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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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Mixture of neural operator experts for learning boundary conditions and model selection

arXiv

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arXiv 2025年

作者： Deighan, Dwyer Actor, Jonas A. Patel, Ravi G. Department of Computer Science and Engineering University at Buffalo The State University of New York BuffaloNY United States Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States

While Fourier-based neural operators are best suited to learning mappings between functions on periodic domains, several works have introduced techniques for incorporating non trivial boundary conditions. However, all previously introduced methods have restrictions that limit their applicability. In this work, we introduce an alternative approach to imposing boundary conditions inspired by volume penalization from numerical methods and Mixture of Experts (MoE) from machine learning. By introducing competing experts, the approach additionally allows for model selection. To demonstrate the method, we combine a spatially conditioned MoE with the Fourier based, Modal Operator Regression for Physics (MOR-Physics) neural operator and recover a nonlinear operator on a disk and quarter disk. Next, we extract a large eddy simulation (LES) model from direct numerical simulation of channel flow and show the domain decomposition provided by our approach. Finally, we train our LES model with Bayesian variational inference and obtain posterior predictive samples of flow far past the DNS simulation time horizon. © 2025, CC BY.

关键词： Large eddy simulation

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Analysis of Predictive Models for Revealing Household Characteristics using Smart Grid Data

Analysis of Predictive Models for Revealing Household Charac...

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Hussein Aly Abdulaziz Al-Ali Abdulla Al-Ali Qutaibah Malluhi Department of Computer Science and Engineering College of Engineering Qatar University Doha Qatar Kindi Center for Computing Research Qatar University Doha Qatar

The Smart Grid Advanced Metering Infrastructure (AMI) has revolutionized the smart grid network, generating vast amounts of data that can be utilized for diverse objectives, one of which is Household Characteristics Classification (HCC). This can help the utility provider profile their customers and tailor their services to meet customer needs. To accomplish this task, we evaluated multiple Machine learning HCC models, with a focus on CNN-based models due to their wide popularity in the field of smart grid signal classification. We evaluated 1D and 2D variants of four different CNN architectures. Our experimental analysis revealed that ResNet-based models achieved the best performance on the task of HCC. Also, we found that 2D models tends to perform better than 1D variants.

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SELF-SUPERVISED VIDEO REPRESENTATION LEARNING WITH MOTION-CONTRASTIVE PERCEPTION

arXiv

引用

arXiv 2022年

作者： Liu, Jinyu Cheng, Ying Zhang, Yuejie Zhao, Rui-Wei Feng, Rui School of Computer Science Shanghai Collaborative Innovation Center of Intelligent Visual Computing Fudan University China Academy for Engineering and Technology Fudan University China

visual-only self-supervised learning has achieved significant improvement in video representation learning. Existing related methods encourage models to learn video representations by utilizing contrastive learning or designing specific pretext tasks. However, some models are likely to focus on the background, which is unimportant for learning video representations. To alleviate this problem, we propose a new view called long-range residual frame to obtain more motion-specific information. Based on this, we propose the Motion-Contrastive Perception Network (MCPNet), which consists of two branches, namely, Motion Information Perception (MIP) and Contrastive Instance Perception (CIP), to learn generic video representations by focusing on the changing areas in videos. Specifically, the MIP branch aims to learn fine-grained motion features, and the CIP branch performs contrastive learning to learn overall semantics information for each instance. Experiments on two benchmark datasets UCF-101 and HMDB-51 show that our method outperforms current state-of-the-art visual-only self-supervised approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Video recording

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CM-HTNet: CNN-Mamba-based Framework for Predicting Hemorrhagic Transformation Risk of AIS Patients using Sequence Relationship Modeling and Multi-Modal Cross Attention

CM-HTNet: CNN-Mamba-based Framework for Predicting Hemorrhag...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Yuxiang Ren, Huanhuan Li, Yongmei Jiang, Jingfeng Xu, Haodong Feng, Jinwang Cui, Shaoguo Chongqing Normal University College of Computer and Information Science China Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital Department of Radiology China First Affiliated Hospital of Chongqing Medical University Department of Radiology China Michigan Technological University Center for Biocomputing and Digital Health Institute of Computing and Cybernetics and Health Research Institute United States Michigan Technological University Department of Biomedical Engineering United States

ISBN: (纸本)9798350386226

Hemorrhagic transformation (HT) is a severe complication of acute ischemic stroke (AIS) that can lead to disability or death. Accurate and timely risk assessment of HT is essential for clinicians to design effective treatment strategies. Previous studies in HT prediction have largely relied on machine learning and radiomics, which demand extensive manual data preprocessing by physicians. While some HT prediction models based on convolutional neural network (CNN) have been developed, they are limited in their ability to capture the sequential relationships between image slices and often lack the focus on crucial information. This study collected non-contrast computed tomography (NCCT) images and clinical data from 512 AIS patients across six hospitals to create a multi-center dataset. Based on the dataset, we propose CM-HTNet, a novel deep learning framework designed to predict HT risk in AIS patients following intravenous thrombolysis (IVT). CM-HTNet mimics the clinical process of reviewing NCCT images by focusing on key slices and integrating information from adjacent slices. It leverages CNNs to extract features from each NCCT slice and utilizes the Selective State-Space Model (SSM) within the Mamba framework to model sequential relationships between slices while prioritizing features relevant to HT prediction. Additionally, CM-HTNet incorporates the Neighborhood Rough Set (KRS) algorithm for clinical feature selection and cross-attention mechanisms to integrate clinical and imaging data for multimodal HT risk prediction. In testing on an external dataset from independent centers, CM-HTNet achieved a prediction accuracy of 88.85% and an AUC of 95.17%, showcasing its strong performance and generalization capabilities. © 2024 IEEE.

关键词： Convolutional neural networks

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Feature Rotation Invariance Learning for Point Cloud Analysis 10

Feature Rotation Invariance Learning for Point Cloud Analysi...

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10th IEEE Smart World Congress, SWC 2024

作者： Shi, Lu Cao, Qi Zhang, Guoqing Yi, Jin Huang, Yansen Cen, Yigang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer Science and Technology Visual Intellgence +X International Cooperation Joint Laboratory of Moe Beijing100044 China University of Glasgow The School of Computing Science 567739 Singapore Beijing Jiaotong University With the Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education The State Key Laboratory of Advanced Rail Autonomous Operation The School of Computer and Information Technology Beijing100044 China Guizhou University Guizhou Lianjian Civil Engineering Quality Testing Monitoring Center Co. Ltd The College of Civil Engineering Guizhou550025 China

ISBN: (纸本)9798331520861

While deep learning has significantly advanced point cloud analysis, extracting effective features from their disordered structure remains challenging. Existing approaches often rely on complex network architectures or manual feature engineering to achieve rotation invariance, which increases inference latency. This paper offers a novel perspective on this challenge, showing that rotation-sensitive models can achieve performance comparable to rotation-invariant models when trained with rotation-augmented data. Given the significant differences in the feature distributions of rotation-sensitive and rotation-invariant models, we propose Feature Rotation Invariance Learning (FRIL), a framework that leverages Distance Metric Learning and rotation-based constraints to help rotation-sensitive models extract rotation-invariant features. This approach eliminates the need for additional modules, enabling robust performance against rotations. Extensive experiments demonstrate that FRIL consistently outperforms existing rotation-invariant methods across various visual tasks. © 2024 IEEE.

关键词： Contrastive Learning

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Development Progress of SWLBM a Framework Based on Lattice Boltzmann Method for Fluid Dynamics Simulation 1

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18th International Symposium on Applied Reconfigurable computing, ARC 2022

作者： Xuesen, Chu Xiang, He Fang, Li Zhao, Liu Guangwen, Yang Department of Computer Science and Technology Tsinghua University Beijing100084 China China Ship Scientific Research Center Wuxi214082 China Taihu Laboratory of DeepSea Technological Science Wuxi214082 China Jiangnan Institute of Computing Technology Wuxi214083 China National Research Center of Parallel Computer Engineering and Technology Beijing100084 China National Supercomputing Center in Wuxi Wuxi214072 China

ISBN: (数字)9783031199837

ISBN: (纸本)9783031199820

SWLBM is a software framework based on Lattice Boltzmann Method (LBM) for Computational Fluid Dynamic (CFD) running on Sunway many-core processors. In this paper, we review the achievements of code developing in early stage and introduce the development progress recently of this software, including the development of parallel optimization for Sunway new-generation supercomputing system, functional extensions of the software like pre-process function for mesh generation from a geometry file with STL file and BMP file, Immersed boundary condition for moving subject simulation. Some applications with SWLBM will be introduced to show the advantage of this software over other CFD code in large-scale simulations. SWLBM is still under development, with the continuous improvement of functions, it will play a greater role in the field of fluid simulation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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SafeZone*: A Graph-Based and Time-Optimal Cooperative 3D Printing Framework

引用

Journal of computing and Information science in Engineering 2025年第6期25卷 061004页

作者： Stone, Ronnie F. P. Ebert, Matthew Zhou, Wenchao Akleman, Ergun Krishnamurthy, Vinayak Sha, Zhenghui Walker Department of Mechanical Engineering University of Texas at Austin Austin TX 78712 J. Mike Walker ’66 Department of Mechanical Engineering Texas A&M University College Station TX 77840 Department of Mechanical Engineering University of Arkansas Fayetteville AR 72701 Visual Computing & Computational Media Department of Computer Science and Engineering Texas A&M University College Station TX 77840 J. Mike Walker ’66 Department of Mechanical Engineering Department of Computer Science and Engineering (Affiliated) Texas A&M University College Station TX 77840

Swarm manufacturing is an emerging manufacturing paradigm that employs a heterogeneous swarm of robots to accomplish complex hybrid manufacturing tasks. Cooperative 3D printing (C3DP), a specialized form of swarm manufacturing, enables multiple printers to collaboratively produce large-scale parts, addressing key tradeoffs in additive manufacturing, such as size, speed, quality, and cost. A fundamental challenge in C3DP is ensuring collision-free, time-optimal printing in a shared workspace. This is a complex problem that can be influenced by factors such as the number of printers, part geometry, printer positioning, mobility, and kinematics. In this article, we present SafeZone*, a collision-free and scalable C3DP framework that optimizes printing time by co-considering the geometry (area and shape) and topology (space-connectivity) of a shared workspace during layer partitioning. We first establish a conceptual framework to mathematically represent the topology of a layer through partition graphs. Then, we use a Voronoi tessellation within a constrained optimization framework to control the partition graph and minimize makespan. The Voronoi sites are associated with printer locations, allowing the framework to integrate physical constraints and facilitating solutions for systems with robotic manipulators. Physical testing in a four-printer scenario with robotic arms confirms that SafeZone* enables collision-free printing, resulting in a printing time reduction of 44.63% when compared to the single-printer scenario. Finally, numerical studies reveal trends in the optimal solutions concerning the chromatic number of their resulting partition graphs and the distribution of the printing areas among printers.

关键词： Additive manufacturing Printing Manufacturing Collisions (Physics) additive manufacturing Optimization Manipulators Robots Topology cooperative 3D printing swarm manufacturing computational geometry Polishing equipment Geometry robotic manipulators

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CDIMC-net: Cognitive Deep Incomplete Multi-view Clustering Network

arXiv

引用

arXiv 2024年

作者： Wen, Jie Zhang, Zheng Xu, Yong Zhang, Bob Fei, Lunke Xie, Guo-Sen Bio-Computing Research Center Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China Pengcheng Laboratory Shenzhen China Department of Computer and Information Science University of Macau Taipa China School of Computer Science and Technology Guangdong University of Technology Guangzhou China Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficult to obtain discriminative common representations. 2) These methods are generally sensitive to noise or outliers since the negative samples are treated equally as the important samples. In this paper, we propose a novel incomplete multi-view clustering network, called Cognitive Deep Incomplete Multi-view Clustering Network (CDIMC-net), to address these issues. Specifically, it captures the high-level features and local structure of each view by incorporating the view-specific deep encoders and graph embedding strategy into a framework. Moreover, based on the human cognition, i.e., learning from easy to hard, it introduces a self-paced strategy to select the most confident samples for model training, which can reduce the negative influence of outliers. Experimental results on several incomplete datasets show that CDIMC-net outperforms the state-of-the-art incomplete multi-view clustering methods. © 2024, CC BY.

关键词： Statistics

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Privacy-aware Data Fusion and Prediction for Smart City Services in Edge computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

引用

2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart Data, SmartData 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

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