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Researchers face difficulties in finding relevant papers to their research interest as the number of scientific publication is rapidly increasing on the web. Scientific paper recommenders have emerged as a leading solution to help researchers by automatically suggesting relevant and useful publications. Several approaches have been proposed on improving recommender systems. However, most existing approaches depend on priori user profiles, and thus they cannot recommend papers to new user. Furthermore, the existing approaches utilize non-public contextual information, and thus it cannot adequately find similarities between papers due to copyright restrictions. Also, the existing approaches consider only single level paper-citation relation to identify similarities between papers. Considering the above challenges, this paper presents a collaborative filtering based recommendation approach for scientific papers that does not depend on priori user profiles and which utilizes only public contextual information. Using citation context, we utilized 2-level paper-citation relations to find hidden associations between papers. The rational underlying this approach is that, two papers are co-occurred with same cited paper(s) and two papers are co-occurring with same citing paper(s) are significantly similar to some extent. To evaluate the performance of the proposed approach, publicly available datasets are used to conduct extensive experiments. The experimental results demonstrate that the proposed approach has significantly outperforms the baseline approaches in terms of precision, recall, F1, mean average precision, and mean reciprocal rank, which are commonly used information retrieval metrics. The novelty of this study is that, with the proposed approach, researchers are able to find relevant and useful publications over the internet regardless of their previous research experiences and research area.

In this work, Schottky diodes based on amorphous indium-gallium-zinc-oxide (IGZO) were fabricated on cellulose microfiber paper substrate. Silver lines used as the Schottky barrier were printed, in parallel to thermally evaporated Cr/Au ohmic contact, using a dispense printer. The morphological and electrical characteristics of the devices are presented. The fabricated diodes exhibited rectification ratios ranging from 3.4 to 34.9 at ±1V with ON voltages that range from 1.1V to 1.4 V, for device lengths (Ag to Au distance) from 145 µm to 894 µm. The diodes were characterized in a temperature range between 25°C and 80°C. They showed a decrease of the ON current when increasing temperature, which is mainly attributed to the change of the cellulose microstructure. Indeed, an opposite of the ON current behavior was registered when the diode was realized on a polyimide substrate. The realized flexible paper-based diodes offer a potential promising choice for printed environmental-friendly electronics.

Paper-based strain sensors have attracted attention as a cost-effective and environmentally friendly approach to wearable electronic devices. This paper proposes a paper-based strain sensor created solely using hand-painting, which is a simple, equipment-free process utilized in green electronics. Commercial printing paper is coated with polydimethylsiloxane diluted with heptane, which provides moisture resistance and mechanical robustness while increasing the hydrophobicity of the surface. The electrodes and conduction channels are fabricated using a pencil for graphite deposition and a brush for the deposition of tin-doped indium oxide nanoparticles, respectively. With a gauge factor of 41.98 in tensile strain tests and 21.36 in compressive strain tests, our strain sensor exhibits a good sensitivity that is similar to that of previously reported paper-based sensors. We also demonstrate that our proposed strain sensor can be used as a weak motion detection device for humans and robots.

We often point to the relative increase in the amount and sophistication of evaluations of visualization systems versus the earliest days of the field as evidence that we are maturing as a field. I am not so convinced. In particular, I feel that evaluations of visualizations, as they are ordinarily performed in the field or asked for by reviewers, fail to tell us very much that is useful or transferable about visualization systems, regardless of the statistical rigor or ecological validity of the evaluation. Through a series of thought experiments, I show how our current conceptions of visualization evaluations can be incomplete, capricious, or useless for the goal of furthering the field, more in line with the “heroic age” of medical science than the rigorous evidence-based field we might aspire to be. I conclude by suggesting that our models for designing evaluations, and our priorities as a field, should be revisited.

The exchange of academic marks between HEIs (Higher Education Institutions) is mandatory in every student mobility programs (i.e. the EU Erasmus Program) but that process remains to present date with insufficient technological support and the absence of a comprehensive reference model that allows the integration of potential technological solutions for the exchange of academic data with existing Academic Information Systems seems to limit greatly the possibility of adopting solutions of this type referred to in the existing literature. This work addresses this issue, conducting an initial bibliographic review aimed at the identification of the fundamental requirements of such an architecture as well as explores some of the technologies that are showing potential for usage in the safe exchange of academic results between partner HEIs, with particular interest in blockchain technology applied in an educational context. info:eu-repo/semantics/publishedVersion

Objective: We propose a MATLAB-based tool to convert electrocardiography (ECG) waveforms from paper-based ECG records into digitized ECG signals that is vendor-agnostic. The tool is packaged as an open source standalone graphical user interface (GUI) based application. Methods and procedures: To reach this objective we: (1) preprocess the ECG records, which includes skew correction, background grid removal and linear filtering; (2) segment ECG signals using Connected Components Analysis (CCA); (3) implement Optical Character Recognition (OCR) for removal of overlapping ECG lead characters and for interfacing of patients’ demographic information with their research records or their electronic medical record (EMR). The ECG digitization results are validated through a reader study where clinically salient features, such as intervals of QRST complex, between the paper ECG records and the digitized ECG records are compared. Results: Comparison of clinically important features between the paper-based ECG records and the digitized ECG signals, reveals intra- and inter-observer correlations of 0.86–0.99 and 0.79–0.94, respectively. The kappa statistic was found to average at 0.86 and 0.72 for intra- and inter-observer correlations, respectively. Conclusion: The clinically salient features of the ECG waveforms such as the intervals of QRST complex, are preserved during the digitization procedure. Clinical and Healthcare Impact: This open-source digitization tool can be used as a research resource to digitize paper ECG records thereby enabling development of new prediction algorithms to risk stratify individuals with cardiovascular disease, and/or allow for development of ECG-based cardiovascular diagnoses relying upon automated digital algorithms.

International audience; The design of a dipole antenna printed on a paper substrate is presented in this paper. The antenna which integrates a compact balun is devoted for dual-band 2.4/5 GHz WLAN applications. The antenna is based on a double-side printed multilayer paper substrate and is fed with a coaxial cable for the testing. The simulated results of the whole structure are also presented in detail and compared with the measured performances.

To improve the accuracy of user implicit rating prediction, we combine the traditional latent factor model (LFM) and bidirectional gated recurrent unit neural network (BiGRU) model to propose a hybrid model that deeply mines the latent semantics in the unstructured content of the text and generates a more accurate rating matrix. First, we utilize the user’s historical behavior (favorites records) to build a user rating matrix and decompose the matrix to obtain the latent factor vectors of users and literature. We also apply the BERT model for word embedding of the research papers to obtain the sequence of word vectors. Then, we apply the BiGRU with the user attention mechanism to mine the research paper textual content and to generate the new literature latent feature vectors that are used to replace the original literature latent factor vectors decomposed from the rating matrix. Finally, a new rating matrix is generated to obtain users’ ratings of noninteractive research papers and to generate the recommendation list according to the user latent factor vector. We design experiments on the real datasets and verify that the research paper recommendation model is superior to traditional recommendation models in terms of precision, recall, F1-value, coverage, popularity and diversity.

Tight assembly of stator windings with no insulation paper damage is a manufacturing challenge. We evaluate different sets of parts according to the following parameters: magnet wire thickness, stator slot smoothness, length of the straight magnet wire after the slot end, and type and amount of insulation cap at the end of the slot. These parameters have discrete values with small differences between them. The damage criterion is the decrease of the insulation paper breakdown voltage after assembly/disassembly of parts, assembled in a small set of designed experiments. Parameter values, i.e., levels at individual experiments are set by an orthogonal experiment matrix. Repetition of each experiment provides statistical significance. Data analysis shows that the additive model alone is not sufficient due to the high correlation of the parameters’ influences. We extend the model to include interparameter influences, which we model by adding a virtual parameter. The extended additive model generates parameter values that do not degrade the insulation paper breakdown voltage within the manufacturing process. These values are verified by repetitions of the control experiment.

The modern HPC scientific software ecosystem is instrumental to the practice of science. However, software can only fulfill that role if it is readily usable. In this position paper, we discuss usability in the context of scientific software development, how usability engineering can be incorporated into current practice, and how software engineering research can help satisfy that objective.

Usability and the use of automated static analysis tools in the software development process have been an evolving subject of research in the last decades. Several studies shed light on issues like high false positive rates and low comprehensibility, which hinder tool adoption for even software engineers. Yet, the tools' perceived usefulness and ease of use play a much larger role when it comes to untrained software developers, as is usually the case in scientific software development. In this paper, we outline a multi-stage interview study to learn more about how scientists come to accept and use static analysis tools.

Replications are rare in visualization research, but if they were more common, it is not unreasonable to believe that they would show a similar rate of unreproducible results as in the psychological and social sciences. While a replication crisis in visualization research would be a helpful wake-up call, examining and correcting the underlying problems in many studies is ultimately more productive.In this paper, we survey the state of replication in visualization research. We examine six threats to the validity of studies in visualization and suggest ways to address them. Finally, we describe possible models for publishing replications that satisfy the novelty criterion that can keep replications from being accepted.

Machine learning (ML) components are increasingly adopted in many automated systems. Their ability to learn and work with novel input/incomplete knowledge and their generalization capabilities make them highly desirable solutions for complex problems. This has motivated the inclusion of ML techniques/components in products for many industrial domains including automotive systems. Such systems are safety-critical systems since their failure may cause death or injury to humans. Therefore, their safety must be ensured before they are used in their operational environment. However, existing safety standards and Verification and Validation (V&V) techniques do not properly address the special characteristics of ML-based components such as non-determinism, non-transparency, instability. This position paper presents the authors' view on the safety of automotive systems incorporating ML-based components, and it is intended to motivate and sketch a research agenda for extending a safety standard, namely ISO 26262, to address challenges posed by incorporating ML-based components in automotive systems.

There has been an increase in the interest in blockchain technologies. While the most popular application of this technology is in the cryptocurrency sector in the developed world, recent studies have shown that they hold promise toward deployment in many sectors such as digital contracts and healthcare in the developing world. This article discusses the various use cases of the blockchain technology, particularly from the point of view of developing countries in general and for India in particular.

Artificial Intelligence (AI), and digital technology in general, has the potential to transform healthcare in every setting, and even more in the developing world, where the current healthcare system simply cannot scale to the growing masses, often in more remote locations. Digital technology— mobile phones, remote sensing, and the Internet—can help improve quality of healthcare delivery, reduce cost of healthcare systems, and improve accessibility. However, in addition to AI, there is the need for an appropriate business model for solving healthcare problems. Digital technologies also have the potential to deliver preventative care, focusing on wellness, in addition to personalized disease treatment. In that context, mobile health platforms are more versatile because they can monitor events at a finer granularity and are often used by “healthy” individuals to track their daily fitness-centric activities. Further, molecular data is available at unprecedented levels and encompass different aspects of the “being”, be in genomic, epigenomic, proteomic, or metagenomic datasets, in other words offering a holistic view of the individual. This can then be integrated with fine-grained physiological signals obtained through wearables. Thus, there are volumes of datasets that need to be annotated, often in multiple formats, offering a multi-dimensional view of the healthy or diseased human. In fact, the human reference genome should have multiple representations based on the race, ethnicity, and the general well-being of the human. This calls for monitoring healthy people at scale, not just diseased people so we get a better understanding of the “ground truth” for the healthy human being. Overall, AI can therefore be thought of as augmented intelligence that can leverage both better observational and interventional capabilities for delivering precision healthcare and augmented monitoring capabilities to the masses at scale.

This Special Issue is assembled from a selection of highly-rated technical papers presented at the 4th IEEE Electron Devices Technology and Manufacturing Conference 2020, EDTM 2020. It took place during April 6-21, 2020 and signified the 1st of its kind as a virtual conference financially-sponsored by the Electron Devices Society (EDS). The original EDTM 2020 was planned as a full three-day event at the Hotel Equatorial , Penang, Malaysia from March 16 to 18, 2020. However, due to the COVID-19 pandemic, EDTM 2020 was held as a virtual event .

For nearly four decades 2-furfural (2-FAL) in transformer oil has been used as a chemical marker to indicate the ageing state of transformer paper insulation. Nevertheless, it has always been a challenge to define an acceptable limit for 2-FAL due to issues related to non-uniform temperature distribution in transformers, stability of 2-FAL in oil and its partitioning between liquid and solid insulation. Therefore, transformer users try to combine the laboratory findings, the operational experience and post-mortem analyses together for predicting ageing state of paper insulation through 2-FAL content in oil. Due to increase in the use of ester liquids in transformers, there is a need to investigate the ability to use 2-FAL as a paper ageing indicator for ester based insulation systems. Laboratory ageing studies have shown that 2-FAL is indeed generated in ester based insulation systems. However, there is a lack of understanding related to the partitioning of 2-FAL in ester based insulation systems. This paper studies the partitioning of 2-FAL in synthetic ester based insulation systems and compares to that in mineral oil based insulation systems with the focus on the effect of paper type and water content in paper. Under the investigated conditions the synthetic ester had higher partitioning coefficients (ratio between the 2-FAL content in oil and paper) than the mineral oil. Partitioning coefficients in both oil types decreased with the increase of the water content in paper. However, change in paper type did not significantly affect the partitioning coefficients in this study.

Improper Input Validation (IIV) is a software vulnerability that occurs when a system does not safely handle input data. Even though IIV is easy to detect and fix, it still commonly happens in practice. In this paper, we study to what extent developers can detect IIV and investigate underlying reasons. This knowledge is essential to better understand how to support developers in creating secure software systems. We conduct an online experiment with 146 participants, of which 105 report at least three years of professional software development experience. Our results show that the existence of a visible attack scenario facilitates the detection of IIV vulnerabilities and that a significant portion of developers who did not find the vulnerability initially could identify it when warned about its existence. Yet, a total of 60 participants could not detect the vulnerability even after the warning. Other factors, such as the frequency with which the participants perform code reviews, influence the detection of IIV. Preprint: https://arxiv.org/abs/2102.06251. Data and materials: https://doi.org/10.5281/zenodo.3996696.

Among all infections, urinary tract infections (UTI) are one of the most common. Nowadays the procedures to analyze urine and consequently detect UTI are often painful and time-consuming. Recent studies about microfluidic paper-based devices have developed the interest of researchers due their outstanding characteristics. In this paper is presented a novel design for a microfluidic paper-based device for screening and analysis of multiple biomarkers from urine samples on diapers. The device consists on a set of eight layers. It was designed based on the previous attempts to improve and overcome some problems detected as the continuous entrance of fluids, the possibility of contamination and the invalidity of results due to communication between different reagent pads. One approach was create a self-locking mechanism that closes the sample inlet in approximately four minutes solving the first two problems. Furthermore, is important that comfort is guaranteed, hence a device with a total thickness of 5,3 mm is presented. This device can keep the results for eight hours and can be used as a low-cost and more effective alternative than conventional methods being a strategy with potential for the diagnostic and analysis of biological samples in the future improving healthcare., This research is mainly supported by Norsk Regionalt Forskningsfond Oslofjordfondet Prosjekt: Touchsensor for enklere og raskere urinprøvetaking og analyse (proj. no: 234972). The support from Regionale Forksningfond Oslofjordfondet forprosjekt (proj. no. 249017, 255893 and 260586) and Forprosjekt – VRIBEDRIFT Vestfold (proj. no. 252173) is also acknowledged. We are also thankful to the funds from National Natural Science Foundation of China (grant no. 61550110253 and 61650410655), Chongqing Research Program of Basic Research and Frontier Technology (nos. cstc2016jcyjA2161, cstc2015jcyjA20023), Science and Technology Research Program of Chongqing Education Commission (no. KJ1600604) and Chongqing Innovation Team of Colleges and Universities (no. CXTDX201601025)., info:eu-repo/semantics/publishedVersion

This work presents an experimental study of second mode positive streamers propagating along mineral oilpaper interfaces. A point-plane arrangement immersed in mineral oil with the paper inclined 60 degrees to the plane electrode is used to create the liquid-solid interface. Kraft paper and a kraft fibril paper, made from cellulosic micro and nano fibrils, with higher density and lower surface roughness are used as the solid materials. High speed shadowgraphy and charge recordings are used to compare the propagation of second mode positive streamers along the mineral oil-kraft paper and mineral oil-kraft fibril paper. Streamers creeping along the mineral oil-kraft paper interface propagate mainly into the liquid, with one or two main filaments. In comparison, the streamers propagating along the kraft fibril paper show a strong reduction of the branching; these streamers consist of a single filament that propagates exactly on the solid surface. Streamers along the kraft fibril paper also have longer propagation time than for the case with kraft paper. Mutual electrostatic shielding between filaments is observed for the streamers creeping on the kraft paper. An electrostatic analysis of the influence of permittivity, density and surface roughness of the solid in the electrical properties of the streamer filaments is also performed.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

The papers in this special section were presented at the Optical Fiber Communications Conference (OFC) 2020, which took place in March 2020 in San Diego, CA.

In this paper the experimental results of a compact low-power rectenna in paper substrate, designed to operate in the Wi-Fi band, are presented. The complete prototype, based on an annular slot antenna and a single-diode rectifier, features a weight of 1.5 grams and shows an RF-to-dc conversion efficiency in the design band of about 40 % for a -10 dBm available input power, of about 28 % at -15 dBm, and in the range [10, 22] % at -20 dBm, corresponding to an output DC voltage in the order of 320, 240 and 60 mV respectively. Additionally, the rectenna features an efficiency higher then 7 % in the whole band 1.8-2.7 dBm for a power density estimated around 3 μW/cm2.

The new type of coronavirus COVID-19 virus was first detected in Wuhan-China. A COVID-19 certified patient is characterized by fever, fatigue, and dry cough. The coronavirus (COVID-19) epidemic is spreading worldwide. In this review paper, a database of X-ray, CT-Scan images from patients with common bacterial pneumonia, confirmed Covid-19 infection, and common cases, were used to automatically detect Coronavirus infection. The purpose of the study was to evaluate the effectiveness of COVID-19 acquisition. During the COVID-19 scenario, the number of infected cases rises in huge number globally. Due to this fact, a vital decision had been taken by medical experts and infected patients to adopt various medical facilities within a reasonable amount of time.

This paper is dedicated to the centenary of a founder of statistical antenna theory - Professor Yakov Shifrin who had been an organizer of the antenna science in the independent Ukraine since the early nineties until 2019. It outlines his life, education, participation in the World War II, scientific achievements, scientific-organizational and social activities, and human features.

Privacy of the individual data, especially in the Health data, is very sensitive and important. Privacy preserving Machine learning is emerging as one of the solutions for the security of data with the utility to create knowledge. In this paper, we have proposed a differential private artificial neural network (DP-ANN) and shows its application to predict the spread and the peak number of COVID-19 cases. We proposed a differential private artificial neural network (DP-ANN) in which laplacian noise has been introduced at activation function level and it has been compared with existing privacy ideas at error function and weights level of ANN. Results show that DP-ANN model with the private activation function produces the result similar to the base ANN model.

The publication of the 2020 Special Section of Invited Papers on Emerging Topics in the Power and Energy Society (PES) received broad interest from diverse readers of the PES community. We continued to publish the special section for another year. After a rigorous peer-review process, a total of 13 papers [A1]–[A13] were accepted for publication in this invited section of the IEEE Open Access Journal of Power and Energy (OAJPE) in 2021.

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time., 12 pages, 3 figures

Gamification is one of the trends in education that is best suited to the current learning environment. Putra BINGO is a transformative assessment tool that uses gamification techniques. This research starts by elaborating the concept of Putra BINGO and its implementation using traditional method with pen and paper. The feedbacks gathered from the implementation are analyzed and applied to develop a multiplayer educational online game using Unity Game Engine. This paper describes details of the development for its two components; the database and the digital online game. Putra Bingo is available on PC and Android devices. It required additional plugins such as Photon Networking to handle game communication between multiple players. This is crucial to accommodate its test case application as an alternative assessment for teamwork skills using Rubrics and questionnaires. Further research will be conducted to enhance its functionality and user experience after the initial development of the digital online version is completed.

International audience; This paper presents a miniaturized microstrip antenna on an air-filled paper substrate. The presence of air in the substrate accounts for low dielectric losses and a high dielectric thickness, resulting in a high radiation efficiency and a large bandwidth. In addition, the edge of the antenna is exploited to feed the antenna with a coplanar line that can be connected to a 50 coaxial probe. This original feeding technique permits the reduction of the frequency of resonance as the air gap increases. The antenna is further miniaturized with the insertion of a slot in the ground plane. As a result, a high degree of miniaturization can be obtained along with a high efficiency. The influence of the air gap and of the slot on the antenna's performances is investigated. Two prototypes with dimensions of 0.29 x 0.29 x 0.065 radiating at 2.45 GHz are fabricated with copper tape and silver ink and the antennas' performances are experimentally validated: a gain of 4.5 dBi and a front-to-back ratio of 9.5 dB are measured, corresponding to a radiation efficiency of 70%. Index Terms-Paper substrate, microstrip antenna, air gap, air-filled, miniaturization, light weight antenna, low cost.

This study utilizes citation analysis and automated topic analysis of papers published in International Conference on Agile Software Development (XP) from 2002 to 2018. We collected data from Scopus database, finding 789 XP papers. We performed topic and trend analysis with R/RStudio utilizing the text mining approach, and used MS Excel for the quantitative analysis of the data. The results show that the first five years of XP conference cover nearly 40% of papers published until now and almost 62% of the XP papers are cited at least once. Mining of XP conference paper titles and abstracts result in these hot research topics: “Coordination”, “Technical Debt”, “Teamwork”, “Startups” and “Agile Practices”, thus strongly focusing on practical issues. The results also highlight the most influential researchers and institutions. The approach applied in this study can be extended to other software engineering venues and applied to large-scale studies.

We investigate end-to-end optimized optical transmission systems based on feedforward or bidirectional recurrent neural networks (BRNN) and deep learning. In particular, we report the first experimental demonstration of a BRNN auto-encoder, highlighting the performance improvement achieved with recurrent processing for communication over dispersive nonlinear channels.

Visible light communication (VLC) is considered a breakthrough wireless communication technology that has been proven capable of achieving very high data rates. This is a key advantage in indoor communication scenarios, since the vast majority of wireless traffic is witnessed in indoor communications. In every wireless system, the trade-off between achievable throughput, transmit signal power and corresponding error rate performance is largely dependent upon the considered modulation format. This is also the case in VLC systems, which are typically characterized by stringent performance requirements. Motivated by this, in the present contribution we introduce the space shift keying (SSK) modulation scheme in the context of non-orthogonal-multiple-access (NOMA) communications, which have been shown to be a performance enhancer of indoor based VLC systems. Based on this, all network users in the considered set up receive the same superimposed signal of all NOMA users, which is transmitted from the activated transmitters corresponding to the multiplexed SSK users information. Based on this and assuming a unique maximum likelihood detection, we quantity the system performance in terms of the corresponding bit error rate (BER) performance at each receiver. This analysis leads to the development of useful insights of theoretical and practical interest, which are expected to be useful in the effective design, implementation and deployment of SSK in NOMA based VLC systems. acceptedVersion

This Special Section follows from a selection of highly-rated, high impact technical papers presented at the 11th International Conference on Computer-Aided Design for Thin Film Transistor Technologies (CAD-TFT) 2020. The CAD-TFT 2020 was held as a virtual conference on November 10th and 11th, 2020 from the operations center at Tianjin, China. The pre-conference virtual presentations on November 9th, 2020 included six tutorials from internationally recognized experts in their technical fields as well as six posters from China, India, and Canada.

Field-Programmable Gate Arrays (FPGAs) are becoming increasingly available via commercial cloud providers, which currently allocate devices on a per-user basis. As the underlying hardware is often underutilized, several proposals for multi-tenant use of FPGA resources have been brought forth, along with some initial work on security attacks in this setting. Simultaneously, high-end FPGAs are being produced with 2.5D integration of multiple distinct dies, called Super Logic Regions (SLRs), onto the same chip. Although one might expect that physical separation of logic onto separate dies could prevent multi-tenant attacks, this paper demonstrates for the first time that cross-SLR information leaks based on sensing voltage changes within the FPGA chip are possible, without physical access to or modification of the boards. The cross-SLR covert channel is characterized analytically and experimentally on five Xilinx Virtex UltraScale+ FPGAs, both locally and on the Amazon and Huawei clouds. Several configurations of the source transmitters and the sink receivers are tested, including their locations, types, and sizes. The power-based channel is shown to have a bandwidth upwards of 4.6 Mbps and accuracy of over 97.6%. Consequently, as physical separation of tenants onto separate dies (SLRs) is an insufficient countermeasure against information leaks, hardware-level architectural improvements are necessary to make secure multi-tenant FPGAs on shared clouds a reality.

Fiber nonlinearities from Kerr effect are considered as major constraints for enhancing the transmission capacity in current optical transmission systems. Digital nonlinearity compensation techniques such as digital backpropagation can perform well but require high computing resources. Machine learning can provide a low complexity capability especially for high-dimensional classification problems. Recently several supervised and unsupervised machine learning techniques have been investigated in the field of fiber nonlinearity mitigation. This paper offers a brief review of the principles, performance and complexity of these machine learning approaches in the application of nonlinearity mitigation.

This paper reviews the results of recent researches aimed to extend the standard-cell based digital design flow to analog building blocks, so that to enhance scalability, reconfigurability and portability across technology nodes and to reduce design effort, time-to-market and costs. In this framework, the application of the proposed fully digital design approach to a wake up oscillator and to a Digital-to-Analog Converter, which are two building blocks widely employed in IoT sensor nodes, is illustrated in detail.

This paper presents the design of a novel rectenna based on a tapered annular slot and a single-diode rectifier, optimized to operate in the Wi-Fi band and in presence of low input power (Pavs=-15 dBm). In order to obtain a compact layout a double-layer architecture has been considered, according to which the interior metal surface of the annular slot is employed as ground plane for the rectifying circuit placed on the other side of the substrate. As a result, a rectenna with an active area of only 40 × 33 mm2 and an efficiency included between 26.5 and 28% has been obtained. The antenna has been fabricated in paper substrate and tested in the anechoic chamber. Then, the rectifier has been designed and optimized within the Advanced Design System suite. Finally, the rectenna performance has been discussed and compared to the State of the Art (SoA).

J.A. Moral-Munoz holds a FPU scholarship (AP2012-1789) from the Spanish Ministry of Education., Citation classics offer an outlook on those papers that have attracted great and historical interest by a research community and that could be also considered the basis of the research field. A new approach, which is called H-Classics, has been developed to identify such highly cited papers. It is based on the H-index and is sensitive to both the own characteristics of the corresponding research discipline and its evolution. The present study provides a useful insight into the development of the intelligent transport systems research field, revealing those scientific actors (authors, countries, and institutions) that have made the biggest research contribution to its development., Spanish Government AP2012-1789

Dielectric response measurements are an important maintenance and diagnostic tool for large oil filled power transformers. Regardless of if the measurements being performed in the time domain or in the frequency domain, the developed tools rely on the dielectric response being linear with excitation voltage magnitude. This paper details work done in an initial investigation into the measurement of the non-linear dielectric response in oil-paper insulated power transformers using frequency domain measurements. Measurements were performed on two transformers, with non-linear response currents measured at the higher excitation voltage levels. It was found that non-linearity in the dielectric response will result in the generation of harmonics currents particularly 3rd and 5th harmonics. There will also be effects on the fundamental current magnitude due to the higher order dielectric susceptibilities. The higher order susceptibilities will thus be evident in the measured fundamental complex admittance of the dielectric.

Cyber Physical Systems (CPSs) refer to a new generation of embedded ICT systems (PCs, smartphones, sensors, actuators, etc.) that are interconnected and collaborating to provide users with a wide range of innovative applications and services. Many application domains, e.g., Emergency management, factories of the future, personalized healthcare, just to name a few, require the definition, design and development of systems able to carry out complex processes that coordinate the services offered by the CPS in the "physical" real world. The physical world, however, is not entirely predictable, and such processes must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this paper, we tackle the above issue and we propose an approach and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events.

International audience; Texture characterization of photographic papers is likely to provide scholars with valuable information regarding artistic practices. Currently, texture assessment remains mostly based on visual and manual inspections, implying long repetitive tasks prone to inter- and even intra-observer variability. Automated texture characterization and classification procedures are thus important tasks in historical studies of large databases of photographic papers, likely to provide quantitative and reproducible assessments of texture matches. Such procedures may, for instance, produce vital information on photographic prints of uncertain origins. The hyperbolic wavelet transform, because it relies on the use of different dilation factor along the horizontal and vertical axes, permits to construct robust and meaningful multiscale and anisotropic representation of textures. In the present contribution, we explore how unsupervised clustering strategies can be complemented both to assess the significance of extracted clusters and the strength of the contribution of each texture to its associated cluster. Graph based filterbank strategies are notably investigated with the aim to produce small size significant clusters. These tools are illustrated at work on a large database of about 2500 exposed and non exposed photographic papers carefully assembled and documented by the MoMA and P. Messier's foundation. Results are commented and interpreted.

Two fundamental tenants of scientific research are that it can be scrutinized and built-upon. Both require that the collected data and supporting materials be shared, so others can examine, reuse, and extend them. Assessing the accessibility of these components and the paper itself can serve as a proxy for the reliability, replicability, and applicability of a field’s research. In this paper, I describe the current state of openness in visualization research and provide suggestions for authors, reviewers, and editors to improve open practices in the field.A free copy of this paper, the collected data, and the source code are available at https://osf.io/qf9na/

This manuscript considers a secondary system sharing the spectrum with the primary system at the same time and on teh same frequency. We propose a cognitive overlay system in which the secondary system relays the primary signal and piggybacks its own data on it. Furthermore, the secondary system uses the channel coding based on the dirty paper coding (DPC). The result of the analysis shows that the proposed scheme allows the secondary system to communicate at the same speed as the primary system without any harm to it.

The fifth generation of mobile communications (5G) is expected to dramatically improve performance compared to preceding standards by offering very high bandwidths and low latencies. To provide this performance, heavy processing is required and must meet strong timing constraints. Reconfigurable computing, managing processing in software and exploiting reconfigurable hardware acceleration, is an innovative approach that should be considered for 5G for its capacity to combine high throughput and high flexibility. This paper presents a case study for Orthogonal Frequency Division Multiplexing (OFDM) computation reconfigurable offloading onto an Field Programmable Gate Array (FPGA). The implementation is based on Open Computing Language (OpenCL) that represents a versatile solution, as this language can be compiled for several architectures, provided that a Host+Accelerator structure is used. The objective of our study is to demonstrate that, by means of hardware offloading, the 5G architecture resources can reach high computational load, avoiding processing stalls and latency increase. Results show that around 15% of the software processing can be freed through hardware acceleration and reallocated to support other tasks.

Data centers are the infrastructure that underpins modern distributed service-oriented systems. They are complex systems-of-systems, with many interacting elements, that consume vast amounts of power. Demand for such facilities is growing rapidly, leading to significant global environmental impact. The data center industry has conducted much research into efficiency improvements, but this has mostly been at the physical infrastructure level. Research into software-based solutions for improving efficiency is greatly needed. However, most current research does not take a holistic view of the data center that considers virtual and physical infrastructures as well as business process. This is crucial if a solution is to be applied in a realistic setting. This paper describes the complex, system-of-systems nature of data centers, and discusses the service models used in the industry. We describe a holistic scheduling system that replaces the default scheduler in the Kubernetes container system, taking into account both software and hardware models. We discuss the initial results of deploying this scheme in a real data center, where power consumption reductions of 10-20% were observed. We show that by introducing hardware modelling into a software-based solution, an intelligent scheduler can make significant improvements in data center efficiency. We conclude by looking at some of the future work that needs to be performed in this area.

This paper presents an intensive overview of the characterization and modeling of advanced 28-nm hulk and FDSOI CMOS processes operating continuously from room down to deep cryogenic temperature.

Data storage in the Cloud became a very popular service. However, delegation of data management results in loss of control from user perspective, in particular regarding the real location where data are stored. Thus, data location verification in the Cloud is a challenging issue. Among the huge methods proposed to consider data location verification, this paper focuses on machine learning based methods, which use network Round Trip Times as main metric. In particular, it provides experimental results based on country-wide dataset collected through Grid'5000 platform. Results show the capacities of regression-based methods to support data location verification at specific accuracy depending on user requirements.

Source code analysis tools are designed to analyze code artifacts with different intents, which span from improving the quality and security of the software to easing refactoring and reverse engineering activities. However, most tools do not come with features to periodically schedule their analysis or to be executed on a battery of repositories, and lack support to combine their results with other analysis tools. Thus, researchers and practitioners are often forced to develop ad-hoc scripts to meet their needs. This comes at the risk of obtaining wrong results (because of the lack of testing) and of hindering replication by other research teams. In addition, the resulting scripts are often not meant to be customized nor designed for incrementality, scalability and extensibility. In this paper we present Graal, which empowers users with a customizable, scalable and incremental approach to conduct source code analysis and enables relating the obtained results with other software project data. Graal leverages on and extends the functionalities of GrimoireLab, a strong free software tool developed by Bitergia, a company devoted to offer commercial software development analytics, and part of the CHAOSS project of the Linux Foundation.