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Engineering 2023年第3期22卷 60-70页

作者：杨晨廖方茵兰舒琳王力翚沈卫明黄国全 School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Computer Science Yan'an UniversityYan'an 716000China School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Department of Production Engineering KTH Royal Institute of TechnologyStockholm 10044Sweden State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and TechnologyWuhan 430074China Department of Industrial and Manufacturing Systems Engineering The University of Hong KongHong Kong 999077China

本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件... 详细信息

本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件上。这种转变意义重大,因为软件可以充当制造系统的“大脑”,并且可以轻松更改或更新以支持快速系统重新配置、运营和演进。随后,边缘计算被引入,以接近终端的计算和存储能力来补充云。另一个关键问题是管理由不同服务质量(QoS)要求的大量物联网(IoT)数据传输而导致的严重网络拥塞。基于SDCM的虚拟化和灵活的网络能力,本研究形式化了面向复杂制造任务集的时间敏感性数据流量控制问题,并考虑了子任务分配和数据路由路径选择。为了解决这一优化问题,提出了一种将遗传算法(GA)、Dijkstra最短路径算法和排队算法相结合的方法。实验结果表明,该方法能有效地防止网络拥塞,减少SDCM中的总通信延迟。

关键词： Cloud manufacturing Edge computing Software-defined networks Industrial Internet of Things Industry 4.0

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Reducing Energy Consumption in NB-IoT by Compressing Data and Aggregating Transmission

Reducing Energy Consumption in NB-IoT by Compressing Data an...

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International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2023

作者： Kheir El Dine, Mohamad Al Haj Hassan, Hussein Nasser, Abbass Zaki, Chamseddine Moawad, Azza Mansour, Ali ENSTA Bretagne LabSTICC UMR CNRS 8265 Brest Cedex 929806 France CCE Department American University of Science and Technology Beirut Lebanon Computer Science Departement American University of Culture and Education ICCS-Lab Beirut Lebanon College of Engineering and Technology American University of the Middle East Egaila54200 Kuwait Cairo Egypt

ISBN: (纸本)9783031481208

The rapid expansion of Internet of Things (IoT) applications necessitates the utilization of efficient Low Power Wide Area Network (LPWAN) technologies. Among these technologies, Narrowband IoT (NB-IoT) has emerged as a highly promising solution due to its energy efficiency, long-range communications, and anticipated scalability. In this study, we conducted simulations to examine the impact of various factors on energy usage in NB-IoT devices. Taking into consideration the significance of energy efficiency in IoT devices, particularly in hard-to-reach locations, we study the effects of data compression on energy consumption. The obtained results revealed that implementing data compression techniques can significantly reduce energy consumption, specifically in areas with limited transmission coverage. Additionally, we study the benefits of expanding data transmission intervals to conserve energy. The findings of this paper emphasize the importance of adopting energy-efficient practices in the development of IoT technology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Internet of things

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Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance

Cross-View Meets Diffusion: Aerial Image Synthesis with Geom...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Ahmad Arrabi Xiaohan Zhang Waqas Sultani Chen Chen Safwan Wshah Department of Computer Science Vermont Artificial Intelligence Lab University of Vermont Intelligent Machines Lab Information Technology University Center for Research in Computer Vision University of Central Florida

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Aerial imagery analysis is critical for many research fields. However, obtaining frequent high-quality aerial images is not always accessible due to its high effort and cost requirements. One solution is to use the Ground-to-Aerial (G2A) technique to synthesize aerial images from easily collectible ground images. However, G2A is rarely studied, because of its challenges, including but not limited to, the drastic view changes, occlusion, and range of visibil-ity. In this paper, we present a novel Geometric Preserving Ground-to-Aerial (G2A) image synthesis (GPG2A) model that can generate realistic aerial images from ground images. GPG2A consists of two stages. The first stage predicts the Bird's Eye View (BEV) segmentation (referred to as the BEV layout map) from the ground image. The second stage synthesizes the aerial image from the predicted BEV layout map and text descriptions of the ground image. To train our model, we present a new multimodal cross-view dataset, namely VIGORv2, built upon VIGOR [64] with newly collected aerial images, maps, and text descriptions. Our extensive experiments illustrate that GPG2A synthesizes better geometry-preserved aerial images than existing models. We also present two applications, data augmentation for cross-view geo-localization and sketch-based region search, to further verify the effectiveness of our GPG2A. The code and dataset are available at https://***/AhmadArrabi/GPG2A.

关键词： Geometry Image segmentation computer vision Costs Image synthesis Geology Computational modeling Layout Diffusion models Data augmentation

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Epistemic Multiagent Reasoning with Collaborative Robots 37

Epistemic Multiagent Reasoning with Collaborative Robots

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37th Italian Conference on Computational Logic, CILC 2022

作者： Soldà, Davide Fabiano, Francesco Dovier, Agostino Constraint and Logic Programming Lab University of Udine Udine33100 Italy Embedding System Unit of Fondazione Bruno Kessler Trento38123 Italy Department of Mathematical Physical and Computer Sciences University of Parma Parma43124 Italy Department of Mathematics Computer Science and Physics University of Udine Udine33100 Italy

Over the last few years, the fields of Artificial Intelligence, Robotics and IoT have gained a lot of attention. This increasing interest has brought, among other things, to the development of autonomous multi-agent systems where robotic entities may interact with each other. As for all the other autonomous settings, also these systems require arbitration. Our work tries to address this problem by presenting a framework that embeds both a classical and a multi-agent epistemic (epistemic, for brevity) planner in a robotic control architecture. The idea is to combine the (i) classical and the (ii) epistemic solvers to model efficiently the interaction with: the (i) physical world and the (ii) information flows, respectively. In particular, the presented architecture starts by planning on the "epistemic level" refining then single-agent world-altering actions thanks to the classical planner. To further optimize the solving process, we also introduce the concept of macros in epistemic planning. Macros, in fact, have been successfully employed in classical planning as they allow for opportune aggregations of actions that may lead to a reduction of plans' length. Finally, the overall framework is exemplified and validated with two Franka Emika manipulators. This allowed us to empirically justify how the combination of the two planning approaches (classical and epistemic), and the introduction of macros, reduce the computational time required by the orchestrating phase. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Robot programming

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GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fields

GazeNeRF: 3D-Aware Gaze Redirection with Neural Radiance Fie...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Alessandro Ruzzi Xiangwei Shi Xi Wang Gengyan Li Shalini De Mello Hyung Jin Chang Xucong Zhang Otmar Hilliges Department of Computer Science ETH Zürich Computer Vision Lab Delft University of Technology NVIDIA School of Computer Science University of Birmingham

We propose GazeNeRF, a 3D-aware method for the task of gaze redirection. Existing gaze redirection methods operate on 2D images and struggle to generate 3D consistent results. Instead, we build on the intuition that the face region and eyeballs are separate 3D structures that move in a coordinated yet independent fashion. Our method leverages recent advancements in conditional image-based neural radiance fields and proposes a two-stream architecture that predicts volumetric features for the face and eye regions separately. Rigidly transforming the eye features via a 3D rotation matrix provides fine-grained control over the desired gaze angle. The final, redirected image is then attained via differentiable volume compositing. Our experiments show that this architecture outperforms naively conditioned NeRF baselines as well as previous state-of-the-art 2D gaze redirection methods in terms of redirection accuracy and identity preservation. Code and models will be released for research purposes.

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Fides: Scalable Censorship-Resistant DAG Consensus via Trusted Components

arXiv

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arXiv 2025年

作者： Xie, Shaokang Kang, Dakai Lyu, Hanzheng Niu, Jianyu Sadoghi, Mohammad Exploratory Systems Lab Department of Computer Science University of California Davis United States School of Engineering University of British Columbia Okanagan campus Canada Department of Computer Science Engineering Southern University of Science and Technology China

Recently, consensus protocols based on Directed Acyclic Graph (DAG) have gained significant attention due to their potential to build robust blockchain systems, particularly in asynchronous networks. In this paper, we propose Fides, an asynchronous DAG-based BFT consensus protocol that leverages Trusted Execution Environments (TEEs) to tackle three major scalability and security challenges faced by existing protocols: (i) the need for a larger quorum size (i.e., at least 3x larger) to tolerate Byzantine replicas, (ii) high communication costs and reliance on expensive cryptographic primitives (i.e., global common coin) to reach agreement in asynchronous networks, and (iii) poor censorship resilience undermining the liveness guarantee. Specifically, Fides adopts four trusted components-Reliable Broadcast, Vertex Validation, Common Coin, and Transaction Disclosure-within TEEs. Incorporating these components enables Fides to achieve linear message complexity, guaranteed censorship resilience, 2x larger quorum size, and lightweight common coin usage. Besides, abstracting these essential components rather than porting the entire protocol into TEE can significantly reduce the Trusted Computing Base (TCB). Experimental evaluations of Fides in local and geo-distributed networks demonstrate its superior performance compared to established state-of-the-art protocols such as Tusk, RCC, HotStuff, and PBFT. The results indicate that Fides achieves a throughput of 400k transactions per second in a geo-distributed network and 810k transactions per second in a local network. Our analysis further explores the protocol's overhead, highlighting its suitability and effectiveness for practical deployment in real-world blockchain systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Blockchain

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A novel clinical artificial intelligence model for disease detection via retinal imaging

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The Innovation 2024年第2期5卷 11-12页

作者： Yidian Fu Liang Ma Sheng Wan Shengfang Ge Zhi Yang Department of Ophthalmology Ninth People’s HospitalShanghai JiaoTong University School of MedicineShanghai 200011China Shanghai Key Laboratory of Orbital Diseases and Ocular Oncology Shanghai 200011China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of EducationNanjing 210000China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing 210000China

Artificial intelligence(AI)is an emerging field in which computerized systems are used to carry out complex tasks in place of *** AI algorithms have been developed for disease diagnosis and prediction and treatment recommendation across various clinical data types,e.g.,chest X-rays,electrocardiograms,and other radiological images.1 In ophthalmology,particularly,great progress has been made in AI systems over the past *** fundus photography(CFP)and optical coherence tomography(OCT),which are readily available in routine clinical practice,are both mainstream and useful retinal imaging modalities in *** September 2023,the 2023 Lasker-Debakey Clinical Medical Research Award was awarded to three scientists for their work on OCT for accurate retinal disease detection.

关键词： clinical routine diagnosis

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Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning

Fine-grained Category Discovery under Coarse-grained supervi...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： An, Wenbin Tian, Feng Chen, Ping Tang, Siliang Zheng, Qinghua Wang, QianYing School of Automation Science and Engineering Xi'an Jiaotong University China School of Computer Science and Technology MOEKLNNS Lab Xi'an Jiaotong University China Department of Engineering University of Massachusetts Boston United States College of Computer Science and Technology Zhejiang University China Lenovo Research

Novel category discovery aims at adapting models trained on known categories to novel categories. Previous works only focus on the scenario where known and novel categories are of the same granularity. In this paper, we investigate a new practical scenario called Fine-grained Category Discovery under Coarse-grained supervision (FCDC). FCDC aims at discovering fine-grained categories with only coarse-grained labeled data, which can adapt models to categories of different granularity from known ones and reduce significant labeling cost. It is also a challenging task since supervised training on coarse-grained categories tends to focus on inter-class distance (distance between coarse-grained classes) but ignore intra-class distance (distance between fine-grained sub-classes) which is essential for separating fine-grained categories. Considering most current methods cannot transfer knowledge from coarse-grained level to fine-grained level, we propose a hierarchical weighted self-contrastive network by building a novel weighted self-contrastive module and combining it with supervised learning in a hierarchical manner. Extensive experiments on public datasets show both effectiveness and efficiency of our model over compared methods. Code and data are available at https://***/Lackel/Hierarchical_Weighted_SCL. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Expression in Vision-Language Models

LLaVA-SG: Leveraging Scene Graphs as Visual Semantic Express...

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

作者： Jingyi Wang Jianzhong Ju Jian Luan Zhidong Deng Department of Computer Science and Technology BNRist THUAI Tsinghua University Beijing China Xiaomi AI Lab Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advances in large vision-language models (LVLMs) typically employ vision encoders based on the Vision Transformer (ViT) architecture. The division of the images into patches by ViT results in a fragmented perception, thereby hindering the visual understanding capabilities of LVLMs. In this paper, we propose an innovative enhancement to address this limitation by introducing a Scene Graph Expression (SGE) module in LVLMs. This module extracts and structurally expresses the complex semantic information within images, thereby improving the foundational perception and understanding abilities of LVLMs. Extensive experiments demonstrate that integrating our SGE module significantly enhances the LVLM’s performance in vision-language tasks, indicating its effectiveness in preserving intricate semantic details and facilitating better visual understanding.

关键词： Visualization computer vision Semantics Signal processing Transformers Acoustics Data mining Speech processing

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Hemonauts Exploring In-Game Remediation for Health Literacy and STEM Learning 22

Hemonauts Exploring In-Game Remediation for Health Literacy ...

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17th International Conference on the Foundations of Digital Games, FDG 2022

作者： Schoemann, Sarah Hardy, Elaissa Disalvo, Betsy Department of Computer Science School of Science and Mathematics Critical Art and Technology Lab College of Charleston United States Emory University School of Medicine United States Georgia Institute of Technology United States

ISBN: (纸本)9781450397957

Hemonauts is a digital game designed to increase STEM content knowledge and promote healthy lifestyle choices among children 9 - 14 years of age. The game provides engaging puzzle-based challenges staged in a world based on the physiology of the human body, depicting the workings of the circulatory system and two chronic diseases. This paper documents findings from a preliminary 10-week study of the game's impact on 199 school-aged students in the U.S. The study leveraged quantitative and qualitative data about game use and learning outcomes which demonstrated that Hemonauts is indeed an effective teaching tool. Furthermore, it revealed insights for the game design community about the cognitive load of learning remediation in games and ways that game mechanics can improve learning in educational play experiences, providing new perspectives on learning through game mechanics as metaphors. © 2022 ACM.

关键词： Health

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