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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Haipeng Du Muyan Jiao Jiageng Zhang Xin Lv Jie Zhang School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China Network Information Center Xi’an Jiaotong University Xi’an China School of Computer Science and Engineering Xi’an University of Technology Xi’an China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Convolutional Neural Networks (CNNs) have significantly advanced computer vision tasks, but their increasing complexity poses challenges for efficient inference, particularly on resource-constrained devices. We present DualConvNet, a novel CNN architecture that enhances inference efficiency through two key innovations: compressed convolutions and reparameterization. Compressed convolutions reduce computational complexity by selectively processing input channel subsets during both training and inference. For inference, we introduce a reparameterization technique that merges the multi-branch structure into a single, efficient operation, significantly improving speed. Experiments on CIFAR-10, CIFAR-100, and ImageNet-1k demonstrate DualConvNet’s effectiveness, consistently outperforming state-of-the-art models in both accuracy and inference speed. On the COCO dataset, DualConvNet shows competitive accuracy in object detection and instance segmentation tasks while substantially reducing GPU latency. Ablation studies validate the impact of our dual-strategy approach, revealing significant improvements in both accuracy and computational efficiency compared to alternative designs. These results demonstrate DualConvNet’s effectiveness in improving inference efficiency while maintaining high accuracy across various tasks and datasets, making it particularly suitable for real-time applications in resource-constrained scenarios.

关键词： Training Technological innovation Accuracy Image coding Computational modeling computer architecture Transformers Computational efficiency Convolutional neural networks Computational complexity

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Nusec and Midesec: datasets for Histopathological Image Analysis

SSRN

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SSRN 2024年

作者： Nemati, Nooshin Samet, Refik Sak, Serpil Hancer, Emrah Kirmizi, Bilge Ayca Yildirim, Zeynep Traoré, Mohamed Department of Computer Engineering Ankara University Ankara06830 Turkey Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Victoria University of Wellington Wellington6140 New Zealand Department of Software Engineering Mehmet Akif Ersoy University Burdur15039 Turkey Department of Pathology Faculty of Medicine Ankara University Ankara06080 Turkey

Hematoxylin and Eosin (H&E) staining stands as the gold standard protocol for the preparationof histopathology tissue samples in breast cancer research. This staining technique facilitates theidentification and differentiation of various cell types within the tissue, providing crucial informationfor pathologists. While deep learning algorithms have shown promise in automating the analysis ofH&E images, their effectiveness relies on extensive datasets for training. Unfortunately, there is ascarcity of publicly available datasets specifically tailored for nuclei and mitosis detection. To addressthis gap, this paper introduces two publicly available datasets, namely NuSeC (Nuclei Segmentationand Classification) and MiDeSeC (Mitosis Detection, Segmentation, and Classification) to serve asvaluable resources for building deep learning models for histopathological image analysis. The NuSeCdataset comprises 75 images for training and an additional 25 images for testing, encompassing around6000 nuclei structures, and the MiDeSeC dataset involves 35 training and 15 test image sets with morethan 500 mitoses. In both datasets, the QuPath tool as a pixel-level annotator is utilized to generatemask images. Moreover, we design two deep learning-based methodologies to conduct experimentalanalysis on the NuSeC and MiDeSeC datasets. The results indicate the potential to achieve substantialperformance on these datasets, facilitating the construction of new models or analysis of existing ones © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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How to Detect ai-Generated Texts?

How to Detect AI-Generated Texts?

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IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Trung T. Nguyen Amartya Hatua Andrew H. Sung Department of Computer Science Winona State University Winona MN USA AI Center of Excellence Fidelity Investments Boston MA USA School of Computing Sciences and Computer Engineering The University of Southern Mississippi Hattiesburg MS USA

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to ***% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research.

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Why Go Full? Elevating Federated Learning Through Partial Network Updates

arXiv

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arXiv 2024年

作者： Wang, Haolin Liu, Xuefeng Niu, Jianwei Guo, Wenkai Tang, Shaojie State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Center for AI Business Innovation Department of Management Science and Systems University at Buffalo BuffaloNY United States Zhongguancun Laboratory Beijing China

Federated learning is a distributed machine learning paradigm designed to protect user data privacy, which has been successfully implemented across various scenarios. In traditional federated learning, the entire parameter set of local models is updated and averaged in each training round. Although this full network update method maximizes knowledge acquisition and sharing for each model layer, it prevents the layers of the global model from cooperating effectively to complete the tasks of each client, a challenge we refer to as layer mismatch. This mismatch problem recurs after every parameter averaging, consequently slowing down model convergence and degrading overall performance. To address the layer mismatch issue, we introduce the FedPart method, which restricts model updates to either a single layer or a few layers during each communication round. Furthermore, to maintain the efficiency of knowledge acquisition and sharing, we develop several strategies to select trainable layers in each round, including sequential updating and multi-round cycle training. Our theoretical analysis and experimental results show that the FedPart method significantly outperforms traditional full-network update strategies, achieving faster convergence, greater accuracy, and reduced communication and computational overhead. © 2024, CC BY.

关键词： Federated learning

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3D-MOLT5: LEVERAGING DISCRETE STRUCTURAL INFORMATION FOR MOLECULE-TEXT MODELING

arXiv

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arXiv 2024年

作者： Pei, Qizhi Yan, Rui Gao, Kaiyuan Zhu, Jinhua Wu, Lijun Gaoling School of Artificial Intelligence Renmin University of China China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China School of Computer Science Wuhan University China Huazhong University of Science and Technology China University of Science and Technology of China China Shanghai AI Laboratory China

The integration of molecular and natural language representations has emerged as a focal point in molecular science, with recent advancements in Language Models (LMs) demonstrating significant potential for comprehensive modeling of both domains. However, existing approaches face notable limitations, particularly in their neglect of three-dimensional (3D) information, which is crucial for understanding molecular structures and functions. While some efforts have been made to incorporate 3D molecular information into LMs using external structure encoding modules, significant difficulties remain, such as insufficient interaction across modalities in pre-training and challenges in modality alignment. To address the limitations, we propose 3D-MolT5, a unified framework designed to model molecule in both sequence and 3D structure spaces. The key innovation of our approach lies in mapping fine-grained 3D substructure representations into a specialized 3D token vocabulary. This methodology facilitates the seamless integration of sequence and structure representations in a tokenized format, enabling 3D-MolT5 to encode molecular sequences, molecular structures, and text sequences within a unified architecture. Leveraging this tokenized input strategy, we build a foundation model that unifies the sequence and structure data formats. We then conduct joint pre-training with multi-task objectives to enhance the model’s comprehension of these diverse modalities within a shared representation space. Thus, our approach significantly improves cross-modal interaction and alignment, addressing key challenges in previous work. Further instruction tuning demonstrated that our 3D-MolT5 has strong generalization ability and surpasses existing methods with superior performance in multiple downstream tasks, such as nearly 70% improvement on the molecular property prediction task compared to state-of-the-art methods. Our code is available at https://***/QizhiPei/3D-MolT5. Copyright © 2

关键词： Molecular docking

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds

Layered Structure Aware Dependent Microservice Placement Tow...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Deze Zeng Hongmin Geng Lin Gu Zhexiong Li School of Computer Science China University of Geosciences Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies.

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Drone assisted Multi sensory Autonomous Driving System

Drone assisted Multi sensory Autonomous Driving System

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第三十一届中国汽车工程学会年会暨展览会（SAECCE 2024）

作者： Zhang Bolun Zhuang Ao Liu Yanbo Han Zeyuan Sun Fuyao Chen Linxianyi Pan Zhenyue Guo Jianmei Xu Wenchao Shen Zanwei Song Libo Li Zhengyu Zhang Zhiyong Dan Yilin Chen Yancheng Sun Weiqi Ji Yifan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University School of Data Science & Engineering East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Intel(China)Ltd.Network and Edge Group(China) Intel(China)Ltd.Data Center and AI Group(China) STMicroelectronics Zhengzhou University

Autonomous driving has experienced rapid development in the past decade. Considering that relying solely on onboard sensors limits the safety and efficiency of autonomous driving, introducing a multi-sensory system to perceive road conditions is an effective solution, with roadside units being a typical attempt of a multi-sensory autonomous driving system. However, roadside systems have issues such as high cost, long construction cycles, low coverage, and low short-tenn utilization rates. Therefore, we propose the Drone-Assisted Multi-Sensory Autonomous Driving System (DAMAD). DAMAD consists of a vehicle perception system, a blind spot detection system, a drone control system, a drone perception system, and an autonomous driving decision system. This paper focuses on the methods of implementing the blind spot identification system and drone sensing system and verifies the performance improvement of DA-MAD on DDQN-based deep learning autonomous driving decision systems in a simulation scenario of a typical bidirectional dual-lane multi-intersection. Simulation results show that DAMAD can significantly improve the safety and efficiency of the autonomous driving decision system under ideal conditions. Additionally, we conducted real-vehicle tests, which demonstrated that DAMAD could help autonomous vehicles perceive obscured environmental information in advance, assisting the autonomous driving system in addressing emergencies.

关键词： autonomous driving drone control DDQN density-based spatial clustering of applications with noise

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A DQN-Based Approach for Online Service Placement in Mobile Edge Computing 16th

A DQN-Based Approach for Online Service Placement in Mobile ...

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16th Eai International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Jie, Xiaogan Liu, Tong Gao, Honghao Cao, Chenhong Wang, Peng Tong, Weiqin School of Computer Engineering and Science Shanghai University Shanghai China Shanghai Key Laboratory of Data Science Shanghai China Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9783030675394

Due to the development of 5G networks, computation intensive applications on mobile devices have emerged, such as augmented reality and video stream analysis. Mobile edge computing is put forward as a new computing paradigm, to meet the low-latency requirements of applications, by moving services from the cloud to the network edge like base stations. Due to the limited storage space and computing capacity of an edge server, service placement is an important issue, determining which services are deployed at edge to serve corresponding tasks. The problem becomes particularly complicated, with considering the stochastic arrivals of tasks, the additional latency incurred by service migration, and the time spent for waiting in queues for processing at edge. Benefiting from reinforcement learning, we propose a deep Q network based approach, by formulating service placement as a Markov decision process. Real-time service placement strategies are output, to minimize the total latency of arrived tasks in a long term. Extensive simulation results demonstrate that our approach works effectively. © 2021, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Reinforcement learning

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Location-guided Head Pose Estimation for Fisheye Image

arXiv

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arXiv 2024年

作者： Li, Bing Zhang, Dong Huang, Cheng Xian, Yun Li, Ming Lee, Dah-Jye The School of Electronics and Information Technology Sun Yat-sen University China The Data Science Research Center Duke Kunshan University Kunshan215316 China The Department of Electric and Computer Engineering Brigham Young University United States

Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection. Serious fisheye lens distortion in the peripheral region of the image leads to degraded performance of the existing head pose estimation models trained on undistorted images. This paper presents a new approach for head pose estimation that uses the knowledge of head location in the image to reduce the negative effect of fisheye distortion. We develop an end-to-end convolutional neural network to estimate the head pose with the multi-task learning of head pose and head location. Our proposed network estimates the head pose directly from the fisheye image without the operation of rectification or calibration. We also created a fisheye-distorted version of the three popular head pose estimation datasets, BIWI, 300W-LP, and AFLW2000 for our experiments. Experiments results show that our network remarkably improves the accuracy of head pose estimation compared with other state-of-the-art one-stage and two-stage methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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CBuild: Cluster-oriented Collaborative Image Building for Containers

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IEEE Transactions on computers 2025年

作者： Huang, Zhuo Fan, Hao Tang, Bin Wu, Song Yu, Chen Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan 430074 China

Starting a container needs to build a container image layer-by-layer if the required image is not available. However, the image building involves downloading a large amount of data, which significantly delays the development and deployment of containerized services. To reduce data downloads and accelerate image building, current methods typically focus on improving data sharing through reconstructing images. Unfortunately, these approaches show limited performance improvement in clusters as they only improve data sharing on a single node. In this paper, we find that there are significant duplicated remote file downloads between nodes in a cluster. Accordingly, we propose cBuild, a distributed file cache to minimize costly image data downloads in cluster environments. Specifically, to enable inter-node image data sharing, cBuild designs a non-intrusive interception mechanism based on network namespace, instead of directly detecting building instructions that dirty images. Based on the distribution characteristics of duplicated files in layers, cBuild places image files among nodes in a balanced manner to prevent transfer bottlenecks caused by hotspot nodes and employs a layer-aware searching strategy to quickly locate the desired files. We implement cBuild on the basis of Docker. Experiments show that cBuild improves building speed by up to 15.3 × and reduces the data downloading by 80%. © 1968-2012 IEEE.

关键词： Container Distributed Dockerfile Image building

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