengdu China Toyota Technological Institute at Chicago School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and they are also affiliated with Digital Futures Stockholm Sweden

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper studies efficient algorithms for dynamic curing policies and the corresponding network design problems to guarantee fast extinction of epidemic spread in a Markov process-based susceptible-infected-susceptible (SIS) model. We provide a computationally efficient curing algorithm based on the curing policy proposed by Drakopoulos, Ozdaglar, and Tsitsiklis (2014). Since the corresponding optimization problem is NP-hard, finding optimal policies is intractable for large graphs. We provide approximation guarantees on the curing budget of the proposed dynamic curing algorithm. To avoid the waiting period included in the original curing policy, we study network design problems to reduce the total infection rate by deleting edges or reducing the weight of edges. To this end, we provide algorithms with provable guarantees. In summary, the proposed curing and network design algorithms together provide an effective and computationally efficient approach that mitigates SIS epidemic spread in networks.

关键词： Epidemics Heuristic algorithms Computational modeling Curing Markov processes Approximation algorithms Linear programming Computational efficiency Resource management Optimization

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Progressive Self-Guided Hardness Distillation for Fine-Grained Visual Classification

Progressive Self-Guided Hardness Distillation for Fine-Grain...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yangdi Wang Wenming Guo Su Xiu Xu ShuoZhi Yuan School of Management and Economics Beijing Institute of Technology Beijing China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Fine-grained visual classification (FGVC) is a challenging problem due to its inherently high intra-class variances and low inter-class variances. Recently, vision transformers (ViTs) have demonstrated their powerful performance in both traditional and FGVC cases. However, compared with most categories, some categories are difficult to classify because of their similar characteristics and postures and their strong background interference, impacting the performance improvement achieved by the utilizd model. In this work, we present a novel method named progressive self-guided hardness distillation (PS-GHD), which defines a classification hardness judgement criterion and utilizes different approaches for the corresponding categories according to this criterion. This method gradually and correctly classifies various categories in three stages through knowledge distillation, so it can correctly classify indistinguishable categories to some extent. We demonstrate the value of PS-GHD by experimenting on four popular fine-grained benchmarks: CUB-200-2011, Nabirds, Stanford Cars, and Stanford Dogs. Our method achieves very competitive results on the four datasets. We also present qualitative results to enhance the interpretability of our model.

关键词： Visualization computer vision Smoothing methods Neural networks Interference Dogs Benchmark testing

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Multi-Class Gaze Detection in a Dynamic Environment

Multi-Class Gaze Detection in a Dynamic Environment

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IEEE Instrumentation and Measurement Technology Conference

作者： Aidan Lochbihler Bruce Wallace Kathleen Van Benthem Chris Herdman Will Sloan Kirsten Brightman Frank Knoefel Shawn Marshall Rafik Goubran Systems and Computer Engineering Carleton University Ottawa Canada Systems and Computer Engineering Carleton University Bruyère Research Institute SAM3 Innovation Hub Ottawa Canada Department of Cognitive Science Carleton University Ottawa Canada Bruyè re Research Institute Elisabeth-Bruyè re Hospital Systems and Computer Engineering Carleton University Ottawa Canada Bruyère Research Institute Elisabeth-Bruyère Hospital Ottawa Hospital Carleton University

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Developing AI tools to identify areas of interest within a dynamic field of view is essential for objective behavioural evaluation of drivers. Video image classification and specifically image segmentation is a key technology to allow for the possibility of physiological and behavioural measurement of drivers. For example, to understand driver attention, one must measure where a driver is looking when driving and this requires segmentation of their field of view into relevant areas of interest, such as windows, mirrors, and dashboard. The present work addresses the challenge of dynamic field of view classification and shows the impact of transfer learning, a new AI tool, on segmentation accuracy. Results from this study demonstrate that transfer learning improves predictive performance by 0.02 to 0.20 Dice when large training sets were used. The resulting performance was >0.80 Dice for all classification tests of driver attention segmentation. This work showed that transfer learning also supported the use of smaller training sets while still providing adequate performance. This finding is key for applications where labeled training data is limited or costly to create. The present results expand the application space for deep learning-based image segmentation models.

关键词： Training Image segmentation Transfer learning Training data Physiology Object recognition Mirrors

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