检索结果-内蒙古大学图书馆

TouchPower: Interaction-based power transfer for power-as-needed devices

学校读者我要写书评

暂无评论

Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2017年第3期1卷 1-20页

作者： Zhang, Tengxiang Yi, Xin Yu, Chun Wang, Yuntao Becker, Nicholas Shi, Yuanchun Tsinghua National Laboratory for Information Science and Technology Computer Science and Technology Department Tsinghua University China Beijing Key Lab of Networked Multimedia Computer Science and Technology Department Tsinghua University China Global Innovation eXchange Institute Computer Science and Technology Department Tsinghua University China Electrical Engineering Department University of Washington United States

The trend toward ubiquitous deployment of electronic devices demands novel low maintenance power schemes to decrease the burden of maintaining such a large number of devices. In this paper, we propose Interaction-based Power Transfer (IPT): a novel power scheme for power-as-needed devices (i.e., devices that only require power during interaction). IPT allows for the removal of built-in batteries on these devices, and to instead be powered up through direct contact interaction with the user (e.g. gripping a mouse, holding a pen). We prove the concept and show the potential of IPT through our TouchPower prototype. TouchPower transfers on-body power to off-body power-as-needed devices through contact between electrodes on a glove worn by the user and those on the target device during the interaction process. We design TouchPower to automatically detect the contact topology at runtime to supply power accordingly, and place electrodes on the glove so that TouchPower is compatible with various interactions with different objects. We also show the methodology of placing electrodes on the device-end, and evaluate it on a mouse and a remote controller. Results show that during interaction, TouchPower is able to provide stable power supply to these devices with only a small sacrifice in regards to interaction naturalness. At last we demonstrate six applications of TouchPower, and discuss the limitations and potential of TouchPower and IPT systems. © 2017 Association for Computing Machinery.

关键词： Energy transfer

Using Microservices for Rapid Creation of Remote Sensing Products

学校读者我要写书评

暂无评论

Using Microservices for Rapid Creation of Remote Sensing Pro...

software Architecture Companion (ICSA-C)

作者： Bo Xiang Zheng Li Yan Liu He Zhang Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences China Department of Computer Engineering and Computer Science University of Concepción Chile Faculty of Engineering and Computer Science Concordia University Canada State Key Laboratory of Novel Software Technology Software Institute Nanjing University China

Driven by the demands of various sensing data products, remote sensing software systems have increasingly been developed and applied to a wide range of fields. Due to the legacy monolithic product line, we used to satisfy the requirements on a project-by-project basis, only with reuse of some fundamental source codes and tools. By employing microservices to refactor our product line, we are now able to follow a divide-and-conquer strategy to maximize the reusability of component business capacities and create satellite remote sensing products at a much faster pace. This paper mainly introduces our microservice-oriented product line, and the data-analytics-visualization (DAV) pattern for logically organizing and managing relevant microservices of different products. In particular, two projects (namely archaeological detection and cultural heritage protection & urban planning and land use) are used to demonstrate how we prevent to develop thematic products from scratch.

关键词： Sensors Cultural differences Remote sensing Satellites Monitoring Portals computer architecture

When full duplex wireless meets non-orthogonal multiple access: Opportunities and challenges

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Chen, Xianhao Liu, Gang Ma, Zheng Zhang, Xi Fan, Pingzhi Chen, Shanzhi Richard Yu, F. Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu610031 China National Mobile Communications Research Laboratory Southeast University Department of Information Science and Engineering KTH Royal Institute of Technology Stockholm Sweden Key Lab of Information Coding and Transmission Southwest Jiaotong University Chengdu610031 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States State Key Laboratory of Wireless Mobile Communications China Academy of Telecommunication Technology Beijing100191 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Systems and Computer Engineering Carleton University OttawaON Canada

Non-orthogonal multiple access (NOMA) is a promising radio access technology for the 5G wireless systems. The core of NOMA is to support multiple users in the same resource block via power or code domain multiplexing, which provides great enhancement in spectrum efficiency and connectivity. Meanwhile, with the recent advance in self-interference (SI) cancelation techniques, full duplex (FD) wireless communication has become a feasible technology enabling radios to receive and transmit simultaneously. This article aims to investigate the combination of these two emerging technologies. At first, several typical scenarios and protocols are presented to illustrate the application of FD technique in NOMA systems. Then, a novel NOMA system with FD base stations (BSs) based on centralized radio access networks (C-RAN) is proposed. Furthermore, power allocation policies are discussed for the proposed scheme, and simulation results are provided to demonstrate its superiority. Finally, challenges and research opportunities of FD NOMA systems are also identified to stimulate the future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Radio access networks

A confidence-aware top-k query processing toolkit on crowdsourcing 43

学校读者我要写书评

暂无评论

A confidence-aware top-k query processing toolkit on crowdso...

43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Li, Yan Kou, Ngai Meng Wangy, Hao Hou, U. Leong Gong, Zhiguo Department of Computer and Information Science University of Macau Macau SAR China State Key Laboratory for Novel Software Technology Nanjing University China

Ranking techniques have been widely used in ubiquitous applications like recommendation, information retrieval, etc. For ranking computation hostile but human friendly items, crowdsourcing is considered as an emerging technique to process the ranking by human power. However, there is a lack of an easy-to-use toolkit for answering crowdsourced top-k query with minimal effort. In this work, we demonstrate an interactive programming toolkit that is a unified solution for answering the crowdsourced top-k queries. The toolkit employs a new confidenceaware crowdsourced top-k algorithm, SPR. The whole progress of the algorithm is monitored and visualized to end users in a timely manner. Besides the visualized result and the statistics, the system also reports the estimation of the monetary cost and the breakdown of each phase. Based on the estimation, end users can strike a balance between the budget and the quality through the interface of this toolkit. © 2017 VLDB.

关键词： Crowdsourcing

MedShapeNet - A Large-Scale Dataset of 3D Medical Shapes for computer Vision

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Li, Jianning Zhou, Zongwei Yang, Jiancheng Pepe, Antonio Gsaxner, Christina Luijten, Gijs Qu, Chongyu Zhang, Tiezheng Chen, Xiaoxi Li, Wenxuan Wodzinski, Marek Friedrich, Paul Xie, Kangxian Jin, Yuan Ambigapathy, Narmada Nasca, Enrico Solak, Naida Melito, Gian Marco Vu, Viet Duc Memon, Afaque R. Schlachta, Christopher De Ribaupierre, Sandrine Patel, Rajnikant Eagleson, Roy Chen, Xiaojun Mächler, Heinrich Kirschke, Jan Stefan de la Rosa, Ezequiel Christ, Patrick Ferdinand Li, Hongwei Bran Ellis, David G. Aizenberg, Michele R. Gatidis, Sergios Küstner, Thomas Shusharina, Nadya Heller, Nicholas Andrearczyk, Vincent Depeursinge, Adrien Hatt, Mathieu Sekuboyina, Anjany Löffler, Maximilian Liebl, Hans Dorent, Reuben Vercauteren, Tom Shapey, Jonathan Kujawa, Aaron Cornelissen, Stefan Langenhuizen, Patrick Ben-Hamadou, Achraf Rekik, Ahmed Pujades, Sergi Boyer, Edmond Bolelli, Federico Grana, Costantino Lumetti, Luca Salehi, Hamidreza Ma, Jun Zhang, Yao Gharleghi, Ramtin Beier, Susann Sowmya, Arcot Garza-Villarreal, Eduardo A. Balducci, Thania Angeles-Valdez, Diego Souza, Roberto Rittner, Leticia Frayne, Richard Ji, Yuanfeng Ferrari, Vincenzo Chatterjee, Soumick Dubost, Florian Schreiber, Stefanie Mattern, Hendrik Speck, Oliver Haehn, Daniel John, Christoph Nürnberger, Andreas Pedrosa, João Ferreira, Carlos Aresta, Guilherme Cunha, António Campilho, Aurélio Suter, Yannick Garcia, Jose Lalande, Alain Vandenbossche, Vicky Van Oevelen, Aline Duquesne, Kate Mekhzoum, Hamza Vandemeulebroucke, Jef Audenaert, Emmanuel Krebs, Claudia van Leeuwen, Timo Vereecke, Evie Heidemeyer, Hauke Röhrig, Rainer Hölzle, Frank Badeli, Vahid Krieger, Kathrin Gunzer, Matthias Chen, Jianxu van Meegdenburg, Timo Dada, Amin Balzer, Miriam Fragemann, Jana Jonske, Frederic Rempe, Moritz Malorodov, Stanislav Bahnsen, Fin H. Seibold, Constantin Jaus, Alexander Marinov, Zdravko Jaeger, Paul F. Stiefelhagen, Rainer Santos, Ana Sofia Lindo, Mariana Ferreira, André Alves, Victor Kamp, Michael Abourayya, Amr Nensa, Felix Hörst, Fabian Brehmer, Alexander Hein Girardetstraße 2 Essen45131 Germany Graz University of Technology Inffeldgasse 16c Graz8010 Austria Graz Austria The Department of Computer Science Johns Hopkins University Malone Hall 3400 N Charles St BaltimoreMD21218 United States Rte Cantonale Lausanne1015 Switzerland The Department of Measurement and Electronics AGH University of Science and Technology Krakow Poland Sierre Switzerland Department of Biomedical Engineering University of Basel Hegenheimermattweg Allschwil167C CH-4123 Switzerland The Boston College 140 Commonwealth Ave Chestnut HillMA02467 United States University Hospital B7-200 339 Windermere Road LondonN6A 5A5 Canada The Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai200240 China The Research Center for Connected Healthcare Big Data ZhejiangLab Zhejiang Hangzhou311121 China The Faculty of Statistics Technical University Dortmund August-Schmidt-Straße 1 Dortmund44227 Germany The Institute of Mechanics Graz University of Technology Kopernikusgasse 24/IV Graz8010 Austria The Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China The Department of Cardiac Surgery Medical University Graz Auenbruggerplatz 29 Graz8036 Austria The Department of Diagnostic and Interventional Radiology University Hospital Giessen Justus-Liebig-University Giessen Klinikstraße 33 Giessen35392 Germany The Department of Mechanical Engineering Mehran University of Engineering and Technology Jamshoro Sindh76062 Pakistan The Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China The Geschäftsführender Oberarzt Abteilung für Interventionelle und Diagnostische Neuroradiologie Universitätsklinikum der Technischen Universität München Ismaningerstr. 22 München81675 Germany icometrix Kolonel Be

Prior to the deep learning era, shape was commonly used to describe the objects. Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from numerous shape-related publications in premier vision conferences as well as the growing popularity of ShapeNet (about 51,300 models) and Princeton ModelNet (127,915 models). For the medical domain, we present a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instrument, called MedShapeNet, created to facilitate the translation of data-driven vision algorithms to medical applications and to adapt SOTA vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. As of today, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via a web interface and a Python application programming interface (API) and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Exemplary, we present use cases in the fields of classification of brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. In future, we will extend the data and improve the interfaces. The project pages are: https://***/ and https://***/Jianningli/*** Codes 68T01 © 2023, CC BY.

关键词： Large dataset

Graduate employment prediction with bias

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Guo, Teng Xia, Feng Zhen, Shihao Bai, Xiaomei Zhang, Dongyu Liu, Zitao Tang, Jiliang Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Dalian116620 China School of Science Engineering and Information Technology Federation University Australia BallaratVIC3353 Australia Computing Center Anshan Normal University Anshan114007 China TAL AI Lab TAL Education Group Beijing100080 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States

The failure of landing a job for college students could cause serious social consequences such as drunkenness and suicide. In addition to academic performance, unconscious biases can become one key obstacle for hunting jobs for graduating students. Thus, it is necessary to understand these unconscious biases so that we can help these students at an early stage with more personalized intervention. In this paper, we develop a framework, i.e., MAYA (Multi-mAjor emploYment stAtus) to predict students’ employment status while considering biases. The framework consists of four major components. Firstly, we solve the heterogeneity of student courses by embedding academic performance into a unified space. Then, we apply a generative adversarial network (GAN) to overcome the class imbalance problem. Thirdly, we adopt Long Short-Term Memory (LSTM) with a novel dropout mechanism to comprehensively capture sequential information among semesters. Finally, we design a bias-based regularization to capture the job market biases. We conduct extensive experiments on a large-scale educational dataset and the results demonstrate the effectiveness of our prediction framework. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

METTLE: a METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Xie, Xiaoyuan Zhang, Zhiyi Yueh Chen, Tsong Liu, Yang Poon, Pak-Lok Xu, Baowen School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC3122 Australia School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Engineering and Technology Central Queensland University Australia MelbourneVIC3000 Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Unsupervised machine learning is the training of an artificial intelligence system using information that is neither classified nor labeled, with a view to modeling the underlying structure or distribution in a dataset. Since unsupervised machine learning systems are widely used in many real-world applications, assessing the appropriateness of these systems and validating their implementations with respect to individual users’ requirements and specific application scenarios/contexts are indisputably two important tasks. Such assessment and validation tasks, however, are fairly challenging due to the absence of a priori knowledge of the data. In view of this challenge, we develop a METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems, abbreviated as METTLE. Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering the specific expectations and requirements of these systems from individual users’ perspectives. To support METTLE, we have further formulated 11 generic metamorphic relations (MRs), covering users’ generally expected characteristics that should be possessed by machine learning systems. To demonstrate the viability and effectiveness of METTLE, we have performed an experiment involving six commonly used clustering systems. Our experiment has shown that, guided by user-defined MR-based adequacy criteria, end users are able to assess, validate, and select appropriate clustering systems in accordance with their own specific needs. Our investigation has also yielded insightful understanding and interpretation of the behavior of the machine learning systems from an end-user software engineering’s perspective, rather than a designer’s or implementor’s perspective, who normally adopts a theoretical approach. Copyright © 2018, The Authors. All rights reserved.

关键词： software testing

PEPS++: Towards extreme-scale simulations of strongly correlated quantum many-particle models on sunway tainhulight

学校读者我要写书评

暂无评论

arXiv 2018年

作者： He, Lixin An, Hong Yang, Chao Wang, Fei Chen, Junshi Wang, Chao Liang, Weihao Dong, Shaojun Sun, Qiao Han, Wenting Liu, Wenyuan Han, Yongjian Yao, Wenjun CAS Key Lab of Quantum Information University of Science and Technology of China Hefei China School of Computer Science & Technology University of Science and Technology of China Hefei China CCSE & CAPT School of Mathematical Sciences NELVT Peking University Beijing China Institute of Software & State Key Laboratory of Computer Science CAS Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The study of strongly frustrated magnetic systems has drawn great attentions from both theoretical and experimental physics. Efficient simulations of these models are essential for understanding their exotic properties. Here we present PEPS++, a novel computational paradigm for simulating frustrated magnetic systems and other strongly correlated quantum many-body systems. PEPS++ can accurately solve these models at the extreme scale with low cost and high scalability on modern heterogeneous supercomputers. We implement PEPS++ on Sunway TaihuLight based on a carefully designed tensor computation library for manipulating high-rank tensors and optimize it by invoking various high-performance matrix and tensor operations. By solving a 2D strongly frustrated J1-J2 model with over ten million cores, PEPS++ demonstrates the capability of simulating strongly correlated quantum many-body problems at unprecedented scales with accuracy and time-to-solution far beyond the previous state of the art. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

Chinese poetry generation with a working memory model

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Yi, Xiaoyuan Sun, Maosong Li, Ruoyu Yang, Zonghan State Key Lab on Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China 6ESTATES PTE LTD Singapore

As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several neural models have been designed for this task. However, among lines of a whole poem, the coherence in meaning and topics still remains a big challenge. In this paper, inspired by the theoretical concept in cognitive psychology, we propose a novel Working Memory model for poetry generation. Different from previous methods, our model explicitly maintains topics and informative limited history in a neural memory. During the generation process, our model reads the most relevant parts from memory slots to generate the current line. After each line is generated, it writes the most salient parts of the previous line into memory slots. By dynamic manipulation of the memory, our model keeps a coherent information flow and learns to express each topic flexibly and naturally. We experiment on three different genres of Chinese poetry: quatrain, iambic and chinoiserie lyric. Both automatic and human evaluation results show that our model outperforms current state-of-the-art methods. Copyright © 2018, The Authors. All rights reserved.

关键词：