检索结果-内蒙古大学图书馆

International Conference on Sustainable Information engineering and Technology (SIET)

作者： Fitra A. Bachtiar Fajar Pradana Rizkia Desi Yudiari Faculty of Computer Science Intelligent System Laboratory Brawijaya University Malang Indonesia Faculty of Computer Science Software Engineering Laboratory Brawijaya University Malang Indonesia Faculty of Computer Science Brawijaya University Malang Indonesia

Finding the right employee in the employee recruitment process in an organization is a challenging tasks. For example, enormous candidates participated in the selection, various parameters to be examined from the candidates. Furthermore, the selection process done manually could take long time during the selection process. In addition, there is a case that sometimes the position of the employee's work is not suitable with the strengths and weaknesses of the candidates. Moreover, in some cases using Curriculum Vitae (CV), Interview, and Assessment Test could not give the optimal selection results. There is a need to overcome this problems to create more comprehensive and could suggest the employee placement based on their characteristics. In this study, Profile Matching is used to comprehensively assess the candidates. In addition, Naïve Bayes is used in the next step to determine or predict the employee placement based on their characteristics. Validation is conducted by performing bivariate correlation to evaluate the Profile Matching and accuracy is used to evaluate the suitability of the employee placement. The results shows that there is a positive correlation with the value of 0.93. Meanwhile, the prediction using Naïve Bayes model shows a 100% accuracy to predict employee placement.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Reviewing continual learning from the perspective of human-level intelligence

arXiv

引用

arXiv 2021年

作者： Chang, Yifan Li, Wenbo Peng, Jian Tang, Bo Kang, Yu Lei, Yinjie Gui, Yuanmiao Zhu, Qing Liu, Yu Li, Haifeng The State Key Laboratory of Fire Science Department of Automation and Institute of Advanced Technology University of Science and Technology of China Hefei China Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China The Department of Electrical and Computer Engineering Mississippi State University United States College of Electronics and Information Engineering Sichuan University Chengdu China Faculty of Geosciences and Environmental Engineering The Southwest Jiaotong University Chengdu China Institute of Remote Sensing and Geographic Information System Peking Universit Beijing China School of Geosciences and Info-Physics Central South University South Lushan Road Changsha410083 China

Humans continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection);ii) infer on new information continuously (information prospection:);iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence;ii) providing a detailed and extensive overview on CL topics;iii) presenting some novel ideas on the potential development of CL. © 2021, CC BY.

关键词： Petroleum reservoir evaluation

来源：评论

学校读者我要写书评

暂无评论

A Progressive background updating based coding scheme for surveillance videos 34

A Progressive background updating based coding scheme for su...

引用

34th IEEE International Conference on Visual Communications and Image Processing, VCIP 2019

作者： Li, Meng Shi, Yunhui Ding, Wenpeng Liu, Xiaojie Liu, Jiaying Yin, Baocai Beijing University of Technology Beijing Key Laboratory of Multimedia and Intelligent Software Technology Faculty of Information Technology Beijing100124 China Baidu Inc China Henan Institute of Technology School of Computer Science and Technology Xinxiang China Peking University China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China

ISBN: (纸本)9781728137230

Background based coding is an effective scheme to improve the coding efficiency of surveillance videos. However, it takes a long time to generate a high quality background picture (BG-picture). And the encoding of the high quality BG-picture will increase the bitrate abruptly. To solve these problems, a progressive background updating based coding scheme is proposed in this paper. In the proposed scheme, the BG-picture is updated block by block. To improve the overall coding efficiency, an importance map is designed to select the valid background blocks (B-blocks) progressively which will be encoded with high quality. It is worth noting that only the valid B-blocks is encoded instead of the entire BG-picture. Compared with the reference software of Versatile Video Coding (VVC), the proposed scheme achieves about 23.3 percent bit-rate saving on average. © 2019 IEEE.

关键词： Video signal processing

来源：评论

学校读者我要写书评

暂无评论

Joint long-term cache updating and short-term content delivery in cloud-based small cell networks

arXiv

引用

arXiv 2020年

作者： Wu, Xiongwei Li, Qiang Li, Xiuhua Leung, Victor C.M. Ching, P.C. Department of Electronic Engineering Faculty of Engineering Chinese University of Hong Kong Shatin Hong Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Peng Cheng Laboratory Shenzhen518052 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users’ quality of service (QoS) and requires substantial power consumption. This paper proposes an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users’ QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users’ content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective tradeoff between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power utilization

来源：评论

学校读者我要写书评

暂无评论

BuildSenSys: Reusing building sensing data for traffic prediction with cross-domain learning

arXiv

引用

arXiv 2020年

作者： Fan, Xiaochen Xiang, Chaocan Chen, Chao Yang, Panlong Gong, Liangyi Song, Xudong Nanda, Priyadarsi He, Xiangjian School of Electrical and Data Engineering Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education China College of Computer Science Chongqing University Chongqing400044 China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui230026 China School of Software BNRist Tsinghua University Beijing100084 China

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys, a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning. First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperf

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Order-Preserving Wasserstein Discriminant Analysis

Order-Preserving Wasserstein Discriminant Analysis

引用

International Conference on computer Vision (ICCV)

作者： Bing Su Jiahuan Zhou Ying Wu Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering Northwestern University Evanston IL

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Supervised dimensionality reduction for sequence data projects the observations in sequences onto a low-dimensional subspace to better separate different sequence classes. It is typically more challenging than conventional dimensionality reduction for static data, because measuring the separability of sequences involves non-linear procedures to manipulate the temporal structures. This paper presents a linear method, namely Order-preserving Wasserstein Discriminant Analysis (OWDA), which learns the projection by maximizing the inter-class distance and minimizing the intra-class scatter. For each class, OWDA extracts the order-preserving Wasserstein barycenter and constructs the intra-class scatter as the dispersion of the training sequences around the barycenter. The inter-class distance is measured as the order-preserving Wasserstein distance between the corresponding barycenters. OWDA is able to concentrate on the distinctive differences among classes by lifting the geometric relations with temporal constraints. Experiments show that OWDA achieves competitive results on three 3D action recognition datasets.

关键词： Training Dimensionality reduction Dispersion Complexity theory Distortion Legged locomotion Prototypes

来源：评论

学校读者我要写书评

暂无评论

A modified lumped parameter model of distribution transformer winding

引用

Global Energy Interconnection 2020年第2期3卷 158-165页

作者： Qingqing Ding Yao Yao Bingqian Wang Jingwei Fu Wei Zhang Chao Zeng Xiaoping Li Stanimir Valtchev State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityHaidian DistrictBeijing100084P.R.China State Grid Hubei Electric Power Co. Research Institute(HBEPRI)Hongshan DistrictWuhan 430077P.R.China Flexible Transmission Engineering Department Nari-Relays Electric Company210003 NanjingP.R.China State Grid Hubei Electric Power Co. LtdHongshan District430077 WuhanP.R.China Department of Electrical and Computer Engineering Faculty of Science and TechnologyUNLPortugal

The modelling of the distribution transformer winding is the starting point and serves as important basis for the transformer characteristics analysis and the lightning pulse response prediction.A distributed parameters model can depict the winding characteristics accurately,but it requires complex *** parameter model requires less calculations,but its applicable frequency range is not *** paper studies the amplitude-frequency characteristics of the lightning wave,compares the transformer modelling methods and finally proposes a modified lumped parameter model,based on the above *** proposed model minimizes the errors provoked by the lumped parameter approximation,and the hyperbolic functions of the distributed parameter *** this modification it becomes possible to accurately describe the winding characteristics and rapidly obtain the node voltage *** proposed model can provide theoretical and experimental support to lightning protection of the distribution transformer.

关键词： Wide band frequency response Distributed parameter model Lumped parameter model Distribution transformer Lightning protection

来源：评论

学校读者我要写书评

暂无评论

Environmentally safe layered crystal produced from hazardous chemical elements: TlPb2BrI4, a new promising detector material

引用

Journal of Alloys and Compounds 2022年 924卷 1页

作者： Khyzhun, O.Y. Vu, Tuan V. Parasyuk, O.V. Fedorchuk, A.O. Fochuk, P.M. Lavrentyev, A.A. Gabrelian, B.V. Levchuk, Ievgen Matt, Gebhard J. Tedde, Sandro F. Schmidt, Oliver Shrestha, Shreetu Brabec, Christoph J. Kityk, I.V. Piasecki, M. Frantsevych Institute for Problems of Materials Science National Academy of Sciences of Ukraine 3 Krzhyzhanovsky Street KyivUA-03142 Ukraine Faculty of Mechanical - Electrical and Computer Engineering School of Engineering and Technology Van Lang University Ho Chi Minh City Viet Nam Department of Inorganic and Physical Chemistry Lesya Ukrainka Volyn National University 13 Voli Avenue LutskUA-43025 Ukraine Department of Inorganic and Organic Chemistry Lviv National University of Veterinary Medicine and Biotechnologies 50 Pekarska Street Lviv79010 Ukraine Yuriy Fed'kovych Chernivtsi National University 2 Kotziubynskoho Street Chernivtsi58012 Ukraine Department of Electrical Engineering and Electronics Don State Technical University 1 Gagarin Square Rostov-on-Don344010 Russia Department of Computational Technique and Automated System Software Don State Technical University 1 Gagarin Square Rostov-on-Don344010 Russia Martensstrasse 7 Erlangen91058 Germany Siemens Healthineers Technology Center Guenther-Scharowsky-Str. 1 Erlangen91058 Germany Electrical Engineering Department Czestochowa University Technology Armii Krajowej 17 Częstochowa42217 Poland Departmentof Experimental Physics and Information-measuring Technology Lesya Ukrainka Volyn National University Voli Avenue 13 Lutsk43025 Ukraine J.Dlugosz University Częstochowa Armii Krajowej 13/15 Częstochowa42200 Poland

Exploring better Gamma, X-Ray and IR-Mid-R detectors, adequately sensitive to ambient temperatures and environmental conditions, is highly desirable. Therefore, following this path, we present detecting opportunities, based on data of experimental and theoretical investigation of the electronic and optical properties, photocurrent behavior at different bias voltages as well as the reflection for the He-Cd cw laser after irradiation by three different IR laser wavelengths, for recently grown TlPb2BrI4 single crystal with strong anisotropic, two-dimensional (2D) layered structure. The X-ray photoelectron spectroscopy brings data on binding energy values of core levels of TlPb2BrI4 for both as-grown and Ar+-ion-treated crystal surfaces as well as for elucidation of the energy distribution of the valence electronic states. In opposite to well-known IR-detector materials, TlPb2BrI4 crystal surface shows high chemical stability concerning Ar+-ion bombardment. Further, we carry out ab initio band-structure calculations to gain curves of total and partial densities of states and to elucidate the principal optical constants of TlPb2BrI4. The present studies indicate that, in spite of the fact that the TlPb2BrI4 crystal contains two very hazardous/toxic chemical elements, thallium and lead;however, it is chemically stable and reveals very low hygroscopic behavior when being exposed to environmental conditions. © 2022 Elsevier B.V.

关键词： X ray photoelectron spectroscopy

来源：评论

学校读者我要写书评

暂无评论

Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

引用

The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care system for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Learning low-dimensional temporal representations supplementary material 35

Learning low-dimensional temporal representations supplement...

引用

35th International Conference on Machine Learning, ICML 2018

作者： Su, Bing Wu, Ying Science and Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

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：