Combining Citation Network Information and Text Similarity for Research Article Recommender Systems

Researchers often need to gather a comprehensive set of papers relevant to a focused topic, but this is often difficult and time-consuming using existing search methods. For example, keyword searching suffers from difficulties with synonyms and multiple meanings. While some automated research-paper recommender systems exist, these typically depend on either a researcher’s entire library or just a single paper, resulting in either a quite broad or a quite narrow search. With these issues in mind, we built a new research-paper recommender system that utilizes both citation information and textual similarity of abstracts to provide a highly focused set of relevant results. The input to this system is a set of one or more related papers, and our system searches for papers that are closely related to the entire set. This framework helps researchers gather a set of papers that are closely related to a particular topic of interest, and allows control over which cross-section of the literature is located. We show the effectiveness of this recommender system by using it to recreate the references of review papers. We also show its utility as a general similarity metric between scientific articles by performing unsupervised clustering on sets of scientific articles. We release an implementation, ExCiteSearch (bitbucket.org/mmmontemore/excitesearch), to allow researchers to apply this framework to locate relevant scientific articles.

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IEEE Author Portal Saves IEEE Access Authors Time and Effort

Submitting your article to IEEE Access is now easier than ever. In a few short steps and without having to copy and paste or retype entire sections of your manuscript, what used to take a few hours can now be accomplished in as little as 15-20 minutes for most users. Sign in to the IEEE Author Portal using your IEEE Account. Upload your article, review what’s been captured to confirm the details, and submit it to IEEE Access for peer review. 

Creating an IEEE Account is easy if you happen to need one. Your IEEE Account will also be used for all production-related processes, creating a more streamlined author experience from the point of peer review all the way through to the publication of your article in IEEE Xplore. Author login credentials on any other type of peer review system (ScholarOne, PaperPlaza, PaperCept, etc.) will not grant you access to the IEEE Author Portal; please use your IEEE Account.

Start a new submission to IEEE Access.

Editors’ Top Article Selections of 2021

Editors’ Top Selections – IEEE Access Articles Published in 2021

Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning

Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb

This paper presents a novel architecture for large intelligent surfaces by leveraging compressive sensing and deep learning tools. The architecture keeps the majority of its reflection matrix elements passive while nearly eliminating the training overhead required for the passive elements.

Read the full article on IEEE Xplore.

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High-Power and High-Responsivity Avalanche Photodiodes for Self-Heterodyne FMCW Lidar System Applications

Zohauddin Ahmad, Yan-Min Liao, Sheng-I Kuo, You-Chia Chang, Rui-Lin Chao, Naseem, Yi-Shan Lee, Yung-Jr Hung, Huang-Ming Chen, Jyehong Chen, Jiun-In Guo, Jin-Wei Shi

A novel top-illuminated InGaAs-based avalanche photodiode with high-power and high-responsivity is proposed here for multiple benefits in self-heterodyne frequency-modulated continuous wave Lidar system applications.

Read the full article on IEEE Xplore.

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Collision Avoidance in Pedestrian-Rich Environments With Deep Reinforcement Learning

Michael Everett, Yu Fan Chen, Jonathan P. How

In an effort to make the navigation of robots collision-free, a deep reinforcement model is effectively employed, which handles a potentially large and time-varying number of nearby, decision-making agents with a single learned policy.

Read the full article on IEEE Xplore.

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Meandering Pattern 433 MHz Antennas for Ingestible Capsules

Michael J. Christoe, Natthaporn Phaoseree, Jialuo Han, Aron Michael, Shaghik Atakaramians, Kourosh Kalantar-Zadeh

An optimum design of a miniature antenna that gives desired signal transmission across extreme conditions of in-body dielectric environment is presented in this article.

Read the full article on IEEE Xplore.

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Highly Sensitive Reflective-Mode Phase-Variation Permittivity Sensor Based on a Coplanar Waveguide Terminated With an Open Complementary Split Ring Resonator (OCSRR)

Lijuan Su, Jonathan Munoz-Enano, Paris Velez, Marta Gil-Barba, Pau Casacuberta, Ferran Martin

Reflective-mode phase-variation microwave sensors devoted to material characterization, where the sensing element is an electrically small planar resonator, are presented in this article.

Read the full article on IEEE Xplore.

 

The Internet of Federated Things (IoFT)

The Internet of Things (IoT) is on the verge of a major paradigm shift. In the IoT system of the future, IoFT, the “cloud” will be substituted by the “crowd” where model training is brought to the edge, allowing IoT devices to collaboratively extract knowledge and build smart analytics/models while keeping their personal data stored locally. This paradigm shift was set into motion by the tremendous increase in computational power on IoT devices and the recent advances in decentralized and privacy-preserving model training, coined as federated learning (FL). This article provides a vision for IoFT and a systematic overview of current efforts towards realizing this vision. Specifically, we first introduce the defining characteristics of IoFT and discuss FL data-driven approaches, opportunities, and challenges that allow decentralized inference within three dimensions: (i) a global model that maximizes utility across all IoT devices, (ii) a personalized model that borrows strengths across all devices yet retains its own model, (iii) a meta-learning model that quickly adapts to new devices or learning tasks. We end by describing the vision and challenges of IoFT in reshaping different industries through the lens of domain experts. Those industries include manufacturing, transportation, energy, healthcare, quality & reliability, business, and computing.

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Optimal Sizing of CPP-GMR Read Sensors for Magnetic Recording Densities of 1–4 Tb/in²

Several studies have confirmed that current-perpendicular-to-the-plane giant magnetoresistance (CPP-GMR) technology is appropriate for next-generation read sensors for ultrahigh areal densities (ADs) of data storage applications. Since the physical dimension of the read sensor is a crucial factor for developing the reader to overcome its limitations, this paper proposes an optimal sizing prediction of the CPP-GMR read heads for ADs of 1-4 Tb/in 2 . Micromagnetic modelling was performed in the simulations. The appropriate length of the stripe height (SH) and the read width (RW) of the readers was estimated based on a consideration of sensor outputs including the readback signal, asymmetry parameter, dibit response and power spectral density (PSD) profile. It was found that a variation of SH and RW lengths had an influential impact on the readback signal waveform. Those affectations were further characterized through the echoes of dibit response showing that shortening the SH length or increasing the RW length could improve the resolution and reduce the distortion occurring in the readback signal. Moreover, the PSD profile indicated that the reader operation became more stable at shorter SH lengths or longer RW lengths. The head response spectrum was also examined. In addition, the magnitude of the bias current was studied in relation to the head response. Lastly, the optimal physical dimension (SH × RW) of the CPP-GMR readers for ADs of 1-4 Tb/in 2 was predicted to be ( 40×48 ) nm, ( 28×29 ) nm, ( 25×26 ) nm and ( 19×20 ) nm, respectively. The results can be utilized to design the CPP-GMR sensors at ultrahigh magnetic recording capacities.

*Published in the IEEE Magnetics Society Section within IEEE Access.

View this article on IEEE Xplore

 

Investigation and Analysis of Novel Skewing in a 140 kW Traction Motor of Railway Cars That Accommodate Limited Inverter Switching Frequency and Totally Enclosed Cooling System

This study facilitated the improvement of no-load back electromotive force (back-EMF) wave form, total harmonic distortion (THD) of back-EMF, and torque ripple using a novel skew angle formula, considering the specific order of a no-load THD. In real usage environments, it is taken into consideration for the fully enclosed cooling system and limited inverter switching frequency of urban railway car traction motors. Since the most railway car traction motors use high-withstand voltage rectangular wires in slot-open structure, a no-load back EMF waveform includes large space slot harmonics, which should be smaller as possible. For 6-step control, the no-load back EMF waveform is important because switching for motor control is performed once after the rotor position is determined. To improve the no-load back EMF waveform and THD, two-dimensional and three-dimensional finite element analysis (FEA) were performed using a novel skew angle formula considering specific harmonic order reduction, while the fundamental amplitude was minimally reduced. A prototype with the novel skew was fabricated and verified. In addition, it was designed by calculating a low current density for a fully enclosed cooling system. A temperature saturation experiment was also performed, and successfully verified. Therefore, we suggest that the no-load back EMF characteristics and torque ripple are improved by applying the novel skew angle instead of a traditional skew angle.

*Published in the IEEE Magnetics Society Section within IEEE Access.

View this article on IEEE Xplore

 

IEEE Magnetics Society Co-Sponsoring the 2022 Joint MMM-Intermag Conference

The IEEE Magnetics Society, which has a permanent section within IEEE Access, will be co-sponsoring the Joint MMM-Intermag Conference in New Orleans, LA, from January 10-14, 2022 with AIP Publishing LLC.  

The 15th Joint MMM-INTERMAG Conference (2022 Joint) is an opportunity for members of the international scientific and engineering communities interested in recent developments in fundamental and applied magnetism to attend and contribute to the technical sessions. The conference will offer both in-person and prerecorded on-demand content.

The technical program will include invited and contributed papers in oral and poster sessions, invited symposia, a plenary session, and an evening session, with about 1500 presentations overall. Listed below are just a few of the Special Events and Sessions occurring during the conference: 

– Tutorial: Quantum  Magnonics
– Special Session: Current Trends in Magnetism,
– Women in Magnetism Event
– Plenary and IEEE Awards Ceremony
– Writing Workshop

 

To learn more about attending or participating in this conference, please visit the conference website.

IEEE Access now offers Paperpal Preflight

IEEE Access now offers Paperpal Preflight as a preparatory tool. Running your manuscript through Paperpal Preflight prior to submission to IEEE Access allows you to receive instant feedback on common errors and omissions in language and technical aspects of your manuscript, to help you improve your manuscript prior to submission. Check your manuscript on Paperpal Preflight by clicking here.

IEEE Access Hosts Permanent Society/Council Sections

Did you know that IEEE Access hosts multiple, permanent Society/Council Sections? These sections are collections of articles that focus on an IEEE Society/Council’s fields of interest, grouped together on IEEE Xplore. Articles submitted to these sections are managed by topically focused Editors from that Society/Council, and undergo high-quality, rigorous peer review in only 4 to 6 weeks.

Publishing open access gives your research maximum visibility, and with the expedited peer review process of IEEE Access, you can share your research with the world faster. View all of the Society/Council Sections that IEEE Access hosts by clicking here.

 

Five IEEE Access Editorial Board Members Recognized as Highly Cited Researchers by Web of Science™

We are proud to announce that 5 of our Editorial Board Members have been recognized as Highly Cited Researchers for 2020 by the Web of Science™. This prestigious acknowledgement recognizes true pioneers within their respective fields who have produced multiple highly-cited papers that rank in the top 1% by citations for field and year in the Web of Science™.

Congratulations to the IEEE Access Editorial Board Members Josep M. Guerrero, Okyay Kaynak, Victor Leung, Michael G. Pecht, and Mugen Peng for this excellent accomplishment.  To learn more about the Highly Cited Researchers 2020 please click here.