Reviewerly/exHarmony huggingface dataset:
Enhancing Accuracy and Precision of eye tracker by head movement compensation and calibration

The deep analysis of eyes movement and fixation according to a particular scenario assists to gain profound insights into the process of information acquisition, mood and mental states of the test subject. Eye tracking technology enables to determine where the eyes of the tested person are focused and how the visual information is attended to. Nowadays, the eye tracking technology is in its mature stage, often realized in glasses or other wearable devices, allowing non-obtrusive recording of individual's eye gazing in wide spectrum of dynamic scenarios. Sometimes, these last generation eye trackers are not equipped with synchronous head mounted forward-looking camera. In order to regard head movement for these eye trackers, the designers outfit them with inertial sensors. This paper deals with head movement compensation based on the data from inertial sensors. The implementation of accelerometers and gyros in eye tracking system is commented.
Spectroscopy and Light Emitting Diodes Based System in Characterizing External Beam Therapy 3 Films for Solar Ultraviolet Measurement

Abstract Gafchromic external beam therapy 3 (EBT3) film has widely been used in medical field applications. Principally, the EBT3 film’s color gradually changes from light green to darker color under incremental exposures by ionizing or even non-ionizing ultraviolet (UV) radiation. Peak absorbance of the EBT3 film can be used to predict absorbed doses by the film. However, until today, related researches still rely on spectrometers for color analysis of EBT3 films. Hence, this paper presents a comparative analysis between results produced by the spectrometer and a much simpler light-emitting diode-photodiode based system in profiling the color changes of EBT3 films after exposure by solar UV radiation. This work has been conducted on a set of 50 EBT3 samples with incremental solar UV exposure (doses). The wavelength in the red region has the best sensitivity in profiling the color changes of EBT3 films for low solar UV exposure measurement. This study foresees the ability of blue wavelength to profile films with a large range of solar UV exposure. The LED (light emitting diode)-based optical system has produced comparable measurement accuracies to the spectrometer and thus, with a potential for replacing the need for a multipurpose spectroscopy system for simple measurement of light attenuation.
An Accurate Negative Survey Using Answer Confidence Level

Negative survey is an effective method to protect the privacy of survey participants and has many applications. Different from normal survey, it hides personal privacy by requiring an individual participant to provide a negative option as the answer to the survey question. Answers of participants can further be reconstructed into the distribution of positive options. However, the current negative survey that assumes all participants are 100 percent confident in their answers introduces some precision loss to the reconstruction process. In this paper, we provide an accurate negative survey by allowing participants to annotate their confidence levels to the answers. In particular, we develop a novel Negative Survey to Positive Survey with Answer Confidence Level (NStoPS-CL) algorithm to reconstruct the negative survey with answer confidence level and further increase the accuracy of reconstruction. Experiments demonstrate that NStoPS-CL reliably improves the reconstruction accuracy by testing answer confidence level under different conditions (i.e., dataset size, number of question options and dataset distributions), while balancing reconstruction accuracy and privacy well.
Passive attack to the controlled secure direct communication with seven-qubit entangled states protocol

It is found that the controlled quantum secure direct communication (CQSDC) based on a seven-qubit entangled state protocol [S. K. Wang, X. W. Zha and H. Wu, Int. J. Theor. Phys. 57, 48 (2018)] can be passively attacked. That is to say, this protocol has the security loophole of information leakage. To be specific, the first bit of the sender’s two-bit message can be deduced by an attacker Eve according to the first and the third bits of Alice’s announcement and the first bit of the controller Charlie’s announcement. Then, a way to fix this loophole is discussed.
Adaptive Binarization for Degraded Document Image via Contrast Enhancement

In this paper, we propose an adaptive binarization method to handle different degraded document images. Firstly, a contrast image robust to background variations caused by different types of degradation is generated after preprocessing the input image. Secondly, an image contrast enhancement technique is used to strengthen the edges of the strokes to make them more complete. Then, the OTSU method is applied on the enhanced contrast image to obtain the stroke edge pixels, and the final binarization image is returned according to the threshold determined from the mean and the standard deviation of the intensity of these edge pixels. Experimental results on two public datasets indicate that the proposed method can deal with various degraded images, improve the continuity of the strokes and achieve superior binarization performance when compared with other methods.
Sentiment-Aware Recommendation System for Healthcare Using Social Media

Over the last decade, health communities (known as forums) have evolved into platforms where more and more users share their medical experiences, thereby seeking guidance and interacting with people of the community. The shared content, though informal and unstructured in nature, contains valuable medical and/or health related information and can be leveraged to produce structured suggestions to the common people. In this paper, at first we propose a stacked deep learning model for sentiment analysis from the medical forum data. The stacked model comprises of Convolutional Neural Network (CNN) followed by a Long Short Term Memory (LSTM) and then by another CNN. For a blog classified with positive sentiment, we retrieve the top-n similar posts. Thereafter, we develop a probabilistic model for suggesting the suitable treatments or procedures for a particular disease or health condition. We believe that integration of medical sentiment and suggestion would be beneficial to the users for finding the relevant contents regarding medications and medical conditions, without having to manually stroll through a large amount of unstructured contents.
Human Sketch Recognition using Generative Adversarial Networks and One-Shot Learning

We introduce a model for face recognition from sketches. The system follows a multi-layered approach and is built by combining sketch to image generation, and face recognition methods. First, sketch to photo generation is achieved by employing cGAN based pix2pix model. Then face recognition is done by One Shot Learning using FaceNet. The sketch recognition model developed through this research is able to yield good results on multiple datasets with the generated images performing with an accuracy close to that with the original images. The average difference from the recognition accuracy as compared to with original images was approximately three percent on the datasets used. Real time recognition using sketches as inputs is also effectively explored through the combination of these two layers of approach.
Multi-Pose Moving Human Detection based on Unmanned Aerial Vehicle in Real-Time

In this paper, a multi-pose human detection system is presented based on Unmanned Aerial Vehicle (UAV) imageries in real-time. A satisfying detection accuracy of multipose human detection in complex outdoor environments can be achieved with no accelerating hardware like graphics processing unit. First, 30,000 positive samples with various appearances are selected manually from different shooting angles and are used to train the convolutional neural network (CNN) classifier. Second, optical flow of the two successive frames taken by onboard camera is used to extract region of interests and a series of post-processing methods, i.e., average filtering, morphological operation and extraction of outer contour are applied to meet the real-time requirement. Then a two-stream CNN combining the appearance and motion information are applied to reduce the false detection accuracy. Field experiments in complex outdoor environments have been performed to verify that the proposed system can be used to detect multi-pose humans with high detection accuracy in real-time.
Using BERT to Process Chinese Ellipsis and Coreference in Clinic Dialogues

This paper focuses on ellipsis and coreference resolution in Chinese dialogue. The experimental data are real medical diagnosis dialogues. New features for machine learning, as well as deep learning approach such as BERT, were used to develop classifiers to detect, classify, and resolve ellipsis and coreference. The experimental results show that rule-based systems, BERT, and neural networks outperform one another in different tasks. The best F-scores of ellipsis and coreference detection were 70.61% and 89.09%, respectively. The best accuracy of ellipsis and coreference classification were 77.96% and 83.09%, respectively. The best accuracy of ellipsis and coreference detection were 80.06% and 82.69%, respectively.
SCIENTIFIC COMMUNICATION IN THE WORLD OF DIGITAL TECHNOLOGIES: CONDITIONS AND PERSPECTIVES REGARDING THE DEVELOPMENT OF SCIENTIFIC JOURNALS

Purpose. The presentation of challenges with which scientific journals and - more broadly - scientific communication, will have to face in the substantive, technological and financial sphere in the conditions of constant evolution of the digital world, dynamic development of new information systems in science (e-library, library 2.0) and new phenomena conditioning the behaviour of Internet users. Method. The views presented in the article are the result of a review of literature regarding scientific journals and empirical research, which was conducted from April to August 2018 among 132 authors representing the world of science and economic practices. Findings. The open access (OA) movement has caused significant changes in the behaviour of people of science in publishing and depositing research results. The prospect of taking over all the functions of scientific journals by scientific repositories still seems to be distant due to the lack of alternative methods for assessing the quality of scientific publications. There are doubts about the division into scientific, institutional repositories and the repositories belonging to scientific journals, which results from unclear business models of individual solutions. The phenomenon of self-publishing is stimulated by the dynamic development of research carried out by business units, skilfully analysing the resources of large data sets and successfully popularising research results in social repositories. This is new quality in the area of information exchange, which requires rapid adaptation on scientific grounds. An insufficiently implemented postulate to popularise scientific knowledge and its transfer to business practice remains an equally important challenge. Research and conclusions limitations. The results of the survey based on the respondents’ subjective assessment should be treated with caution and do not allow to draw general conclusions. The research revealed significant discrepancies in respondents' opinions regarding the future of scientific journals and their prospects for functioning in new information systems. The highest doubts concern the quality assessment system of scientific publications and the business model of scientific repositories: the significant number of stakeholders of the scientific communication system, dispersed in various scientific, political and economic systems, further limits the possibility of formulating unambiguous decisions in this respect. Originality. The presented article formulates challenges for scientific journals whose functions are being increasingly taken over by scientific and social repositories. In contrast to the previously published works, this suggests solutions in the field of artificial intelligence, which will enable complete change in the way of publishing and validation of knowledge as well as quality control of scientific research. Type of work. Review article.
Prediction of 3D rotation and translation from 2D images

The prediction of three-dimensional (3D) rotation and translation can be retrieved from two-dimensional (2D) images to build 3D models from large collections of images. In this paper, the process starts by extracting the features of images via transfer learning approach from Deep Neural Network model called VGG19. Even though the features extracted from VGG19 are usually adopted in image recognition application; in this research, we apply these features to the prediction model to obtain rotation and translation parameters. Due to the large size of the feature dimensions, it is necessary to perform dimensional reduction technique called latent semantic analysis (LSA) to decrease the feature dimensions and remain only the important ones. Then, the regression estimation technique based on the idea of Support Vector Machine (SVM) is used to predict the rotation and translation parameters. The accuracy is estimated by comparing the prediction results with the corresponding ground truth set. The average errors of rotation and translation of 3D prediction from 2D images are approximately 0.2419 degrees and 1.35 meters respectively.
BLOOM'S DIGITAL TAXONOMY AND MODEL OF DIGITAL TRANSFORMATION OF EDUCATION IN THE EDUCATIONAL PROCESS OF UNIVERSITY

The article describes the integration of digital technologies in the educational process by SAMR model (Substitution, Augmentation, Modification, Redefinition) developed by Ruben Puentedura. The stages of the use of digital technologies in the educational process are considered. The application of these technologies to improve learning is justified. The examples are given that demonstrate the stage of routine use of digital technologies at the levels of Substitution and Augmentation, and the stage of innovative use of digital technologies at the levels of Modification and Redefinition. Bloom’s taxonomy is described, which is learning through a set of concepts that begin with lower-order thinking skills and advance to higher-order thinking skills. The relationship between the SAMR model and the development of higher-order cognitive skills in Bloom’s taxonomy was studied. An example of the joint use of the SAMR model and Bloom’s taxonomy in the framework of the formation of digital competencies of future teachers when studying the discipline “Digital education technologies” is considered.
Model of Multi-sensor Short-Term Task Assignment

This paper establishes a short-time sensor task time allocation model for air-to-air combat scenarios. First, the sensor tracking capability is quantified based on the Cramer's border of the parameter estimation method. Next, the sensor search capability is quantified based on the superposed SNR of the signal detection theory. Finally, the sensor management optimization function, constraints and their respective management effectiveness indicators with the shortest working time under short-term task conditions are proposed. The simulation results show that under the premise of the same sensor working time, the short-term task assignment model improves the tracking accuracy by 50% on average. The target tracking completion rate $\gamma$ 0 needs to be maintained at more than 85% of the mission requirements.
Fast and efficient calculations of structural invariants of chirality

Chirality describes the structural properties or configuration of an object, and it plays an important role in fields such as physics, chemistry and biology. It is critical to identify achiral objects and determine symmetry plane of them in shape analysis, and chiral invariants play essential role in above tasks. However, chiral invariants are often complex and time-consuming to calculate in practical applications. In this paper, we present five chiral invariants based on the concept of generating functions. Three of the five chiral invariants decode the expression of a chiral index, and they are the core part of this chiral index. In addition, this paper provides three new chiral invariants. These five chiral invariants have three characteristics: (1) Their results on the shape before and after the reflection transformation are opposite to each other. (2) The five chiral invariants have brief form and low time complexity (O(n)), the order and degree of them are not more than four. (3) They can be used to determine the global symmetry plane of reflection symmetry objects, and they help to deal with false zeros problem. The five chiral invariants give the invariants under reflection transformation from the geometrical point of view, and the experimental results show that they are effective and efficient. In conclusion, our invariants can be used for determining archiral objects and mutual symmetry shape, as well as for determining symmetry plane of reflection symmetry objects.
PaperWork

Electronic paper (e-paper) is a display technology that aims to imitate conventional paper. Currently most e-paper applications on handheld devices are restricted to digital book readers. Few studies explore the potential of e-paper on input oriented applications. In this paper, we introduce a novel e-paper application PaperWork, which allows users to offload their commonly used office applications from a PC to an e-paper device remotely. There are considerable challenges when building a system for a resource constrained e-paper device which we will highlight in this work. In addition to presenting a new e-paper system we also conduct a usability study of this system.
Research on Offline Handwritten Chinese Character Recognition Based on Deep Learning

Offline Handwritten Chinese Character Recognition (HCCR) is not only a research difficulty, but also a research hotspot. With the rapid development of deep learning, offline HCCR which based on deep learning has been widely studied and made a Breakthrough Development in methods and performance. CASIA-HWDB1.1 handwritten Chinese character database which was collected by Institute of Automation, Chinese Academy of Sciences is adopted in the paper. In the paper, tensorflow software platform is used, and the structure of convolution neural network (CNN) is optimized. The neural network model used in the paper has been trained and tested. It can be concluded that the recognition rate of offline HCCR has been improved. The training model has been successfully applied to the off-line HCCR system based on Android, and good results have been achieved.
Combining Online Clustering and Rank Pooling Dynamics for Action Proposals

The action proposals problem consists in developing efficient and effective approaches to retrieve, from untrimmed long videos, those temporal segments which are likely to contain human actions. This is a fundamental task for any video analysis solution, which will struggle to detect activities in a large-scale video collection without the proposals step, needing hence to apply an action classifier at every time location, in a temporal sliding window strategy, a pipeline which is clearly unfeasible. While all previous action proposals solutions are supervised, we introduce here a novel strategy that works in an unsupervised fashion. We rely on an online agglomerative clustering algorithm to build an initial set of proposals/clusters. Then a novel filtering approach is proposed, which uses the dynamics of the proposals discovered by the clustering, to measure their actioness, and proceeds to filter them accordingly. Our experiments show that our model improves the supervised state-of-the-art approaches when the number of proposals is controlled.
Augmented Reality with Hand Gestures Control for Electronic Medical Record

Medical records are generally used in medical institution to keep track of patients' information and their health conditions. Information such as family members, past surgeries and medication taken are recorded and accessible for future references. Medical images of the patient, such as Magnetic Resonance Imaging (MRI) scan and Computed Tomography (CT scan) are available in a 2D printout and are shown to the patient. With the advancement of technology and augmented reality platform, this medical image can be brought to life to enhance the understanding of it. This paper presents the work on developing an application that employs Augmented Reality (AR) technology to enhance users' experience with Electronic Medical Record (EMR). It allows patients to have an in-depth look at their 3D organs as well as their past medical record. With the integration of LEAP motion device, the 3D organs can be controlled using hand gestures. Three 3D models (kidney, heart and liver) were developed to demonstrate the feasibility of using AR as an alternative solution for storing users' medical records. Based on the users' feedback obtained through user acceptance test sessions, the responses are very encouraging.
Physical fights back

Current transformational developments in automotive user interface (UI) technology are causing a shift in emphasis from safety and efficiency to emotion and flexibility. The many factors to consider in parallel make this a difficult process, in which technological affordances all too easily push the user to the background. To address this issue, this paper introduces an interaction model linking the different tangible control elements, including smartphone functionality, and shows how non-driving-related activities (e.g. climate control, multimedia access) can be represented physically. Next, a working prototype is presented that supports the design and development of novel tactile UIs. By integrating layers of sensors and actuators, a flexible UI is created that pushes technology to the background, giving proper attention to the user again and enabling effective research on how to make the digital world tangible for users.
Translating Manipulated Idioms (EN&gt;ES) in the Word Sketch Scenario

This paper presents a teaching proposal for translation/interpreting undergraduate students. Following a corpus-based methodology, the main objective of this proposal is to train students on how to exploit the possibilities offered by Sketch Engine for the creation of ad hoc phraseological equivalences. More precisely, we will examine the potential of its functionality Word Sketch as a convenient tool in those cases where the manipulation of idioms and the absence of one-to-one phraseological correspondences may pose problems to translation. The experimental setting comprised an introductory seminar on the convergence of corpora, phraseology and translation, followed by a hands-on session, where trainee translators were presented with some case-studies for which they had to create ad hoc phraseological equivalents for the manipulated idioms in the source text (ST) and had to justify both the translation process and product. Overall, the insights gained from analysing the results obtained will allow us to determine to what extent trainee translators are able to translate manipulated idioms in the Word Sketch scenario.