检索结果-内蒙古大学图书馆

Towards Dynamic and Safe Configuration Tuning for Cloud databases

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Zhang, Xinyi Wu, Hong Li, Yang Tan, Jian Li, Feifei Cui, Bin School of CS Key Laboratory of High Confidence Software Technologies Peking University China Database and Storage Laboratory Damo Academy Alibaba Group China Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications China China

Configuration knobs of database systems are essential to achieve high throughput and low latency. Recently, automatic tuning systems using machine learning methods (ML) have shown to find better configurations compared to experienced database administrators (DBAs). However, there are still gaps to apply the existing systems in production environments, especially in the cloud. First, they conduct tuning for a given workload within a limited time window and ignore the dynamicity of workloads and data. Second, they rely on a copied instance and do not consider the availability of the database when sampling configurations, making the tuning expensive, delayed, and unsafe. To fill these gaps, we propose OnlineTune, which tunes the online databases safely in changing cloud environments. To accommodate the dynamicity, OnlineTune embeds the environmental factors as context feature and adopts contextual Bayesian Optimization with context space partition to optimize the database adaptively and scalably. To pursue safety during tuning, we leverage the black-box and the white-box knowledge to evaluate the safety of configurations and propose a safe exploration strategy via subspace adaptation. We conduct evaluations on dynamic workloads from benchmarks and real-world workloads. Compared with the state-of-the-art methods, OnlineTune achieves 14.4%∼165.3% improvement on cumulative performance while reducing 91.0%∼99.5% unsafe configuration recommendations. Copyright © 2022, The Authors. All rights reserved.

关键词： database systems

GOV: A Verification Method for Smart Contract Gas-Optimization

学校读者我要写书评

暂无评论

GOV: A Verification Method for Smart Contract Gas-Optimizati...

IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Yuan Huang Rong Wang Xiangping Chen Xiaocong Zhou Ziyan Wang School of Software Engineering Sun Yat-Sen University Zhuhai China Guangdong Key Laboratory for Big Data Analysis and Simulation of Public Opinion School of Journalism and Communication Sun Yat-Sen University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China

Developers may not understand the Gas mechanism of Ethereum, so many smart contracts consume a lot of unnecessary Gas. To address this issue, existing studies have proposed several methods to optimize the code of the contracts to reduce Gas consumption. To verify the effectiveness, most of the methods deploy a private chain to make verification. However, a more reasonable way is to employ the real transactions on Ethereum to trigger the contracts before and after optimization, and then compare the Gas consumption. To achieve this goal, we proposed a method, GOV, to estimate the Gas consumption of the optimized contract by using the real transactions on Ethereum. Our method enables the optimized contract to follow the execution path of the contract before optimization, thus solving the problem of inconsistent execution paths before and after optimization. A preliminary evaluation shows that GOV can effectively estimate the Gas consumption of optimized contract.

关键词： Codes Smart contracts software quality software reliability Security Optimization

Assisted Design of High-Performance Electrochromic Devices Using Multilayer Perceptron Neural Network with High R2

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Li, Muyun Cai, Qingyue Lai, Huayi Ning, Honglong Kong, Sifan Guo, Chenxiao Jiang, Bocheng Su, Guoping Yao, Rihui Peng, Junbiao Guangdong Basic Research Center of Excellence for Energy & Information Polymer Materials State Key Laboratory of Luminescent Materials and Devices School of Materials Sciences and Engineering South China University of Technology Guangzhou510640 China Aberdeen Institute of Data Science and Artificial Intelligence South China Normal University Foshan528225 China School of Software South China Normal University Foshan528225 China

Electrochromic devices (ECDs), serving as thermal insulation and decorative components, have been extensively employed across various domains. To meet diverse application demands in different contexts, the product parameters of ECDs exhibit continual variations, notably within the electrochromic layer. In this work, a multilayer perceptron neural network was leveraged to comprehend the transition from material parameter alterations to device performance parameter changes. Specifically, 336 groups of electrochromic solutions containing different concentrations and voltages were prepared. Subsequently, a model comprising three hidden layers and three output neurons was developed, expanding the dataset simulation to 2000 groups, yielding an R2 of 0.975 on modulation amplitude. Through this model, accurate estimations of performance parameters of numerous unmanufactured liquid-based ECDs were achieved, with projected outcomes highlighting a peak modulation amplitude of 78.38%@620nm. It has been verified that the modulation amplitude prediction error was below 5% and demonstrated stable cyclic performance over 1000 times (97.93%@1.45V) , thereby underscoring the feasibility of our approach. This research serves as a pertinent instance of the synergy between multilayer perceptron neural networks and ECDs, thereby advancing the deployment of neural network in the innovation and application of chemical engineering, materials and other fields. © 2024, The Authors. All rights reserved.

关键词： Thermal insulation

Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

学校读者我要写书评

暂无评论

IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

Higher-Order Topological Insulators by Ml-Enhanced Topology Optimization

学校读者我要写书评

暂无评论

SSRN

SSRN 2023年

作者： Du, Zongliang Luo, Jiachen Xu, Zhiang Jiang, Zhenhao Ding, Xianggui Cui, Tianchen Guo, Xu State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment Department of Engineering Mechanics Dalian University of Technology Dalian116023 China Ningbo Institute Dalian University of Technology Ningbo315016 China School of Data Science The Chinese University of Hongkong 518000 Hong Kong

Energy in lower dimensions, such as edges or corners, can be robustly confined by the higher-order topological insulators (HOTIs) against defects. Being promising for wave manipulation, the unique property of HOTIs is protected by the associated topological invariant and bulk band. Nevertheless, it is still challenging to fast design topologically nontrivial continuum unit cells with highly localized edge/corner modes in photonic and phononic systems. In the present work, with the help of a multitask learning model simultaneously predicting the discrete-valued topological invariant and continuum-valued bandgap range, a fast design framework is developed using an explicit topology optimization method for mechanical HOTIs. In the solution process, the machine learning model and backpropagation algorithm are integrated into a MultiStart solver to accelerate the design efficiency by 3-4 orders than the traditional topology optimization method. Furthermore, a novel mechanical imaging device illustrates the applications of the optimized HOTI with highly localized corner states. This AI-enhanced design paradigm can be easily extended for the fast design of optimized topological materials among various physical systems. © 2023, The Authors. All rights reserved.

关键词： Machine learning

A cross Transformer for image denoising

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Tian, Chunwei Zheng, Menghua Zuo, Wangmeng Zhang, Shichao Zhang, Yanning Lin, Chia-Wen School of Software Northwestern Polytechnical University Shaanxi Xi’an710129 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Shaanxi Xi’an710129 China School of Computer Science and Technology Harbin Institute of Technology Heilongjiang Harbin150001 China Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Engineering Central South University Hunan Changsha410083 China School of Computer Science Northwestern Polytechnical University Shaanxi Xi’an710129 China Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu30013 Taiwan

Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to obtain good performance in image denoising. However, how to obtain effective structural information via CNNs to efficiently represent given noisy images is key for complex scenes. In this paper, we propose a cross Transformer denoising CNN (CTNet) with a serial block (SB), a parallel block (PB), and a residual block (RB) to obtain clean images for complex scenes. A SB uses an enhanced residual architecture to deeply search structural information for image denoising. To avoid loss of key information, PB uses three heterogeneous networks to implement multiple interactions of multi-level features to broadly search for extra information for improving the adaptability of an obtained denoiser for complex scenes. Also, to improve denoising performance, Transformer mechanisms are embedded into the SB and PB to extract complementary salient features for effectively removing noise in terms of pixel relations. Finally, a RB is applied to acquire clean images. Experiments illustrate that our CTNet is superior to some popular denoising methods in terms of real and synthetic image denoising. It is suitable to mobile digital devices, i.e., phones. Codes can be obtained at https://***/hellloxiaotian/CTNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Image denoising

Heterogeneous and plaintext checkable signcryption for integrating IoT in healthcare system

学校读者我要写书评

暂无评论

Journal of Systems Architecture 2024年 154卷

作者： Hadabi, Abdalla Qu, Zheng Yeh, Kuo-Hui Chen, Chien-Ming Kumari, Saru Xiong, Hu Network and Data Security Key Laboratory of Sichuan Province School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of Information Management National Dong Hwa University Hualien974 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

Preserving the confidentiality and integrity of data transmission is paramount in the Internet of Things (IoT)-based healthcare systems. Current encryption techniques that allow plaintext checks primarily serve a specific cryptosystem, lacking the adaptability to work with a diverse system incorporating various cryptographic methods. To address this, we present a unique online/offline heterogeneous signcryption scheme with plaintext checkable encryption (HOOSC-PCE). This approach enables the transition of signcrypted messages from an identity-based cryptosystem (IBC) to a public key infrastructure (PKI) system, improving information interoperability. A key aspect of our scheme is its capacity to allow cloud servers to perform plaintext queries, facilitating efficient data searches using plaintext keywords over encrypted data. Furthermore, the signcryption process is divided into online and offline phases. The online phase handles tasks that require fewer resources, while the offline phase carries out more resource-intensive preparatory tasks. We rigorously tested the HOOSC-PCE scheme's security by proving it secure under the Random Oracle Model (ROM). Meanwhile, compared to similar work, it effectively reduces computation costs by 46.39%, 19.45%, 18.73%, and 13.25% across offline encryption, online encryption, decryption, and search algorithms. The results indicate that the HOOSC-PCE is secure and efficient, confirming its feasibility for IoT-based healthcare systems. © 2024 Elsevier B.V.

关键词： Internet of things

Hierarchical Local-Global Transformer for Temporal Sentence Grounding

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Fang, Xiang Liu, Daizong Zhou, Pan Xu, Zichuan Li, Ruixuan Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wangxuan Institute of Computer fTechnology Peking University No. 128 Zhongguancun North Street Beijing100080 China School of software Dalian University of Technology Dalian116024 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

This paper studies the multimedia problem of temporal sentence grounding (TSG), which aims to accurately determine the specific video segment in an untrimmed video according to a given sentence query. Traditional TSG methods mainly follow the top-down or bottom-up framework and are not end-to-end. They severely rely on time-consuming post-processing to refine the grounding results. Recently, some transformer-based approaches are proposed to efficiently and effectively model the fine-grained semantic alignment between video and query. Although these methods achieve significant performance to some extent, they equally take frames of the video and words of the query as transformer input for correlating, failing to capture their different levels of granularity with distinct semantics. To address this issue, in this paper, we propose a novel Hierarchical Local-Global Transformer (HLGT) to leverage this hierarchy information and model the interactions between different levels of granularity and different modalities for learning more fine-grained multi-modal representations. Specifically, we first split the video and query into individual clips and phrases to learn their local context (adjacent dependency) and global correlation (long-range dependency) via a temporal transformer. Then, a global-local transformer is introduced to learn the interactions between the local-level and global-level semantics for better multimodal reasoning. Besides, we develop a new cross-modal cycle-consistency loss to enforce interaction between two modalities and encourage the semantic alignment between them. Finally, we design a brand-new cross-modal parallel transformer decoder to integrate the encoded visual and textual features for final grounding. Extensive experiments on three challenging datasets (ActivityNet Captions, Charades-STA and TACoS) show that our proposed HLGT achieves a new state-of-the-art performance, demonstrating its effectiveness and computational efficiency. Copyright ©

关键词： Semantics

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Bhattacharjya, Abanti Islam, Md Manowarul Uddin, Md Ashraf Talukder, Md. Alamin Azad, A.K.M. Aryal, Sunil Paul, Bikash Kumar Tasnim, Wahia Abdulllah Almoyad, Muhammad Ali Moni, Mohammad Ali School of Information Technology Deakin University Waurn Ponds Campus Geelong Australia Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh Department of Software Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh Riyadh13318 Saudi Arabia Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Bangladesh Department of Basic Medical Sciences College of Applied Medical Sciences King Khalid University Saudi Arabia Artificial Intelligence & Data Science Faculty of Health and Behavioural Sciences The University of Queensland Australia

With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously. © 2024, CC BY.

关键词： Topology