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Artificial Intelligence For Internet of Things (AIIoT), International Conference on

作者： N Roshan Ahmed S. Shridevi School of Computer Science and Engineering Vellore Institute of Technology Chennai India Center for Advanced Data Science Vellore Institute of Technology Chennai India

ISBN: (数字)9798350372120

ISBN: (纸本)9798350372137

Text-based Statistical steganography is one of the most non-human detectable methods of embedding hidden messages in plain text format which is useful in concealing information. Steganalysis is its counter, the process of detecting if a text has any encrypted data in it. This paper applies quantum computing to create DeepKet Embedding, which optimizes the space requirements for word embeddings similar to Word2Vec. DeepKet is benchmarked against existing embedding layers and a significant size reduction is achieved while maintaining accuracy for steganalysis.

关键词： Steganography Quantum computing Accuracy Benchmark testing Internet of Things Artificial intelligence

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Diving into Underwater: Segment Anything Model Guided Underwater Salient Instance Segmentation and A Large-scale dataset 41

Diving into Underwater: Segment Anything Model Guided Underw...

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41st International Conference on Machine Learning, ICML 2024

作者： Lian, Shijie Zhang, Ziyi Li, Hua Li, Wenjie Yang, Laurence Tianruo Kwong, Sam Cong, Runmin Hainan University China China Ministry of Education China Huazhong University of Science and Technology China St. Francis Xavier University Canada LIngnan University School of Data Science Hong Kong Shandong University School of Control Science and Engineering China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

With the breakthrough of large models, Segment Anything Model (SAM) and its extensions have been attempted to apply in diverse tasks of computer vision. Underwater salient instance segmentation is a foundational and vital step for various underwater vision tasks, which often suffer from low segmentation accuracy due to the complex underwater circumstances and the adaptive ability of models. Moreover, the lack of large-scale datasets with pixel-level salient instance annotations has impeded the development of machine learning techniques in this field. To address these issues, we construct the first large-scale underwater salient instance segmentation dataset (USIS10K), which contains 10,632 underwater images with pixel-level annotations in 7 categories from various underwater scenes. Then, we propose an Underwater Salient Instance Segmentation architecture based on Segment Anything Model (USIS-SAM) specifically for the underwater domain. We devise an Underwater Adaptive Visual Transformer (UA-ViT) encoder to incorporate underwater domain visual prompts into the segmentation network. We further design an out-of-the-box underwater Salient Feature Prompter Generator (SFPG) to automatically generate salient prompters instead of explicitly providing foreground points or boxes as prompts in SAM. Comprehensive experimental results show that our USIS-SAM method can achieve superior performance on USIS10K datasets compared to the state-of-the-art methods. datasets and codes are released on Github. Copyright 2024 by the author(s)

关键词： Pixels

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Co-Occurrence Matters: Learning Action Relation for Temporal Action Localization

arXiv

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arXiv 2023年

作者： Cao, Congqi Wang, Yizhe Lu, Yue Zhang, Xin Zhang, Yanning The National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710129 China

Temporal action localization (TAL) is a prevailing task due to its great application potential. Existing works in this field mainly suffer from two weaknesses: (1) They often neglect the multi-label case and only focus on temporal modeling. (2) They ignore the semantic information in class labels and only use the visual information. To solve these problems, we propose a novel Co-Occurrence Relation Module (CORM) that explicitly models the co-occurrence relationship between actions. Besides the visual information, it further utilizes the semantic embeddings of class labels to model the co-occurrence relationship. The CORM works in a plug-and-play manner and can be easily incorporated with the existing sequence models. By considering both visual and semantic co-occurrence, our method achieves high multi-label relationship modeling capacity. Meanwhile, existing datasets in TAL always focus on low-semantic atomic actions. Thus we construct a challenging multi-label dataset UCF-Crime-TAL that focuses on high-semantic actions by annotating the UCF-Crime dataset at frame level and considering the semantic overlap of different events. Extensive experiments on two commonly used TAL datasets, i.e., MultiTHUMOS and TSU, and our newly proposed UCF-Crime-TAL demenstrate the effectiveness of the proposed CORM, which achieves state-of-the-art performance on these datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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SPHERICAL QUASI-INTERPOLATION USING SCALED ZONAL KERNELS

arXiv

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arXiv 2024年

作者： Sun, Zhengjie Gao, Wenwu Sun, Xingping School of Mathematics and Statistics Nanjing University of Science and Technology Nanjing China School of Big Data and Statistics Anhui University Hefei China Department of Mathematics Missouri State University SpringfieldMO65897 United States

We propose and study a new quasi-interpolation method on spheres featuring the following two-phase construction and analysis. In Phase I, we analyze and characterize a large family of zonal kernels (e.g., the spherical version of Poisson kernel, Gaussian, compactly-supported radial kernels), so that the underlying spherical convolution operators (upon the introduction of a scaling parameter) attains a high-order of approximation to target functions. In Phase II, we discretize the spherical integrals utilizing quadrature rules of optimal order to produce the final quasi-interpolants. Numerical experiments demonstrate that the new quasi-interpolation algorithm is robust and amenable to integrated as well as distributed ways of implementation. Moreover, the underlying error-analysis shows that by fine-tuning the scaling parameter in the radial kernels employed, the resulting quasi-interpolants achieve a well-balanced trade-off between approximation and sampling *** Codes 43A90, 41A25, 41A55, 65D12, 65D32 Copyright © 2024, The Authors. All rights reserved.

关键词： Spheres

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Effects of the urban landscape on heatwave-mortality associations in Hong Kong: comparison of different heatwave definitions

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Frontiers of Environmental science & Engineering 2024年第1期18卷 145-156页

作者： Jinglu Song Yi Lu Thomas Fischer Kejia Hu Department of Urban Planning and Design Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China Department of Architecture and Civil Engineering City University of Hong KongKowloon TongHong Kong 999077China Environmental Assessment and Management Research Centre School of Environmental SciencesUniversity of LiverpoolLiverpool L35TRUK Research Unit for Environmental Sciences and Management Faculty of Natural and Agricultural SciencesNorth West UniversityPotchefstroom 31750South Africa Institute of Big Data in Health Science School of Public HealthZhejiang UniversityZijingang CampusHangzhou 310058China

Despite increased attention given to potential modifiers of temperature-mortality associations, evidence for variations between different urban landscape characteristics remains limited. It is in this context that in this paper effect modifications of multiple urban landscape characteristics are explored under different heatwave definitions for different age groups and gender in Hong Kong, China. Daily meteorological data and heatwave-related mortality counts from 2008 to 2017 were collected from the Hong Kong Census and Statistics Department, China. A case-only design was adopted, combined with logistic regression models to examine the modification effects of five urban landscape characteristics under six heatwave definitions. Stratified analyses were conducted to investigate age- and gender-specific effect modifications. It is found that individuals living in greener areas experienced lower levels of mortality during or immediately after heatwaves. In contrast, a higher building density and nighttime land surface temperature (LST) were associated with a higher heatwave-related mortality risk. Pronounced effect modifications of these urban landscape characteristics were observed under hotter and longer heatwaves, and in older adults (age ≥ 65 years) and males. The findings provide a scientific basis for policymakers and practitioners when considering measures for coping with hotter, longer, and more frequent heatwaves in the context of global climate change.

关键词： Urban landscape Heatwave Mortality Effect modification Intra-urban differences Health risk reduction

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Generating Targeted Universal Adversarial Perturbation against Automatic Speech Recognition via Phoneme Tailoring

Generating Targeted Universal Adversarial Perturbation again...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yujun Chen, Yanqu Wang, Jiakai Hu, Jin Tao, Renshuai Liu, Xianglong State Key Laboratory of Complex & Critical Software Environment Beihang University China School of Computer Science and Engineering Beihang University China College of Computer Science Beijing University of Technology China Zhongguancun Laboratory China School of Computer and Information Technology Beijing Jiaotong University China Institute of Data Space Hefei Comprehensive National Science Center China

ISBN: (纸本)9798350368741

There is a growing concern about adversarial attacks against automatic speech recognition (ASR) systems. Although research into targeted universal adversarial examples (AEs) has progressed, current methods are constrained by inefficient exploitation of audio features, demonstrating insufficient attack ability and robustness in the physical world. To solve this problem, we propose a phoneme-tailored attack (PTA) to improve the quality of the generated AEs. Specifically, to improve attack ability, we propose a Diverse Audio Composition Enrichment method, which enhances the utilization of audio features through phoneme-level slicing and recombination. To adapt AEs to complex environments, we propose a Natural Noise Pattern Guidance method to align AEs with natural noise patterns to improve their robustness. Experiments show that our method achieves an average accuracy of more than 72.34% and 98% with and without a norm constraint, and also demonstrates excellent performance in terms of generalization across datasets and resilience to MP3 compression. © 2025 IEEE.

关键词： Automatic speech recognition natural noise pattern phoneme-level targeted universal adversarial examples

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An Optimal Power System Multi-objective Environmental-Economic Planning Based on Moth-Flame Optimization 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Chu, Shu-Chuan Nguyen, Trong-The Nguyen, Vinh-Tiep Nguyen, Trinh-Dong Dao, Thi-Kien College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China University of Information Technology Ho Chi Minh City700000 Viet Nam Vietnam National University Ho Chi Minh City700000 Viet Nam Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

A power system’s optimization under economic planning concerns scheduling sectors as a challenging reality task with its characteristics with non-convex, high-dimensional, nonlinear problems. This study introduces an optimal power system environmental and economic scheduling based on a multi-moth-flame optimizer (MMFO). A multi-objective programming mathematical model uses the Pareto optimal frontier to obtain the Pareto non-inferiority optimal solution, generate the optimal configuration scheme, and search for the optimal global solution in the feasible region. Calculation results of a power system with IEEE test case of 15 and 40 generator sets demonstrate the high convergence and strong adaptability of the algorithm suggested in addressing the environmental-economic dispatch of the power system. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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Multilingual Synopses of Movie Narratives: A dataset for Vision-Language Story Understanding

arXiv

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arXiv 2024年

作者： Sun, Yidan Yu, Jianfei Li, Boyang College of Computing and Data Science Nanyang Technological University Singapore School of Computer Science and Engineering Nanjing University of Science and Technology China

Story video-text alignment, a core task in computational story understanding, aims to align video clips with corresponding sentences in their descriptions. However, progress on the task has been held back by the scarcity of manually annotated video-text correspondence and the heavy concentration on English narrations of Hollywood movies. To address these issues, in this paper, we construct a large-scale multilingual video story dataset named Multilingual Synopses of Movie Narratives (M-SYMON), containing 13,166 movie summary videos from 7 languages, as well as manual annotation of fine-grained video-text correspondences for 101.5 hours of video. Training on the human annotated data from M-SYMON outperforms the SOTA methods by 15.7 and 16.2 percentage points on Clip Accuracy and Sentence IoU scores, respectively, demonstrating the effectiveness of the annotations. As benchmarks for future research, we create 6 baseline approaches with different multilingual training strategies, compare their performance in both intra-lingual and cross-lingual setups, exemplifying the challenges of multilingual video-text alignment. The dataset is released at: https://***/insundaycathy/M-SyMoN © 2024, CC BY.

关键词： Video analysis

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Generative Zero-shot Learning Compound Fault Diagnosis of Bearings 2

Generative Zero-shot Learning Compound Fault Diagnosis of Be...

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2nd International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2021

作者： Xu, Juan Li, Kang Hefei University of Technology School of Computer and Information Key Laboratory of Knowledge Engineering with Big Data Hefei China

ISBN: (纸本)9781665427470

Diagnosis of compound faults remains a challenge during fault diagnosis of bearings, owing to the different fault parameters coupling, fault characteristics diversity, and the exponential increasement of the number of possible failure modes. Current compound faults diagnostic methods, which are usually based on supervised or semi-supervised learning, require sufficient labeled or unlabeled training data for each compound faults. In industrial scenarios, neither labeled nor unlabeled training data of compound faults are usually difficult to collect and sometimes even inaccessible, whereas single faults samples are easy to obtain. Based on these issues, we construct a novel generative zero-shot learning (ZSL) compound faults diagnosis model identifies unseen compound faults using only single faults samples as training set. This model comprises several modules, namely semantic vector definition, feature extractor, generative adversarial modules. Firstly, we devise a unified semantic vector definition method for expressing single and compound faults based on theoretical correlation of characteristics between single fault and compound faults vibration data. Secondly, a CNN-based feature extractor is designed for extraction the fault features from the time-frequency domain of vibration data. Thirdly, a generative adversarial module performs adversarial training of semantic vectors and fault features of single faults to learn the mapping relationship between the fault features and the fault semantic vectors. Once trained, the generator is able to generate compound fault features using the compound fault semantic vectors, rather than any compound fault samples. Finally, the K-nearest neighbor method is adopted to identify the unseen compound faults by measuring the distance between the extracted feature from the testing compound fault samples and the generated features. The effectiveness of the proposed method is verified on a self-built bearing test stand. The results show

关键词： Semantics

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LSU-NET: Lightweight Automatic Organs Segmentation Network for Medical Images

LSU-NET: Lightweight Automatic Organs Segmentation Network f...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yujie Ding Shenghua Teng Zuoyong Li Xiao Chen College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

UNet and its variants have widespread applications in medical image segmentation. However, the substantial number of parameters and computational complexity of these models make them less suitable for use in clinical settings with limited computational resources. To address this limitation, we propose a novel Lightweight Shift U-Net (LSU-Net). We integrate the Light Conv Block and the Tokenized Shift Block in a lightweight manner, combining them with a dynamic weight multi-loss design for efficient dynamic weight allocation. The Light Conv Block effectively captures features with a low parameter count by combining standard convolutions with depthwise separable convolutions. The Tokenized Shift Block optimizes feature representation by shifting and capturing deep features through a combination of the Spatial Shift Block and depthwise separable convolutions. Dynamic adjustment of the loss weights at each layer approaches the optimal solution and enhances training stability. We validated LSU-Net on the UWMGI and MSD Colon datasets, and experimental results demonstrate that LSU-Net outperforms most state-of-the-art segmentation architectures.

关键词： Training Image segmentation Convolution Dynamic scheduling Stability analysis Resource management Speech processing Standards Monitoring Biomedical imaging

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