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IEEE International Conference on Data Mining (ICDM)

作者： Lina Ren Ruizhang Huang Shengwei Ma Yongbin Qin Yanping Chen Chuan Lin State Key Laboratory of Public Big Data Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and Technology Guizhou University Guiyang China Department of Information Engineering Guizhou Light Industry Technical College Guiyang China

In this paper, a deep multi-kernel clustering network, named DMKCN, is proposed to learn a high-quality and structurally separable kernel representation for the clustering task. Specifically, a multi-kernel learner is proposed to choose a suitable kernel function by learning a suitable combination of kernel functions automatically. A kernel-aid encoder module, consisting of a series of multi-kernel learners, is proposed to learn the structurally separable kernel representation. Besides, a dual self-supervised mechanism, consisting of a kernel self-supervised strategy and a representation self-supervised strategy, is designed to uniformly optimize the kernel representation learning and structural partition. The kernel self-supervised strategy is developed to supervise the multi-kernel learners with the consideration of an objective of clustering task, the representation self-supervised strategy is developed to guide the optimization of kernel representation learning by reconstructing the raw data. Extensive experiments on six real-world datasets demonstrate the outstanding performance of our proposed DMKCN.

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Optimal Targeted Advertising Strategy for Secure Wireless Edge Metaverse

Optimal Targeted Advertising Strategy for Secure Wireless Ed...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Hongyang Du Dusit Niyato Chunyan Miao Jiawen Kang Dong In Kim Interdisciplinary Graduate Program School of Computer Science and Engineering the Energy Research Institute @ NTU Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of of Automation Guangdong University of Technology China Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (纸本)9781665435413

Recently, Metaverse has attracted increasing attention from both industry and academia, because of the significant potential to integrate real and digital worlds ever more seam-lessly. By combining advanced wireless communications, edge computing and virtual reality (VR) technologies into Metaverse, a multidimensional, intelligent and powerful wireless edge Meta-verse is created for future human society. In this paper, we design a privacy preserving targeted advertising strategy for the wireless edge Metaverse. Specifically, a Metaverse service provider (MSP) allocates bandwidth to the VR users so that the users can access Metaverse from edge access points. To protect users' privacy, the covert communication technique is used in the downlink. Then, the MSP can offer high-quality access services to earn more profits. Motivated by the concept of “covert”, targeted advertising is used to promote the sale of bandwidth and ensure that the advertising strategy cannot be detected by competitors who may make counter-offer and by attackers who want to disrupt the services. We derive the best advertising strategy in terms of budget input, with the help of the Vidale-Wolfe model and Hamiltonian function. Furthermore, we propose a novel metric named Meta-Immersion to represent the user's experience feelings. The performance evaluation shows that the MSP can boost its revenue with an optimal targeted advertising strategy, especially compared with that without the advertising.

关键词： Wireless communication Privacy Metaverse Bandwidth Virtual reality Downlink User experience

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Neural foundations of mental simulation: future prediction of latent representations on dynamic scenes 23

Neural foundations of mental simulation: future prediction o...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Aran Nayebi Rishi Rajalingham Mehrdad Jazayeri Guangyu Robert Yang McGovern Institute for Brain Research MIT Cambridge MA Cambridge MA and Reality Labs Meta New York NY Cambridge MA and Department of Brain and Cognitive Sciences MIT Cambridge MA and Department of Electrical Engineering and Computer Science MIT

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models. We find that "scale is not all you need", and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction. In fact, only one class of models matches these data well overall. We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so. Finally, we find that not all foundation model latents are equal. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of egocentric sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms a

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What did I sign? A study of the impenetrability of legalese in contracts 43

What did I sign? A study of the impenetrability of legalese ...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Martínez, Eric Mollica, Francis Liu, Yufei Podrug, Anita Gibson, Edward Brain and Cognitive Sciences MIT Cambridge United States School of Informatics University of Edinburgh Edinburgh United Kingdom Psychology Wellesley College Wellesley United States Electrical Engineering and Computer Science MIT Cambridge United States

Legal documents, in the form of terms of service agreements and other private contracts, are now an increasingly prevalent part of everyday life. While legal documents have long been acknowledged to be difficult to understand without training, it remains an open question whether the ever-increasing exposure to contracts might have mitigated this difficulty. Moreover, insofar as this difficulty has persisted, there remains no systematic analysis of which linguistic structures contribute most heavily to the processing difficulty of legal texts, nor whether this difficulty is heightened for those with less language experience. Here, we investigate these issues, and in a well-powered experiment find evidence that (a) both recall and comprehension of legal propositions in a contract are hindered by use of a legal register relative to plain-English translations;(b) certain linguistic structures, such as center-embedding, hinder recall to a greater degree than others, such as passive voice;and (c) language experience influences comprehension of legal propositions. Surprisingly, language experience did not influence recall, nor was there an interaction between legal register and language experience on recall or comprehension. These findings suggest that legal language poses heightened difficulties for those with less language experience–who tend to be of lower socioeconomic status and with diminished access to the justice system–and that eliminating complex features of legalese would benefit those of all reading levels. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Contracts

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Act as you wish: fine-grained control of motion diffusion model with hierarchical semantic graphs 23

Act as you wish: fine-grained control of motion diffusion mo...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Peng Jin Yang Wu Yanbo Fan Zhongqian Sun Yang Wei Li Yuan School of Electronic and Computer Engineering Peking University Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China Tencent AI Lab China School of Electronic and Computer Engineering Peking University Shenzhen China and Peng Cheng Laboratory Shenzhen China and AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School China

Most text-driven human motion generation methods employ sequential modeling approaches, e.g., transformer, to extract sentence-level text representations automatically and implicitly for human motion synthesis. However, these compact text representations may overemphasize the action names at the expense of other important properties and lack fine-grained details to guide the synthesis of subtly distinct motion. In this paper, we propose hierarchical semantic graphs for fine-grained control over motion generation. Specifically, we disentangle motion descriptions into hierarchical semantic graphs including three levels of motions, actions, and specifics. Such global-to-local structures facilitate a comprehensive understanding of motion description and fine-grained control of motion generation. Correspondingly, to leverage the coarse-to-fine topology of hierarchical semantic graphs, we decompose the text-to-motion diffusion process into three semantic levels, which correspond to capturing the overall motion, local actions, and action specifics. Extensive experiments on two benchmark human motion datasets, including HumanML3D and KIT, with superior performances, justify the efficacy of our method. More encouragingly, by modifying the edge weights of hierarchical semantic graphs, our method can continuously refine the generated motion, which may have a far-reaching impact on the community. Code and pre-trained weights are available at https://***/jpthu17/GraphMotion.

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OSM: Leveraging model checking for observing dynamic 1 behaviors in aspect-oriented applications

arXiv

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arXiv 2024年

作者： AlSobeh, Anas Mohammad Ramadan Department of Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Program of Information Technology School of Computing College of Engineering Computing Technology and Mathematics Southern Illinois University CarbondaleIL United States

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect oriented programming

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Development of Automatic Plant Irrigation System using Soil Moisture Sensors for Precision Agriculture of Chili

Development of Automatic Plant Irrigation System using Soil ...

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2020 International Conference on Smart Technology and Applications, ICoSTA 2020

作者： Priandana, Karlisa Wahyu, Ramadina Al-Fatihah Computer Science Department Bogor Indonesia Computer Engineering Study Program Vocational School Bogor Indonesia

ISBN: (纸本)9781728130835

In agriculture, water management is crucial to reduce the amount of water used for irrigation. This study aims to develop an automatic plant irrigation system to be implemented in the Greenhouse of the Department of computer science, IPB University. The system is made of solenoid valves and soil moisture sensors whose amount is adjusted to the number of plants. This automatic plant irrigation system was created to support the development of smart and precision farming in Indonesia. Irrigation is done automatically and precisely for each plant by using a solenoid valve and a relay. The system prototype has been tested with different soil moisture values. Experiment results show that the prototype has successfully read the soil moisture value and can do the irrigation properly according to the specified functional requirements. © 2020 IEEE.

关键词： Soil moisture

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Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generation

arXiv

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arXiv 2025年

作者： Wang, Xiao Wang, Fuling Wang, Haowen Jiang, Bo Li, Chuanfu Wang, Yaowei Tian, Yonghong Tang, Jin Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China The First Affiliated Hospital of Anhui University of Chinese Medicine Hefei230022 China Peng Cheng Laboratory Shenzhen China Harbin Institute of Technology Shenzhen China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing China

X-ray image based medical report generation achieves significant progress in recent years with the help of the large language model, however, these models have not fully exploited the effective information in visual image regions, resulting in reports that are linguistically sound but insufficient in describing key diseases. In this paper, we propose a novel associative memory-enhanced X-ray report generation model that effectively mimics the process of professional doctors writing medical reports. It considers both the mining of global and local visual information and associates historical report information to better complete the writing of the current report. Specifically, given an X-ray image, we first utilize a classification model along with its activation maps to accomplish the mining of visual regions highly associated with diseases and the learning of disease query tokens. Then, we employ a visual Hopfield network to establish memory associations for disease-related tokens, and a report Hopfield network to retrieve report memory information. This process facilitates the generation of high-quality reports based on a large language model and achieves state-of-the-art performance on multiple benchmark datasets, including the IU X-ray, MIMIC-CXR, and Chexpert Plus. The source code of this work is released on https://***/Event-AHU/Medical_Image_Analysis. Copyright © 2025, The Authors. All rights reserved.

关键词： Associative storage

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SAM+nnUNet: Deep-learning-based Head and Neck Tumor Segmentation on FDG PET

SAM+nnUNet: Deep-learning-based Head and Neck Tumor Segmenta...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： Z. Fang Z. Lu H. Liu Y. Liu G. S.P. Mok Department of Electrical and Computer Engineering Faculty of Science and Technology Biomedical Imaging Laboratory (BIG) University of Macau Macau China Department of Nuclear Medicine The Affiliated Hospital Southwest Medical University Luzhou China Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Macau China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

FDG PET is a useful tool for diagnosis, staging and treatment monitoring on Head and Neck (H&N) tumors. Segmentation is usually performed for the tumors while the deep learning technique is emerging as an efficient yet precise alternate to manual segmentation. The performance of the conventional CNN-based segmentation networks is limited by insufficient training data. The Segment Anything Model (SAM) has been released by Meta, initiating a paradigm shift to a “smart” interactive segmentation that provides competitive performance when properly prompted. In this study, we proposed a SAM+nnUNet for H&N tumor segmentation on FDG PET to enhance the original SAM, in which the prompting process can be time-consuming and require a high degree of expertise. The HECKTOR2022 PET/CT dataset was used, with 420 patient images for training, 50 cases for validation, and 54 cases for testing. The training dataset of PET was used to fine-tune the mask decoder of pre-trained SAM, while both PET and CT were used to train an nnUNet. The trained nnUNet was first used to perform a preliminary segmentation of the tumors on the testing dataset, which was used to generate bounding box prompts for the SAM input after visual inspection and manual adjustment if necessary. Thresholding segmentation based on 42% of maximum intensity and various U-Net variants are implemented for comparison. Dice Similarity Coefficient (DSC), Hausdorff Distance at 95th percentile (HD95), and SUVmean of segmented tumors compared to the ground truth were analyzed. Our results indicate that 5 out of 54 cases need manual adjustment for the nnUNet-based prompts for SAM. SAM+nnUNet performs best in all quantitative indices. We conclude that our proposed method is promising for H&N tumor segmentation on PET.

关键词： Training Image segmentation Visualization Head Training data Manuals Artificial neural networks

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Spatial Gradient Guided Learning and Semantic Relation Transfer for Facial Landmark Detection 27th

Spatial Gradient Guided Learning and Semantic Relation Trans...

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27th International Conference on MultiMedia Modeling, MMM 2021

作者： Wang, Jian Li, Yaoyi Lu, Hongtao Key Lab of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783030678319

Pixel-wise losses are widely used in heatmap regression networks to detect facial landmarks, however, those losses are not consistent with the evaluation criteria in testing, which is evaluating the error between the highest pixel position in the predicted heatmap and the ground-truth heatmap. In this paper, we proposed a novel spatial-gradient consistency loss function (called Grad loss), which maintains a similar spatial structure in the heatmap with ground-truth. To reduce the quantization error caused by downsampling in the network, we also propose a new post-processing strategy based on the Gaussian prior. To further improve face alignment accuracy, we introduce Spatial-Gradient Enhance attention and Relation-based Reweighing Module to transfer semantic information and spatial information between high-resolution and low-resolution representations. Extensive experiments on several benchmarks (e.g., 300W, AFLW, COFW, WFLW) show that our method outperforms the state-of-the-art by impressive margins. © 2021, Springer Nature Switzerland AG.

关键词： Face recognition

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