Reviewerly/exHarmony huggingface dataset:
Entanglement-preserving limit cycles from sequential quantum measurements and feedback

Entanglement generation and preservation is a key task in quantum information processing, and a variety of protocols exist to entangle remote qubits via measurement of their spontaneous emission. We here propose feedback methods, based on monitoring the fluorescence of two qubits and using only local pi-pulses for control, to increase the yield and/or lifetime of entangled two-qubit states. Specifically, we describe a protocol based on photodetection of spontaneous emission (i.e. using quantum jump trajectories) which allows for entanglement preservation via measurement undoing, creating a limit cycle around a Bell states. We then demonstrate that a similar modification can be made to a recent feedback scheme based on homodyne measurement (i.e. using diffusive quantum trajectories), [L. S. Martin and K. B. Whaley, arXiv:1912.00067] in order to increase the lifetime of the entanglement it creates. Our schemes are most effective for high measurement efficiencies, and the impact of less-than-ideal measurement efficiency is quantified. The method we describe here combines proven techniques in a novel way, complementing existing protocols, and offering a pathway towards generating and protecting entangled states so that they may be used in various applications on demand.
The role of augmented reality in robotics

Purpose The purpose of this paper is to provide an insight into how augmented reality (AR) technologies are being applied to robotics. Design/methodology/approach Following an introduction and a brief historical background to AR, this first provides examples of AR applications in robot programming. It then gives examples of recent research into AR-based robot teleoperation. Research activities involving the virtual fixtures (VF) technique are then discussed and finally, brief conclusions are drawn. Findings Because AR concepts were first investigated in the 1990s, applications involving robotics have been widely studied. Programming with the aid of AR devices, such as the HoloLens headset, can be simplified and AR methods, including the VF technique, can improve the accuracy and speed of teleoperation, manipulation and positional control tasks. They can also provide visual or haptic feedback which leads to more intuitive operation and significantly reduces the cognitive load on the operator. Originality/value This provides an insight into the growing role of AR in robotics by providing examples of recent research in a range of applications.
A Survey on Hand Gesture Recognition Using Machine Learning and Infrared Information

The research consists in the hand gestures are movements that convey information and thus complement oral communication or by themselves constitute a form of communication between people. The function of a hand gesture recognition system is to identify the type of movement, from a given set of movements, and the instant when that movement is performed. Gesture recognition systems have multiple applications including sign language translation, bionics, human-machine interaction, gamming, and virtual reality. For this reason, hand gesture recognition is a problem where many researchers have focused their attention too. In this context, in this paper, we present a systematic literature review for hand gesture recognition using ma-chine learning and infrared information. This work has been made because there is no work in the scientific literature that reviews gesture recognition systems based on machine learning and infrared information. In this work, we answer the research question: what is the architecture of the proposed models for hand gesture recognition based on machine learning and infrared information? For answering this research question, we used Kitchenham methodology. Finally, in this work, we also present trends and gaps with respect to the problem analyzed.
Compactly Supported Tensor Product Complex Tight Framelets with Directionality

Construction of directional compactly supported tensor product complex tight framelets cptTP-ℂTF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> is discussed. The construction algorithm employs optimization techniques and puts extensive emphasis on frequency response and spatial localization of the underlying one-dimensional tight framelet filter banks. A concrete example of cptTP-ℂTF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> is provided. Numerical experiments show that such constructed cptTP-ℂTF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sub> have good performance in image denoising.
Human Behavior Recognition Algorithm Based on HOG Feature and SVM Classifier

Human behavior analysis is a hot research in the field of computer vision. It has broad application prospects in the fields of intelligent monitoring, human-computer interaction, motion analysis and virtual reality. In order to improve the accuracy of human behavior recognition, a human behavior recognition method based on HOG feature and SVM classifier is proposed. First, the HOG features of the training set and the test set are extracted. Then, the multi-class problem is transformed into multiple dual-class problems, and multiple SVM classifiers are trained by using the HOG features. Finally, the trained classifiers are employed to recognize the human walking and waving behavior. Experimental results show that the recognition rate of walking and waving is 87.5% for the UIUC database. The human behavior recognition method proposed in this paper can effectively improve the recognition rate.
The synergistic combination of fuzzy C-means and ensemble filtering for class noise detection

Purpose The purpose of this study is to enhance data quality and overall accuracy and improve certainty by reducing the negative impacts of the FCM algorithm while clustering real-world data and also decreasing the inherent noise in data sets. Design/methodology/approach The present study proposed a new effective model based on fuzzy C-means (FCM), ensemble filtering (ENS) and machine learning algorithms, called an FCM-ENS model. This model is mainly composed of three parts: noise detection, noise filtering and noise classification. Findings The performance of the proposed model was tested by conducting experiments on six data sets from the UCI repository. As shown by the obtained results, the proposed noise detection model very effectively detected the class noise and enhanced performance in case the identified class noisy instances were removed. Originality/value To the best of the authors’ knowledge, no effort has been made to improve the FCM algorithm in relation to class noise detection issues. Thus, the novelty of existing research is combining the FCM algorithm as a noise detection technique with ENS to reduce the negative effect of inherent noise and increase data quality and accuracy.
Research of Dimension Reduction Algorithms of Feature Space in Data Analysis Task

Today data describing a particular subject area, often contain a large number of different features that determine the properties of any processes or objects. At the same time, these data sets can reach enormous sizes, so working with data turns into very resource-intensive and long processes. Reducing the dimension of the feature space entails a reduction in the used memory and getting rid of noisy and duplicate information. To reduce the space, various machine-learning algorithms are used, each of which has its own individual degree of efficiency and validity for its area of application. Client databases are no exception, they often contain large amounts of information, some of which are redundant. Therefore, reducing the space of signs in client data is a topic whose relevance is only increasing every day. This paper presents a study of such methods as reduction of features with low variability, Univariate feature selection, recursive feature elimination, selection based on a decision tree, principal component analysis. Based on the results of the study, requirements are formulated for an algorithm for reducing the dimension of the feature space specifically for the task of working with client data.
Square Root Partial-Update Kalman Filter

The Partial-Update Kalman Filter is a recent development that, in a very simple fashion, extends the uncertainty and nonlinearities which the extended or unscented Kalman filters are able to tolerate. However, to date its application has been limited to filters using a full covariance matrix representations or through a wasteful mapping between square root and full covariance representations. In this paper, the Square Root Partial-Update Kalman Filter is developed and demonstrated. This new approach benefits from both the numerical stability inherent in the square root filter and the robustness of the partial update while operating directly on the square root representation of the uncertainty. The paper details the algorithm and implementation and provides a nonlinear filtering example to demonstrate the increased robustness of the partial update form of the square root filter.
GAN-Based Steganography with the Concatenation of Multiple Feature Maps

Steganography has been widely used to conceal secret information in multimedia content. Using generative adversarial networks (GAN) where two subnetworks compete against each other, steganography can learn good distortion measurement. Nevertheless, the convergence speed of GAN is usually slow, and the performance of GAN-based steganography has large room to improve. In this paper, we propose a new GAN-based spatial steganographic scheme. The proposed learning framework consists of two parts: a steganographic generator and a steganalytic discriminator. The former generates stego images, and the latter evaluates their steganography security. Different from existing GAN-based steganography, we reconstruct the generator by combining multiple feature maps, and then expand the maximum number of feature channels to 256. The reconstruction generator can effectively generate a sophisticated probability map, which is used to calculate optimal distortion measurement and further provides a better guidance for adaptive information embedding. Comprehensive experimental results show that, with the same discriminant network, the anti-steganalysis performance of our method is better than that of ASDL-GAN scheme and Yang’s scheme.
Deep neural networks for Arabic information extraction

Purpose The purpose of this paper is to present a system based on deep neural networks to extract particular entities from natural language text, knowing that a massive amount of textual information is electronically available at present. Notably, a large amount of electronic text data indicates great difficulty in finding or extracting relevant information from them. Design/methodology/approach This study presents an original system to extract Arabic-named entities by combining a deep neural network-based part-of-speech tagger and a neural network-based named entity extractor. Firstly, the system extracts the grammatical classes of the words with high precision depending on the context of the word. This module plays the role of the disambiguation process. Then, a second module is used to extract the named entities. Findings Using deep neural networks in natural language processing, requires tuning many hyperparameters, which is a time-consuming process. To deal with this problem, applying statistical methods like the Taguchi method is much requested. In this study, the system is successfully applied to the Arabic-named entities recognition, where accuracy of 96.81 per cent was reported, which is better than the state-of-the-art results. Research limitations/implications The system is designed and trained for the Arabic language, but the architecture can be used for other languages. Practical implications Information extraction systems are developed for different applications, such as analysing newspaper articles and databases for commercial, political and social objectives. Information extraction systems also can be built over an information retrieval (IR) system. The IR system eliminates irrelevant documents and paragraphs. Originality/value The proposed system can be regarded as the first attempt to use double deep neural networks to increase the accuracy. It also can be built over an IR system. The IR system eliminates irrelevant documents and paragraphs. This process reduces the mass number of documents from which the authors wish to extract the relevant information using an information extraction system.
FACE RECOGNITION TECHNIQUES

The problem of face recognition is discussed. The main methods of recognition are considered. The calibrated stereo pair for the face and calculating the depth map by the correlation algorithm are used. As a result, a 3D mask of the face is obtained. Using three anthropomorphic points, then constructed a coordinate system that ensures a possibility of superposition of the tested mask.
Decomposed Cyclegan for Single Image Deraining With Unpaired Data

Most previous learning-based methods required paired rain image data. In practice, however, paired rain data cannot be collected. Inspired by adopting unpaired data in task of translation, in this paper we present a new method for rain removal using unpaired data. We noticed that direct use of unpaired training data may have problems, such as color shifts and background blurs. Thus, we formulate DCycleGAN, a new deep framework that decomposes the input rain image into the foreground and background parts, then produces a rain mask to guide the rain generation via re-formulated cycle-consistency constraints. Particular, the framework can simultaneously learn the foreground and background portions of the rain image, which can better remove the rain streak. Experimental results demonstrate the effectiveness of our method when trained on unpaired data.
Website Quality Assessment: A Case Study of Chinese Airlines

The importance of Chinese tourism is growing and so is the level of competition of the companies related to it. The websites of these companies is a key element. It is very important that websites - visited by clients for information search, decision making, and subsequent booking - respond to the expectations of users. The web pages must be designed according to criteria such as web quality. The aim of this paper was the construction of a model for the analysis of the quality of the websites. We took as a reference the Index Web Evaluation (IEW). Four blocks were identified: accessibility, speed, navigability/usability, and content quality. The model was used to analyze the websites of 25 Chinese airlines. For this, we attended to the items contained in this index, modified and expanded it, and adapted it to fulfill the objective of the study. According to the scores, the weights of each aspect and dimension, a ranking of the websites of the Chinese airlines was presented. The results indicated that international airlines had a better website. Accessibility was a weak point on the web. Within the category of navigability, the languages that the websites were translated into was another weak point for most companies. On the other hand, one of the strong points was the quality of the content of the web pages. In addition, points of improvement were provided for each of the airlines analyzed.
An array-assisted deep learning approach to seismic phase-picking

Rapid increase of seismic data poses challenges to seismic data processing, in which phase-picking is a crucial procedure and accordingly attracts attention from seismologists. Among the breakthroughs in deep learning methods for seismic data processing, seismologists have especially proposed many seismic phase-picking methods based on deep learning, which have shown much better accuracy than traditional automatic phase-picking methods. However, further improvements are needed to meet the requirements for processing continuous seismic records. Here we propose a new approach combining seismic array scheme and U-Net, an end-to-end neural network developed in image segmentation. Previous studies have shown that U-Net can classify waveforms and determine the onset of seismic phases accurately with the help of contracting and expanding paths. Based on U-Net, we first design a deep learning phase picker (PP) model with a long (40 s) time window. To avoid difficulties in time window selection and interference between P- and S-phase identifications, the model involves two networks with the same structure, which pick the P and S phases separately. To improve model training, we employ data transformation to expand the sample set, add typical noises to the sample set for better resemblance of real data, and slightly increase the weight of the seismic signal containing seismic records for a better balance between precision and recall. We also adopt a more effective array-assisted phase picker (APP) model which correlates the picking results from different stations to minimize misidentification. Using data from Hi-net and Alibaba Aftershock AI Capture Contest, we design training and test sets of different sizes to examine the performance and generalization ability of the model. Results show that the APP can effectively pick the body-wave phases on continuous records with a good generalization ability. The relationship between the model performance and training set is such that the model performance is directly related to the data quality rather than the size of the training set, and there is no serious overfitting even for a small training set. Statistics further show that the model has no special requirements for the locations of time windows and can handle seismic records with large epicentral distances. By comparison with other models (model with short time windows or the one of simultaneous picking of P- and S-phases), we find that APP can successfully process event-crowded waveform data and effectively reduce the influence of noise. Finally, we apply the trained model to a seismic network in the Inner Mongolia. Results show that the number of earthquakes detected by the APP is 30 times the manually-detected one, and that 98.1% of the events in the catalog are detected by the APP. The successful application to a seismic network different from the training set suggests that the APP is robust and has strong generalization ability.
MedDialog: A Large-scale Medical Dialogue Dataset

Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs. To facilitate the research and development of medical dialogue systems, we build two large-scale medical dialogue datasets: MedDialog-EN and MedDialog-CN. MedDialog-EN is an English dataset containing 0.3 million conversations between patients and doctors and 0.5 million utterances. MedDialog-CN is an Chinese dataset containing 1.1 million conversations and 4 million utterances. To our best knowledge, MedDialog-(EN,CN) are the largest medical dialogue datasets to date. The dataset is available at this https URL
Clustering clinical and health care processes using a novel measure of dissimilarity for variable-length sequences of ordinal states

Clinical and health care processes are often summarised through sequences of ordinal data describing patient’s state over time. Identifying patterns in these sequences can provide valuable insights into patient progression trajectories for the purposes of clinical monitoring and quality assurance. However, both the variation in the length of each sequence and the ordinal nature of observable states present challenges to pattern identification. In this paper, we address these challenges by presenting a novel measure of dissimilarity for comparing two or more variable-length ordinal sequences that can be used in conjunction with conventional clustering methods to identify patterns in patient progression trajectories. We provide practical guidance on how this can be achieved, and demonstrate it in the context of identifying patterns in post-stoke recovery trajectories.
Two-way MANOVA with unequal cell sizes and unequal cell covariance matrices in high-dimensional settings

In this paper, we discuss a two-way multivariate analysis of variance in high-dimensional settings. With a high-dimensional setting, we propose new approximate tests that work well under the following conditions: 1. The error vectors do not necessarily follow a multivariate normal distribution, 2. The cell sizes are unequal, 3. The cell covariance matrices are unequal, and 4. The dimension p is much larger than the total cell size n. The accuracy of the proposed tests with finite samples is shown through simulations for a variety of high-dimensional scenarios.
Towards explainable AI planning as a service

Explainable AI is an important area of research within which Explainable Planning is an emerging topic. In this paper, we argue that Explainable Planning can be designed as a service -- that is, as a wrapper around an existing planning system that utilises the existing planner to assist in answering contrastive questions. We introduce a prototype framework to facilitate this, along with some examples of how a planner can be used to address certain types of contrastive questions. We discuss the main advantages and limitations of such an approach and we identify open questions for Explainable Planning as a service that identify several possible research directions.
The Crisis of Managerial Information Flows in Education: Theoretical Aspects and Social Realities

The article considers theoretical aspects for the study and understanding of contemporary educational policies with the application of the concepts of bureaucracy. It also presents the results of an interregional study of information flows in the Russian education system. The redundancy, intensity and formalization of these flows constitute one of the painful aspects of work in this sphere, along with unfairly low wages. The article analyzes the materials of extensive sample surveys and the timing of the loads of the main job groups in the education system, data on document flow in institutions of various types and levels, and expert assessments in the three regions of the country. The amount and structure of information flows and paper work lead to institutional dysfunctions, they serve as a means for administrative pressure and imitation of manageability in education system. The article considers their impact on the social and professional well-being of educators. The author concludes that the main source of paper pressure and paper genocide in the education system is a management strategy that ignores the public mission of education and its multifaceted social effectiveness. This strategy expresses distrust of educators, contains ignorance towards public-state governance and has become a dead end for the Russian education system, a major obstacle to maintaining its potential, development and progress in view of growing global competition.
Extended Local Ternary Pattern for Face Anti-spoofing

The Local Binary Patterns (LBP) finds its use in texture classification because of it is simplicity. The extension of LBP that is Local Ternary Pattern (LTP) is a three valued code which is better descriptor than LBP and resistant to noise. But LTP is not invariant to gray-level transformations. To overcome this deficiency of LTP, we propose to use Extended Local Ternary Patter (ELTP) for face anti-spoofing system. The ELTP uses an auto-adaptive method for threshold value selection to make it immune to gray-level variance. Experiments were performed by using all the three descriptors (LBP, LTP and ELTP) and the results are compared. The proposed face anti-spoofing method performs better than LBP and LTP.