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ShadowTutor: distributed Partial Distillation for Mobile Video DNN Inference 20

ShadowTutor: Distributed Partial Distillation for Mobile Vid...

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49th International conference on parallel processing (ICPP)

作者： Chung, Jae-Won Kim, Jae-Yun Moon, Soo-Mook Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781450388160

Following the recent success of deep neural networks (DNN) on video computer vision tasks, performing DNN inferences on videos that originate from mobile devices has gained practical significance. As such, previous approaches developed methods to offload DNN inference computations for images to cloud servers to manage the resource constraints of mobile devices. However, when it comes to video data, communicating information of every frame consumes excessive network bandwidth and renders the entire system susceptible to adverse network conditions such as congestion. Thus, in this work, we seek to exploit the temporal coherence between nearby frames of a video stream to mitigate network pressure. That is, we propose ShadowTutor, a distributed video DNN inference framework that reduces the number of network transmissions through intermittent knowledge distillation to a student model. Moreover, we update only a subset of the student's parameters, which we call partial distillation, to reduce the data size of each network transmission. Specifically, the server runs a large and general teacher model, and the mobile device only runs an extremely small but specialized student model. On sparsely selected key frames, the server partially trains the student model by targeting the teacher's response and sends the updated part to the mobile device. We investigate the effectiveness of ShadowTutor with HD video semantic segmentation. Evaluations show that network data transfer is reduced by 95% on average. Moreover, the throughput of the system is improved by over three times and shows robustness to changes in network bandwidth.

关键词： Deep neural networks distributed inference knowledge distillation video semantic segmentation

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Detection of nuclear cataract in retinal fundus image using radialbasis FunctionbasedSVM 6

Detection of nuclear cataract in retinal fundus image using ...

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6th International conference on parallel, distributed and Grid Computing, PDGC 2020

作者： Behera, Manoj Kumar Chakravarty, S. Gourav, Apurwa Dash, Satyabrata Centurion University of Technology Management Dept. of Computer Science and Engg. Odisha India Gandhi Engineering College Dept. of Computer Science and Engg. Orissa Bhubaneswar India

ISBN: (纸本)9781728171326

Nuclear Cataract is a common eye disease that generally occurs at elder age. But if it's not detected at its earlier state, then it may affect vision and can live permanently. In this work, to detect the cataract an automated model proposed based on image processing and machine learning techniques. The input to the proposed model is, a set of fundus retinal images. For training the model, the image dataset consists of two types ofimages healthy and cataract affected. From each input retinal image a binary image, consisting of blood vessels is generated, using image processing techniques like image Filtration, segmentation and thresholding. These set of binary images are used as the feature matrix for defining the classifier by using a well-known machine learning technique Support vector machine (SVM). For validation and compression of the model, different kernels of SVM like linear, polynomial and RBF are applied and tested. Out of all, Radial Basis Function (RBF) based SVM performs good with an overall accuracy of 95.2 % and able to produce result in real time. © 2020 IEEE.

关键词： Support vector machines

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A Novel Approach to Quantum-Resistant Selective Encryption for Agricultural Sensors with Limited Resources

A Novel Approach to Quantum-Resistant Selective Encryption f...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Aditya Jagatha Akshay Kappala Mahesh Kamepalli Varghese Vaidyan Eric Yocam Yong Wang Gurcan Comert Dakota State University USA DSU USA North Carolina a T University USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Selective Encryption involves extracting critical features from the data and applying highly secure encryption to those features, while the remaining data is stored using less resource-intensive encryption techniques. Discrete Wavelet Transforms (DWT) provides a means to extract these essential features. Previous works on selective encryption using DWT have explored hardware-specific implementations, such as using a General Purpose GPU (GPGPU). However, in the context of agricultural images captured by edge devices with limited computational capabilities, leveraging a GPGPU would introduce additional hardware requirements and restrict application potential. We present a selective encryption methodology utilizing parallel CPU processing to accelerate calculations, addressing these limitations. Given the advancements in quantum computing, there is a need to ensure the employed encryption methods are secure against potential quantum attacks. We implement NIST-proposed standards: ML-KEM-1024 for key encapsulation and ML-DSA for signature verification, ensuring quantum-resistant security. Our approach provides a security analysis and performance evaluation. We demonstrate significant visual degradation of encrypted data, with mean PSNR of 4.7201 decibel (dB) and SSIM of 0.0003, indicating strong resistance to statistical attacks. Performance improvements range from 21.47% to 52.43% compared to full AES-256 encryption across various file sizes. We discuss optimizations for handling different data sizes and compare our approach's security and performance with existing state-of-the-art methods. This MLPQE method offers a balanced solution for securing agricultural images on resource-constrained edge devices while ensuring long-term data protection against emerging quantum threats.

关键词： Performance evaluation Resistance Visualization Quantum computing image edge detection Streaming media Feature extraction Encryption Discrete wavelet transforms Streams

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Displacement detection method of QR code reference object based on computer vision 18

Displacement detection method of QR code reference object ba...

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18th IEEE Int Symp on parallel and distributed Proc with Applicat (ISPA) / 10th IEEE Int Conf on Big Data and Cloud Comp (BDCloud) / IEEE Int Symp on Social Comp and Networking (SocialCom) / IEEE Int Conf on Sustainable Comp and Commun (SustainCom)

作者： Yang, Qiuhui Ji, Xiaoyu Liang, Yanyan Wuzhou Univ Guangxi Coll & Univ Key Lab Image Proc & Intellig Wuzhou Peoples R China Wuzhou Univ Sch Data Sci & Software Engn Wuzhuo Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China Beijing Inst Technol Zhuhai Peoples R China

ISBN: (纸本)9781665414852

In order to realize the concrete quality monitoring, the displacement detection method based on computer vision is adopted by adding the identification label to the concrete specimen The traditional camera calibration methods often need to make standard checkerboard to calculate the camera parameters. If users use different mobile phones to shoot the same scene, it is impossible to calibrate each mobile camera in advance. Therefore, aiming at the problem that the traditional camera calibration method can't meet the needs of some application scenarios, this paper proposes a method to calibrate the camera by extracting a small number of feature points in the image. Experimental data show that the method can calibrate the camera accurately, and then calculate the reference moving distance according to the basic principle of computer vision, and it has good flexibility.

关键词： Displacement detection camera calibration feature point identification label

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Learning to Process Topic Aware Queries on Geo-Textual Streaming Data

Learning to Process Topic Aware Queries on Geo-Textual Strea...

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Mayur Patil Department of Computer Science and Engineering University of California Riverside Center for Geospatial Sciences University of California Riverside

In the past few years, location-aware systems have become an integral and important part of modern applications, including social media applications that have seen an unprecedented rise in popularity. Fast-paced, highly dynamic streams of geo-textual data are generated from such applications consisting of millions of users who demand fast query processing and performance. During a search in location-based systems, topic-aware search allows retrieving relevant data based on a specified ‘topic’ of interest. This paper presents efficient and scalable techniques to process topic-aware spatial keyword search on streaming data. Modern applications require quick system responses for topic-aware queries with spatial and keyword filters. processing such large amounts of geo-tagged stream data is time-consuming and inefficient with current methods for topic-based search. To solve this problem, we build techniques for indexing and processing by introducing an index called Topic Aware Grid (TAG). TAG integrates spatial and topic information using a variation of the Grid index where cells of the index are attached to local topic models. We perform extensive experiments on real datasets with real and synthetic query workloads that verify the increased performance of our work over alternate techniques for geo-textual streams.

关键词： Social networking (online) Query processing Keyword search Information filters Indexing

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EgpuIP: An Embedded GPU Accelerated Library for image processing

EgpuIP: An Embedded GPU Accelerated Library for Image Proces...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Luhan Wang Haipeng Jia Yunquan Zhang Kun Li Cunyang Wei State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Microsoft Research

With the advances of embedded GPUs' programming models like GLES and OpenCL, the mobile processor has gained more parallel computing capability, which enables real-time image processing on portable devices. GLES is an excellent option to implement high-performance image processing on embedded GPUs due to its low hardware overhead. However, most GLES studies only focus on porting specific algorithms to embedded GPUs without a general optimization guide. In order to address the pending problems, this paper presents effective performance optimization chains to guide the optimization of image processing algorithms by using GLES. The image processing algorithms can be divided into three modes including data-independent, data-sharing and data-related according to their memory access and calculation characteristics. Based on this classification, our optimization chains contain four optimization directions. 1) Optimizing access to off-chip memory for the memory-bound data-independent algorithm; 2) Exploiting data locality by utilizing shared memory and cache for the data-sharing algorithm; 3) Redesigning the algorithm to optimize the sharing of computational results between threads for the data-related algorithm; 4) Making full use of computation resource for the above three algorithms. Based on these optimization chains, we design an embedded GPU accelerated image processing library, EgpuIP. To evaluate specific optimization methods and performance improvements, we employ histogram equalization, Gaussian pyramid and integral filter as the representative algorithm from EgpuIP and adequately optimize them guided by the proposed optimization chains. Compared to the OpenCV, experiments show that the three algorithms in EgpuIP provide up to $\mathbf{19}\times,\ \mathbf{88}\times$ , and $\mathbfprocessing\times$ speedup respectively.

关键词： image processing Instruction sets Graphics processing units Optimization methods Programming parallel processing Libraries

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Topological Dependencies in Deep Learning for Mobile Edge: distributed and Collaborative High-Speed Inference

Topological Dependencies in Deep Learning for Mobile Edge: D...

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Electronics and Renewable Systems (ICEARS), International conference on

作者： Yousef Methkal Abd Algani A Suresh Kumar Md. Abul Ala Walid Balu S Priya Velayutham A. Sasi Kumar Department of Mathematics The Arab Academic College for Education in Israel-Haifa Israel Department of Computer Science and Engineering Rathinam Technical Campus Coimbatore Tamil Nadu India Khulna University of Engineering & Technology (KUET) Khulna Bangladesh Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Namakkal Tamil Nadu India Department of Computer Science and Engineering Paavai Engineering College Namakkal Tamil Nadu India Inurture Education Solutions Pvt Ltd Bangalore Department Of Cloud Technology & Data Science Institute Of Engineering & Technology Srinivas University Karnataka State India

Edge computing is now widely deployed across the globe. Although the Internet serves as the foundation for edge computing, the actual usefulness of this type of computing is seen when it is combined with the process of obtaining data from sensors and deriving relevant information from that data. It is predicted that in the not-too-distant era, most edge devices will be equipped with intelligent systems powered by deep learning. Unfortunately, in order to train, methods based on deep learning require a significant quantity of data of a high standard, and these methods are quite costly in terms of the amount of processing, memory, and power that they use. distributed deep neural networks, or DDNNs, are something suggested by using distributed computing hierarchies. The cloud, fog, and devices make up these tiers. Although a DDNN can facilitate the interpretation of a DNN in the cloud, it also allows for interpretation to be carried out quickly and precisely on the edges and on end devices by making use of shallow parts of the neural network. With the help of a scalable cloud-based infrastructure, a DDNN can expand both in terms of volume of its neural network and the number of users it serves around the world. For DNN applications, the distributed nature of DDNNs results in improvements to sensor fusion, fault tolerance in the system, and data privacy. In order to implement DDNN, first the portions of a DNN are mapped onto a dispersed computing structure. By learning both components together, the devices' need become limited for both connectivity and energy while the model provided the value of the selected features in the cloud. The final product includes provision for instinctive sensor fusion as well as fault tolerance that has been built directly into the system. This study demonstrates as a proof of concept that a DDNN may make use of the geographical variety of sensors to improve the accuracy of object detection and lower the cost of communication. The suggested

关键词： Deep learning Training Cloud computing Fault tolerance image edge detection Computational modeling Fault tolerant systems

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Generalizable Whole Slide image Classification with Fine-Grained Visual-Semantic Interaction

Generalizable Whole Slide Image Classification with Fine-Gra...

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Ying Chen Yifei Chen Rongshan Yu Wenxian Yang Liansheng Wang Bowen Ding Yuchen Han School of Informatics Xiamen University Huawei Aginome Scientific Xiamen China Department of Pathology Shanghai Chest Hospital Shanghai Jiao Tong University School of Medicine

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Whole Slide image (WSI) classification is often formu-lated as a Multiple Instance Learning (MIL) problem. Re-cently, Vision-Language Models (VLMs) have demonstrated remarkable performance in WSI classification. However, existing methods leverage coarse-grained pathogenetic de-scriptions for visual representation supervision, which are insufficient to capture the complex visual appearance of pathogenetic images, hindering the generalizability of mod-els on diverse downstream tasks. Additionally, processing high-resolution WSIs can be computationally expensive. In this paper, we propose a novel “Fine-grained Visual-Semantic Interaction” (FiVE) framework for WSI classi-fication. It is designed to enhance the model's general-izability by leveraging the interaction between localized visual patterns and fine-grained pathological semantics. Specifically, with meticulously designed queries, we start by utilizing a large language model to extract fine-grained pathological descriptions from various non-standardized raw reports. The output descriptions are then reconstructed into fine-grained labels used for training. By introducing a Task-specific Fine-grained Semantics (TFS) module, we enable prompts to capture crucial visual information in WSIs, which enhances representation learning and aug-ments generalization capabilities significantly. Further-more, given that pathological visual patterns are redun-dantly distributed across tissue slices, we sample a subset of visual instances during training. Our method demon-strates robust generalizability and strong transferability, dominantly outperforming the counterparts on the TCGA Lung Cancer dataset with at least 9.19% higher accu-racy in few-shot experiments. The code is available at: https://***/lslrius/WSI_FiVE.

关键词： Training Representation learning Visualization Pathology Computational modeling Large language models Semantics

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Deep learning based image processing for proactive data collecting system for autonomous vehicle

Deep learning based image processing for proactive data coll...

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Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Winter), ACIS International Winter conference on

作者： Trong-Hop Do Ngan-Linh Nguyen Hoang-Thong Vo Thanh-Binh Nguyen Ngo Tan Vu Khanh University of Information Technology Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam University of Economics Ho Chi Minh City Vietnam

ISBN: (纸本)9781665418430;9781665448260

Autonomous vehicle is the topic that gains a lot of attentions in recent years. To enable autonomous vehicle, a huge amount of data needs to be collected and processed. One of the most promising method to improve the accuracy of data collection is using vehicle communication. With the help of vehicle communication, controlling and warning signal can be transmitted between vehicles. Consequently, necessary information such as identity, position, speed, moving direction, future behavior can be obtained with higher accuracy. This paper focuses on a type of vehicle communication system called optical camera communication. One of the most important components of optical camera communication system is image processing. In this paper, a deep learning-based image processing algorithm is proposed for optical camera communication used for proactive data collecting system for autonomous vehicle. The experiment results show that the proposed algorithm can achieve much higher performance compared to its counterparts.

关键词： Communication systems image processing Optical imaging Light emitting diodes Cameras Task analysis Autonomous vehicles

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Recommending the Most Confusing images to the Annotators via Confusion Graph for the Large-Scale Face Dataset Annotation 11

Recommending the Most Confusing Images to the Annotators via...

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11th International conference on Graphics and image processing (ICGIP)

作者： Zhao, Lei Qiao, Peng Dou, Yong Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha 410073 Peoples R China

ISBN: (纸本)9781510635241

image set annotation is an important task in the supervised training of the deep neural network. Manual and data-driven dataset annotation methods are commonly used approaches. Both of them have shortcomings, especially in the case of the dataset requiring professional knowledge, which leads to high cost with manual annotation methods and poorly diversified annotation samples with data-driven annotation methods. Although the recommendation annotation method based on cosine similarity using deep neural network features takes advantages of manual annotation and data-driven method, there are still problems such as low accuracy and click-through rate. In order to improve the recommendation accuracy and click-through rate, we propose a confusion graph recommendation annotation method, which builds a confusion graph based on the Largest Margin Nearest Neighbor (LMNN) distance among deep neural network features, to recommend the most confusing images to annotators. In this paper, we made ablation studies on the self-built child face dataset in terms of Precision, mAP (mean Average Precision), and CTR (click-through-rate). The experimental results show that the proposed method achieves superior performance, compared with the cosine similarity recommendation annotation method and the manual annotation method.

关键词： Machine-assisted manual annotating confusion graph largest margin nearest neighbor distance deep learning

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