检索结果-内蒙古大学图书馆

CDC: Classification driven compression for bandwidth efficient edge-cloud collaborative deep learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Dong, Yuanrui Zhao, Peng Yu, Hanqiao Zhao, Cong Yang, Shusen National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Department of Computing Imperial College London United Kingdom

The emerging edge-cloud collaborative Deep Learning (DL) paradigm aims at improving the performance of practical DL implementations in terms of cloud bandwidth consumption, response latency, and data privacy preservation. Focusing on bandwidth efficient edge-cloud collaborative training of DNN-based classifiers, we present CDC, a Classification Driven Compression framework that reduces bandwidth consumption while preserving classification accuracy of edge-cloud collaborative DL. Specifically, to reduce bandwidth consumption, for resource-limited edge servers, we develop a lightweight autoencoder with a classification guidance for compression with classification driven feature preservation, which allows edges to only upload the latent code of raw data for accurate global training on the Cloud. Additionally, we design an adjustable quantization scheme adaptively pursuing the tradeoff between bandwidth consumption and classification accuracy under different network conditions, where only fine-Tuning is required for rapid compression ratio adjustment. Results of extensive experiments demonstrate that, compared with DNN training with raw data, CDC consumes 14.9 less bandwidth with an accuracy loss no more than 1.06%, and compared with DNN training with data compressed by AE without guidance, CDC introduces at least 100% lower accuracy loss. Copyright © 2020, The Authors. All rights reserved.

关键词： Classification (of information)

SurveilEdge: Real-time video query based on collaborative cloud-edge deep learning

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Wang, Shibo Yang, Shusen Zhao, Cong National Engineering Laboratory for Big Data Analytics Xi’an Jiaotong University China Department of Computing Imperial College London United Kingdom

The real-time query of massive surveillance video data plays a fundamental role in various smart urban applications such as public safety and intelligent transportation. Traditional cloud-based approaches are not applicable because of high transmission latency and prohibitive bandwidth cost, while edge devices are often incapable of executing complex vision algorithms with low latency and high accuracy due to restricted resources. Given the infeasibility of both cloud-only and edge-only solutions, we present SurveilEdge, a collaborative cloud-edge system for real-time queries of large-scale surveillance video streams. Specifically, we design a convolutional neural network (CNN) training scheme to reduce the training time with high accuracy, and an intelligent task allocator to balance the load among different computing nodes and to achieve the latency-accuracy tradeoff for real-time queries. We implement SurveilEdge on a prototype1 with multiple edge devices and a public Cloud, and conduct extensive experiments using real-world surveillance video datasets. Evaluation results demonstrate that SurveilEdge manages to achieve up to 7× less bandwidth cost and 5.4× faster query response time than the cloud-only solution;and can improve query accuracy by up to 43.9% and achieve 15.8× speedup respectively, in comparison with edge-only approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Bandwidth

Awareness requirement and performance management for adaptive systems: a survey

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Qader, Shko Vimal, S. Chhabra, Amit Yaseen, Zaher Mundher Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientifc Research KRI Sulaymaniyah46001 Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Sydney Australia Sydney Australia Information Technology Department Kent Institute Australia Sydney Australia Department of Information Technology University College of Goizha Kurdistan Region Sulaimani Iraq Data Analytics Lab Department of Artifcial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31621 Saudi Arabia Department of Computer Science College of Science Charmo University KRI Chamchamal46023 Iraq Department of Information Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaymaniyah46001 Iraq

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). © 2023, CC BY-NC-ND.

关键词： Adaptive systems

Instantons in 4 Theories: Transseries, Virial Theorems and Numerical Aspects

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Giorgini, Ludovico T. Jentschura, Ulrich D. Malatesta, Enrico M. Rizzo, Tommaso Zinn-Justin, Jean Nordita Royal Institute of Technology Stockholm University Stockholm106 91 Sweden Department of Physics Missouri University of Science and Technology RollaMO65409 United States Department of Computing Sciences Bocconi University via Sarfatti 25 Milan20136 Italy Institute for Data Science and Analytics Bocconi University Milano20136 Italy Dipartimento di Fisica Sapienza Università di Roma P.le Aldo Moro 5 Rome00185 Italy CNR Rome unit P.le A. Moro 5 Rome00185 Italy IRFU/CEA Paris-Saclay Gif-sur-Yvette91191 Cedex France

We discuss numerical aspects of instantons in two- and three-dimensional 4 theories with an internal O(N) symmetry group, the so-called N-vector model. Combining asymptotic transseries expansions for large argument with convergence acceleration techniques, we obtain high-precision values for certain integrals of the instanton that naturally occur in loop corrections around instanton configurations. Knowledge of these numerical properties are necessary in order to evaluate corrections to the large-order factorial growth of perturbation theory in 4 theories. The results contribute to the understanding of the mathematical structures underlying the instanton configurations. © 2024, CC BY.

关键词： Perturbation techniques

Experiences on applying SPL engineering techniques to design a (Re) usable ontology in the energy domain 31

学校读者我要写书评

暂无评论

Experiences on applying SPL engineering techniques to design...

31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019

作者： Cuenca, Javier Larrinaga, Felix Curry, Edward Electronics and Computing Department Mondragon University Faculty of Engineering Mondragon Spain Insight Centre For Data Analytics National University of Ireland Galway Ireland

ISBN: (纸本)1891706489

Global ontologies must provide a balance of reusability-usability to minimize the ontology reuse effort in different applications. To achieve this balance, ontology design methods focus on designing layered ontologies that classify into abstraction layers the common domain knowledge (reused by most applications) and the variant domain knowledge (reused by specific application types). This classification is performed from scratch by domain experts and ontology engineers. Hence, the design of reusable and usable ontologies that represent complex domains takes a lot of effort. Considering how common and variant software features are classified when designing Software Product Lines (SPLs), we argue that SPL engineering techniques can facilitate the domain knowledge classification taking as reference existing ontologies. In this paper, we show the experiences of applying SPL and ontology design techniques in combination to design a reusable and usable global ontology for the energy domain. Domain experts and ontology engineers evaluated the proposed method. The results show that SPL engineering techniques enable a systematic and accurate domain knowledge classification, thus saving ontology design effort. © 2019 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Ontology

Comparative analysis of SIMILAR to RCD ONE(SRO)family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

学校读者我要写书评

暂无评论

Journal of Cotton Research 2024年第1期7卷 45-57页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture FaisalabadSub-Campus BurewalaVehari 61010Pakistan Department of Chemistry and Biochemistry University of AgricultureFaisalabad 38400Pakistan Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad 38400Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38400Pakistan Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud ComputingCentre for Advanced Studies in Agriculture and Food SecurityUniversity of Agriculture FaisalabadFaisalabad 38400Pakistan Department of Plant Pathology University of CaliforniaDavisCA 95616USA State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangHenan 455000China Federal Seed Certification and Registration Department Ministry of National Food Security and ResearchIslamabad 44090Pakistan School of Agriculture Sciences Zhengzhou UniversityZhengzhou 450000China

Background SRO(Similar to RCD1)genes family is largely recognized for their importance in the growth,develop-ment,and in responding to environmental ***,genome-wide identification and functional characteri-zation of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribu-tion of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target 12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes dur-ing ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

关键词： Cotton SRO miRNAs Gene duplications Gene expression Ovule development

Enhancing Dental Caries Identification with Deep Learning: A Study of Convolutional Neural Networks and Transfer Learning Approaches

学校读者我要写书评

暂无评论

SSRN

SSRN 2024年

作者： Mannan, Fayqa Adeel, Muhammad Arif, Erssa Li, Jianqiang Ullah, Amin Nawazish, Samra Mahmood, Tariq Rehman, Amjad Shoukat, Ijaz Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Government College University Faisalabad38000 Pakistan Department of Computer Science School of Science National Textile University Faisalabad37610 Pakistan Department of Computer Science Faculty of Computing Riphah International University Faisalabad38000 Pakistan Beijing Engineering Research Center for IoT Software and Systems 100124 China Department of Computer Science Faculty of IT and Computer Science University of Central Punjab Lahore Pakistan School of Computer Science and Engineering Southeast University Nanjing China Software Engineering Department University of Engineering and Technology Taxila Pakistan Artificial Intelligence & Data Analytics Lab CCIS Prince Sultan University Riyadh11586 Saudi Arabia Faculty of information Sciences University of Education Vehari Campus Vehari61100 Pakistan

Various deep learning techniques have been employed to diagnose dental caries using X-ray images. In this study, we utilized deep learning models, including Convolutional Neural Networks (CNNs) and transfer learning models such as Visual Geometry Group (VGG16), VGG19, ResNet50, and Inception V3, to identify dental caries in periapical radiographs. The traditional CNN model comprises three sets of 2D convolutional layers, followed by an activation layer, a max-pooling layer, a flatten layer, one dense layer, another activation layer, dropout, another dense layer, and a final activation layer. For the VGG models, ResNet50, and Inception V3, the top convolutional layers were frozen to prevent retraining of the model, with only the last layers trained to produce the required output. The accuracy achieved by CNN, VGG16, VGG19, ResNet50, and Inception V3 was 90%, 96%, 73%, 70%, and 73%, respectively. VGG16 exhibited the highest accuracy among the models. Hyperparameters were carefully selected using a grid search approach, and the results were validated using the Shuffle-Split-Cross (SSC) validation method. These findings underscore the effectiveness of transfer learning in analyzing medical images, particularly in the diagnosis of dental caries. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

Multi-omics Integration based Predictive Model for Survival Prediction of Lung Adenocarcinaoma

学校读者我要写书评

暂无评论

Multi-omics Integration based Predictive Model for Survival ...

2019 Grace Hopper Celebration India, GHCI 2019

作者： Malik, Vidhi Dutta, Shayoni Kalakoti, Yogesh Sundar, Durai DAILAB Indian Institute of Technology Delhi Department of Biochemical Engineering and Biotechnology Delhi India Language of Technical Computing: Data Analytics MathWorks Delhi India

ISBN: (纸本)9781728142647

Background: Lung adenocarcinoma (LUAD) patients majorly tend to poor clinical outcomes. A biomarker or gene signature built using multi-omics dataset along with clinical features that could predict survival in these patients would have a significant clinical impact, enabling earlier detection of mortality risk and personalized therapy. Methods: To identify a novel multi-omics signature along with clinical features associated with overall survival, we analyzed LUAD patient's single omics datasets for Copy number variations (CNV), protein, methylation, mutation, RNA, mi-RNA that were extracted from The Cancer Genome Atlas (TCGA). Neighborhood component analysis, a feature reduction algorithm was applied to the large feature space for all the single omics data set to select the optimal number of combinations of best feature predictors. These selected features for each singe omics dataset were coupled to integrate multiple inputs and fed into an Support vector machine (SVM), Neural network pattern recognizer and RUS ensemble boost to build the survival prediction model. An external cohort was used to validate the prediction models. Results: We identified a critical feature space for multi-omics-based integration that could effectively stratify these LUAD patients into our critical survival classes with 92.9% accuracy using our neural network-based model, and receiver operating characteristic (ROC) analysis indicated that the signature had a powerful predictive ability. Moreover, a predictive pipeline was established based on the above signature integrated with clinicopathological features. The performance in terms of prediction accuracy for single-omics data as input for validation was not as good as the performance of our model, as it requires multi-omics data as an input and improves performance accuracy of our classifier. Lastly, the signature was validated by an external cohort from excluded patients retrieved for Group I and II study on our best performing classifi

关键词： RNA

Cosmology with fast radio bursts in the era of SKA

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Ji-Guo Zhao, Ze-Wei Li, Yichao Zhang, Jing-Fei Li, Di Zhang, Xin Department of Physics College of Sciences Northeastern University Shenyang110819 China Northeastern University Shenyang110819 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

We present a forecast of the cosmological parameter estimation using fast radio bursts (FRBs) from the upcoming Square Kilometre Array (SKA), focusing on the issues of dark energy, the Hubble constant, and baryon density. We simulate 105 and 106 localized FRBs from a 10-year SKA observation, and find that: (i) using 106 FRB data alone can tightly constrain dark-energy equation of state parameters better than CMB+BAO+SNe, providing an independent cosmological probe to explore dark energy;(ii) combining the FRB data with gravitational-wave standard siren data from 10-year observation with the Einstein Telescope, the Hubble constant can be constrained to a sub-percent level, serving as a powerful low-redshift probe;(iii) using 106 FRB data can constrain the baryon density Ωbh to a precision of ∼ 0.1%. Our results indicate that SKA-era FRBs will provide precise cosmological measurements to shed light on both dark energy and the missing baryon problem, and help resolve the Hubble tension. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmology