检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Abdelghany, Hazem A. Tindemans, Simon H. de Weerdt, Mathijs M. la Poutréa, Han Department of Electrical Sustainable Energy Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Amsterdam Netherlands Electrical and Control Engineering Department Arab Academy for Science Technology and Maritime Transport Cairo Egypt

Increased uptake of variable renewable generation and further electrification of energy demand necessitate efficient coordination of flexible demand resources to make most efficient use of power system assets. Flexible electrical loads are typically small, numerous, heterogeneous and owned by self-interested agents. Considering the multi-temporal nature of flexibility and the uncertainty involved, scheduling them is a complex task. This paper proposes a forecast-mediated real-time market-based control approach (F-MBC) for cost minimizing coordination of uninterruptible time-shiftable (i.e. deferrable) loads. F-MBC is scalable, privacy preserving, and usable by device agents with small computational power. Moreover, F-MBC is proven to overcome the challenge of mutually conflicting decisions from equivalent devices. Simulations in a simplified but challenging case study show that F-MBC produces near-optimal behaviour over multiple time-steps. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

来源：评论

学校读者我要写书评

暂无评论

Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network

Plot2API: Recommending Graphic API from Plot via Semantic Pa...

引用

IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Zeyu Wang Sheng Huang Zhongxin Liu Meng Yan Xin Xia Bei Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University China School of Big Data and Software Engineering Chongqing University Chongqing China College of Computer Science and Technology Zhejiang University Hangzhou China Faculty of Information Technology Monash University Australia

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely *** the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API *** collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.

关键词： Deep learning Visualization Semantics Neural networks Data visualization software Task analysis

来源：评论

学校读者我要写书评

暂无评论

Usefulness of high-level parallel compositions in genomics 32

Usefulness of high-level parallel compositions in genomics

引用

32nd European Modeling and Simulation Symposium, EMSS 2020

作者： Rossainz-López, Mario Zúñiga-Herrera, Sarahí Pineda, Iván Olmos Pineda-Torres, Ivo Capel-Tuñón, Manuel Faculty of Computer Science Autonomous University of Puebla San Claudio Avenue and South 14th Street City San Manuel Puebla Puebla72000 Mexico Software Engineering Department College of Informatics and Telecommunications ETSIIT University of Granada Daniel Saucedo Aranda s/n Granada18071 Spain

ISBN: (纸本)9788885741454

This work shows the use of parallel objects to build High Level Parallel Compositions or HLPC and their usefulness in genomics through four case studies related to sequencing DNA chains. The first two case studies are combinatorial optimization problems: grouping fragments of DNA sequences and the parallel exhaustive search (PES) of RNA strings that help the sequence and assembly of DNAs in the construction of gnomes. The third case study shows the implementation of a Convolutional Neuronal Network as a Parallel Object Composition to solve the problem of the recognition of DNA sequences from a database with 4 types of hepatitis C virus (type 1, 2, 3 and 6). The results of this classification were obtained in terms of percentages of training precision and validation precision. The fourth and final case study shows the problem of sequence typing (STP) as a form of DNA sequence classification. It is particularized in a proposal for a parallel solution to find conserved regions of sequences that help discriminate between different types of hepatitis C virus, through the creation of a decision tree using HLPC. We show the algorithms that solves these problems using modeling and parallel simulation, their design and implementation as HLPC and the performance metrics in their parallel execution using multicores, video accelerator card and CPU-SET or processors with shared-distributed memory. © 2020 The Authors.

关键词： DNA sequences

来源：评论

学校读者我要写书评

暂无评论

Image Processing-Based Flood Detection 10th

Image Processing-Based Flood Detection

引用

10th National Technical Seminar on Underwater System Technology, NUSYS 2018

作者： Ariawan, Angga Pebrianti, Dwi Ronny Akbar, Yudha Maulana Margatama, Lestari Bayuaji, Luhur Magister of Computer Science Universitas Budi Luhur Jakarta12260 Indonesia Faculty of Engineering Universitas Budi Luhur Jakarta12260 Indonesia Faculty of Information Technology Universitas Budi Luhur Jakarta12260 Indonesia Faculty of Computer Science and Software Engineering Universiti Malaysia Pahang Pekan Malaysia Faculty of Electrical and Electronics Engineering Universiti Malaysia Pahang Pekan Malaysia

ISBN: (纸本)9789811337079

This paper discusses about the design of an online flood detection and early warning system which integrated to using Raspberry-PI and optical sensor. Raspberry-PI is a single board of computer which in this case we design as an image processor to process image obtained from the webcam and update the result to the twitter. This research can help some of the citizens who live near the river to get the updated information regarding water conditions and the possibility of flooding so that they can take action to secure their properties and families as soon as possible. We use OpenCV as an image processing application. The steps are as follows: (1) Region of Interest to create a portion of an image to filter or perform some other operation. (2) Brightness and contrast adjustment in order to get brighter and better image before the next process. (3) Grayscale and threshold to create segmentation object with Otsu-thresholding. (4) Edge detection algorithm to find edge points on a roughly horizontal water line and riverbank height. By using the above method, the system can read and monitor the water level of a river or other water bodies. If the water level exceeds the specific threshold, the system will generate notification as early warning for the possibility of flooding by uploading the text and image to the twitter regarding that condition. The citizens will get the information if they follow that account (early warning system) on Twitter. The result of this simulation using prototype that we have made is that the system can read the water conditions with an increase in accuracy reaching 99.6%. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Water levels

来源：评论

学校读者我要写书评

暂无评论

Toward trainability of quantum neural networks

arXiv

引用

arXiv 2020年

作者： Zhang, Kaining Hsieh, Min-Hsiu Liu, Liu Tao, Dacheng UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering and Information Technologies University of Sydney Australia Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology Sydney Australia

Quantum Neural Networks (QNNs) have been recently proposed as generalizations of classical neural networks to achieve the quantum speed-up. Despite the potential to outperform classical models, serious bottlenecks exist for training QNNs;namely, QNNs with random structures have poor trainability due to the vanishing gradient with rate exponential to the input qubit number. The vanishing gradient could seriously influence the applications of large-size QNNs. In this work, we provide a first viable solution with theoretical guarantees. Specifically, we prove that QNNs with tree tensor architectures have gradients that vanish polynomially with the qubit number. Moreover, our result holds irrespective of which encoding methods are employed. We numerically demonstrate QNNs with tree tensor structures for the application of binary classification. Simulations show faster convergent rates and better accuracy compared to QNNs with random structures. Copyright © 2020, The Authors. All rights reserved.

关键词： Qubits

来源：评论

学校读者我要写书评

暂无评论

Recurrent feature reasoning for image inpainting

arXiv

引用

arXiv 2020年

作者： Li, Jingyuan Wang, Ning Zhang, Lefei Du, Bo Tao, Dacheng National Engineering Research Center for Multimedia Software School of Computer Science and Institute of Artificial Intelligence Wuhan University China UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia

Existing inpainting methods have achieved promising performance for recovering regular or small image defects. However, filling in large continuous holes remains difficult due to the lack of constraints for the hole center. In this paper, we devise a Recurrent Feature Reasoning (RFR) network which is mainly constructed by a plug-and-play Recurrent Feature Reasoning module and a Knowledge Consistent Attention (KCA) module. Analogous to how humans solve puzzles (i.e., first solve the easier parts and then use the results as additional information to solve difficult parts), the RFR module recurrently infers the hole boundaries of the convolutional feature maps and then uses them as clues for further inference. The module progressively strengthens the constraints for the hole center and the results become explicit. To capture information from distant places in the feature map for RFR, we further develop KCA and incorporate it in RFR. Empirically, we first compare the proposed RFR-Net with existing backbones, demonstrating that RFR-Net is more efficient (e.g., a 4% SSIM improvement for the same model size). We then place the network in the context of the current state-of-the-art, where it exhibits improved performance. The corresponding source code is available at: https://***/jingyuanli001/ RFR-Inpainting Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Preparing software Quality Assurance Professionals: Metamorphic Exploration for Machine Learning

Preparing Software Quality Assurance Professionals: Metamorp...

引用

2019 IEEE International Conference on engineering, Technology and Education, TALE 2019

作者： Yang, Sen Towey, Dave Zhou, Zhi Quan Chen, T.Y. University of Sydney Faculty of Engineering Information Technologies Australia University of Nottingham Ningbo School of Computer Science China University of Wollongong School of Computing and Information Technology Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9781728126654

computer science (CS) subjects have been rapidly growing in popularity, and demand for CS education and training has put increasing pressure on teaching resources in higher education (HE) and elsewhere. HE in the People's Republic of China (PRC) has also been developing, with one product of this evolution being Sino-foreign HE institutions (SfHEIs). Much of the popularity growth for CS can be linked to the growth of CS-based technology and innovation, especially in the form of Artificial Intelligence (AI) and Machine Learning (ML). AI/ML-based innovation has been forecast to offer increases in quality of life for consumers. However, AI/ML systems face a challenge for software quality assurance (SQA): They are so-called 'untestable systems' - identifying the correctness of AI/ML system outputs or behaviour may not be feasible. Preparing SQA professionals to be able to ensure AI/ML SQA will require innovative and creative education and training. An SQA approach called metamorphic testing (MT) has a proven track record of alleviating the oracle problem, and has great potential as a testing methodology for AI/ML systems. Metamorphic exploration (ME) is a new addition to the MT literature, and involves developing the user's understanding of the system under study. This paper reports on experiences at an SfHEI of using ME and MT to test an AI/ML system. © 2019 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

On the development of a web extension for text authentication on Google Chrome 2

On the development of a web extension for text authenticatio...

引用

2nd International Conference on Electrical, computer and Communication engineering, ECCE 2019

作者： Kabir, Muhammad Nomani Tayan, Omar Alginahi, Yasser Hasan, Md Munirul Rahman, Md Arafatur Faculty of Computer Systems Software Engineering University Malaysia Pahang Kuantan Pahang Malaysia Dept. of Computer Engineering NOOR Research Center College of Computer Science and Engineering Taibah University Madinah Saudi Arabia Dept. of Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (纸本)9781538691113

Web extension is a software that can be installed on a web browser. A web-extension link is displayed as an icon on the toolbar of the browser. Based on browsing activity, the extension works automatically or by clicking the extension icon depending on the functionalities made in the extension software. In this work, we developed a web extension on Google Chrome browser to verify online texts by simply clicking on an extension button. Upon clicking the button, the underlying algorithm in the extension software retrieves the texts from the current web-page being displayed. Verification and authentication of texts are performed by comparing the retrieved texts with text database. According to the comparison, texts are highlighted in colors. We consider authentication of Arabic Hadith texts for a case study. The authentic Hadith texts are highlighted by green color;authentic texts with partial diacritics, by yellow;and unauthentic texts, by red. This technique can also be used to authenticate laws, constitutions and Government documents in any language. © 2019 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Protected Bidding Against Compromised Information Injection in IoT-Based Smart Grid 2nd

Protected Bidding Against Compromised Information Injection ...

引用

2nd EAI International Conference on Smart Grid and Internet of Things, SGIoT 2018

作者： Bhuiyan, Md Zakirul Alam Zaman, Mdaliuz Wang, Guojun Wang, Tian Rahman, Md. Arafat Tao, Hai Department of Computer and Information Science Fordham University New York CityNY United States School of Computer Science and Educational Software Guangzhou University Guangzhou China Department of Computer Science and Technology Huaqiao University Xiamen China Faculty of Computer Systems and Software Engineering Universiti Malaysia Pahang Pekan Malaysia Department of Computer Science Baoji University of Arts and Sciences BaojiShaanxi China

ISBN: (纸本)9783030059279

The smart grid is regarded as one of the important application field of the Internet of Things (IoT) composed of embedded sensors, which sense and control the behavior of the energy world. IoT is attractive for features of grid catastrophe prevention and decrease of grid transmission line and reliable load fluctuation control. Automated Demand Response (ADR) in smart grids maintain demand-supply stability and in regulating customer side electric energy charges. An important goal of IoT-based demand-response using IoT is to enable a type of DR approach called automatic demand bidding (ADR-DB). However, compromised information board can be injected into during the DR process that influences the data privacy and security in the ADR-DB bidding process, while protecting privacy oriented consumer data is in the bidding process is must. In this work, we present a bidding approach that is secure and private for incentive-based ADR system. We use cryptography method instead of using any trusted third-party for the security and privacy. We show that proposed ADR bidding are computationally practical through simulations performed in three simulation environments. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Conceptual Design of a Generic Nodal Abstraction (GNA) for a Human-Agent Collaboration Systems

Conceptual Design of a Generic Nodal Abstraction (GNA) for a...

引用

International Conference on Green engineering Technology and Applied Computing 2019, IConGETech2 019 and International Conference on Applied Computing 2019, ICAC 2019

作者： Abbas Mohammed, Khudhair Abdul Majid, Mazlina Sharifuddin Ahmad, Mohd Faculty of Computer Systems and Software Engineering University Malaysia Pahang Pahang Kuantan Malaysia College of Computer Science and Information Technology Universiti Tenaga Nasional Selangor Kajang43000 Malaysia

The issues of modelling software agents in collaborative systems present some problems and difficulties caused by the diversity of tasks and the procedures attached to them. Consequently, such diversity poses a critical challenge to implement scheduled tasks. In this paper, we propose collaborative human-agent system in which a software agent collaborates and cooperates with its human counterpart in several job positions of an organization. The goal of the GNA approach is to improve collaboration between humans when performing tasks within a workflow process. The GNA is built based on the concept of a node which comprises of a human, agents, and their shared functions. This concept espouses the notion that agents could be deployed to assist humans in many common workflow processes and to handle mundane tasks. Furthermore, a multi-agent system is advocated as an application or platform for managing distributed data. Specifically, this paper describes a conceptual design of a generic approach based on the notion of nodes to construct a Generic Nodal Abstraction (GNA) approach that is deployable as a modeling and simulation platform for organizational processes. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Multi agent systems

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：