Reviewerly/exHarmony huggingface dataset:
KART: Privacy Leakage Framework of Language Models Pre-trained with Clinical Records

Nowadays, mainstream natural language pro-cessing (NLP) is empowered by pre-trained language models. In the biomedical domain, only models pre-trained with anonymized data have been published. This policy is acceptable, but there are two questions: Can the privacy policy of language models be different from that of data? What happens if private language models are accidentally made public? We empirically evaluated the privacy risk of language models, using several BERT models pre-trained with MIMIC-III corpus in different data anonymity and corpus sizes. We simulated model inversion attacks to obtain the clinical information of target individuals, whose full names are already known to attackers. The BERT models were probably low-risk because the Top-100 accuracy of each attack was far below expected by chance. Moreover, most privacy leakage situations have several common primary factors; therefore, we formalized various privacy leakage scenarios under a universal novel framework named Knowledge, Anonymization, Resource, and Target (KART) framework. The KART framework helps parameterize complex privacy leakage scenarios and simplifies the comprehensive evaluation. Since the concept of the KART framework is domain agnostic, it can contribute to the establishment of privacy guidelines of language models beyond the biomedical domain.
Interdependencies of Gender and Race in Contextualized Word Embeddings

Recent years have seen a surge in research on the biases in word embeddings with respect to gender and, to a lesser extent, race. Few of these studies, however, have given attention to the critical intersection of race and gender. In this case study, we analyze the dimensions of gender and race in contextualized word embeddings of given names, taken from BERT, and investigate the nature and nuance of their interaction. We find that these demographic axes, though typically treated as physically and conceptually separate, are in fact interdependent and thus inadvisable to consider in isolation. Further, we show that demographic dimensions predicated on default settings in language, such as in pronouns, may risk rendering groups with multiple marginalized identities invisible. We conclude by discussing the importance and implications of intersectionality for future studies on bias and debiasing in NLP.
Current Trends in Digital Education Reform

The article analyzes current trends in the digital transformation of education in order to train personnel for the digital economy. The article considers the impact of digitalization on modern school and University education and analyzes its positive and negative aspects for the modern educational environment. It is shown how the rapid change in teaching methods and forms during the COVID-19 pandemic outbreak led to the accumulation of experience in virtual contacts between teachers and students, as well as between students. The advantages of personification and personalization of the educational process are presented. The idea of creating educational clusters as a new model for integrating education and business is analyzed. Attention is paid to the features of training of generations Z and Alpha.
Customisable assistive plans as dynamic composition of services with normed-QoS

Ambient assisted living solutions (AAL) aim to improve the quality of life, especially of elderly with cognitive and physical disabilities, by providing assistive tasks such as medication management, reminders of daily activities or medical tests. However, the loss of physical functions (such as mobility, eyesight and hearing), as well as the loss of cognitive functions (starting from amnesia to severe disabilities as dementia or Alzheimer), require appropriate solutions in order to offer personalised assistive tasks according to user needs and characteristics. In this paper, we propose a novel methodology that is based on the integration of a service-oriented approach with normative reasoning to automatically generate assistive tasks customised for different target user’s profiles, and deployable in any AAL environment. The formalisation of the conceptual steps of the methodology is provided, together with a framework implementing them. The advantages of dynamic customisation obtained by decoupling the functional and not functional aspects of assistive technology are shown through a validation scenario.
Adjust-free adversarial example generation in speech recognition using evolutionary multi-objective optimization under black-box condition

This paper proposes a black-box adversarial attack method to automatic speech recognition systems. Some studies have attempted to attack neural networks for speech recognition; however, these methods did not consider the robustness of generated adversarial examples against timing lag with a target speech. The proposed method in this paper adopts Evolutionary Multi-objective Optimization (EMO) that allows it generating robust adversarial examples under black-box scenario. Experimental results showed that the proposed method successfully generated adjust-free adversarial examples, which are sufficiently robust against timing lag so that an attacker does not need to take the timing of playing it against the target speech.
Improved differentiable neural architecture search for single image super-resolution

Abstract Deep learning has shown prominent superiority over other machine learning algorithms in Single Image Super-Resolution (SISR). In order to reduce the efforts and resources cost on manually designing deep architecture, we use differentiable neural architecture search (DARTS) on SISR. Since neural architecture search was originally used for classification tasks, our experiments show that direct usage of DARTS on super-resolutions tasks will give rise to many skip connections in the search architecture, which results in the poor performance of final architecture. Thus, it is necessary for DARTS to have made some improvements for the application in the field of SISR. According to characteristics of SISR, we remove redundant operations and redesign some operations in the cell to achieve an improved DARTS. Then we use the improved DARTS to search convolution cells as a nonlinear mapping part of super-resolution network. The new super-resolution architecture shows its effectiveness on benchmark datasets and DIV2K dataset.
Using R packages 'tmap', 'raster' and 'ggmap' for cartographic visualization: An example of dem-based terrain modelling of Italy, Apennine Peninsula

The main purpose of this article is to present the use of R programming language in cartographic visualization demonstrating using machine learning methods in geographic education. Current trends in education technologies are largely influenced by the possibilities of distance-learning, e-learning and selflearning. In view of this, the main tendencies in modern geographic education include active use of open source GIS and publicly available free geospatial datasets that can be used by students for cartographic exercises, data visualization and mapping, both at intermediate and advanced levels. This paper contributes to the development of these methods and is fully based on the datasets and tools available for every student: the R programming language and the free open source datasets. The case study demonstrated in this paper show the examples of both physical geographic mapping (geomorphology) and socio-economic geography (regional mapping) which can be used in the classes and in self-learning. The objective of this research includes geomorphological modelling of the terrain relief in Italy and regional mapping. The data include dem SRTM90 and datasets on regional borders of Italy embedded in R packages 'maps' and 'mapdata'. Modelling references to the characteristics of slope, aspect, hillshade and elevation, their visualization using R packages: 'raster' and 'tmap'. Regional mapping of Italy was made using main package 'ggmap' with the 'ggplot2' as a wrapper. The results present five thematic maps (slope, aspect, hillshade, elevation and regions of Italy) created in R language. Traditionally used in statistical analysis, R is less known as a perfect tool in geographic education. This paper contributes to the development of methods in geographic education by presenting new technologies of the machine learning methods of mapping.
Improving information technology training of the future specialists in the higher educational establishments in the conditions of digital economy

The paper is devoted to one of the most important directions of development of the system of higher education in the era of digitalization, i.e. the information technology training of future specialists. In this work, the need to improve the information technology training of bachelors and masters in the university is substantiated as one of the components of fundamental training of a modern specialist for the digital economy of Russia. On the basis of analysis of relevant theoretical, methodical, and technological approaches of domestic and foreign scientists and practitioners to the issues of improving information and technological training of students in the university, the authors of the article: (i) consider the distinctive features of modern information technology training of students requiring close cooperation of higher education institutions with the labor market in order to promptly adjust the content of curricula in accordance with employers' requests, as well as to follow the concept of advanced education; (i) emphasize that, despite the digital transformation of educational process, changing forms, and methods of teaching, and competences, the most important requirement for the updated system of information technology training of students is to maintain its fundamental nature; (ii) emphasize the importance for future specialists to have programming skills, and their readiness to interact with information systems and technologies, as well as the ability to engage an independent and productive dialogue with computers, make decisions and perform assigned professional tasks and manage complicated automated complexes; (iii) justify and systematize the principles of selection of the content of information technology training for the higher educational establishments' students, taking into account the requirements of Federal State Educational Standards for the Higher Education (FSES HE), domestic and foreign standards in the field of information technology, as well as the concept of advanced education; (iv) offer a system of methodical and technological approaches to improving the information technology training of future specialists in the higher educational establishments using practice-oriented and optimization approaches, advanced education, and e-learning using distance education technologies.
Semi-Automatic Framework for Traffic Landmark Annotation

We present a semi-automatic annotation method to build a large dataset for traffic landmark detection, where traffic landmarks include traffic signs, traffic lights as well as road markings. Labor-intensive bounding box tagging is a huge challenge to generate a large dataset for detection algorithms. To mitigate the labor, we adopt a high-definition (HD) map and a positioning system. We propose a process to align the HD map and images semi-automatically. Through the registration, the annotations of the HD map can be directly tagged onto traffic landmarks in the images. To make full use of the HD map for the dataset generation, we annotate the traffic landmarks with reference points, following the way that they are represented in the HD map, instead of the bounding boxes. The proposed semi-automatic method speeds up the annotation by a factor of 3.19, as compared to the manual annotation. Our dataset consists of about 150,000 images and includes about 470,000 annotated traffic landmarks. We train a deep neural network on our dataset to detect the traffic landmarks, and its performance is evaluated using a novel evaluation metric. Moreover, we show that the pretrained traffic landmark detection network is effective in detecting traffic landmarks in other countries using the bounding box by fine-tuning.
A Camouflage Text-Based Password Approach for Mobile Devices against Shoulder-Surfing Attack

Authentication in mobile devices is inherently vulnerable to attacks and has the weakness of being susceptible to shoulder-surfing attack. Shoulder-surfing attack is a type of attack that uses direct observation techniques such as looking over someone’s shoulder to get information. This paper aims to introduce a novel way of concealing the password within a contingent of randomly selected entries. In particular, the traditional password concept where what you input is what you get is redefined by proposing the camouflage characters approach. Based on this approach, three defensive techniques are introduced for mobile devices. By using an Android platform, the introduced techniques are implemented. Experimental studies are conducted in order to evaluate both security and usability perspectives. The empirical results showed that the proposed approach is reasonably resistant against shoulder-surfing attacks and usable for participants. Moreover, it is possible to choose very short passwords, while insuring that the password remains hidden amongst a large number of key presses. Based on the achieved results, the proposed approach is recommended to be a new avenue in the field of security to produce very simple and yet very complicated passwords, to be observed by the attacker, at the same time.
Towards a "better normal": educational experiences in Design in Latin America during the COVID-19 pandemic

The emergence of the COVID-19 pandemic has demanded the adoption of extraordinary measures of quarantine and social distancing, impacting educational institutions worldwide. Schools and campuses – which used to be spaces for social exchange – had to cease face-to-face instruction and shift to remote learning with no prior planning or training, which posed several challenges to education systems around the globe. In Latin America – responsible, today, for over half of the planet’s daily COVID-19-related deaths - this scenario is even more dramatic. The diverse socioeconomic levels of the student population is a major challenge for online teaching, as institutions cannot provide computer training, equipment and connectivity to all those in need. In spite of all challenges, universities which are part of AUSJAL (Association of Universities Entrusted to the Society of Jesus in Latin America) are making every effort to offer online classes during this pandemic, since education plays a pivotal role in these countries. This paper presents a selection of Design educational experiences conducted in AUSJAL universities during this pandemic. Design educators from Brazil, Colombia, Ecuador and Nicaragua present educational methods and strategies for dealing with this critical situation. In conclusion, we discuss how their innovative and engaging teaching ideas are paving the way towards not simply a new, but a “better normal” in Latin America.
Cybersecurity and related challenges during the COVID-19 pandemic

Lee Kim is the director of privacy and security/thought advisory at HIMSS in Chicago, Ill. The author has disclosed no financial relationships related to this article.
Smartphone-Based Single-Camera Stereo-DIC System: Thermal Error Analysis and Design Recommendations

Coupling a smartphone-based stereo-DIC system using a single smartphone and a 3D-printed four-mirror adapter has been recently proposed by the authors as a cost-effective and field-portable 3D displacements measurement tool. However, this system is found to be very sensitive to temperature variation inside the smartphone and the ambient temperature variation around the system, which further can introduce displacement and strain errors into the measured results. Interestingly, the image coordinate drifts of image points that are directly associated with these thermal errors cannot be well explained by the existing temperature-induced camera deformation models. To eliminate the large thermal errors and realize high-accuracy 3D deformation measurement, a comprehensive thermal error analysis is performed for the smartphone-based single-camera stereo-DIC system. The theoretical estimations based on the existing temperature-induced camera deformation model are compared with the experimental observations. Then, all the potential error sources that can introduce thermal errors in the smartphone-based single-camera stereo-DIC system are analyzed and discussed. Several comparison tests confirm that the thermal errors are caused by both the inner structural changes and the deformation from the adapter, and that the thermal errors from the adapter are much larger than those caused by the inner deformation inside the smartphone camera. Finally, a modified mirror adapter was fabricated and proven less sensitive to the temperature variation. This research is expected to explain all the potential thermal error sources in the smartphone-based single-camera stereo-DIC system and further contribute to the correction of the thermal errors and the optimal design of the adapter.
Results Validation about the Adoption Model of Information and Communication Technologies Applied to the Technical University of Cotopaxi

Within the last years, the utilization of ICT has developed within the different education levels and particularly in universities that these count with new technological resources much advanced within the particular case of the university during which the investigation is developing the study is, to enhance the Educational Learning process implementing a replacement adoption model of ICT, with which it´s possible to offer a solution to the educational process. A methodology is realized of study supported the systematical Review of the literature of the models of the ICT citing as an example the primary model that there appeared the Model TAM who was developed by [1] and of the Web 2 and Web 3 and therefore the ICT within the Universities of the planet. The investigation threw the results expected with the creation of the new model of adoption of ICT, with the utilization of collaborative hardware during a Learning Environment (CLE) for creating the constructs, alpha of Cronbach, variance, and interrelations and therefore the use of statistical software because the Minitab validated the raised hypotheses. Educational Technologies are very important nowadays for the Development of each country.
Zero-Cost Proxies for Lightweight NAS

Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models need to be evaluated before choosing the best one. To reduce the computational power and time needed, a proxy task is often used for evaluating each model instead of full training. In this paper, we evaluate conventional reduced-training proxies and quantify how well they preserve ranking between multiple models during search when compared with the rankings produced by final trained accuracy. We propose a series of zero-cost proxies, based on recent pruning literature, that use just a single minibatch of training data to compute a model's score. Our zero-cost proxies use 3 orders of magnitude less computation but can match and even outperform conventional proxies. For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0.82, compared to 0.61 for EcoNAS (a recently proposed reduced-training proxy). Finally, we use these zero-cost proxies to enhance existing NAS search algorithms such as random search, reinforcement learning, evolutionary search and predictor-based search. For all search methodologies and across three different NAS datasets, we are able to significantly improve sample efficiency, and thereby decrease computation, by using our zero-cost proxies. For example on NAS-Bench-101, we achieved the same accuracy 4$\times$ quicker than the best previous result. Our code is made public at: this https URL.
Detection of Strategic Targets of Interest in Satellite Images using YOLO

This paper details about training a convolutional neural network for object detection and classification of a custom generated dataset from google earth satellite images. The objects of interest in satellite images are strategic targets such as nuclear power plants and oil refineries. The deep learning network is YOLO version 3 which has shown significant improvement in detecting smaller objects. YOLO v3 is three times faster than SSD and its AP metric for COCO dataset is on par with SSD. Hence YOLO v3 is a faster detector in comparison to SSD. But AP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.75</sub> is low when compared with RetinaNet due to higher localization error. On the other hand, the simpler network of YOLO, Tiny YOLO v3 takes lesser detection time. The objective of experimentation is to evaluate the performance of YOLO v3 and Tiny YOLO v3 for objects from satellite imagery. Google earth satellite images consumes less time and provides cost effective solution rather than acquiring the overhead images through unmanned aerial vehicles (UAVs) and drones. The objects size varies from 120 pixels to 1250 pixels. The experimental results demonstrate its detection capability, metrics results. GeForce RTX GPU was used for training the network.
Sentiment analysis of Chinese text based on Elmo-RNN model

Abstract Traditional word embedding technologies such as Word2Vec and GloVe can only produce a single semantic vector, and can not get the specific meaning of polysemous words in the text combined with context. To solve this problem, a sentiment analysis model of Chinese text based on Elmo and recurrent neural network is proposed. The model uses Elmo model to learn the pre training corpus, and the BiLSTM network structure in Elmo model makes the word vector generated by Elmo model have context sensitive characteristics, which is more accurate in the expression of polysemous words, and this is not achieved by the traditional word embedding technology. Then, the model uses the recurrent neural network to extract the deep-seated features of the word vector and fuse the features. Finally, the softmax function is used to realize the sentiment classification of text. Experimental results show that Elmo-RNN can effectively improve the accuracy of text sentiment analysis.
Detection of clear sky instants from high frequencies pyranometric measurements of global horizontal irradiance

Solar resource assessment by clear sky models is of great importance in the solar energy field: verifying the performance of photovoltaic systems during stable conditions, clouds effects evaluation, the determination of geographical areas where irradiation is more uncertain and the preparation of forecasts with sky cameras. But before using these models they must be validated against high performances soil measurements. Since there is no radiometric sensor that measures clear-sky radiation, then historical clear-sky time periods must be identified only from long-term allsky irradiation records. The contribution of this study is to exploit the ground measurements, analyze them and retrieve the information they contain concerning the clear sky instants. The study will be performed by comparing the clear sky instants identified by an algorithm proposed by Reno and Hansen with a physical clear sky model. This comparison is made using high frequency global horizontal irradiation (GHI) data from high performances meteorological station installed at Benguerir in Morocco.