Reviewerly/exHarmony huggingface dataset:
Universal adversarial perturbations for multiple classification tasks with quantum classifiers

Abstract Quantum adversarial machine learning is an emerging field that studies the vulnerability of quantum learning systems against adversarial perturbations and develops possible defense strategies. Quantum universal adversarial perturbations are small perturbations, which can make different input samples into adversarial examples that may deceive a given quantum classifier. This is a field that was rarely looked into but worthwhile investigating because universal perturbations might simplify malicious attacks to a large extent, causing unexpected devastation to quantum machine learning models. In this paper, we take a step forward and explore the quantum universal perturbations in the context of heterogeneous classification tasks. In particular, we find that quantum classifiers that achieve almost state-of-the-art accuracy on two different classification tasks can be both conclusively deceived by one carefully-crafted universal perturbation. This result is explicitly demonstrated with well-designed quantum continual learning models with elastic weight consolidation method to avoid catastrophic forgetting, as well as real-life heterogeneous datasets from hand-written digits and medical MRI images. Our results provide a simple and efficient way to generate universal perturbations on heterogeneous classification tasks and thus would provide valuable guidance for future quantum learning technologies.
Impact of Hairdressers’ Motivation to Participate in Volunteer Activities on Job Satisfaction

Purpose: This study seeks to investigate the impact of hairdressers’ motivation to participate in volunteer activities on their job satisfaction.Methods: The research subjects were hairdressers living in Gwangju Metropolitan City and Jeonlanamdo who have experience in volunteer activities, and a survey was conducted from December 1 to December 30, 2022. For data analysis, descriptive statistics, correlation, and multiple regression analyses were performed on 211 copies, excluding surveys with incomplete responses, by using the SPSS v. 25.0 Statistical Package Program. Results: The analysis showed the followingresults: First, self-development motivation, cause-directed motivation, external motivation, and leisure motivation as subfactors of the motivation to participate in volunteer activities had a statistically significant positive (+) correlation with cosmetology job satisfaction and interpersonal satisfaction as subfactors of job satisfaction (p&lt;0.001); Second, self-development motivation and leisure motivation were found to have a statistically significant positive (+) effect on cosmetology job satisfaction; Third, leisure motivation, self-development motivation, and cause-directed motivation were found to have a statistically significant positive (+) effect on interpersonal satisfaction.Conclusion: It can be confirmed that self-development motivation and leisure motivation are important factors in hairdressers' job satisfaction as subfactors of their motivation to participate in volunteer activities. For interpersonal satisfaction, it can be confirmed that leisure motivation, self-development motivation, and cause-directed motivation are important factors among the subfactors of motivation to participate in volunteer activities.
SetRank: A Setwise Bayesian Approach for Collaborative Ranking in Recommender System

The recent development of recommender systems has a focus on collaborative ranking, which provides users with a sorted list rather than rating prediction. The sorted item lists can more directly reflect the preferences for users and usually perform better than rating prediction in practice. While considerable efforts have been made in this direction, the well-known pairwise and listwise approaches have still been limited by various challenges. Specifically, for the pairwise approaches, the assumption of independent pairwise preference is not always held in practice. Also, the listwise approaches cannot efficiently accommodate “ties” and unobserved data due to the precondition of the entire list permutation. To this end, in this article, we propose a novel setwise Bayesian approach for collaborative ranking, namely, SetRank, to inherently accommodate the characteristics of user feedback in recommender systems. SetRank aims to maximize the posterior probability of novel setwise preference structures and three implementations for SetRank are presented. We also theoretically prove that the bound of excess risk in SetRank can be proportional to \(\sqrt {M/N}\) , where M and N are the numbers of items and users, respectively. Finally, extensive experiments on four real-world datasets clearly validate the superiority of SetRank compared with various state-of-the-art baselines.
Purpose of Using e-ShodhSindhu by Indian Institute of Technology Faculty: A Study

The study is about the purpose of using e-ShodhSindhu e-Resources by Indian Institute of Technology faculty in India. Professors, Associate Professors and Assistant Professors of selected seven Indian Institute of Technology were used as samples for the study. Objectives: To find out the core purpose and augmented purpose of using e-ShodhSindhu e-Resources, to know the advantages of using e-ShodhSindhu e-Resources, to know the importance, to know the satisfaction and to find out IIT faculty will recommend e-ShodhSindhu e-Resources to others. Methodology: The survey and questionnaire methods were used to gather data. Seven hundred and seventy questionnaires were distributed to selected Indian Institute of Technology (seven Indian IITs). Results and discussions: Most of the respondents are from IIT Bombay, the majority of the respondents are male, it is evident that users between the ages of 36 and 45 are the majority, majority of the respondents are Professors, research is the core purpose of using e-ShodhSindhu e-Resources, the highest mean value of 4.42 is for to-be-up-to-date in subject IIT faculty use e-ShodhSindhu e-Resources, the majority of IIT faculty thinks that searchability and convenient to use are the advantages of using e-ShodhSindhu e-Resources, majority of IIT faculty think that information content and coverage by e-ShodhSindhu is useful and IIT faculty members are satisfied with e-ShodhSindhu e-Resources.
Everything flows... On the role of knowledge sources and the significance of comparative research in the modern world

Contemporary changes in the socio-cultural reality, associated with the revolution in the field of information and the development of new technologies, provide a context for reflection on the problem of the sources of knowledge. The wide access to resources of various data, information and knowledge, related nowadays, in particular, to questions about the manners and limits of using artificial intelligence (AI) at the borderlines of science and education, provokes reflection on the role of contemporary science and its representatives. The reflections are undertaken from the perspective of comparative education, which is treated as a scientific discipline that studies different materials (sources) and constitutes also a field (source) of knowledge. The text presents three aspects of reflection: the origins and development of pedagogical comparativism; its contemporary opportunities and challenges; and the role of the researcher embedded in a vision of the future. Comparative education is presented as the discipline which, through actions and works of comparativists, establishes the value of progress and continuity as inherent developmental forces in culture.
Quantum circuit compilation and hybrid computation using Pauli-based computation

Pauli-based computation (PBC) is driven by a sequence of adaptively chosen, non-destructive measurements of Pauli observables. Any quantum circuit written in terms of the Clifford+<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gate set and having <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>T</mml:mi></mml:math> gates can be compiled into a PBC on <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math> qubits. Here we propose practical ways of implementing PBC as adaptive quantum circuits and provide code to do the required classical side-processing. Our schemes reduce the number of quantum gates to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo stretchy="false">)</mml:mo></mml:math> (from a previous <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:msup><mml:mi>t</mml:mi><mml:mn>3</mml:mn></mml:msup><mml:mrow class="MJX-TeXAtom-ORD"><mml:mo>/</mml:mo></mml:mrow><mml:mi>log</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> scaling) and space/time trade-offs are discussed which lead to a reduction of the depth from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mi>log</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>O</mml:mi><mml:mo stretchy="false">(</mml:mo><mml:mi>t</mml:mi><mml:mo stretchy="false">)</mml:mo></mml:math> within our schemes, at the cost of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>t</mml:mi></mml:math> additional auxiliary qubits. We compile examples of random and hidden-shift quantum circuits into adaptive PBC circuits. We also simulate hybrid quantum computation, where a classical computer effectively extends the working memory of a small quantum computer by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> virtual qubits, at a cost exponential in <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math>. Our results demonstrate the practical advantage of PBC techniques for circuit compilation and hybrid computation.
Using Heat Maps to Optimize Educational Business Processes in The Skill Box Company

&lt;p&gt;The article addresses data analysis methods for solving the problem of choosing the best solution in order to optimize resources using the example of Skillbox company. The problems of choosing information resources are considered and solved, taking into account market offers. The author considers various methods of data analysis with an emphasis on minimizing the resources required for their processing. In the article, the author describes the practical application of the "Digital Assistant" program, which was developed by the Skillbox company to measure product metrics and analyze the behavior of students. Using the big data analysis methods described by the author, ways to optimize educational business processes that lead to an increase in the profitability of educational courses are considered. Heat maps are considered as one of the main ways to analyze big data, and their practical application is described. The author describes the relationship between business metrics and changes occurring with the educational product and suggests methods and frequency of surveys and feedback processing about the educational path of students.&lt;/p&gt;
Estimating the silica content and loss-on-ignition in the North American Soil Geochemical Landscapes datasets: a recursive inversion approach

A novel method of estimating the silica (SiO 2 ) and loss-on-ignition (LOI) concentrations for the North American Soil Geochemical Landscapes (NASGL) project datasets is proposed. Combining the precision of the geochemical determinations with the completeness of the mineralogical NASGL data, we suggest a ‘reverse normative’ or inversion approach to first calculate the minimum SiO 2 , water (H 2 O) and carbon dioxide (CO 2 ) concentrations in weight percent (wt%) in these samples. These can be used in a first step to compute minimum and maximum estimates for SiO 2 . In a recursive step, a ‘consensus’ SiO 2 is then established as the average between the two aforementioned SiO 2 estimates, trimmed as necessary to yield a total composition (major oxides converted from reported Al, Ca, Fe, K, Mg, Mn, Na, P, S and Ti elemental concentrations + ‘consensus’ SiO 2 + reported trace element concentrations converted to wt% + ‘normative’ H 2 O + ‘normative’ CO 2 ) of no more than 100 wt%. Any remaining compositional gap between 100 wt% and this sum is considered ‘other’ LOI and likely includes H 2 O and CO 2 from the reported ‘amorphous’ phase (of unknown geochemical or mineralogical composition) as well as other volatile components present in soil. We validate the technique against a separate dataset from Australia where geochemical (including all major oxides) and mineralogical data exist on the same samples. The correlation between predicted and observed SiO 2 is linear, strong ( R 2 = 0.91) and homoscedastic. We also compare the estimated NASGL SiO 2 concentrations with a sparser, publicly available continental-scale survey over the conterminous USA, the ‘Shacklette and Boerngen’ dataset. This comparison shows the new data to be a reasonable representation of SiO 2 values measured on the ground over the conterminous USA. We recommend the approach of combining geochemical and mineralogical information to estimate missing SiO 2 and LOI by the recursive inversion approach in datasets elsewhere, with the caveat to always validate results.
Copula Modeling of Serially Correlated Multivariate Data with Hidden Structures

We propose a copula-based extension of the hidden Markov model (HMM) which applies when the observations recorded at each time in the sample are multivariate. The joint model produced by the copula extension allows decoding of the hidden states based on information from multiple observations. However, unlike the case of independent marginals, the copula dependence structure embedded into the likelihood poses additional computational challenges. We tackle the latter using a theoretically-justified variation of the EM algorithm developed within the framework of inference functions for margins. We illustrate the method using numerical experiments and an analysis of room occupancy. Supplementary materials for this article are available online.
Development of solar radiation prediction model using minimax algorithm

Abstract Recently, many studies are carried out on solar energy (SE) and global solar radiation (GSR) estimation. When the literature is examined, there are many GSR estimation models developed with different algorithms in different regions of the world. Although some of these models are seen to perform acceptable all over the world, the performance of some models differs according to their location. The Minimax algorithm is a decision‐making algorithm that maximizes the winning potential while minimizing the probability of losing. In this study, a new Angstorm model was developed for GSR with minimax algorithm. The most important aspect of this article in terms of originality is that the GSR estimation model is being developed with the minimax algorithm for the first time. The MATLAB program was used while developing the prediction model with the minimax algorithm. Different statistical error tests were used to evaluate the performance of the results. When these test results are evaluated in general, it is seen that the minimax algorithm gives acceptable results in GSR estimation.
Time-multiplexed programmable continuous-variable photonic quantum computing

Photonic quantum computing has been dramatically scaled up recently by the promising approach of combining the &#99;ontinuous-variable" and &#116;ime-multiplexing" schemes. We report on our recent development of time-multiplexed programmable continuous-variable photonic quantum computing, including a programmable loop-based quantum processor for single-mode multi-step Gaussian gates and a programmable time-multiplexed squeezed light source for initial state preparation. Our works pave the way to large-scale programmable continuous-variable quantum computing.
Characterizing the noise output by a fiber-based entangled photon source

By simultaneously measuring individual and coincidence counts of two single photon detectors connected to a fiber-based entangled photon source and fitting the results with a theoretical model of the photon generation rates, we determine the average number of polarization-entangled photon pairs generated by four-wave mixing and the average number of noise photons generated by Raman scattering and self-phase modulation of the pump as a function of power. In contrast to previous efforts to characterize fiber-based entangled photon sources, this method does not require additional coherence-based measurements to distinguish SPM photons from FWM and Raman-scattered photons.
A patch-based regression convolutional neural network for motion blur estimation

Non-uniform motion blur, including effects commonly encountered in blur associated with atmospheric turbulence, can be estimated as a superposition of locally linear uniform blur kernels. Linear uniform blur kernels are modeled using two parameters, length and angle. In recent work, we have demonstrated the use of a regression-based Convolutional Neural Network (CNN) for robust blind estimation of the length and angle blur parameters of linear uniform blur kernels. In this work we extend the approach of regression-based CNNs to analyze patches in images and estimate the parameters of a locally-linear motion blur kernel, allowing us to model the blur field. We analyze the effectiveness of this patch-based approach versus patch size for two problems: synthetic images generated as a superposition of locally linear blurs, and synthetic images generated with a Zernike polynomial-based wavefront distortion applied at the pupil plane.
Part Deviation Correction Method Based on Geometric Feature Recognition

To realize the automatic loading process of parts, one of the core tasks is to identify the geometric contour of the part's surface and the angular direction. Since the angular direction of each part is not the same when it arrives at the loading position, for example, there are two same types of parts with the same pattern, when they arrive at the loading position, the pattern on one part may be on the right side of the part surface, and the pattern on the other part may be on the left side of the part surface, the gripper of the mechanical arm needs to rotate above the parts in order to grab the parts during each loading process. If the rotation angle is wrong, there will be an impact between the gripper and the parts. Therefore, in order to solve the problem of different angles, this paper proposes a method of parts deviation correction based on geometric features. In this work, firstly, the acquired image is preprocessed, the image background is separated, and the geometric features of the parts are obtained. Then edge detection is used to obtain the set of edge pixels to obtain the contour in the image. Finally, the image moment and measurement model are used to output angular direction. Through 500 repeated detection experiments, the results show that this method can perform better angular direction correction. The maximum angular direction difference is 0.073°, which is 0.856° and 1.793° higher than the Least square method and Hough transform circle detection accuracy, respectively. The average detection time is 1.89 s and is 0.336 s and 1.39 s less than the Least square method and Hough transform circle detection, which meets the requirements of industrial applications.
Research on Chaotic SPWM Strategy Based on Fuch Map

The application of chaotic SPWM can have SPSP(spread spectrum) effect on harmonic interference by taking advantage of the continuous spectral characteristics of chaotic signals, thus the higher order harmonics generated by the high frequency action of the switching device tend to be distributed over a wider frequency range, and the spreading effect of chaotic modulation depends on the attribute of the chaotic sequence. That is why it is significant to ensure the pseudo-random variation of the chaotic signal during the operation of the system. To address the problem that the chaotic sequence often falls into the immobile or cyclic state due to the improper selection of the sequence initial value or system parameters when using traditional map functions, the new discrete Fuch map is introduced into the chaotic modulation. It has been shown in the existing literature that the Fuch map does not depend on the initial value of the sequence and the choice of the system parameters, but always can generate chaos phenomenon provided the initial value is not zero. To validate the SPSP effect of chaotic modulation based on Fuch map, a simulated inverter platform is built in Simulink. The simulation results show that the chaotic SPWM based on Fuch map can significantly improve the harmonic interference problem of system, and the spreading spectrum effect is slightly better than that of the conventional Logistic map. The Fuch map can also guarantee that the chaotic sequences would not fall into equilibrium or periodic cyclic states.
Super-resolution of Ray-tracing Channel Simulation via Attention Mechanism based Deep Learning Model

As an emerging approach, deep learning plays an increasingly influential role in channel modeling. Traditional ray tracing (RT) methods of channel modeling tend to be inefficient and expensive. In this paper, we present a super-resolution (SR) model for channel characteristics. Residual connection and attention mechanism are applied to this convolutional neural network (CNN) model. Experiments prove that the proposed model can reduce the noise interference generated in the SR process and solve the problem of low efficiency of RT. The mean absolute error of our channel SR model on the PL achieves the effect of 2.82 dB with scale factor 2, the same accuracy as RT took only 52% of the time in theory. Compared with vision transformer (ViT), the proposed model also demonstrates less running time and computing cost in SR of channel characteristics.
A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how LIME generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Hongming Tang and Shiyu Yue have equally contributed to this work.
Machine Learning Based Classification and Prediction of Electromagnetic Absorption in Electrical Reverberation Chambers

A method is proposed to quickly classify devices with regard to their susceptibility to absorb power from an ambient electromagnetic field with the help of an electrical reverberation chamber. Different absorption classes are implemented via a set of labeled training devices. Frequency depending S-parameters are considered as data. The machine learning environment consists of an auto-encoder to reduce the dimension of the input data and several machine learning approaches for classification.