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arXiv 2023年

作者： Zhang, Zhe Wu, Gaochang Zhang, Jing Zhu, Xiatian Tao, Dacheng Chai, Tianyou The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China The Surrey Institute for People-Centred Artificial Intelligence Centre for Vision Speech and Signal Processing University of Surrey Guildford United Kingdom The School of Computer Science Faculty of Engineering University of Sydney Sydney Australia

Unsupervised Domain Adaptive Semantic Segmentation (UDA-SS) aims to transfer the supervision from a labeled source domain to an unlabeled and shifted target domain. The majority of existing UDA-SS works typically consider images whilst recent attempts have extended further to tackle videos by modeling the temporal dimension. Although two lines of research share the major challenges – overcoming the underlying domain distribution shift, their studies are largely independent. It causes several issues: (1) The insights gained from each line of research remain fragmented, leading to a lack of holistic understanding of the problem and potential solutions. (2) Preventing the unification of methods and best practices across two scenarios (images and videos) will lead to redundant efforts and missed opportunities for cross-pollination of ideas. (3) Without a unified approach, the knowledge and advancements made in one scenario may not be effectively transferred to the other, leading to suboptimal performance and slower progress. Under this observation, we advocate unifying the study of UDA-SS across video and image scenarios, enabling a more comprehensive understanding, synergistic advancements, and efficient knowledge sharing. To that end, we explore the unified UDA-SS from a general domain augmentation perspective, serving as a unifying framework, enabling improved generalization, and potential for cross-pollination, ultimately contributing to the practical impact and overall progress. Specifically, we propose a Quad-directional Mixup (QuadMix) method, characterized by tackling intra-domain discontinuity, fragmented gap bridging, and feature inconsistencies through four-directional paths designed for intra- and inter-domain mixing within an explicit feature space. To deal with temporal shifts within videos, we incorporate optical flow-guided feature aggregation across spatial and temporal dimensions for fine-grained domain alignment, which is extendable to image scenarios

关键词： Semantic Segmentation

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Construction and Evaluation of Benchmark Da-Tasets For Adversarial Ai Research

Construction and Evaluation of Benchmark Da-Tasets For Adver...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Parikshit N. Mahalle P. K. Miniappan Ashmeet Kaur S. Ganga Yuvraj Parmar Sahana B S Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India Department of Mechanical Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of IT Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement UG Management JAIN (Deemed to be University) Bangalore Karnataka India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Standard offensive AI datasets need to be made and tested to improve the performance of AI models. Using adversarial diversity-driven dataset building (ADDC), this work shows new ways to create datasets and ways to measure safety and power. The DCA fakes several aggressive events to make sure that the benchmark dataset has a variety of strikes and pauses within it. When the REA is run, it shows how well AI models can handle certain threats. A vulnerability avoidance algorithm (VMA) finds holes in an AI model and stops them from being used. We compare the suggested plan to ADS, APDA, RMAAI, AVP, CABG, and AASF based on several factors that affect results. The suggested method both lowers the number of mistakes and raises the variety, which improves the dataset and makes sure that a full review is done. artificial intelligence models that use the suggested method are less likely to be hacked online. First, well-known datasets for study into hostile artificial intelligence are put together and studied.

关键词： Power measurement Reviews Stability criteria Government Benchmark testing Solids Robustness Safety artificial intelligence Standards

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Early Classification of Lung Cancer Based on Cell Morphology Features 1st

Early Classification of Lung Cancer Based on Cell Morphology...

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1st International Conference on Advanced Network Technologies and Computational intelligence, ICANTCI 2024

作者： Byeon, Haewon Soni, Mukesh Deb, Nabamita Rivera, Richard Sharma, Nilesh Vijay Kaushal, Chetna Department of Digital Anti-Aging Healthcare Inje University Gimhae Korea Republic of Dr. D. Y. Patil School of Science and Technology Tathawade Pune India Department of Information Technology Gauhati University Guwahati India Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Artificial Intelligence and Data Science SNJB′s Late Sau. K. B. Jain College of Engineering Nashik Chandwad India Institute of Engineering and Technology Chitkara University Chitkara University Punjab India

ISBN: (纸本)9783031860683

In response to the challenges of limited annotated lung cancer pathology images with complex cell morphology, we propose a lung cancer pathology image classification method based on contrastive learning of cell morphology features. This approach integrates highly reliable unlabelled data into the training data through contrastive learning to address the issue of insufficient labelled data. Building upon the foundation of k-nearest neighbour contrastive learning, we introduce farthest and nearest neighbour contrastive learning, simultaneously using the farthest and nearest neighbour images for contrastive learning. This enhances the performance of contrastive learning by increasing the difficulty of positive sample learning and diversifying the dataset. We employ a ResNet50 encoder with deformable convolutions and dynamic convolutions to improve the extraction of cell morphology features. Experimental results demonstrate that, especially when labelled data is scarce, our classification method effectively leverages cell feature information from both labelled and unlabelled cancer pathology images, resulting in superior classification performance compared to existing methods. When applied to 80%, 60%, and 40% labeled data, respectively, CCCM outperforms other approaches with superior performance metrics, reaching F1 Score of 0.9584, 0.9488, and 0.9386. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Liu, Jiangchuan Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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Order-preserving pattern mining with forgetting mechanism

arXiv

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arXiv 2024年

作者： Li, Yan Ma, Chenyu Gao, Rong Wu, Youxi Li, Jinyan Wang, Wenjian Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin300400 China School of Artificial Intelligence Hebei University of Technology Tianjin300400 China School of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen518055 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China School of Computer and Information Technology Shanxi University Taiyuan237016 China Key Laboratory of Knowledge Engineering with Big Data The Ministry of Education of China Hefei University of Technology Hefei230009 China

Order-preserving pattern (OPP) mining is a type of sequential pattern mining method in which a group of ranks of time series is used to represent an OPP. This approach can discover frequent trends in time series. Existing OPP mining algorithms consider data points at different time to be equally important;however, newer data usually have a more significant impact, while older data have a weaker impact. We therefore introduce the forgetting mechanism into OPP mining to reduce the importance of older data. This paper explores the mining of OPPs with forgetting mechanism (OPF) and proposes an algorithm called OPF-Miner that can discover frequent OPFs. OPF-Miner performs two tasks, candidate pattern generation and support calculation. In candidate pattern generation, OPF-Miner employs a maximal support priority strategy and a group pattern fusion strategy to avoid redundant pattern fusions. For support calculation, we propose an algorithm called support calculation with forgetting mechanism, which uses prefix and suffix pattern pruning strategies to avoid redundant support calculations. The experiments are conducted on nine datasets and 12 alternative algorithms. The results verify that OPF-Miner is superior to other competitive algorithms. More importantly, OPF-Miner yields good clustering performance for time series, since the forgetting mechanism is employed. Copyright © 2024, The Authors. All rights reserved.

关键词： Miners

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Camera-Conditioned Stable Feature Generation for Isolated Camera Supervised Person Re-IDentification

arXiv

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arXiv 2022年

作者： Wu, Chao Ge, Wenhang Wu, Ancong Chang, Xiaobin School of Artificial Intelligence Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Computer Science and Engineering Sun Yat-sen University China

To learn camera-view invariant features for person Re-IDentification (Re-ID), the cross-camera image pairs of each person play an important role. However, such cross-view training samples could be unavailable under the ISolated Camera Supervised (ISCS) setting, e.g., a surveillance system deployed across distant scenes. To handle this challenging problem, a new pipeline is introduced by synthesizing the cross-camera samples in the feature space for model training. Specifically, the feature encoder and generator are end-to-end optimized under a novel method, Camera-Conditioned Stable Feature Generation (CCSFG). Its joint learning procedure raises concern on the stability of generative model training. Therefore, a new feature generator, σ-Regularized Conditional Variational Autoencoder (σReg. CVAE), is proposed with theoretical and experimental analysis on its robustness. Extensive experiments on two ISCS person Re-ID datasets demonstrate the superiority of our CCSFG to the competitors. 1 © 2022, CC BY.

关键词： Cameras

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Leveraging LSTM-based Auto-Encoders for Covid-19 Severity Prediction and Classification

Leveraging LSTM-based Auto-Encoders for Covid-19 Severity Pr...

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Recent Advances in science and engineering Technology (ICRASET), International Conference on

作者： Supriya Rai Komal Madhukar Birare K.S. Bhuvaneshwari Tarang Bhatnagar S. Jayasree Ashu Katyal School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka India IT Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence Data science Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of S&H Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350388602

ISBN: (纸本)9798350388619

The primary purpose of this study activity is to give its share to the battle against the growing threats posed by COVID-19 all over the world, which has over one million cases. According to the World Health Organisation (WHO), more than seven million individuals have been reported to have passed away as a result of; the work is focused on building an optimization-based deep learning model that will assist in identifying COVID-19 patients based on early clinical information. The suggested model is pretty accurate for the prediction of COVID-19 and improves the strictness of the forecasts of the ailment. This is accomplished by combining the medical and personal data from a variety of populations. In the preliminary stage, the only things that can be determined are the pre-processing phase and the characteristics that are pertinent for classification using ESDO. The efficacy is characterised by the use of a classifier that combines LSTM and AE. This allows for the determination of the degree of severity, which may range from mild to moderate to severe, with a precision of $\mathbf{9 9 \%} \mathbf{. 0. 0 2}$, and a level of accuracy of $\mathbf{9 8}$. Therefore, future study will concentrate on applying the offered approach for the affected regions in India exclusively, and efforts will also be made to compare the suggested methodology to larger databases, if any such databases exist.

关键词： COVID-19 Training Deep learning Humanities Accuracy databases Predictive models Feature extraction Long short term memory Optimization

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Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing

arXiv

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arXiv 2023年

作者： Shahab, M. Hashim Buttar, Hasan Mujtaba Mehmood, Ahsan Aman, Waqas Ur Rahman, M. Mahboob Nawaz, M. Wasim Pervaiz, Haris Abbasi, Qammer H. Electrical Engineering Department Information Technology University Lahore54000 Pakistan Doha Qatar Department of Computer Engineering The University of Lahore Lahore54000 Pakistan School of Computer Science and Electronic Engineering University of Essex United Kingdom James Watt School of Engineering University of Glasgow GlasgowG12 8QQ United Kingdom Artificial Intelligence Research Centre Ajman University Ajman United Arab Emirates

Non-intrusive load monitoring (NILM) or energy disaggregation aims to extract the load profiles of individual consumer electronic appliances, given an aggregate load profile of the mains of a smart home. This work proposes a novel deep-learning and edge computing approach to solve the NILM problem and a few related problems as follows. 1) We build upon the reputed seq2-point convolutional neural network (CNN) model to come up with the proposed seq2-[3]-point CNN model to solve the (home) NILM problem and site-NILM problem (basically, NILM at a smaller scale). 2) We solve the related problem of appliance identification by building upon the state-of-the-art (pre-trained) 2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are fine-tuned two custom datasets that consist of Wavelets and short-time Fourier transform (STFT)based 2D electrical signatures of the appliances. 3) Finally, we do some basic qualitative inference about an individual appliance’s health by comparing the power consumption of the same appliance across multiple homes. Low-frequency REDD dataset is used for all problems, except site-NILM where REFIT dataset has been used. As for the results, we achieve a maximum accuracy of 94.6% for home-NILM, 81% for site-NILM, and 88.9% for appliance identification (with Resnet-based model). © 2023, CC BY.

关键词： Edge computing

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High-dimensional orbital angular momentum multiplexing nonlinear holography

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Advanced Photonics 2021年第1期3卷 40-46页

作者： Xinyuan Fang Haocheng Yang Wenzhe Yao Tianxin Wang Yong Zhang Min Gu Min Xiaoa Nanjing University College of Engineering and Applied SciencesSchool of PhysicsNational Laboratory of Solid State MicrostructuresNanjingChina University of Shanghai for Science and Technology School of Optical-Electrical and Computer EngineeringCentre for Artificial-Intelligence NanophotonicsShanghaiChina University of Arkansas Department of PhysicsFayettevilleArkansasUnited States

Nonlinear holography has been identified as a vital platform for optical multiplexing holography because of the appearance of new optical ***,due to nonlinear wave coupling in nonlinear optical processes,the nonlinear harmonic field is coupled with the input field,laying a fundamental barrier to independent control of the interacting fields for *** propose and experimentally demonstrate high-dimensional orbital angular momentum(OAM)multiplexing nonlinear holography to overcome this *** dividing the wavefront of the fundamental wave into different orthogonal OAM channels,multiple OAM and polarization-dependent holographic images in both the fundamental wave and second-harmonic wave have been reconstructed independently in the spatial frequency domain through a type-II second harmonic generation ***,this method can be easily extended to cascadedχ2 nonlinear optical processes for multiplexing in more wavelength channels,leading to potential applications in multicasting in optical communications,multiwavelength display,multidimensional optical storage,anticounterfeiting,and optical encryption.

关键词： orbital angular momentum nonlinear holography second harmonic generation multiplexing holography

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Interpreting Industrial IoT data Streams Through Fuzzy Querying With Hysteretic Fuzzy Sets on Apache Kafka

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IEEE Transactions on Fuzzy Systems 2024年第8期32卷 4671-4684页

作者： Bożena Małysiak-Mrozek Bartłomiej Ryba Marek Molęda Che-Lun Hung Witold Pedrycz Weiping Ding Dariusz Mrozek Department of Distributed Systems and Informatic Devices Silesian University of Technology Gliwice Poland Department of Applied Informatics Silesian University of Technology Gliwice Poland Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei City Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Türkiye School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

In industrial settings, querying data streams from Internet of Things (IoT) devices benefits from utilizing elastic criteria to enhance the interpretability of the current state of the monitored environment. Fuzzy sets provide this elasticity, enabling the aggregation and representation of similar values in a human-comprehensible manner. However, many sensor signals exhibit temporal oscillations, leading to varying interpretations of the signal based on its current trend (rising or falling). This hysteresis in signal (and subsequently of the production device) interpretation inspired us to introduce this phenomenon into data stream processing, resulting in the novel concept of hysteretic fuzzy sets. This article demonstrates how fuzzy searching and grouping can be applied to IoT sensor signals in flexible Big data stream processing on Apache Kafka. We illustrate the impact of data stream querying with KSQL queries involving fuzzy sets (encompassing fuzzy filtering of data stream events, fuzzy transformation of data stream attributes, fuzzy grouping, and joining) on the flexibility of executed operations and computational resources utilized by the Kafka processing engine. Finally, our experiments with hysteretic fuzzy sets while analyzing sensor signals in power plants demonstrate that this novel approach effectively reduces the number of alarms while monitoring the state of the production machine.

关键词： Streams Fuzzy sets Internet of Things Big data Monitoring Production Real-time systems

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