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arXiv 2024年

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at this url. © 2024, CC BY.

关键词： Semantics

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EVMAS: An energy-aware virtual machine allocation scheme in fog centers 2

EVMAS: An energy-aware virtual machine allocation scheme in ...

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2nd International Conference for Emerging technology, INCET 2021

作者： Naik, Madhusudan Barik, Lalbihari Kandpal, Meenakshi Patra, Sudhansu Shekhar Jena, Sudarson Barik, Rabindra K. Department of Computer Science Hakimpada Angul India King Abdul Aziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia KIIT Deemed to Be University School of Computer Engineering Bhubaneswar India Sambalpur University SUIIT Department of Computer Science and Engineering Sambalpur India

ISBN: (纸本)9781728170299

Due to the rapid growth of the Industrial IoT (IIoT), social media and digitization, and wireless communication technology in various sectors, data volume is increasing rapidly. Cloud computing is an emerging solution with fog computing assistance for handling and processing a huge volume of data. It is a soar of means to improve the quality of services provided to users through cloud computing, which has been more overwhelmed by the massive data flow. Transmitting all the data to the cloud and getting it back from the cloud causes high latency and requires high network bandwidth. In the IIoT applications, there is enough energy needed in the fog layer, a promising area to be handled by the cloud service providers. An important factor that contributes to the energy consumption in fog servers is VM allocation. This paper proposes EVMAS, i.e., an energy-aware VM allocation scheme based on Lagrange's undetermined multiplier with the Hessian matrix, which improves the Quality of Service (QoS) in parallel with service level agreement (SLA). The suggested technique outperforms in comparison to the other state of the algorithm. © 2021 IEEE.

关键词： Fog computing

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STMDNet: A Lightweight Directional Framework for Motion Pattern Recognition of Tiny Targets

arXiv

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arXiv 2025年

作者： Xu, Mingshuo Luan, Hao Hao, Zhou Daniel Peng, Jigen Yue, Shigang The School of Mathematics and Computing Science University of Leicester LeicesterLE1 7RH United Kingdom The Tianjin Key Laboratory of Information Sensing and Intelligent Control School of Automation and Electrical Engineering Tianjin University of Technology and Education Tuanjin 300222 China The Machine Life and Intelligence Research Center Guangzhou University Guangzhou510006 China

Recognizing motions of tiny targets—only few dozen pixels—in cluttered backgrounds remains a fundamental challenge when standard feature-based or deep learning methods fail under scarce visual cues. We propose STMDNet, a model-based computational framework to Recognize motions of tiny targets at variable velocities under low-sampling frequency scenarios. STMDNet designs a novel dual-dynamics-and-correlation mechanism, harnessing ipsilateral excitation to integrate target cues and leakage-enhancing-type contralateral inhibition to suppress large-object and background motion interference. Moreover, we develop the first collaborative directional encoding-decoding strategy that determines the motion direction from only one correlation per spatial location, cutting computational costs to one-eighth of prior methods. Further, simply substituting the backbone of a strong STMD model with STMDNet raises AUC by 24%, yielding an enhanced STMDNet-F. Evaluations on real-world low-sampling frequency datasets show state-of-the-art results, surpassing the deep learning baseline. Across diverse speeds, STMDNet-F improves mF1 by 19%, 16%, and 8% at 240Hz, 120Hz, and 60Hz, respectively, while STMDNet achieves 87 FPS on a single CPU thread. These advances highlight STMDNet as a next-generation backbone for tiny target motion pattern recognition and underscore its broader potential to revitalize model-based visual approaches in motion detection. Code is available at https://***/MingshuoXu/STMDNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal encoding

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Design and Verification of an Aromatherapy Feedback System for Mental Fatigue Based on Physiological Signals

Design and Verification of an Aromatherapy Feedback System f...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tao Sun Fuze Tian Hua Jiang Qinglin Zhao Bin Hu Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China School of Medical Technology Beijing Institute of Technology Beijing China CAS Center for Excellence in Brain Science and Institutes for Biological Sciences Shanghai Institutes for Biological Sciences Chines Academy of Sciences Shanghai China Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Lanzhou China

Mental fatigue is a prevalent issue in contemporary society and can negatively affect physical performance and concentration, increasing the likelihood of adverse consequences due to inattention during productive activities. Therefore, it becomes increasingly important to address and eliminate fatigue within a specific period of time. Aromatherapy, as a form of Complementary Alternative Medicine (CAM), is a non-invasive, cost-effective, and efficient method to combat fatigue. Previous studies have assessed the effects of specific aromatherapy oils using scales, but there is a lack of objective and reliable physiological indicators to prove the effectiveness of aromatherapy. Hence, this paper seeks to establish a model illustrating the effects of aromatic essential oil gases on the human body. A multimodal physiological fatigue signal acquisition system that integrates aromatherapy feedback was designed. In addition, an experimental paradigm was developed to explore the potential of aromatherapy in mitigating mental fatigue. Electroencephalogram (EEG) and Electrocardiogram (ECG) signals were collected, allowing for the analysis of time-frequency domain features in EEG and ECG signals, as well as Heart Rate Variability (HRV) features in ECG signals. Our findings indicate that specific aromatic gases demonstrate effectiveness in reducing mental fatigue. Furthermore, we employed the Support Vector Machine (SVM) algorithm to classify the state of human mental fatigue. Based on the classification results, the release of aromatic gas was controlled to provide targeted aromatic feedback. This innovative approach offers a promising avenue for objectively assessing and addressing mental fatigue through aromatherapy interventions.

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Automatic classification and isolation of cracks on masonry surfaces using deep transfer learning and semantic segmentation

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Journal of Building Pathology and Rehabilitation 2023年第1期8卷 1-14页

作者： Aliu, Abdulmalik Adozuka Ariff, Nor Rima Muhamad Ametefe, Divine Senanu John, Dah College of Built Environment Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Centre of Studies for Building Surveying College of Built Environment Universiti Teknologi Mara (UiTM) Selangor Shah Alam 40450 Malaysia Wireless Communication Technology Group College of Engineering Department of Electrical Engineering Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia School of Information Science College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) Selangor Puncak Perdana 40150 Malaysia

Masonry structures represent a significant portion of the built environment and are among the most commonly constructed buildings worldwide. However, manually identifying cracks in masonry walls is a dominant yet flawed approach, owing to its arduous, time-consuming, subjective, and ambiguous nature, particularly in inaccessible areas. Artificial intelligence, through computer vision and deep learning, has exhibited competence in several identification and classification tasks. Despite the success of existing models, their generalization efficacy remains largely unexplored. Motivated by this, we propose a crack classifier based on deep transfer learning and data augmentation, which we evaluate on various masonry images to determine its generalization performance. Our method achieved sensitivity, specificity, precision, and F1-score of 99.98%, 99.95%, 99.95%, and 99.96%, respectively. Additionally, we employed two types of semantic segmentation techniques, canny edge and heatmap, to isolate the identified cracks and compared their efficacy. Both techniques accurately isolated the crack region effectively. However, we observed some differences in their performance. The canny edge segmentation method was more effective at detecting sudden changes in edge structure, resulting in sharper and more precise edges around the crack region. Conversely, the heatmap segmentation method was better at highlighting the color contrast around the crack region, which helped to concentrate the color around the crack region and make it stand out more prominently. Overall, our findings demonstrate that automated masonry crack detection using deep transfer learning with data augmentation is effective for detecting cracks in varied masonry surfaces, with significant potential applications in building maintenance and safety. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Canny edge Crack detection Data augmentation Deep transfer learning Heatmap Masonry

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A Two-Party Combined Cryptographic Scheme and Its Application

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Chinese Journal of Electronics 2023年第4期21卷 687-691页

作者： Shengbao WANG Qi XIE Qiang TANG Peng ZENG Wei CHEN School of Information Science and Engineering Hangzhou Normal University Hangzhou China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China New Star Institute of Applied Technology Hefei China Faculty of EWI University of Twente The Netherlands Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China

In this paper, we extend Haber and Pinkas' notion of combined (cryptographic) scheme to the two-party setting, which is shown to be a useful tool in some real-world application which we name the “2-boss problem”. In a two-party combined scheme, a single public key associated with two independent private keys and one escrow decryption key is provided. Any ciphertext encrypted under the public key can be simultaneously decrypted by the three keys. Meanwhile, the two private keys can also be used as signing keys to achieve non-repudiation service. We provide formal security definitions for two-party combined schemes, and present a simple and efficient scheme. Our construction is derived from bilinear pairings, and the security is based on the Bilinear Diffie-Hellman (BDH) assumption.

关键词： Public key Switches Non-repudiation

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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S3PaR: Section-Based Sequential Scientific Paper Recommendation for Paper Writing Assistance

SSRN

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SSRN 2023年

作者： Santosa, Natasha Christabelle Liu, Xin Han, Hyoil Miyazaki, Jun Department of Computer Science School of Computing Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo152-8550 Japan 2-3-26 Aomi Koto-ku Tokyo135-0064 Japan School of Information Technology Illinois State University 100 N University St NormalIL61761 United States

A scientific paper recommender system (RS) is very helpful for literature searching in that it (1) helps novice researchers explore their own field and (2) helps experienced researchers explore new fields outside their area of expertise. However, existing RSs usually recommend relevant papers based on users' static interests, i.e., their citation from past publication(s) or reading histories. We have therefore come up with a unique recommendation task based on users' dynamic interests during their paper-writing activity. This dynamism is revealed in (for example) the topic shift while writing the introduction vs. related works section. To implement this task, we developed a new pipeline called Section-based Sequential Scientific Paper Recommendation (S3PaR), which recommends papers based on the context of the given user's currently written paper section. Our experiments demonstrate that this unique task is feasible and that our proposed pipeline outperforms existing standard RS baselines. © 2023, The Authors. All rights reserved.

关键词： Paper

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Halftone Image Steganography Based on Reassigned Distortion Measurement 7th

Halftone Image Steganography Based on Reassigned Distortion...

引用

7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Xu, Wenbo Liu, Wanteng Lin, Cong Wang, Ke Wang, Wenbin Lu, Wei School of Computer Science and Engineering Guangdong Key Laboratory of Information Security Technology Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Guangzhou510006 China School of Statistics and Mathematics Guangdong University of Finance and Economics Guangzhou510320 China Guangdong Science and Technology Innovation Monitoring and Research Center Guangzhou510033 China

ISBN: (纸本)9783030786113

Most state-of-the-art halftone image data hiding methods aim to preserve good visual quality when embedding messages, while ignoring the statistical security. This paper proposed a halftone steganographic scheme that improves the visual quality and the statistical security of the anti-steganalysis. First, a general distortion measurement for halftone images based on human visual system (HVS) model is proposed. Utilizing the Least-Mean-Square (LMS) method, halftone images can be converted to grayscale images and the objective image quality assessment is applied to evaluate the distortion caused by flipping pixels. Different distortion measurements can be derived from different image quality assessments. Then, to further measure the embedding distortions, we combine these distortion measurements to construct a reassigned distortion measurement based on the controversial pixels prior (CPP) rule. Finally, syndrome-trellis code (STC) is employed to minimize the number of flipping pixels. Experimental results have shown that the proposed steganographic scheme achieves strong statistical security with high capacity and visual quality. © 2021, Springer Nature Switzerland AG.

关键词： Image quality

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Local Neighbor Propagation on Graphs for Robust Feature Matching

SSRN

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SSRN 2023年

作者： Guo, Hanlin Xiao, Guobao Su, Lumei Zhou, Jiaxing Wang, Dahan Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control School of Electrical Engineering and Automation Xiamen University of Technology China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Information Science and Engineering Xiamen University China College of Computer and Control Engineering Minjiang University China Fujian Key Laboratory of Pattern Recognition and Image Understanding School of Computer and Information Engineering Xiamen University of Technology China

Establishing reliable correspondences between two sets of feature points is a critical preprocessing step in many computer vision and pattern recognition tasks. In this paper, we propose a novel robust Local Neighbor Propagation on Graphs based feature matching (LNPG) method, to obtain good correspondences for feature matching. LNPG starts from a novel neighborhood graph construction strategy. The strategy leverages the spatial consistency constraint to generate a series of neighborhood sets, and employs the residual information to preserve the local neighborhood relationships of potential inliers (i.e., true matches). Subsequently, LNPG incorporates local neighbor propagation into the graph to enhance connections between data in different neighborhoods, by using the path-based similarity measurement and the adaptive graph partition. In addition, LNPG includes a novel consistency-filtering-based clustering algorithm for robust feature matching. This clustering algorithm introduces a reliable neighborhood consistency measure function and an effective cluster merging criterion for cluster detection and cluster merging during the clustering process, respectively. Overall, LNPG not only effectively distinguishes inliers from outliers, but also reliably classifies inliers into different transformation models between pairs of images. Experiments on publicly available datasets with different types of image transformations show the superiority of our LNPG in comparison with other state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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