Reviewerly/exHarmony huggingface dataset:
Experimental demonstration of tight duality relation in three-path interferometer*

Bohr’s principle of complementarity has a long history and it is an important topic in quantum theory, among which the famous example is the duality relation. The relation between visibility C and distinguishability D , <?CDATA ${C}^{2}+{D}^{2}\le 1$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msup> <mml:mrow> <mml:mi>C</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>+</mml:mo> <mml:msup> <mml:mrow> <mml:mi>D</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> <mml:mo>≤</mml:mo> <mml:mn>1</mml:mn> </mml:math> , has long been recognized as the only representative of the duality relation. However, recent researches have shown that this inequality is not good enough because it is not tight for multipath interferometers. Meanwhile, a tight bound for the multipath interferometer has been put forward. Here we design and experimentally implement a three-path interferometer coupling with path indicator states. The wave property of photons is characterized by l 1 -norm coherence measure, and the particle property is based on distinguishability of the indicator states. The new duality relation of the three-path interferometer is demonstrated in our experiment, which bounds the union of a right triangle and a part of elliptical area inside the quadrant of a unit circle. Data analysis confirms that the new bound is tight for photons in three-path interferometers.
Quantizing Lévy flights

The Caldeira-Leggett model of quantum Brownian motion is generalized using a generic velocity-dependent coupling. That leads to the description of a set of models able to capture Markovian and non-Markovian versions of Brownian and L\'evy statistics, depending on the functional form of the coupling and on the spectral function of the reservoir. One specific coupling force is found that establishes a connection with L\'evy statistics of cold atoms in \textit{Sisyphus} laser cooling. In the low-velocity limit, this also gives rise to additional inertia of the Brownian particle, reproducing the Abraham-Lorentz equation from first principles for a superohmic bath. Through path-integral quantization in Euclidean time, the environment is integrated out, leaving a set of non-local effective actions. These results further serve as starting points for several numerical calculations, particularly decoherence properties of non-ohmic baths.
Food safety Knowledge Graph and Question Answering System

The issue of food safety in recent years has always been the focus of public opinion. Every time there are unqualified foods, it will cause widespread panic and rumor spread, which has a great impact on social stability. Therefore, this paper crawled the data of unqualified foods officially released in recent years from the network, and designed the extraction algorithm of food general entities, food domain entities and relationships between entities for these data. The extracted entity pairs were stored in the gStore database. In order to solve the problem of association of knowledge in knowledge graph, this paper also designed the food safety ontology which organized the concepts, classifications and relationships about food production and food inspection. Finally, this paper also built an intelligent question answering system by means of gStore's http service to help person grasp the unqualified food information through natural language.
E-Entrepreneurship and Economic Development: the Case of EU Countries

Abstract The aim of our paper is to identify how entrepreneurs from European Union (EU) countries use information and communications technologies (ICTs) in their business activities. We also propose to identify if there are differences in the use of ICTs by entrepreneurs, according to level of economic development of EU countries. In order to achieve these goals, we analyse a sample of EU countries, by including them into two groups, according to the stage of their economic development. For analysing the data, we use several methods (the logical-constructive method, comparative methods and benchmarking). The benchmarking method helps us to estimate indicators at country level and to compare them between countries. Our results indicate that e-entrepreneurship in developed countries is more advanced compared to developing countries. There are also significant differences regarding the use of informational technologies between types of firms by their size. Small enterprises use in a lower proportion ICTs in their activity compared with large firms. Through the content of our research, we emphasize that in order to adapt to the rapid changing environment and also to the changes in the consumer’s behaviour the enterprises should focus on introducing the ICTs in their activity to face the competition. Also, government policies should pay more attention to supporting development of information technology infrastructure.
Modeling the process of forming students’ general pedagogical ICT competence on the basis of system-and-activity approach

Г. С. Вяликова
A Hybrid Fuzzy System via Topic Model for Recommending Highlight Topics of CQA in Developer Communities

Question-answering (QA) websites supply a quickly growing source of useful information in numerous areas. These platforms present novel opportunities for online users to supply solutions, they also pose numerous challenges with the ever-growing size of the QA community. QA sites supply platforms for users to cooperate in the form of asking questions or giving answers. Stack Overflow is a massive source of information for both industry and academic practitioners, and its analysis can supply useful insights. Topic modeling of Stack Overflow is very beneficial for pattern discovery and behavior analysis in programming knowledge. In this paper, we propose a framework based on the Latent Dirichlet Allocation (LDA) algorithm and fuzzy rules for question topic mining and recommending highlight latent topics in a community question-answering (CQA) forum of developer community. We consider a real dataset and use 170,091 programmer questions in the R language forum from the Stack Overflow website. Our result shows that LDA topic models via novel fuzzy rules can play an effective role for extracting meaningful concepts and semantic mining in question-answering forums in developer communities.
AI based Book Recommender System with Hybrid Approach

In recent years, recommender systems have become increasingly popular and are applied to a diverse range of applications, due to development of items and its various types available, that leaves the users to choose from abundant provided options.Recommendations typically speed up searches and make it easier for users to access content that they are interested in, and also surprise them with offers they would have never searched for.By using filtering methods for pre-processing the data, recommendations are provided either through collaborative filtering or through content-based filtering.In this paper, we demonstrate a recommendation model that involves Matrix Factorization as a collaborative filtering solution used for providing recommendations.And with further application of artificial intelligence over the previously obtained results from collaborative filtering, the final precise list of top recommended items is listed for the user thereby using a hybrid approach in recommendation.Thus, the recommender model provides personalised recommendations.
Students’ Cognition and Attitude in Writing Descriptive Text

College students are in formal operational in period of cognitive development which they can use symbols logically related to abstract concepts, acquire flexibility in thinking as well as the capacities for abstract thinking and mental hypothesis and consider possible alternatives reasoning and problem solving. However, the students’ writings have some mistakes in terms of using surface features such as spelling, grammar, usage, punctuation, grammar, vocabulary and tenses. This study aimed to analyze about the students’ cognition and attitude in writing descriptive text. The design of this study was descriptive qualitative by conducting the in-depth interview and questionnaire administration. The data of this study were the sentences in descriptive text and transcript of interview. Moreover, the data source were 2 students of 7th semester at STBA – PIA Medan. The results showed that 1) the dominant type of students’ attitude in writing descriptive text was cognitive domain followed by affective and conative domains which could be interpreted that the students had positive attitude regarding to the writing of descriptive text, 2) the students’ cognition in writing descriptive text was having the stages of writing process which consists of pre-writing, drafting, revising, editing and publishing, and 3) the reasons of applying the students’ cognition and attitude in descriptive text were they had the goals and purposes in their thoughts when they were writing. Moreover, they believed that writing could improve their skills in learning English as a foreign language.
Design of learning modules writing factual text based on discovery learning by using mobile devices

Innovation and development of language teaching materials that are appropriate to the development of science and technology need to be done.At present, the development of knowledge and technology has an impact on changing generations.The use of technological devices, such as digital media, mobile applications, websites, etc., has resulted in a change in needs that allows all activities to move faster, one of which is information and knowledge.That has a big impact on education.One teaching material that can be developed is a learning module.At this time, the learning module developed can be done using a variety of digital devices, one of which is mobile.By using a mobile device, practical learning modules are used and can be equipped with various media.The module developed is a learning module writing factual text on the basis of Discovery Learning.This research is R & D (research & development) using a 4-D model (define, design, develop, and dessiminate).Based on the results of validation and trials it was concluded that the learning module is valid, practical, and effective in learning Indonesian.
Forged Multinational Currency Identification and Detection System using Deep Learning Algorithm

Now a days as many counterfeit banknotes are manufactured and circulated in global market, which results in significant damage and harm to society.Recognizing the currency's originality is a very difficult task to general person.Because of advancement in generating highly accurate fake currency.There are several techniques available, such as automatic sorting of banknotes in payment facilities, automated payment machines or sales machines, which consists of several tasks such as identification of banknote type, classification of recirculation fitness and detection of fake banknotes.Banknote identification is the most important approach, based on an image processing system.There are many techniques used in the classification of banknotes by different countries that has been conducted experiments on separate image data sets of each country.Deep learning is a machine learning technique which analyzes & learns the features of the original note.Using the neural networks, the most important aspect is to find more essential features.In the age of big data, in which vast amounts of data must be processed for any application in the real world, the superior techniques are deep learning.In this study, banknotes from various countries are examined by extracting their minute features in carefully and analyzing them using deep learning.Proposed system recommended a Convolutional Neural Network algorithm to detect Forged banknote using dataset of multiple country currency.This approach is chosen to achieve high accuracy with good performance with respect to loss and accuracy in training and validation in terms of huge dataset.So it helps individuals to avoid personal economic damage caused by counterfeiters.
Signal Modulation Classification Based on Deep Belief Network

Modulation classification plays an important role in civil and military fields such as software defined radio, electronic countermeasure and intelligent demodulator. Due to the difficulty of feature extraction in traditional signal modulation classification algorithm, this paper proposes a signal modulation classification algorithm based on deep belief network. The proposed algorithm does not need to extract the signal features, and uses the I/Q data to classify signal directly. The simulation results show that the classification performance of the proposed algorithm is better than traditional machine learning algorithm, when the simulation condition is same.
Prediction of Next Sensor Event and its Time of Occurrence using Transfer Learning across Homes

We present results on the prediction of sequential sensor events and time of occurrence using transfer learning with Recurrent Neural Network with Long Short-Term Memory, between five apartments. Our dataset has been collected from real homes with one resident each and contains data from a set of 13–17 sensors, depending on the apartment, including motion, magnetic, and power sensors. We compare the prediction accuracy and the required dataset size for the prediction when each apartment is modelled individually, and when transfer learning is used. Transfer learning is used in two configurations — a) training with data from four apartments and fine-tuning and testing on each of the target apartments, and b) training with one apartment and fine-tuning and testing on each of the target apartments. In our best prediction models, a top accuracy of 87% is attained when predicting the next sensor event, and 81% when predicting both the next sensor event and the mean time elapsed to the next sensor event. There is a variability of 10% in the attained prediction accuracy across apartments. For a small number of events in the target dataset, having a network pre-trained with data from four apartments and fine-tuned with the target apartment provides the best prediction models.
Experimental dataset on preparation and characterization of black sand mineral-based as photocatalyst

Heterogeneous photocatalysis with natural minerals (geocatalysis) has been considered for a wide variety of redox processes, which require the participation of metals either as catalysts or promoters in the reactive system. Black mineral sand as raw material (RM), which is a natural composite frequently found in coastal deposits, was used for the preparation of a semiconductor potential. Due to iron content of mineral, it was applied several magnetic fields (0.0311 and 0.1645 Tesla) to obtain the fractions M1 and M2, respectively. The fraction that was not magnetically attracted was named NM. The chemical, structural and optical properties of each obtained composite were characterized by Zeta potential (ζ), X-ray fluorescence (XRF), energy-dispersive X-Ray analysis (EDX), scanning electron microscopy (SEM), BET area, thermal gravimetric analysis (TGA), X-ray diffraction (XRD) patterns, Fourier transform infrared (FT-IR) and UV–Vis spectroscopy techniques.
A Self-Ensemble Approach for Partial Multi-Label Learning

Partial multi-label learning (PML), which tackles the problem where each training instance is associated with multiple candidate labels which only a subset are valid. In this paper, we propose a simple but effective batch-wise PML model, PML-SE, which tackles PML problem with a self-ensemble approach (SE), namely the ensembles of model and predictions. Specially, PML-SE introduces a teacher model to refine a more reliable soft label matrix of each training batch by iteratively ensembling the current learned prediction network with the formal one in an online manner. Besides, it adopts a MixUp data augmentation scheme to enhance the robustness of the prediction network against the redundant irrelevant labels. In addition, we form self-ensemble label predictions through a consistency cost to boost the performance of the prediction network. Extensive experiments are conducted on synthesized and real-world PML datasets, while the proposed approach demonstrates the state-of-the-art performance for partial multi-label learning.
Textual fiducial detection in breast conserving surgery for a near-real time image guidance system

Breast cancer is the most common cancer in American women, and is the second most deadly. Current guidance approaches for breast cancer surgery provide distance to a seed implanted near the tumor centroid. Large deformations between preoperative imaging and surgical presentation, coupled with the lack of tumor extent information leads to difficulty in ensuring complete tumor resection. Here we propose a novel image guidance platform that utilizes character-based fiducials for easy detection and small fiducial points for precise and accurate localization. Our system is work-flow friendly, and near-real time with use of stereo cameras for surface acquisition. Using simple image processing techniques, the proposed technique can localize fiducials and character labels, providing updates without relying on video history. Character based fiducial labels can be recognized and used to determine correspondence between left and right images in a pair of stereo cameras, and frame to frame in a sequence of images during a procedure. Letters can be recognized with 89% accuracy using the MATLAB built in optical character recognition function, and an average of 81% of points can be accurately labeled and localized. The stereo camera system can determine surface points with accuracy below 2mm when compared to optically tracked stylus points. These surface points are incorporated to a four-panel guidance display that includes preoperative supine MR, tracked ultrasound, and a model view of the breast and tumor with respect to optically tracked instrumentation.
An executable modeling and analyzing approach to C4ISR architecture

To analyze the behavioral model of the command, control, communication, computer, intelligence, surveillance, reconnaissance (C4ISR) architecture, we propose an executable modeling and analyzing approach to it. First, the meta concept model of the C4ISR architecture is introduced. According to the meta concept model, we construct the executable meta models of the C4ISR architecture by extending the meta models of fUML. Then, we define the concrete syntax and executable activity algebra (EAA) semantics for executable models. The semantics functions are introduced to translating the syntax description of executable models into the item of EAA. To support the execution of models, we propose the executable rules which are the structural operational semantics of EAA. Finally, an area air defense of the C4ISR system is used to illustrate the feasibility of the approach.
Robust hepatic vessels segmentation model based on noisy dataset

Automatic hepatic vessel segmentation from computed tomography (CT) images is essential in computer-assisted liver surgery. However, because of the error-prone and time-consuming manual annotation, it is impractical to obtain the fully correct training labels of hepatic vein (HV) and highly branched portal vein (PV), which largely restricts the development of deep learning methods on hepatic vessel segmentation. To reduce the noise label interference, this paper builds a robust hepatic vessel segmentation model via analyzing the probability distribution relationship between noisy annotation labels and unobserved correct ones, and apply it to deep neural networks (DNNs). Meanwhile, for inferior vena cava (IVC) close to liver and PV in extrahepatic area, segmentation methods are also represented to enhance the completeness of hepatic vessel structure. Experiments, which are conducted on a public hepatic vessel dataset with noise interference, indicates that our model can decrease misclassified regions and increase the vessel recognition probability, simultaneously.
Visualization of quality of 3D tomographic images in construction of digital rock model

Various types of tomography are widely employed in oil and gas industry for studying structure of rocks.Using X-ray or FIB-SEM tomography, a 3D model of a core sample is constructed for mathematical simulations of fluid flow in porous media and evaluation of physical characteristics of rock.Since images have various defects and distortions, there is a problem of selection of a fragment with the best quality from the initial 3D image.At the moment this operation is made manually on the basis of an expert's opinion and takes significant time.In this paper, we investigate applicability of existing non-reference quality metrics for evaluation of tomographic images and propose the approach for visualization of spatial change of 3D image quality.The method includes the construction of central cross-section; plotting graphs of quality and similarity measures for each slice over the cross-section; generation of combined heat map of quality of cubic fragments with various size.The proposed approach significantly accelerates and makes less subjective selection of the best region for further simulations in digital rock workflow.The choice of colour scale is considered to facilitate the analysis of graphical information for people with colour vision deficiency.
Integrative Teaching of Mathematics as a Means of a Forming Modern Economist

Introduction: A modern specialist in practically any field should not only possess professional expertise but also have a good command of modern computer technologies and main mathematical methods of data modeling and processing. It is employers’ requirements and, at the same time, students’ demands to receive this exact type of education. The study examines various ideas of educator-researchers striving to change the educational process to match the new goals. Meanwhile, the authors place the main emphasis on teaching mathematics. Drawing on the experience of teaching at the Financial University under the Government of the Russian Federation, the authors propose an integrative model of teaching mathematics that utilizes the contextual approach and content-based teaching. Materials and Methods: The main methods of the study are the analysis of scientific works and pedagogical modeling based on the following methodological approaches: competency-based, integrative, activity-based, as well as the concept of the zone of proximal development. The authors place special importance on the contextual approach and content-based teaching. Results: The analysis of works confirms the significance and relevance of interdisciplinary courses. The necessary modernization of the educational process and its contents is based on the integrative approach. The contextual approach is increasingly used in higher education to reinforce professional orientation. As for content-based education, it is currently used only in foreign language teaching. Within this study, the authors propose an integrative model of teaching mathematics at economics universities. To enforce the focus on the application in basic student training, the contextual approach is utilized which allows giving professional substance to the content of an educational course. The second part of the model is content-based teaching. The authors enhance its potential uses and believe that studying interconnected subjects together allows one to show common patterns and makes the educational process more efficient, intensive and mindful. Discussion and Conclusions: The relevance of having command of mathematical methods and computer technologies for multidisciplinary specialists makes it possible to generalize the creative model by adding various professional contexts.