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Bootstrapping ViTs: Towards Liberating Vision Transformers from Pre-training

Bootstrapping ViTs: Towards Liberating Vision Transformers f...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Haofei Duan, Jiarui Xue, Mengqi Song, Jie Sun, Li Song, Mingli Zhejiang Univ Hangzhou Zhejiang Peoples R China Zhejiang Univ Shanghai Inst Adv Study Hangzhou Zhejiang Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV). With the general-purpose Transformer architecture replacing the hard-coded inductive biases of convolution, ViTs have surpassed CNNs, especially in datasufficient circumstances. However, ViTs are prone to over-fit on small datasets and thus rely on large-scale pre-training, which expends enormous time. In this paper, we strive to liberate ViTs from pre-training by introducing CNNs' inductive biases back to ViTs while preserving their network architectures for higher upper bound and setting up more suitable optimization objectives. To begin with, an agent CNN is designed based on the given ViT with inductive biases. Then a bootstrapping training algorithm is proposed to jointly optimize the agent and ViT with weight sharing, during which the ViT learns inductive biases from the intermediate features of the agent. Extensive experiments on CIFAR-10/100 and ImageNet-1k with limited training data have shown encouraging results that the inductive biases help ViTs converge significantly faster and outperform conventional CNNs with even fewer parameters.

关键词： Training Computer vision Upper bound Neural networks Training data network architecture Transformers

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Optimized Resource Scheduling for open Source Mobile network Heterogeneous Computing Engines: A Comparative Study of DRF and H-DRF Algorithms

Optimized Resource Scheduling for Open Source Mobile Network...

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2024 International conference on Information Technology, Communication Ecosystem and Management, ITCEM 2024

作者： Chang, Ying Zhu, Qinghua Beijing China

ISBN: (纸本)9798331542696

With the rapid development of the communications and computer industries, the demand for efficient data processing has surged. Single-processor architectures struggle to meet the high communication rate requirements of technologies such as V2X and AI, leading to a shift towards heterogeneous computing. Although heterogeneous computing improves data processing speed, it exacerbates the resource allocation problem. To address the shortcomings of the DRF scheduling method, this paper proposes an H-DRF scheduling approach based on DRF. Subsequently, it provides a conceptual framework for implementing H-DRF. To prevent issues such as project resource allocation failures and the allocation of critical resources, a weighting parameter is introduced to adjust the resource share ratios. An algorithmic approach is then outlined, and the H-DRF scheduling method is implemented in MATLAB. The performance of DRF and H-DRF is compared by allocating resources under identical system resources and project demands, leading to a conclusion. Finally, a comparative analysis of various parameters involved in the resource allocation process using DRF and H-DRF is conducted, explaining the superiority of H-DRF over DRF. Under the same conditions, H-DRF achieves higher resource utilization and better task throughput. © 2024 ieee.

关键词： Computing power

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An Architecture for Microprocessor-Executable Skin Cancer Classification 8

An Architecture for Microprocessor-Executable Skin Cancer Cl...

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8th ieee International conference for Convergence in Technology, I2CT 2023

作者： Rondón, Carlos Vicente Niño Carvajal, Diego Andrés Castellano Delgado, Byron Medina Casadiego, Sergio Alexander Castro Ibarra, Dinael Guevara Universidad Francisco de Paula Santander Electricidad & Electrónica Cúcuta Colombia

ISBN: (纸本)9798350334012

Skin cancer ranks as the most common malignant tumor among all types of cancer. Melanoma accounts for 1% of all cancer cases. However, it is responsible for the majority of deaths from this type of cancer. According to the American Cancer Society, it is expected that 99,780 new cases of melanoma will be diagnosed and about 7,650 people will die from this type of cancer. This work presents an executable architecture on reduced plate systems for skin cancer classification, complemented with image enhancement and feature enhancement stages, information extraction using VGG16 network architecture, feature reduction applying Principal Component Analysis and classification stage using gradient augmented decision trees (XGBoost). The architecture was tested on Raspberry Pi 4B reduced board system and developed with Python programming language and open-source libraries. In turn, the images processed and used are part of the ISIC Challenge Dataset. An average power value of 2.93 W out of a maximum of 3.6 W was obtained in the execution of the diagnostic tool. In turn, the minimum required software architecture response time was 0.09 seconds. The demand for the execution of the diagnostic tool in the Central Processing Unit was on average 20.63 % over a maximum value of 24.5 % respectively. On the other hand, the results at the software level of the architecture were compared with the scientific literature and presented improvements of about 9 % in terms of accuracy in skin cancer classification. The diagnostic tool is replicable and affordable due to reduced hardware requirements and cost of implementation. © 2023 ieee.

关键词： network architecture

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Deep Hierarchical Semantic Segmentation

Deep Hierarchical Semantic Segmentation

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Liulei Zhou, Tianfei Wang, Wenguan Li, Jianwu Yang, Yi Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Univ Technol Sydney AAII ReLER Sydney NSW Australia Zhejiang Univ CCAI Hangzhou Peoples R China Baidu Res Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Existing work is often aware of flatten labels and predicts target classes exclusively for each pixel. In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. We devise H SSN , a general HSS framework that tackles two critical issues in this task: i) how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and ii) how to leverage the hierarchy information to regularize HSS network learning. To address i), HSSN directly casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. To solve ii), HSSN first explores inherent properties of the hierarchy as a training objective, which enforces segmentation predictions to obey the hierarchy structure. Further, with hierarchy-induced margin constraints, HSSNreshapes the pixel embedding space, so as to generate well-structured pixel representations and improve segmentation eventually. We conduct experiments on four semantic segmentation datasets (i.e., Mapillary Vistas 2.0, City-scapes, LIP, and PASCAL-Person-Part), with different class hierarchies, segmentation network architectures and backbones, showing the generalization and superiority of HSSN.

关键词： Training Visualization Computer vision Shape Lips Semantics network architecture

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An Optimal Allocation Framework of Security Virtual network Functions in 6G Satellite Deployments 19

An Optimal Allocation Framework of Security Virtual Network ...

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19th ieee Annual Consumer Communications and networking conference, CCNC 2022

作者： Petrosino, Antonio Piro, Giuseppe Grieco, Luigi Alfredo Boggia, Gennaro Bari Italy CNIT Consorzio Nazionale Interuniversitario per le Telecomunicazioni Italy

ISBN: (纸本)9781665431620

In the emerging 6G satellite deployments, the interaction between Non-Terrestrial network terminals and satellite constellations will generate a large surface of attack, which requires the design of novel security architectures. The scientific literature suggests to implement security services as Virtual network Functions, installed onboard the satellites. The dynamic orchestration of these services, however, still represents a challenging and open research issue. To bridge this gap, this paper presents a novel approach willing to allocate security Virtual network Functions across satellites, in a dynamic and optimal way. To this end, an optimization problem is formulated by deeply considering the intermittent connectivity between terminals on the Earth and the satellite constellation, the limited computational capabilities of satellites, and the need to provide secure Virtual network Functions before a given time deadline. Then, the Tabu Search algorithm is used to solve the optimization problem and achieve preliminary results in realistic scenarios. The study illustrates the feasibility of the proposed approach and highlights the issues to address in future research activities. © 2022 ieee.

关键词： Satellites

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Towards Long-Range Pixels Connection for Context-Aware Semantic Segmentation

Towards Long-Range Pixels Connection for Context-Aware Seman...

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4th ieee-EMBS International conference on Wearable and Implantable Body Sensor networks (BSN) / 18th ieee-EMBS International conference on Biomedical and Health Informatics (BHI)

作者： Khan, Muhammad Zubair Lee, Yugyung Khan, Muazzam A. Munir, Arslan Univ Missouri Sch Comp & Engn Kansas City MO 64110 USA Quaid I Azam Univ Dept Comp Sci Islamabad Pakistan Kansas State Univ Dept Comp Sci Manhattan KS 66506 USA

ISBN: (数字)9781665487917

ISBN: (纸本)9781665487917

Semantic segmentation is one of the challenging tasks in computer vision. Before the advent of deep learning, hand-crafted features were used to semantically extract the region-of-interest (ROI). Deep learning has recently achieved enormous response in semantic image segmentation. The previously developed U-Net inspired architectures operate with continuous stride and pooling operations, leading to spatial data loss. Also, the methods lack establishing long-term pixels connection to preserve context knowledge and reduce spatial loss in prediction. This article developed encoder-decoder architecture with a sequential block embedded in long skip-connections and densely connected convolution blocks. The network non-linearly combines the feature maps across encoder-decoder paths for finding dependency and correlation between image pixels. Additionally, the densely connected convolutional blocks are kept in the final encoding layer to reuse features and prevent redundant data sharing. The method applied batch-normalization to reduce internal covariate shift in data distributions. We have used LUNA, ISIC2018, and DRIVE datasets to reflect three different segmentation problems (lung nodules, skin lesions, vessels) and claim the effectiveness of the proposed architecture. The network is also compared with other techniques designed to highlight similar problems. It is found through empirical evidence that our method shows promising results when compared with other segmentation techniques.

关键词： deep learning semantic segmentation image analysis pixels connection convolution neural network

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To Embed or Not to Embed SHA in Programmable network Interface Cards 18

To Embed or Not to Embed SHA in Programmable Network Interfa...

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18th International conference on network and Service Management (CNSM) - Intelligent Management of Disruptive network Technologies and Services

作者： Mafioletti, Diego Rossi Martinello, Magnos Ribeiro, Moises R. N. Ruffini, Marco Slyne, Frank Univ Fed Espirito Santo Vitoria ES Brazil Trinity Coll Dublin Dublin Ireland Fed Inst Espirito Santo Colatina Brazil

ISBN: (纸本)9783903176515

Cryptographic hash functions are widely used to provide from digital time stamping to authenticity and digital signatures, mapping an extensive collection of messages into a small set of message digests and help to secure network connection and data, consequently consuming CPU resources. P4 enables data plane customisation using a high-level programming language to facilitate in-network computing development across diverse hardware targets, including network Interface Cards (NICs). Currently, most P4 targets do not implement secure hash functions due to a lack of hardware instructions or the absence of formal functions to expose their native hardware-based implementation. Moreover, many applications and protocols cannot be instantiated using in-network computing due to stringent requirements based on these hash functions. In order to empower the security and other hash-based applications, in this paper we propose and implement a P4 shared object library for a secure hash algorithm 2 (SHA-2). Our goal is to enable SHA-2 to be used as an embedded network Function (eNF), overcoming the lack of support in a SmartNIC architecture, in order to address the latency and throughput requirements of Service Function Chain (SFC) forwarding performance within the network Function Virtualization (NFV) paradigm. Thus, our prototype is evaluated against kernel-level open vSwitch (OvS) and user-space Data Plane Development Kit (DPDK) implementations. The outcomes demonstrate different tradeoffs over each platform, from the randomness added by the OS to the high cost of executing the aforesaid function using a network programmable device, leading us to highlight the best choice for each specific application.

关键词： In-network Computing P4 programming Cryptographic hash functions

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Tutorial: Evolutionary Design Methods in Electronic Design Automation

Tutorial: Evolutionary Design Methods in Electronic Design A...

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ieee International conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Lukas Sekanina Faculty of Information Technology Brno University of Technology Brno Czech Republic

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

The use of evolutionary algorithms (EAs) for the automated design of programs, electronic circuits, neural networks, and other computational structures has become a fruitful approach in the last two decades. The advantage of EAs is that they can handle the design process in a holistic, multi-objective way and create solutions with unique properties. This tutorial surveys the key ingredients of EAs and focuses mainly on genetic programming. It presents several techniques (such as incorporating formal verification methods and surrogate models) to improve the scalability of the method. Examples of evolved solutions (approximate arithmetic circuits, neural network architectures, and image filters) that show unique properties compared to conventional designs are presented and discussed.

关键词： Surveys Filters Scalability Design methodology Neural networks Genetic programming Tutorials Performance gain Integrated circuit modeling Formal verification

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Cross-Validating open Source In-Vehicle TSN Simulation Models With a COTS Hardware Testbed

Cross-Validating Open Source In-Vehicle TSN Simulation Model...

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ieee Vehicular networking conference (VNC)

作者： Darinela Andronovici Ion Turcanu Jannusch Bigge Christoph Sommer Luxembourg Institute of Science and Technology (LIST) Luxembourg Faculty of Computer Science TU Dresden Germany

ISBN: (数字)9798350362701

ISBN: (纸本)9798350362718

We tackle the challenges of accurately replicating modern automotive network architectures, particularly those reliant on automotive Ethernet and Time-Sensitive networking (TSN), in simulation environments. We describe an open-source TSN simulation model tailored to match a real-world TSN testbed built using off-the-shelf hardware. We address key challenges such as hardware and software imperfections as well as varying data traffic patterns from automotive sensors. By incorporating real data traces from LIDAR and camera sensors, we improve simulation accuracy. Our findings underscore the importance of accounting for hardware and software nuances to ensure faithful simulation results, thus advancing the reliability of automotive network simulations.

关键词： Accuracy Laser radar Ethernet network architecture Traffic control Cameras Hardware

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Optimal Relocation of Virtualized PDC in Edge-Cloud architectures under Dynamic Latency Conditions

Optimal Relocation of Virtualized PDC in Edge-Cloud Architec...

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2022 ieee International conference on Electrical, Computer, and Energy Technologies, ICECET 2022

作者： Tzanis, Nikolaos Brodimas, Dimitrios Plakas, Konstantinos Birbas, Michael Birbas, Alexios Dep. of Research Athens Greece University of Patras Dept. of Electrical and Computer Engineering Patras Greece

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

The increasing penetration of Phasor Measurement Units (PMUs) in the power grid, combined with the advancements in the communication technologies and the cloud-edge continuum, enable the deployment of sophisticated and demanding synchrophasor-based applications. In synchrophasor networks, Phasor Data Concentrators (PDCs) are responsible for the collection, aggregation and time synchronization of PMU datasets and their transmission to the target applications according to specific latency constraints. PMU to PDC latency will be continuously impacted by the deployment of new applications, prone to violations of network related key performance indicators (KPIs). In this work, we present a cloud-edge architecture, which facilitates the continuous monitoring of the target application's performance (Level of measurements Completeness -LoC) and provides an optimal PDC relocation solution through a binary integer linear programming algorithm. PDC relocation becomes feasible by the monitoring of the communication latency at points of interest and by the employment of virtual PDC instances at various nodes. © 2022 ieee.

关键词： network function virtualization

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