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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhong, Guojin Hu, Jinhong Chen, Jiajun Yuan, Jin Pan, Wenbo College of Computer Science and Electronic Engineering Hunan University China GuangDong Engineering Technology Research Center of Intelligent Service of Urban and Rural Planning and Construction China School of Robotics Hunan University China CRRC Zhuzhou Institute China

ISBN: (纸本)157735897X

Image change captioning (ICC) poses great challenges stemming from describing subtle differences between two similar images in natural language, significantly increasing the complexity of feature extraction and cross-modal learning compared to the image captioning task. Existing ICC methods often suffer from two key challenges: 1) Massive irrelevant information of uni-image features leads to suboptimal visual difference representations;2) Imprecise inter-modality correspondence degrades the quality of generated captions. This paper proposes a Difference-aware Contrastive Diffusion Model with Adversarial Perturbations (DECIDER) for ICC due to the excellent performance of diffusion models in image/text generation. Technically, difference-aware cross-modal learning is developed to suppress irrelevant information and learn compact yet robust visual difference representations. This is achieved by optimizing a novel objective mathematically derived from the information bottleneck principle that excels in filtering redundant features and highlighting differences. Furthermore, we propose to dynamically generate “hard” positive and negative samples via adversarial perturbations, which are involved in contrastive diffusion training with a tighter variational bound. This design encourages our DECIDER to excavate and construct complex correspondences between visual differences and captions, thereby improving generalization performance. Extensive experiments on four datasets demonstrate that DECIDER significantly exceeds state-of-the-art performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Fully implantable and battery-free wireless optoelectronic system for modulable cancer therapy and real-time monitoring

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npj Flexible Electronics 2023年第1期7卷 119-130页

作者： Kiho Kim In Sik Min Tae Hee Kim Do Hyeon Kim Seungwon Hwang Kyowon Kang Kyubeen Kim Sangun Park Jongmin Lee Young Uk Cho Jung Woo Lee Woon-Hong Yeo Young Min Song Youngmee Jung Ki Jun Yu Functional Bio-integrated Electronics and Energy Management Lab School of Electrical and Electronic EngineeringYonsei University50 Yonsei-roSeodaemun-guSeoul 03722Republic of Korea Center for Biomaterials Biomedical Research InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Fusion Research and Collaboration Biomedical Research InstituteSeoul National University HospitalSeoul 03080Republic of Korea School of Electrical Engineering and Computer Science(EECS) Gwangju Institute of Science and Technology(GIST)Gwangju 61005Republic of Korea PA1 Foundry BusinessSamsung Electronics1 Samsungjeonja-roHwaseong-siGyeonggi-doRepublic of Korea R&D Center DNA Lab PharmaresearchSeongnam 13486Republic of Korea KU-KIST Graduate School of Converging Science and Technology Korea UniversitySeoul 136-705Republic of Korea School of Materials Science and Engineering Energy Materials for Soft Electronics LaboratoryPusan National UniversityBusan 46241Republic of Korea IEN Center for Human-Centric Interfaces and Engineering and George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University School of MedicineAtlantaGA 30332USA Institute for Robotics and Intelligent Machines Parker H.Petit Institute for Bioengineering and BiosciencesInstitute for MaterialsNeural Engineering CenterGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Electronic Engineering YU-Korea Institute of Science and Technology(KIST)InstituteYonsei University50Yonsei-roSeodaemun-guSeoul 03722Republic of Korea

Photodynamic therapy(PDT)is attracting attention as a next-generation cancer treatment that can selectively destroy malignant tissues,exhibit fewer side effects,and lack pain during *** PDT systems have recently been developed to resolve the issues of bulky and expensive conventional PDT systems and to implement continuous and repetitive *** implantable PDT systems,however,are not able to perform multiple functions simultaneously,such as modulating light intensity,measuring,and transmitting tumor-related data,resulting in the complexity of cancer ***,we introduce a flexible and fully implantable wireless optoelectronic system capable of continuous and effective cancer treatment by fusing PDT and hyperthermia and enabling tumor size monitoring in *** system exploits micro inorganic light-emitting diodes(μ-LED)that emit light with a wavelength of 624 nm,designed not to affect surrounding normal tissues by utilizing a fully programmable light intensity ofμ-LED and precisely monitoring the tumor size by Si phototransistor during a long-term implantation(2–3 weeks).The superiority of simultaneous cancer treatment and tumor size monitoring capabilities of our system operated by wireless power and data transmissions with a cell phone was confirmed through in vitro experiments,ray-tracing simulation results,and a tumor xenograft mouse model in *** all-in-one single system for cancer treatment offers opportunities to not only enable effective treatment of tumors located deep in the tissue but also enable precise and continuous monitoring of tumor size in real-time.

关键词： optoelectronic enable battery

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DDCM: Cracking Anonymized Facial Images Using Denoising Diffusion Cryptanalytic Model

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IEEE Transactions on Consumer Electronics 2025年

作者： Jiang, Donghua Ni, Jiangqun Alasbali, Nada Nafea, Ibtehal Liu, Qingliang Ahmad, Jawad Boulila, Wadii Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China Sun Yat-sen University School of Cyber Science and Technology Shenzhen518107 China Peng Cheng Laboratory Department of New Networks Shenzhen518052 China King Khalid University College of Computer Science Department of Informatics and Computing Systems Abha61421 Saudi Arabia Taibah University College of Computer Science and Engineering Medina41477 Saudi Arabia Qingdao University of Science and Technology School of Mathematics and Physics Qingdao266061 China Prince Mohammad Bin Fahd University Cybersecurity Center Alkhobar Saudi Arabia Prince Sultan University Robotics and Internet-of-Things Laboratory Riyadh12435 Saudi Arabia

Currently, in consumer electronics industry, quite a number of E-healthcare products highly depend on consumers' facial biometrics for both disease diagnosis and identity authen-tication. In light of this, various facial privacy-preserving approa-ches oriented towards the internet of medical things have been proposed, including visual cryptography and desensitization tech-nique. Nevertheless, few attentions have been paid to investigating their countermeasure, i.e., facial privacy analysis, owning to significant distribution discrepancy between the original and anony-mized facial images. To this end, a general Denoising Diffusion Cryptanalytic Model (DDCM) is proposed in this paper to recon-struct the original facial images in a coarse-to-fine manner from their encrypted ones with diverse anonymous approaches. Spec-ifically, to accurately reconstruct the original facial image without any prior knowledge, a customized initial prediction module with an encoder-decoder structure is introduced by taking advantage of its leaked visual cues to generate the coarse face. Furthermore, the residual map between the original and coarse facial images is explicitly modeled with the output of the customized prediction module as a conditional prior, thereby obtaining a refined facial image. Finally, extensive experiments demonstrate that the propo-sed DDCM could not only effectively crack multiple handcrafted facial anonymization approaches, such as blurring, pixelation and thumbnail-preserving encryption, but also outperform the prev-ious works (e.g., Pix2Pix) in terms of image fidelity and attack success rate. © 1975-2011 IEEE.

关键词： Anonymity

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Negotiated Reasoning: On Provably Addressing Relative Over-Generalization

arXiv

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arXiv 2023年

作者： Sheng, Junjie Li, Wenhao Jin, Bo Zha, Hongyuan Wang, Jun Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai China School of Data Science The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China School of Software Engineering Tongji University Shanghai China

Over-generalization is a thorny issue in cognitive science, where people may become overly cautious due to past experiences. Agents in multi-agent reinforcement learning (MARL) also have been found suffering relative over-generalization (RO) as people do and stuck to sub-optimal cooperation. Recent methods have shown that assigning reasoning ability to agent can mitigate RO algorithmically and empirically, but there has been a lack of theoretical understanding of RO, let alone designing provably RO-free methods. This paper first proves that RO can be avoided when the MARL method satisfies a consistent reasoning requirement under certain conditions. Then we introduce a novel reasoning framework, called negotiated reasoning, that first builds the connection between reasoning and RO with theoretical justifications. After that, we propose an instantiated algorithm, Stein variational negotiated reasoning (SVNR), which uses Stein variational gradient descent to derive a negotiation policy that provably avoids RO in MARL under maximum entropy policy iteration. The method is further parameterized with neural networks for amortized learning, making computation efficient. Numerical experiments on many RO-challenged environments demonstrate the superiority and efficiency of SVNR compared to state-of-the-art methods in addressing RO. © 2023, CC BY.

关键词： Multi agent systems

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Multi-Prompt with Depth-Partitioned Cross-Modal Learning

arXiv

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arXiv 2023年

作者： Tian, Yingjie Wang, Yiqi Guo, Xianda Zhu, Zheng Chen, Long Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Waytous China PhiGent Robotics China

In recent years, soft prompt learning methods have been proposed to fine-tune large-scale vision-language pretrained (VLP) models for various downstream tasks. These methods typically combine learnable prefix tokens with class tokens as inputs for models with frozen parameters. However, we argue that these methods often employ uni-prompts to describe the diverse prefixes of classes, which are suboptimal in that they fail to prompt classes’ diverse attributes or other useful environmental information. This study introduces a partitioned multimodal multi-prompt (PMPO), a technique that extends a soft prompt from a single prompt to multiple prompts. To avoid the trivial solution, we creatively propose to divide the depths of a visual encoder and connect learnable prompts to the partitioned visual depths, enabling different prompts to capture the hierarchical contextual information of visual representations. Furthermore, to maximize the advantages of multi-prompt learning, we incorporate prior information derived from manually designed templates into the multi-prompt, thus improving the generalization ability of our approach. We evaluate the effectiveness of our approach on three standard tasks: new class generalization, cross-dataset evaluation, and domain generalization. For instance, our method achieves a 79.28 harmonic mean averaged over 11 diverse image recognition datasets (+7.62 compared to that of CoOp), demonstrating significant competitiveness compared to the state-of-the-art prompting methods. The code is available at PMPO. Copyright © 2023, The Authors. All rights reserved.

关键词： Image recognition

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IoT in Health Care Study by Text Analysis 2

IoT in Health Care Study by Text Analysis

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Dhumal, Yashomati R Gobinath, V.M. Jangirala, Srinivas Vidhya, M. Prakash, A. Shinde, Arundhati College of Engineering Electronics Department Pune India Karpaga Vinayaga College of Engineering and Technology Department of Robotics and Automation Chengalpattu India O. P. Jindal Global University Jindal Global Business School Sonipat India Panimalar Institute of Technology Department of Computer Science and Engineering Varadarajapuram India Veltech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India College of Engineering Electronics Department Pune India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

The Internet of Things (IoT) should be a new buzzword that emerged little by little (IoT). Organizations, governments, and the scientific community have already understood the relevance of IoT in the past few years, with a growing number of publications. A key component of this progress was IoT in health, but little research has been conducted on aspirations and potential developments. Only as the solution, they integrate bibliometric analysis and data mining to evaluate the longitudinal patterns of the IoT devices to Medical in this study. From 2000 to 2021, 778 articles were derived from the Web of science. Abstract textual data indicate that articles were divided into 30 groups, revealing eight IoT in health trends. The development of this field is worldwide, with distinctive patterns that should be noted to promote some areas. © 2022 IEEE.

关键词： Internet of things

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ZSM-based Management and Orchestration of 3GPP Network Slicing: An Architectural Framework and Deployment Options

arXiv

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arXiv 2022年

作者： Sajjad, Muhammad Mohtasim Jayalath, Dhammika Tian, Yu-Chu Bernardos, Carlos J. School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia School of Computer Science Universidad Carlos III de Madrid Spain

Driven by closed-loop automation, the Zero-Touch Network and Services Management (ZSM) framework offers invigorating features for the end-to-end management and orchestration of a sliced network. Although ZSM is considered a promising framework by 3GPP, there is a lack of concrete ZSM-based solutions for the 3GPP Network Slicing management. This article presents an architectural framework of a ZSM-based management system for 3GPP Network Slicing. The proposed framework employs recursive ZSM management domains to meet the specific management requirements of the standard 3GPP Network Slicing framework. Two deployment options are considered for the presented framework. The first option features the integration of the standard services of the 3GPP Management System within ZSM. The other one considers ZSM as a complementary system for the standard 3GPP Management System. From these deployment options, some key questions are identified on ZSM’s interoperability with the existing 3GPP Network Slicing systems. Finally, for each option, the architectural and operational feasibility of the ZSM interoperation with 3GPP Network Slicing systems is shown through an example use case. Copyright © 2022, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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3D Registration of the Point Cloud Data Using Parameter Adaptive Super4PCS Algorithm in Medical Image Analysis 4

3D Registration of the Point Cloud Data Using Parameter Adap...

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4th International Conference on Digital Medicine and Image Processing, DMIP 2021

作者： Su, Shun Song, Guoli Zhao, Yiwen The State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences China

ISBN: (纸本)9781450386487

In this article, we use the parameter-adaptive Super4PCS algorithm to achieve high-precision registration of medical point clouds. First, generate the corresponding point cloud from the biological data (CT, MRI) to be registered. Then analyze the characteristics of the point cloud to be registered, and use it to adaptively set the parameters of Super4PCS, and finally perform point cloud registration. We compare the performance of six different algorithms with their accuracy and robustness. The accuracy, robustness of our method are the best. At the same time, no parameter input is required which is very convenient for medical workers. Experiments on medical models demonstrate the efficiency and robustness of our algorithm. © 2021 Owner/Author.

关键词： Image registration

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CDM-MPC: An Integrated Dynamic Planning and Control Framework for Bipedal Robots Jumping

arXiv

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arXiv 2024年

作者： He, Zhicheng Wu, Jiayang Zhang, Jingwen Zhang, Shibowen Shi, Yapeng Liu, Hangxin Sun, Lining Su, Yao Leng, Xiaokun Department of Computer Science Harbin Institute of Technology Harbin150001 China Beijing100080 China School of Mechatronics Engineering Harbin Institute of Technology Harbin150080 China Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215000 China

Performing acrobatic maneuvers like dynamic jumping in bipedal robots presents significant challenges in terms of actuation, motion planning, and control. Traditional approaches to these tasks often simplify dynamics to enhance computational efficiency, potentially overlooking critical factors such as the control of centroidal angular momentum (CAM) and the variability of centroidal composite rigid body inertia (CCRBI). This paper introduces a novel integrated dynamic planning and control framework, termed centroidal dynamics model-based model predictive control (CDM-MPC), designed for robust jumping control that fully considers centroidal momentum and non-constant CCRBI. The framework comprises an optimization-based kinodynamic motion planner and an MPC controller for real-time trajectory tracking and replanning. Additionally, a centroidal momentum-based inverse kinematics (IK) solver and a landing heuristic controller are developed to ensure stability during high-impact landings. The efficacy of the CDM-MPC framework is validated through extensive testing on the full-sized humanoid robot KUAVO in both simulations and experiments. Copyright © 2024, The Authors. All rights reserved.

关键词： Model predictive control

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Semantic Level Image Retrieval in Domain Adaptive Semantic Segmentation

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Procedia computer science 2024年 242卷 1163-1170页

作者： Siyu Zhu Fenfen Zhou Kai Li Haonan Wen Minghao Liu Computer School Beijing information science and technology university 102206 No.55 Taihang Road Changping District Beijing China Beijing Key Laboratory of Information Service Engineering Beijing Union University 100101 Beijing China. College of Robotics Beijing Union University 100027 Beijing China National Engineering Research Center of Visual Technology School of Computer Science Peking University 100871 Beijing China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Road Haidian District Beijing 100190 Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences No.19(A) Yuquan Road Shijingshan District Beijing 100049 P.R. China

The traditional domain adaptation task is not sufficient in mining inter domain image context. We propose a solution from the perspective of image semantic level retrieval. Image semantic level retrieval is based on the high-level semantics of images, retrieving the similarity or correspondence between images in terms of content and scene between domains. Our work focuses on the meaning, structure, and contextual information expressed by the target image, and retrieves the most relevant image from the source domain to improve the accuracy of domain adaptation. Furthermore, in response to the negative transfer brought about by image retrieval, we will introduce a category level weight adjustment module to further improve the accuracy of semantic level retrieval and weaken the negative impact caused by low precision categories. Finally, we demonstrated the effectiveness of semantic level image retrieval through experiments.

关键词： Image retrieval Domain adaptation Semantic segmentation Transfer learning

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