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An emerging alternative to conventional Augmented Reality (AR) glasses designs, Beaming displays promise slim AR glasses free from challenging design trade-offs, including battery-related limits or computational budget-related issues. These beaming displays remove active components such as batteries and electronics from AR glasses and move them to a projector that projects images to a user from a distance (1-2 meters), where users wear only passive optical eyepieces. However, earlier implementations of these displays delivered poor resolutions (7 cycles per degree) without any optical focus cues and were introduced with a bulky form-factor eyepiece (50 mm thick). This paper introduces a new milestone for beaming displays, which we call HoloBeam. In this new design, a custom holographic projector populates a micro-volume located at some distance (1-2 meters) with multiple planes of images. Users view magnified copies of these images from this small volume with the help of an eyepiece that is either a Holographic Optical Element (HOE) or a set of lenses. Our HoloBeam prototypes demonstrate the thinnest AR glasses to date with a submillimeter thickness (e.g., HOE film is only 120 um thick). In addition, HoloBeam prototypes demonstrate near retinal resolutions (24 cycles per degree) with a 70 degrees-wide field of view., Comment: 15 pages, 18 Figures, 1 Table, 1 Listing

The time-to-failure for oil-impregnated paper (OIP) insulation is governed by two primary aging mechanisms: electrical and thermal. The electrical life can be represented as an Inverse Power Law, where lifetime is inversely proportional to applied electric field. The process of thermal aging on the other hand is established by Arrhenius Law, which relates the rate of aging exponentially to temperature. Due to thermal aging, the structure of insulation is altered owing to chemical changes like oxidation, polymerization, and cellulose degradation. For life estimation of a service-aged high-pressure gas filled (HPGF) cables, electrical endurance tests are normally performed at controlled voltage levels to estimate the time to breakdown. However, it is equally necessary to investigate how thermal aging influence changes in the electrical life of insulation. Therefore, in this paper, firstly short-term ramped stress tests are carried out on elevated thermal aged OIP samples extracted from already field-aged HPGF to find a rough estimate of breakdown voltages at different temperatures. Then, long-term electro-thermal step stress tests are performed on the samples to establish a correlation of temperature on the electrical life of the OIP insulation. The long-term stress tests produce reliable breakdown statistics and Maximum Likelihood Estimation of Inverse Power Law fitted on 2-parameter Weibull distributed breakdown data indicate a reduction of model parameter, n from 13.61 to 7.38 with an increase in temperature from 45 to 75 °C and a constant shape factor, beta of 1.50. The dissipation factor, tandelta related to the aging also shows an increase with temperature across a wide frequency range and is inversely proportional to the breakdown voltage.

Inclusion and accessibility in visualization research have gained increasing attention in recent years. However, many challenges still remain to be solved on the road toward a more inclusive, shared-experience-driven visualization design and evaluation process. In this position paper, we discuss challenges and speculate about potential solutions, based on related work, our own research, as well as personal experiences. The goal of this paper is to start discussions on the role of accessibility and inclusion in visualization design and evaluation., Comment: 8 pages, IEEE VIS BELIV 2022 workshop, preprint

Memory corruption bugs continue to plague lowlevel systems software, generally written in unsafe programming languages. In order to detect and protect against such exploits, many pre- and post-deployment techniques exist. In this position paper, we propose and motivate the need for a hybrid approach for the protection against memory safety vulnerabilities, combining techniques that can identify the presence (and absence) of vulnerabilities pre-deployment with those that can detect and mitigate such vulnerabilities post-deployment. Our proposed hybrid approach involves three layers: hardware runtime protection provided by capability hardware, software runtime protection provided by compiler instrumentation, and static analysis provided by bounded model checking and symbolic execution. The key aspect of the proposed hybrid approach is that the protection offered is greater than the sum of its parts – the expense of postdeployment runtime checks is potentially reduced via information obtained during pre-deployment analysis. During pre-deployment analysis, static checking can be guided by runtime information.

International audience; This paper presents the design and characterisation of an antenna on a paper-based substrate. The antenna is designed on a nanocellulose foil optimised with a highly conductive ink based on silver nanoparticles. The electrical and dielectric properties of the materials were characterised prior to the antenna simulation. The antenna is designed to operate at the ISM 868 MHz frequency band and has a maximum gain of +1.7 dBi. Its development is part of the first step in the implementation of a flexible battery-free geolocation tag, remotely powered by UHF Energy sources in the European RFID spectrum between 865.6 MHz and 867.6 MHz. An accurate characterisation procedure was carried out and the results show good agreement with the simulations.

The deterioration of the insulation in the windings of power transformers affects their lifespan. A commercial insulated Continuously Transposed Conductor (CTC) was studied experimentally, numerically and analytically. The purpose was to understand the mechanisms governing the mechanical failure of the insulating paper, and to achieve a criterion for predicting failure under different conditions. Samples of that insulated CTC were extracted from a coil and aged at 150∘C for different durations inside vessels filled with naphthenic oil. Then the degree of polymerisation and tensile, compressive and shear mechanical properties of the insulation were measured/estimated. Aged insulated CTC samples were subjected to three-point bending tests, producing deformations compatible with a short circuit, and the fractures in the insulation were analysed. The bending test over a CTC sample was simulated by means of a FEM Program. The Norris failure model, applicable to a lamina, was adapted to the studied insulation materials. The predictions of that failure criterion agreed with experimental observations.

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.

The single-mode bosonic channel is addressed with classical interference in the modulation and with side information at the transmitter. This model can viewed as the quantum counterpart of the classical random-parameter Gaussian channel. Based on Costa's writing-on-dirty-paper result (1983), the effect of the channel parameter can be canceled even when the decoder has no side information, and regardless of the input power constraint. For both homodyne and heterodyne detection with a coherent-state protocol, the model reduces to a classical channel with either real or complex-valued Gaussian noise. Thereby, by applying Costa's dirty paper coding strategy, we observe that the effect of the classical interference can be canceled for those channels as well. Then, we consider the bosonic channel with joint detection, for which the classical results do not apply, and derive a dirty-paper coding lower bound. Furthermore, considering the special case of a pure-loss bosonic channel, we demonstrate that the optimal coefficient for dirty paper coding is not necessarily the MMSE estimator coefficient as in the classical setting.

With increasing demands on capacity and height- ened user density, it is imperative to propose approaches that exploit and utilise all possible degrees of freedom (DoF) of a MIMO system. This directly translates to increased channel estimation accuracy. With this motivation, in this paper, a novel MIMO antenna array system with a receiver of N antennas that includes a joint estimation of Direction-of-Arrival (DOA) and Direction-of-Departure (DOD) of the desired user in the presence of other multiple access users, is presented. This is achieved by using the concept of the “Manifold Extender” which increases the dimensionality of the observation space of the received vector- signals, and thus increases the DoF from N to N Next where Next is the extended dimension. The performance of the receiver is evaluated against the existing spatial-only and virtual receivers by computer simulation studies.

This paper is the first to provide a thorough system design overview along with the fusion methods selection criteria of a real-world cooperative autonomous driving system, named Infrastructure-Augmented Autonomous Driving or IAAD. We present an in-depth introduction of the IAAD hardware and software on both road-side and vehicle-side computing and communication platforms. We extensively characterize the IAAD system in the context of real-world deployment scenarios and observe that the network condition that fluctuates along the road is currently the main technical roadblock for cooperative autonomous driving. To address this challenge, we propose new fusion methods, dubbed "inter-frame fusion" and "planning fusion" to complement the current state-of-the-art "intra-frame fusion". We demonstrate that each fusion method has its own benefit and constraint.

Ultraviolet (UV) irradiation was used as a tool Kraft paper. Using measurements of the DP as a function of irradiation time, a theoretical model was used to predict the local value of DP in the surface layer of the paper. After characterization of their mechanical properties, selected samples were exposed to oil flows mimicking ONAN, OFAF and reclamation conditions. The presence of a surface layer of low D Pled to significant surface roughening under high rates of oil flow,but no significant erosion. The findings indicate that the DP value at the surface is more critical than the average (or measured) D Pin determining the extent of oil flow related damage.

Our understanding of COVID-19 pandemic epidemiology has many gaps, with many challenges arising on a global scale. This paper looks at the problem at a smaller geographical scale, the extent of the campus of a large organization. Equipped with an asymptomatic testing program and rough location data from the campus wireless network, we make the case that epidemiological models may be informed from this new source of data, which offers fidelity at the temporal resolution of seconds and spatial resolution of a Wi-Fi cell size, in particular for the tasks of pinpointing clusters of cases and contexts of infection transmission. We sketch the design of a system that fuses the two foregoing information streams and explain how the result can be incorporated into standard epidemiological models of communicable disease, both for better parameter estimation in elementary models, as well as for providing spatial inputs into more sophisticated models. We conclude with logistical and privacy considerations we have encountered in an associated ongoing study, to inform similar efforts at other organizations.

The use of vegetable oil (natural ester) in electrical devices like power transformers is increasing due to their high biodegradability and better safety. The lifespan of power trans- formers is mainly defined by cellulose insulation condition, which usually works together with dielectric oil as electrical insulation and also as mechanical winding protector and compactor. That is why the aim and results of this research shows us not only the dielectric parameters evolution, but also the relationship between the mechanical factors and the moisture content of thermal accelerated ageing processes, with commercial vegetable oil, of Kraft paper and Diamond Dotted Paper (DDP). These are two of the most common insulating materials in electric power transformers. In addition, the new tests have been done by a different method of paper ageing analysis. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement Nº 823969 - BIOTRAFO.

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 formulation of good academic paper titles in English is challenging for intermediate English authors (particularly students). This is because such authors are not aware of the type of titles that are generally in use. We aim to realize a support system for formulating more effective English titles for intermediate English and beginner authors. This study develops an extractive title generation system that formulates titles from keywords extracted from an abstract. Moreover, we realize a title evaluation model that can evaluate the appropriateness of paper titles. We train the model with titles of top-conference papers by using BERT. This paper describes the training data, implementation, and experimental results. The results show that our evaluation model can identify top-conference titles more effectively than intermediate English and beginner students., 6 pages, 3 figures, 4 tables, 2 algorithms

Image classification is a major application domain for conventional deep learning (DL). Quantum machine learning (QML) has the potential to revolutionize image classification. In any typical DL-based image classification, we use convolutional neural network (CNN) to extract features from the image and multi-layer perceptron network (MLP) to create the actual decision boundaries. On one hand, QML models can be useful in both of these tasks. Convolution with parameterized quantum circuits (Quanvolution) can extract rich features from the images. On the other hand, quantum neural network (QNN) models can create complex decision boundaries. Therefore, Quanvolution and QNN can be used to create an end-to-end QML model for image classification. Alternatively, we can extract image features separately using classical dimension reduction techniques such as, Principal Components Analysis (PCA) or Convolutional Autoencoder (CAE) and use the extracted features to train a QNN. We review two proposals on quantum-classical hybrid ML models for image classification namely, Quanvolutional Neural Network and dimension reduction using a classical algorithm followed by QNN. Particularly, we make a case for trainable filters in Quanvolution and CAE-based feature extraction for image datasets (instead of dimension reduction using linear transformations such as, PCA). We discuss various design choices, potential opportunities, and drawbacks of these models. We also release a Python-based framework to create and explore these hybrid models with a variety of design choices.

Reconfigurable intelligent surface (RIS)-assisted communications is one of the promising candidates for next generation wireless networks by controlling the propagation environment dynamically. In this study, a channel modeling strategy for RIS-assisted wireless networks is introduced in sub-6 GHz bands by considering both far-field and near-field behaviours in transmission. We also proposed an open-source physical channel simulator for sub-6 GHz bands where operating frequency, propagation environment, terminal locations, RIS location and size can be adjusted. It is demonstrated via extensive computer simulations that an improved achievable rate performance is obtained in the presence of RISs for both near-field and far-field conditions., NA

Multi-level modeling (MLM) as part of object-oriented modeling aims at fully utilizing the expressive power of multiple abstraction levels. While these levels where initially used to define domain-specific modeling languages, i.e. for linguistic purposes, the MLM community has long argued that there is much more to gain by tapping into ontological abstraction levels. While MLM is a rather specialized research field, there are now quite a number of different proposals. There is thus an opportunity to develop a uniform core of MLM that then possibly can become part of a standard and be taken up by the larger modeling community.

The amount of scholarly data has been increasing dramatically over the last years. For newcomers to a particular science domain (e.g., IR, physics, NLP) it is often difficult to spot larger trends and to position the latest research in the context of prior scientific achievements and breakthroughs. Similarly, researchers in the history of science are interested in tools that allow them to analyze and visualize changes in particular scientific domains. Temporal summarization and related methods should be then useful for making sense of large volumes of scientific discourse data aggregated over time. We demonstrate a novel approach to analyze the collections of research papers published over longer time periods to provide a high-level overview of important semantic changes that occurred over the progress of time. Our approach is based on comparing word semantic representations over time and aims to support users in a better understanding of large domain-focused archives of scholarly publications. As an example dataset we use the ACL Anthology Reference Corpus that spans from 1979 to 2015 and contains 22,878 scholarly articles., Comment: 4 pages, JCDL-2021

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

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.

Development of dependable multi-/many-core systems requires assurance that the system is operable in a range of conditions, subjected to both functional and non-functional requirements. To achieve this, tools need to be implemented that can enable exploration of design options and be able to detect deficiencies earlier to avoid costly system re-design. In this work we discuss the challenges of design of multi-core realtime systems with timing assurance and discuss what are the requirements for modelling, testing and analysis tools. Digital Twin-based predictive modelling and fast design space evaluation are studied that work toward addressing these challenges.

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.

The Plenary Session begins with welcome remarks and introduction from the Conference Chair, Kevin Zhang, followed by the Technical Program Chair, Makoto Ikeda, providing an overview of ISSCC 2021. The Plenary Sessions (1A and 1B) will feature four distinguished keynote speakers. In line with the Conference theme “Integrated Intelligence is the Future of Systems,” the plenary presentations will address important innovation trends that are paving the path forward in this emerging era of Integrated Intelligence. This year, with the virtual format, an Awards Ceremony video will be posted that recognizes major technical and professional accomplishments presented by the IEEE, Solid-State-Circuits Society (SSCS), and ISSCC.

In HVDC converter transformers, the potential DC component in electric stresses imposed on valve windings would unfavorably cause space charge accumulation inside oil-paper insulation. Insulation degradation and aging may have great effects on space charge behaviors in oil-paper insulation. This paper focuses on the effect of the low molecular weight (LMA) and the moisture on space charge phenomenon in oil impregnated-paper insulation. Pulsed electro-acoustic (PEA) technique was used in this study to measure space charge behaviors in 400 $\mu$ m thick, dry and moist single-layer paper sample impregnated in mineral oil prepared with different amounts of formic acid. The measurements were carried out at $30 ^ { \circ } \mathrm { C }$ under a field strength of 20 kV/mm. It was found that LMA greatly strengthens the homo charge accumulation. The combination of LMA and moisture results in even more active space charge accumulations. In dry samples, the existence of LMA causes slower charge decaying rate. However, the introduction of the moisture accelerates the charge dissipation.

This paper focuses on the potential and practice of alternative forms of assessment to the conventional written exam. Readers will be interested in issues such as why would a staff member want to replace the written exam and how can I be sure that the replacement is equally effective at capturing student attainment. The paper summarises a trial at the University of Sheffield where the written exam was replaced by a computer quiz which was marked automatically.

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.

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.

We consider the problem of communicating over a channel that randomly "tears" the message block into small pieces of different sizes and shuffles them. For the binary torn-paper channel with block length $n$ and pieces of length ${\rm Geometric}(p_n)$, we characterize the capacity as $C = e^{-\alpha}$, where $\alpha = \lim_{n\to\infty} p_n \log n$. Our results show that the case of ${\rm Geometric}(p_n)$-length fragments and the case of deterministic length-$(1/p_n)$ fragments are qualitatively different and, surprisingly, the capacity of the former is larger. Intuitively, this is due to the fact that, in the random fragments case, large fragments are sometimes observed, which boosts the capacity.

Distributed smart and connected community applications of the present and future will continuously sense perishable data on which they make decisions. We say the data are perishable when they represent information sensed at a past point in time, possibly at a distant part of the distributed application, and for which the ground truth could have since changed. This paper discusses actions the sensing source, the network, and the controller can take to make the best possible decisions given that we know only a delayed version of the truth. Some of these actions to preserve freshness of data are based on known real-time techniques such as Time Sensitive Networking. To these we add Information Theory applied on a message-by-message basis and cross-layer coordination between content and network handling. Both the unexpectedness (surprisal) and timeliness (freshness) properties of perishable information can be encoded using a Shannon information entropy framework. Examples of smart community systems of the future for which these concepts are relevant are given.

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.

The recognition of the development stage of creepage discharge of oil-paper insulation under AC-DC combined voltage is the basis of fault monitoring and diagnosis of converter transformers, and there are few related studies. In this paper, the AC-DC combined voltage with a ratio of 1:1 was used to study the development stage recognition method of creepage discharge of oil-paper insulation under the cylinder-plate electrode structure. Firstly, the pulse current method was used to collect the discharge signals in the process of creepage discharge development. Finally, based on the online sequential extreme learning machine (OS-ELM), the above characteristic parameters were used to recognize the development stage of oilpaper insulation creepage discharge. The research results show that when the size of the sample training set in the OS-ELM algorithm is closed to the number of hidden layer neurons, higher recognition accuracy can be obtained, and the type of activation function has less influence on it. Based on the OS-ELM algorithm, the developmental stage of the creepage discharge is recognized. The development process of the creepage discharge is recognized as four stages, which is the same as the trend of the characteristic parameters of the whole creepage discharge development process, and the recognition accuracy is 91.4%. The algorithm has fast rate, high accuracy and batch training data characteristics, which can be widely used in the field of online monitoring and evaluation of electrical equipment status.

The uneven utilisation of modes of transport has a big impact on traffic in transport pathway infrastrutures. For motor vehicles for instance, this situation explains rapid road deterioration and the large amounts of money invested in maintenance and development due to overuse. There are many approaches to managing this problem; however, the impact of individual users in infrastructural maintenance is mostly ignored. In this position paper, we hypothesise that important changes torwards a more efficient use of the transport network start with its users and their behavioural changes. To this end, we introduce our vision on how to employ data driven, intelligent agent-based modelling, incorporating human factors aspects, as a toolset to understand travellers and to stimulate behavioural changes. The aim is to achieve better balanced and integrated mobility usage within the transport network. The idea is explored with a few guided questions, and a methodology is proposed. We employ 1) cognitive work analysis to investigate the reasons for travellers' mode choice; 2) computational intelligence to extract and represent knowledge from related datasets; 3) agent-based modelling to represent the real-world and to observe both individual and emergent behaviours. Future directions to adapt our methodology to alternative smart mobility projects are also discussed.

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.

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.

In this work, a large area, low cost and flexible polymer/paper-based double touch mode capacitive pressure sensor is demonstrated. Garage fabrication processes are used which only require cutting, taping and assembly of aluminum (Al) coated polyimide (PI) foil, PI tape and double-sided scotch tape. The presented pressure sensor operates in different pressure regions i.e. normal (0 to 7.5 kPa), transition (7.5 to 14.24 kPa), linear (14.24 to 54.9 kPa) and saturation (above 54.9 kPa). The advantages of the demonstrated double touch mode capacitive pressure sensors are low temperature drift, long linear range, high pressure sensitivity, precise pressure measurement and large die area. The linear output along with a high sensitivity range (14.24 to 54.9 kPa pressure range) of the sensor are utilized to wirelessly control the movement of a robotic arm with precise rotation and tilt movement capabilities.

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.

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

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

Literature analysis facilitates researchers better understanding the development of science and technology. The conventional literature analysis focuses on the topics, authors, abstracts, keywords, references, etc., and rarely pays attention to the content of papers. In the field of machine learning, the involved methods (M) and datasets (D) are key information in papers. The extraction and mining of M and D are useful for discipline analysis and algorithm recommendation. In this paper, we propose a novel entity recognition model, called MDER, and constructe datasets from the papers of the PAKDD conferences (2009-2019). Some preliminary experiments are conducted to assess the extraction performance and the mining results are visualized.

This paper considers a cell-free massive MIMO system with multiple-antenna access points and single-antenna users. The APs use conjugate beamforming to beamform the data to all users in the network. Total energy efficiency maximization is investigated. This optimization problem is nonconvex and thus difficult to solve. Existing solutions are based on second-order optimization methods in connection with convex approximations. These methods have been shown to perform very well but their complexity does not scale favorably with the network size. To tackle this issue, in this paper we propose to use a first-order method for nonconvex programming to our energy efficiency problem. Compared to the second-order methods, the proposed method achieves the same performance, while its run time is much faster. Thus, it is considered as a feasible solution for resource allocation in cell-free massive MIMO systems.

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.