检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Gao, Wanling Tang, Fei Wang, Lei Zhan, Jianfeng Lan, Chunxin Luo, Chunjie Huang, Yunyou Zheng, Chen Dai, Jiahui Cao, Zheng Zheng, Daoyi Tang, Haoning Zhan, Kunlin Wang, Biao Kong, Defei Wu, Tong Yu, Minghe Tan, Chongkang Li, Huan Tian, Xinhui Li, Yatao Lu, Gang Shao, Junchao Wang, Zhenyu Wang, Xiaoyu Ye, Hainan State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing Academy of Frontier Sciences and Technology University of Chinese Academy of Sciences Alibaba Baidu Tencent *** NetEase ByteDance China National Institute of Metrology Zhihu Lenovo Paypal Moqi Microsoft Research Asia Huawei *** CloudTa Intellifusion

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent computing era where AI is widely employed to augment services. In this context, many innovative AI algorithms, systems, and architectures are proposed, and thus the importance of benchmarking and evaluating them rises. However, modern Internet services adopt a microservice-based architecture and consist of various modules. The diversity of these modules and complexity of execution paths, the massive scale and complex hierarchy of datacenter infrastructure, the confidential issues of data sets and workloads pose great challenges to benchmarking. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

来源：评论

学校读者我要写书评

暂无评论

An Automatic Tooth Position and Dental Disease Detection Algorithm Based on YOLOv8

An Automatic Tooth Position and Dental Disease Detection Alg...

引用

International Conference on computer and Communications (ICCC)

作者： Hongliang Wang Ke Diao Lin Wu Xuerao Li Xin Zhou Xiaozhou Liu Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Shenyang China School and Hospital of Stomatology China Medical University Liaoning Provincial Key Laboratory of Oral Diseases Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China

ISBN: (数字)9798331507077

ISBN: (纸本)9798331507084

With the development of digital dental imaging technology, panoramic tomographic images have become an effective tool for diagnosing dental diseases. However, due to the diversity and complexity of dental lesions, accurately identifying tooth positions and pathological conditions from these images remains challenging. To address the issue of low target detection accuracy caused by the high similarity and complex textures of teeth, this paper introduces YOLOv8-Teeth, an enhanced teeth detection algorithm designed for accurate identification of teeth positions and dental diseases from panoramic radiographs. key innovations include integrating the C2f_ConvNeXtv2 module for improved feature extraction, employing the ASPP module for optimized multi-scale feature capture, and utilizing Focal-EloU Loss for refined bounding box regression. Experimental results on a custom dataset of 476 panoramic radiographs demonstrate significant performance gains, achieving a mean average precision (mAP) of 91.3%, precision of 93.6%, and recall of 88.7%. These improvements offer promising potential for early diagnosis and treatment planning in dental care.

关键词： Technological innovation Accuracy Computational modeling Teeth Tomography Feature extraction Real-time systems Dentistry Detection algorithms Diseases

来源：评论

学校读者我要写书评

暂无评论

Optimization Space Exploration of Hardware Design for CRYSTALS-KYBER

Optimization Space Exploration of Hardware Design for CRYSTA...

引用

Asian Test Symposium (ATS)

作者： Yixuan Zhao Zhiteng Chao Jing Ye Wen Wang Yuan Cao Shuai Chen Xiaowei Li Huawei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Yale University US Hohai University China Rock Solid Security Lab FiberHome Co. Ltd. China

ISBN: (数字)9781728174679

ISBN: (纸本)9781728174686

Public key cryptography is important in the global communication digital infrastructure. However, the emergence of quantum computer and Shor algorithm has greatly threatened the security of public key cryptography. The CRYSTALS-KYBER, as a lattice-based KEM algorithm, passed three rounds of a global solicitation for post-quantum cryptography algorithms held by the National institute of Standards and technology (NIST). This paper explores the implementation and optimization space of hardware design according to CRYSTALS-KYBER algorithm. We analyze its software code and try different strategies to optimize the hardware implementation, and conduct comparative analysis in terms of area and speed. The experimental results show that the performance can be greatly improved by moderately optimizing the loops. In comparison with optimal results of the work [12], our optimizations improve the performance by up to 74.6% for encapsulation algorithm and 54.4% for decapsulation algorithm.

关键词： Cryptography Optimization Hardware Pipeline processing Encapsulation computers Space exploration

来源：评论

学校读者我要写书评

暂无评论

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

arXiv

引用

arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Improving the Network Energy Efficiency in MapReduce systems

Improving the Network Energy Efficiency in MapReduce Systems

引用

International Conference on computer Communications and Networks

作者： Lin Wang Fa Zhang Zhiyong Liu Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese State Key Laboratory for Computer Architecture ICT Chinese Academy of Sciences Beijing China

Apart from servers, the energy consumed by enormous amount of network devices in data centers also emerges as a big problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering to consolidate flows and shut down unused devices, not considering another important factor, virtual machine assignment, which has been shown to have a big influence on traffic engineering. Moreover, the lack of information about upper layer applications leads to misunderstand the traffic patterns of the network. This may result in poor effectiveness in the traffic-based optimization in practice. In this paper, we aim to achieve better network energy efficiency in MapReduce systems by combining virtual machine assignment and traffic engineering. By exploiting the characteristics of MapReduce applications, we provide a unified model to describe this problem. Due to its NP-hardness, a general framework is proposed to solve it, where virtual machines are first clustered and then different virtual machine assignments are generated greedily while a local search procedure is used to improve them. The local search procedure depends on the results of an energy-efficient routing provided by GEERA. GEERA is an approximate algorithm designed to select routing paths for flows. Experimental results confirm the efficiency of GEERA, as well as the overall framework. By using this framework, up to 20% more energy savings can be achieved compared with sole traffic engineering solutions.

关键词： Energy efficiency Data center networks MapReduce Virtual machine assignment Traffic engineering

来源：评论

学校读者我要写书评

暂无评论

Fault tolerant communication algorithm for network on chip

引用

Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2007年第4期19卷 508-514页

作者： Zhang, Lei Li, Huawei Li, Xiaowei Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Beijing 100080 China Advanced Test Technology Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University Chinese Academy of Sciences Beijing 100049 China

This paper proposes a random routing algorithm with end-to-end feedback. Random routing has the capability of handling random transmission errors efficiently with high forwarding speed. End-to-end feedback promises the correctness of transmission and reduces the power consumption. Experimental results demonstrated that our random routing algorithm has lower latency, lower power consumption, and can provide on-chip communication with high reliability.

关键词： Error correcting and detecting Fault tolerant Flooding algorithm Network on chip Random routing Random walk Reliability

来源：评论

学校读者我要写书评

暂无评论

Model checking cooperative multi-agent systems in BDI logic

引用

Journal of Information and Computational Science 2012年第5期9卷 1185-1194页

作者： Chen, Qingliang Su, Kaile Wu, Lijun Xu, Zhaocheng School of Electronics Engineering and Computer Science Peking University Beijing 100871 China Department of Computer Science Jinan University Guangzhou 510632 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute for Integrated and Intelligent Systems Griffith University Brisbane Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Traditional temporal logics such as LTL (Linear Temporal Logic) and CTL (Computation Tree Logic) have shown tremendous success in specifying and verifying hardware and software systems. However, this kind of logic can only model the dynamic behaviors for a single system, and fall short in capturing the joint and concurrent aspects in the composition of systems, such as the interactions and coordinations of agents in Multi-agent systems. Although ATL (Alternating-time Temporal Logic) can solve the problem to a certain extent, by preceding the temporal operators with a selective path quantifier, to specify that the interactions between entities inside the system can make sure that there is a strategy to reach a certain state, it can not formalize the rational mental attitudes of the agents such as the Belief, Desire and Intention (BDI) which plays an essential role in decision making during cooperation with other agents. This paper will investigate this problem by proposing a logic of ATLBDI (Alternating-time Temporal Logic of Belief Desire and Intention) and discuss the algorithmic issues such as model checking algorithms and the computational complexity, along with examples to show its effectiveness. We conclude that model checking for ATLBDI is completely tractable and is in PTIME-Complete, which is quite an optimistic and promising result for further applications. © 2012 by Binary Information Press.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

Evolutionary Reinforcement Learning via Cooperative Coevolution

arXiv

引用

arXiv 2024年

作者： Hu, Chengpeng Liu, Jialin Yao, Xin Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous System Southern University of Science and Technology Shenzhen China Department of Computing and Decision Sciences Lingnan University Hong Kong

Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural networks. To address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) algorithm. Inspired by cooperative coevolution, CoERL periodically and adaptively decomposes the policy optimisation problem into multiple subproblems and evolves a population of neural networks for each of the subproblems. Instead of using genetic operators, CoERL directly searches for partial gradients to update the policy. Updating policy with partial gradients maintains consistency between the behaviour spaces of parents and offspring across generations. The experiences collected by the population are then used to improve the entire policy, which enhances the sampling efficiency. Experiments on six benchmark locomotion tasks demonstrate that CoERL outperforms seven state-of-the-art algorithms and baselines. Ablation study verifies the unique contribution of CoERL's core ingredients. Copyright © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-scale Contrastive Learning for Gastroenteroscopy Classification

Multi-scale Contrastive Learning for Gastroenteroscopy Class...

引用

Annual IEEE Symposium on computer-Based Medical systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Online updates on data warehouses via judicious use of solid-state storage

Online updates on data warehouses via judicious use of solid...

引用

作者： Athanassoulis, Manos Chen, Shimin Ailamaki, Anastasia Gibbons, Philip B. Stoica, Radu School of Engineering and Applied Sciences Harvard University CambridgeMA02138 United States State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences no. 6 Kexueyuan South Road Zhongguancun Haidian District Beijing China Intel Labs Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States École Polytechnique Fédérale de Lausanne Lausanne Switzerland IBM Research Zurich Saumerstrasse 4 8803 Ruschlikon Switzerland

Data warehouses have been traditionally optimized for read-only query performance, allowing only offline updates at night, essentially trading off data freshness for performance. The need for 24x7 operations in global markets and the rise of online and other quickly reacting businesses make concurrent online updates increasingly desirable. Unfortunately, state-of-the-art approaches fall short of supporting fast analysis queries over fresh data. The conventional approach of performing updates in place can dramatically slow down query performance, while prior proposals using differential updates either require large in-memory buffers or may incur significant update migration cost. This article presents a novel approach for supporting online updates in data warehouses that overcomes the limitations of prior approaches by making judicious use of available SSDs to cache incoming updates. We model the problem of query processing with differential updates as a type of outer join between the data residing on disks and the updates residing on *** present MaSM algorithms for performing such joins and periodicmigrations, with smallmemory footprints, low query overhead, low SSD writes, efficient in-place migration of updates, and correct ACID support. We present detailed modeling of the proposed approach, and provide proofs regarding the fundamental properties of the MaSM algorithms. Our experimentation shows that MaSM incurs only up to 7% overhead both on synthetic range scans (varying range size from 4KB to 100GB) and in a TPC-H query replay study, while also increasing the update throughput by orders of magnitude. © 2015 ACM.

关键词： Data warehouses

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：