Proceedings Of IEEE Conference Research Articles

Why IEEE Xplore Matters for Research Trend Analysis in the Energy Sector

The paper aims to briefly compare and analyze the results of queries to IEEE Xplore and the leading abstract databases Scopus and Web of Science to identify research trends. Some errors were revealed in the Author Keywords in Web of Science. Therefore, a more detailed analysis that involved comparing various types of key terms was made only for IEEE Xplore and Scopus platforms. The study employed IEEE Access journal metadata as indexed on both platforms. Sample matching for IEEE Xplore and Scopus was achieved by comparing DOI. The IEEE Xplore metadata contains more key term types, which provides an advantage in analyzing research trends. Using NSPEC Controlled Terms from expert-compiled vocabulary provides more stable data, which gives an advantage when considering the change of terms over time. Apriori, an algorithm for finding association rules, was used to compare the co-occurrence of the terms for a more detailed description of sample subjects on both platforms. VOSviewer was used to analyze trends in scientific research based on IEEE Xplore data. The 2011-2021 ten-year period was divided into two sub-intervals for comparing the occurrence of Author Keywords, IEEE Terms, and NSPEC Controlled Terms. Bibliometric data of the IEEE conference proceedings was used to illustrate the importance of context in estimating the growth rate of publishing activity on a topic of interest.

Detection of 3D face masks with thermal infrared imaging and deep learning techniques

Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef

Learning 3D Shape Completion Under Weak Supervision

We address the problem of 3D shape completion from sparse and noisy point clouds, a fundamental problem in computer vision and robotics. Recent approaches are either data-driven or learning-based: Data-driven approaches rely on a shape model whose parameters are optimized to fit the observations; Learning-based approaches, in contrast, avoid the expensive optimization step by learning to directly predict complete shapes from incomplete observations in a fully-supervised setting. However, full supervision is often not available in practice. In this work, we propose a weakly-supervised learning-based approach to 3D shape completion which neither requires slow optimization nor direct supervision. While we also learn a shape prior on synthetic data, we amortize, i.e., learn, maximum likelihood fitting using deep neural networks resulting in efficient shape completion without sacrificing accuracy. On synthetic benchmarks based on ShapeNet (Chang et al. Shapenet: an information-rich 3d model repository, 2015. arXiv:1512.03012) and ModelNet (Wu et al., in: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2015) as well as on real robotics data from KITTI (Geiger et al., in: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2012) and Kinect (Yang et al., 3d object dense reconstruction from a single depth view, 2018. arXiv:1802.00411), we demonstrate that the proposed amortized maximum likelihood approach is able to compete with the fully supervised baseline of Dai et al. (in: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), 2017) and outperforms the data-driven approach of Engelmann et al. (in: Proceedings of the German conference on pattern recognition (GCPR), 2016), while requiring less supervision and being significantly faster.

Knowledge Machines: Digital Transformations of the Sciences and Humanities by Eric T. Meyer, Ralph Schroeder

Reviewed by: Knowledge Machines: Digital Transformations of the Sciences and Humanities by Eric T. Meyer, Ralph Schroeder Patricia Galloway (bio) Knowledge Machines: Digital Transformations of the Sciences and Humanities. By Eric T. Meyer and Ralph Schroeder. Cambridge, MA: MIT Press, 2015. Pp. 224. $40. This volume was written by two members of the Oxford Internet Institute, both of whom have been involved with internet-enabled research projects for more than ten years. Their interest here is in defining and exemplifying what they refer to as e-research: “the use of digital tools and data for the distributed and collaborative production of knowledge” (p. 4). Eric Meyer and Ralph Schroeder argue that this new landscape is transforming all disciplines in terms of research styles, discipline configurations, collaborations, participants (academia, governments, and the private sector may collaborate and projects may encourage crowd-sourced contributions), and available tools (including the Grid of distributed computing resources and libraries of data for sharing and reuse). There are findings offered here, however, that demonstrate that the humanities do not seem to be as interested in e-research as the sciences. But the tool of choice for the creation of a sociology of e-research is the social sciences, reinforced by the realist approaches of Ian Hacking and Harry Collins and stripped of constructionist theories in favor of sociotechnical interaction network research, using, almost entirely, digital methods like bibliometrics and webometrics, applying e-research to e-research. The chapter that outlines this part of the work stems from a project in which large databases were used to detect the growth of e-research, first via funding trends throughout the developed world and then via publications on the topic of e-research as represented in the Scopus database. The poor coverage in this database of humanities publication venues is outshouted by complaints that humanists adhere to their own citation and publication styles (just as computer science and physics scholars have done). Perhaps because of this data bias this research suggests that computing and engineering have not only been central to e-research but have been driving it. It is not clear that this is simply because of a revolution in knowledge based entirely on computation. To some it might seem obvious that a huge effort is being made to justify governmental investments made in the grid of supercomputing centers (see the list of leading institutions in table 3.4) and to occupy the large numbers of graduate students who have begun flocking into European and North American computer science programs and filling up the rosters of the Association for Computing Machinery and IEEE conference proceedings. The authors point to this behavior but fail to see in it the same kind of disciplinary loyalty that is deplored for humanists. The authors make their argument for the transformative features of this new kind of research using an interesting group of case studies in [End Page 1045] which they and their students have participated. One chapter stresses collaborative computation of varied communities through the connection of multiple machines and datasets in the Swiss BioGRID; the participation of academic and citizen astronomers through the crowd-sourced Galaxy Zoo; and literary analysis by both humanities academics and fans through a wiki developed around the works of Thomas Pynchon. Another chapter details how distributed data has been pulled together for online sharing through the SPLASH project to track individual humpback whales on a global scale; the GAIN project that accumulates “psychiatric genetics” data on blood, phenotypes, and genomes to which contributing scientists would have access for research on bipolar disorder; and the Swedish National Data Service’s accumulation of longitudinal data on many aspects of the Swedish population. This chapter provides the opportunity to discuss the problematic issue of privacy that may stand in the way of sharing data on human individuals. This book is a worthwhile read even if—as the authors fully admit—it is a snapshot daring to make a prediction before all the data are in. It is especially interesting as an expression of a view from a particular perspective, which blames archivists (stereotypically described as denizens of “dusty archives”) for failing to steer researchers to digital data and argues...

MINING CONFERENCE PROCEEDINGS FOR CORPORATE TECHNOLOGY KNOWLEDGE MANAGEMENT

An organization's knowledge gained through technical conference attendance is generally isolated to the individual(s) attending the event. The aggregate corporate knowledge is extremely limited, unless the organization institutes a process to document and transfer that knowledge to the organization. Even if such a process exists, the knowledge gains are limited to the experiences and communication skills of the individuals attending the conference. Many conference proceedings are now published and provided to attendees in electronic format, such as on CD-ROM and/or published on the internet, such as IEEE conference proceedings listed at . These proceedings provide a rich repository that can be mined. Paper abstract compilations reflect "hot topics," as defined by the researchers in the field, and delineate the technical approaches being applied. R&D profiling can more fully exploit recorded conference proceedings' research to enhance corporate knowledge. This paper illustrates the potential in profiling conference proceedings through use of WebQL information retrieval and TechOasis (VantagePoint) text mining software. It shows how tracking research patterns and changes over a sequence of conferences can illuminate R&D trends, map dominant issues, and spotlight key research organizations.

Enhancing Access to IEEE Conference Proceedings

SUMMARY The IEEE Xplore online delivery system provides researchers at science and engineering libraries with the abstracts and full text PDF files of IEEE conference proceedings. The University of Akron recently added full text PDF hotlinks to the print records of both serial and monographic IEEE conference proceedings, permitting the searching, viewing, printing, and downloading of individual articles. These changes have resulted in significantly greater use of the proceedings.

First Call for Papers: International IEEE Conference on the Business of Electronic Product Reliability and Liability

First Call for Papers: International IEEE Conference on the Business of Electronic Product Reliability and Liability, January 13–14, 2003 City University of Hong Kong Hong Kong, China; January 15–17, 2003 Shenzhen, ChinaYou are cordially invited to submit papers to an International IEEE Conference on the Business of Electronic Product Reliability and Liability. The conference will be held in Hong Kong and Shenzhen, China (transportation to Shenzhen will be provided). The purpose of the Conference is to present the latest advancements in electronic product reliability and liability, and to discuss critical international issues as they affect the global technical and business community. The goal is to help electronics companies design and manufacture cost-effective reliable products, and encourage efficient supply-chain management. We currently have 15 industry experts who will be presenting papers on reliability and liability. Papers are now invited from other industry participants as well as researchers from academic and government organizations on the following topics: An electronic form of a one-page, 300-word abstract with name, address, phone and fax numbers, and e-mail address must be submitted to the technical committee by e-mail at wywong@ee.cityu.edu.hk on/before August 15, 2002. Instructions for preparing the full paper will be sent to authors whose abstracts are accepted after review. Accepted papers will be published in a formal IEEE Conference Proceedings with an ISBN number. Participants will be required to register for the conference upon notification of acceptance of their full papers. August 15, 2002 Abstract SubmissionSeptember 15, 2002 Abstract AcceptanceNovember 15, 2002 Full Paper DueMs. Angie WongSecretariat of International IEEE ConferenceEPA CenterDepartment of Electronic EngineeringCity University of Hong Kong83 Tat Chee Avenue, Hong KongTel: (852) 2788 7379Fax: (852) 2788 7579e-mail: wywong@ee.cityu.edu.

First IEEE symposium on biomedical imaging.

First IEEE symposium on biomedical imaging.

IEEE conference proceedings take you to the frontier in electronics and computing...[advertisement

IEEE conference proceedings take you to the frontier in electronics and computing...[advertisement

IEEE conference proceedings take you to the frontier in electronics and computing...[advertisement

IEEE conference proceedings take you to the frontier in electronics and computing...[advertisement