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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

引用

International Conference on Signal Processing Proceedings (ICSP)

作者： Caiqing Jian Yongbin Qin Lihui Wang Chen Ye Xinyu Cheng GState Key Laboratory of Public Big Data College of Computer Science and Technology Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Guizhou University Guiyang China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma.

关键词： Instance segmentation Measurement Visualization Adhesives Shape Semantic segmentation Glands Signal processing algorithms Signal processing Market research

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Bidirectional Logits Tree: Pursuing Granularity Reconcilement in Fine-Grained Classification

arXiv

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arXiv 2024年

作者： Lu, Zhiguang Xu, Qianqian Bao, Shilong Yang, Zhiyong Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences China

This paper addresses the challenge of Granularity Competition in fine-grained classification tasks, which arises due to the semantic gap between multi-granularity labels. Existing approaches typically develop independent hierarchy-aware models based on shared features extracted from a common base encoder. However, because coarse-grained levels are inherently easier to learn than finer ones, the base encoder tends to prioritize coarse feature abstractions, which impedes the learning of fine-grained features. To overcome this challenge, we propose a novel framework called the Bidirectional Logits Tree (BiLT) for Granularity Reconcilement. The key idea is to develop classifiers sequentially from the finest to the coarsest granularities, rather than parallelly constructing a set of classifiers based on the same input features. In this setup, the outputs of finer-grained classifiers serve as inputs for coarser-grained ones, facilitating the flow of hierarchical semantic information across different granularities. On top of this, we further introduce an Adaptive Intra-Granularity Difference Learning (AIGDL) approach to uncover subtle semantic differences between classes within the same granularity. Extensive experiments demonstrate the effectiveness of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Coarse-grained modeling

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Helicity controlled spin Hall angle in the 2D Rashba altermagnets

arXiv

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arXiv 2024年

作者： Chen, Weiwei Zeng, Longhai Zhu, W. Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province College of Sciences China Jiliang University Hangzhou310018 China School of Science Westlake University Hangzhou310024 China

We investigate the efficiency of charge-to-spin conversion in two-dimensional Rashba altermagnets, a class of materials that merge characteristics of both ferromagnets and antiferromagnets. Utilizing quantum linear response theory, we quantify the longitudinal and spin Hall conductivities in this system and demonstrate that a substantial enhancement of the spin Hall angle is achieved below the band crossing point through the dual effects of relativistic spin-orbit interaction and nonrelativistic altermagnetic exchange interaction. Additionally, we find that skew scattering and topology-related intrinsic mechanisms are almost negligible in this system, which contrasts with conventional ferromagnetic Rashba systems. Our findings not only advance the understanding of spin dynamics in Rashba altermagnets but also pave the way for novel strategies in manipulating charge-to-spin conversion via the sophisticated control of noncollinear in-plane and collinear out-of-plane spin textures. © 2024, CC0.

关键词： Spin dynamics

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-...

引用

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

作者： Kaijun Deng Dezhi Zheng Jindong Xie Jinbao Wang Weicheng Xie Linlin Shen Siyang Song Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Department of Computer Science University of Exeter

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code is available at https://***/CVI-SZU/DEGSTalk.

关键词： Hair Training Three-dimensional displays Dynamics Signal processing Rendering (computer graphics) Noise measurement Speech processing Faces Videos

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T3: Multi-modal Tailless Triple-Flapping-Wing Robot for Efficient Aerial and Terrestrial Locomotion

arXiv

引用

arXiv 2025年

作者： Xu, Xiangyu Zheng, Zhi Wang, Jin Chen, Yikai Huang, Jingyang Wu, Ruixin Yu, Huan Lu, Guodong The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310058 China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

Flapping-wing robots offer great versatility;however, achieving efficient multi-modal locomotion remains challenging. This paper presents the design, modeling, and experimentation of T3, a novel tailless flapping-wing robot with three pairs of independently actuated wings. Inspired by juvenile water striders, T3 incorporates bio-inspired elastic passive legs that effectively transmit vibrations generated during wing flapping, enabling ground movement without additional motors. This novel mechanism facilitates efficient multi-modal locomotion while minimizing actuator usage, reducing complexity, and enhancing performance. An SE(3)-based controller ensures precise trajectory tracking and seamless mode transition. To validate T3’s effectiveness, we developed a fully functional prototype and conducted targeted modeling, real-world experiments, and benchmark comparisons. The results demonstrate the robot’s and controller’s outstanding performance, underscoring the potential of multi-modal flapping-wing technologies for future aerial-ground robotic applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Antenna grounds

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A Machine Learning-Based Unified Framework for Multidimensional Biological Age Estimation

SSRN

引用

SSRN 2024年

作者： Yu, Qi Da, Lijuan Ma, Qian Huang, Yushu Dong, Yue Liu, Yuan Li, Xiaoyu Wu, Xifeng Li, Zilin Li, Wenyuan Center of Clinical Big Data and Analytics The Second Affiliated Hospital Department of Big Data in Health Science School of Public Health Zhejiang University School of Medicine Zhejiang Hangzhou China Zhejiang Provincial Key Laboratory of Intelligent Preventive Medicine China School of Social Sciences Tsinghua University Beijing China School of Mathematics and Statistics Northeast Normal University Jilin Changchun China

Objective: Aging varies between individuals as well as between organs within an individual. This study aims to develop a machine learning-based unified framework to comprehensively assess and interpret biological aging, where each base component is used to assess the health status of different organ systems. Method: 264,874 individuals at baseline from the UK biobank were included. The ensemble model was constructed to estimate biological age (BA) by combining the predictions of different base models, where the base models estimated organ health using organ-specific data. Phenome-wide association study (PheWAS) and Cox proportional-hazards models were used to assess the associations between various BA accelerations (the contrast between BA and chronological age (CA)) and the risk of aging-related morbidity and mortality. Causal mediation analysis was performed to investigate the mediated effect of biological aging in the association between lifestyle/environmental factors and all-cause mortality. Result: Our estimated BA was significantly correlated with CA in relatively healthy individuals (mean absolute error (MAE): 4.480 years;Pearson correlation: 0.717, P © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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No-Reference Stereoscopic Image Quality Assessment Based on Binocular Collaboration

SSRN

引用

SSRN 2024年

作者： Guo, Wenzhong Wang, Hanling Ke, Xiao Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fujian Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou University Fuzhou350116 China

Stereoscopic images usually consist of a left view, a right view, and depth information. Stereoscopic/3D image quality assessment (SIQA) is often more challenging compared to 2D-IQA due to the usual scene differences between left and right views and the complexity of the binocular vision fusion process. To address the problem of quality prediction bias of multi-distortion images, we investigated the visual physiology and the processing of visual information by the primary visual cortex of the human brain and proposed a no-reference stereoscopic image quality evaluation method. The method mainly includes an innovative end-to-end NR-SIQA neural network with a picture patch generation algorithm. The algorithm generates a saliency map by fusing the left and right views and then guides the image cropping in the database based on the saliency map. The proposed models are validated and compared based on publicly available databases. The results show that the model and algorithm together outperform the state-of-the-art NR-SIQA metric in the LIVE 3D database and the WIVC 3D database, and have excellent results in the specific noise metric. The model generalization experiments demonstrate a certain degree of generality of our proposed model. © 2024, The Authors. All rights reserved.

关键词： database systems

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

引用

International Conference on Mechatronics (ICM)

作者： Zhenyu Li Ruixin Wu Jin Wang Chuanqi Hu Zhi Zheng Jun Meng Guodong Lu School of Mechanical Engineering Zhejiang University Hangzhou China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou China Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Zhejiang University Hangzhou China Robotics Research Center of Yuyao City Ningbo China Polytechnic Institute Zhejiang University Hangzhou China College of Electrical Engineering Zhejiang University Hangzhou China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331533892

ISBN: (纸本)9798331533908

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture.

关键词： Point cloud compression Visualization Three-dimensional displays Trajectory planning Heuristic algorithms Collaboration Real-time systems Safety Optimization Quadrotors

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