Reviewerly/exHarmony huggingface dataset:
Quantum Error Mitigation for Quantum State Tomography

Quantum state tomography (QST) is the task of statistically constructing the density matrix of an unknown quantum state by measuring its several copies.The presence of noise in the QST setup can considerably degrade the fidelity between the constructed density matrix and the actual state.We consider a noisy QST setup with depolarizing noise and attempt to mitigate the effects of noise by quantum error mitigation (QEM).We compare the performance of different QEM methods with the same resources and find that the measurement error mitigation and zero noise extrapolation provide the best performance in terms of maximizing the fidelity between the state and its density matrix.
Analysis of the misalignment effect and the characterization method for imprinting continuous phase plates

Continuous phase plates (CPPs) are increasingly being used to realize beam shaping and smoothing in high-power laser systems. With computer controlled optical surfacing (CCOS) technology, CPPs can be imprinted with high accuracy by a series of processing iterations, in which the characterization of the imprinted CPP surface plays a key role. However, the form accuracy evaluation is sensitive to the misalignment caused by the difference between the designed and measured coordinates. In this paper, the matching problem, which is the critical part of characterization, is first summarized as a least squares problem in accordance with the processing principle of CPPs. Then, the misalignment effect on the form error evaluation is analyzed. Necessary attention is paid to the CPP features and the sensibility analysis for different misalignments is conducted. To improve the efficiency and accuracy, an automatic characterization method based on image registration and nonlinear optimization is presented. Considering the smoothness of the CPP surface, the height difference tracing method is proposed to evaluate the matching performance and embedded into the characterization method. Finally, a series of simulations and experiments were undertaken to verify the performance of the proposed characterization method. The results demonstrated the feasibility of the proposed method, indicating that it can provide the reliable form error evaluation with sub-nanometer accuracy for imprinted CPPs.
Research on the Efficiency of Intelligent Algorithm for English Speech Recognition and Sentence Translatione

Machine translation has been gradually widely used to improve the efficiency of English translation.This paper briefly introduced the English speech recognition algorithm based on the back-propagation (BP) neural network algorithm and the machine translation algorithm based on the long short-term memoryrecurrent neural network (LSTM-RNN) algorithm.Then, the machine translation algorithm was simulated and compared with BP-RNN and RNN-RNN algorithms.The results showed that the BP neural network algorithm had a lower word error rate and shorter recognition time compared with the manual recognition approach; the LSTM-RNN-based machine translation algorithm had the lowest error rate for the translation of speech recognition results, and the translation gained the highest rating in the evaluation of ten professional translators.
Understanding, Modeling and Simulating Unintended Positional Drift during Repetitive Steering Navigation Tasks in Virtual Reality

Virtual steering techniques enable users to navigate in larger Virtual Environments (VEs) than the physical workspace available.Even though these techniques do not require physical movement of the users (e.g. using a joystick and the head orientation to steer towards a virtual direction), recent work observed that users might unintentionally move in the physical workspace while navigating, resulting in Unintended Positional Drift (UPD).This phenomenon can be a safety issue since users may unintentionally reach the physical boundaries of the workspace while using a steering technique.In this context, as a necessary first step to improve the design of navigation techniques minimizing the UPD, this paper aims at analyzing and modeling the UPD during a virtual navigation task.In particular, we characterize and analyze the UPD for a dataset containing the positions and orientations of eighteen users performing a virtual slalom task using virtual steering techniques.Participants wore a head-mounted display and had to follow three different sinusoidal-like trajectories (with low, medium and high curvature) using a torso-steering navigation technique.We analyzed the performed motions and proposed two UPD models: the first based on a linear regression analysis and the second based on a Gaussian Mixture Model (GMM) analysis.Then, we assessed both models through a simulation-based evaluation where we reproduced the same navigation task using virtual agents.Our results indicate the feasibility of using simulation-based evaluations to study UPD.The paper concludes with a discussion of potential applications of the results in order to gain a better understanding of UPD during steering and therefore improve the design of navigation techniques by compensating for UPD.
Elongated Small Object Detection from Remote Sensing Images Using Hierarchical Scale-Sensitive Networks

The detection of elongated objects, such as ships, from satellite images has very important application prospects in marine transportation, shipping management, and many other scenarios. At present, the research of general object detection using neural networks has made significant progress. However, in the context of ship detection from remote sensing images, due to the elongated shape of ship structure and the wide variety of ship size, the detection accuracy is often unsatisfactory. In particular, the detection accuracy of small-scale ships is much lower than that of the large-scale ones. To this end, in this paper, we propose a hierarchical scale sensitive CenterNet (HSSCenterNet) for ship detection from remote sensing images. HSSCenterNet adopts a multi-task learning strategy. First, it presents a dual-direction vector to represent the posture or direction of the tilted bounding box, and employs a two-layer network to predict the dual direction vector, which improves the detection block of CenterNet, and cultivates the ability of detecting targets with tilted posture. Second, it divides the full-scale detection task into three parallel sub-tasks for large-scale, medium-scale, and small-scale ship detection, respectively, and obtains the final results with non-maximum suppression. Experimental results show that, HSSCenterNet achieves a significant improved performance in detecting small-scale ship targets while maintaining a high performance at medium and large scales.
Computer-generated frequency-carrier moiré profilometry

A three-dimensional measurement method based on frequency-carrier computer-generated moiré fringe is proposed. By projecting two sinusoidal gratings with π phase difference each other onto the measured object respectively and capturing the corresponding deformed patterns, the AC component of the deformed pattern can be extracted. When this AC component is respectively multiplied by actively designed computer-generated sinusoidal grating and cosinoidal grating with the same frequency, two new fringes containing frequency-carrier moiré components can be obtained. After low-pass filtering, the frequency-carrier moiré fringes can be extracted to achieve object’s phase retrieval. As long as the DC component of the actively designed sinusoidal grating and cosinoidal grating are all controlled to be zero, the effect of inaccuracy elimination of the deformed pattern’s background light component on measuring accuracy can be reduced evidently. The frequency of the actively designed grating can be determined as 1/4 lines per pixel to ensure the maximum distance between each adjacent spectrum peaks of the new generated fringe so as to get the best measurement effect. The proposed method has good application ability in the limited number of projection frames and other scenes, and can achieve high measurement accuracy by projecting only two fringe patterns. Experimental results verify the feasibility and validity of the proposed method.
An Improved Variable Kernel Density Estimator Based on L2 Regularization

The nature of the kernel density estimator (KDE) is to find the underlying probability density function (p.d.f) for a given dataset. The key to training the KDE is to determine the optimal bandwidth or Parzen window. All the data points share a fixed bandwidth (scalar for univariate KDE and vector for multivariate KDE) in the fixed KDE (FKDE). In this paper, we propose an improved variable KDE (IVKDE) which determines the optimal bandwidth for each data point in the given dataset based on the integrated squared error (ISE) criterion with the L2 regularization term. An effective optimization algorithm is developed to solve the improved objective function. We compare the estimation performance of IVKDE with FKDE and VKDE based on ISE criterion without L2 regularization on four univariate and four multivariate probability distributions. The experimental results show that IVKDE obtains lower estimation errors and thus demonstrate the effectiveness of IVKDE.
Integration of Data Mining Techniques in SCADA System for Optimal Operation of Hydropower Plants

The hydropower sector has gained more importance in the last decade since renewable resources have recorded a worldwide investment boom. Based on its advantages referring to the timely response at peak energy demand with excellent flexibility, hydropower plants must operate as efficiently as possible to cover the energy variations produced by the renewable sources. In the paper, the data mining techniques integrated into a special Knowledge Discovery module of the SCADA system have been proposed to assist the Decision-Maker (DM) in the optimal operation of the hydropower plants. The main advantages refer to the high efficiency of the data processing process and shortening the time interval in which the DM makes the decisions.
DATA-INFORMED EMULATORS FOR MULTI-PHYSICS SIMULATIONS

Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, ranging from functional approximations to dynamical approximations, to build such emulators for coupled thermal, hydrological, mechanical and chemical processes that occur near an engineered barrier system in the nuclear waste repository. Two nonlinear approximators, random forests and neural networks, are deployed to capture the complexity of the physics-based model and to identify its most significant hydrological and geochemical parameters. Our emulators capture the temporal evolution of the Uranium distribution coefficient of the clay buffer, and identify its functional dependence on these key parameters. The emulators' accuracy is further enhanced by assimilating relevant simulated predictors and clustering strategy. The relative performance of random forests and neural networks shows the advantage of ensemble learning in random forests algorithm, especially for highly nonlinear problems with limited data.
WIP: End-to-End Analysis of Adversarial Attacks to Automated Lane Centering Systems

Machine learning techniques, particularly those based on deep neural networks (DNNs), are widely adopted in the development of advanced driver-assistance systems (ADAS) and autonomous vehicles.While providing significant improvement over traditional methods in average performance, the usage of DNNs also presents great challenges to system safety, especially given the uncertainty of the surrounding environment, the disturbance to system operations, and the current lack of methodologies for predicting DNN behavior.In particular, adversarial attacks to the sensing input may cause errors in systems' perception of the environment and lead to system failure.However, existing works mainly focus on analyzing the impact of such attacks on the sensing and perception results and designing mitigation strategies accordingly.We argue that as system safety is ultimately determined by the actions it takes, it is essential to take an end-to-end approach and address adversarial attacks with the consideration of the entire ADAS or autonomous driving pipeline, from sensing and perception to planing, navigation and control.In this paper, we present our initial findings in quantitatively analyzing the impact of a type of adversarial attack (that leverages road patch) on system planning and control, and discuss some of the possible directions to systematically address such attack with an end-to-end view.
The Effect of Online Interaction Types and Acceptance of Technology Factors on Student Satisfaction with Online Learning During the COVID-19 Pandemic

In reducing the spread of the COVID-19 virus, all face-to-face learning processes are replaced with onlinebased learning.Student satisfaction is needed in an online learning.Online interaction and technology acceptance are required in online learning.Learner-content interaction, learner-instructor interaction, and learner-learner interaction are types of online interaction.Technology acceptance is described by the two factors of technology acceptance model (TAM).The two factors are perceived usefulness and perceived ease of use.Therefore, this study aims to examine the effect of online interaction types and technology acceptance factors on student satisfaction with online learning.The study was conducted on 205 students at a university in Jakarta.The research method used is a quantitative method by distributing online questionnaires.Based on simple linear regression analysis, this study indicate that all three types of online interaction has a positive effect on student satisfaction with online learning.This study also shows that two factors of technology acceptance effect student satisfaction with online learning.This study found that duration of online learning gives different result in student satisfaction.Overall, the results of this study indicate that material, communication with instructor and other students in online learning effects the student satisfaction.In addition, the longer online learning takes, the students feel more satisfied in learning because more material is obtained.Furthermore, students' perceptions on one of communication platform can help and ease students in online learning so that student satisfaction increases.
Physion: Evaluating Physical Prediction from Vision in Humans and Machines

While machine learning algorithms excel at many challenging visual tasks, it is unclear that they can make predictions about commonplace real world physical events. Here, we present a visual and physical prediction benchmark that precisely measures this capability. In realistically simulating a wide variety of physical phenomena -- rigid and soft-body collisions, stable multi-object configurations, rolling and sliding, projectile motion -- our dataset presents a more comprehensive challenge than existing benchmarks. Moreover, we have collected human responses for our stimuli so that model predictions can be directly compared to human judgments. We compare an array of algorithms -- varying in their architecture, learning objective, input-output structure, and training data -- on their ability to make diverse physical predictions. We find that graph neural networks with access to the physical state best capture human behavior, whereas among models that receive only visual input, those with object-centric representations or pretraining do best but fall far short of human accuracy. This suggests that extracting physically meaningful representations of scenes is the main bottleneck to achieving human-like visual prediction. We thus demonstrate how our benchmark can identify areas for improvement and measure progress on this key aspect of physical understanding.
Evaluation of Abstraction Capabilities and Detection of Discomfort with a Newscaster Chatbot for Entertaining Elderly Users

We recently proposed a novel intelligent newscaster chatbot for digital inclusion. Its controlled dialogue stages (consisting of sequences of questions that are generated with hybrid Natural Language Generation techniques based on the content) support entertaining personalisation, where user interest is estimated by analysing the sentiment of his/her answers. A differential feature of our approach is its automatic and transparent monitoring of the abstraction skills of the target users. In this work we improve the chatbot by introducing enhanced monitoring metrics based on the distance of the user responses to an accurate characterisation of the news content. We then evaluate abstraction capabilities depending on user sentiment about the news and propose a Machine Learning model to detect users that experience discomfort with precision, recall, F1 and accuracy levels over 80%.
Low Quality and Recognition of Image Content

Assessment of visual encryption of video and image content requires a reliable estimation of content recognizability and low quality. As pointed out in the literature, current methods are insufficient and research into this topic, as well as into the relation between low quality and recognizability, is still lacking. This lack of research is primarily due to a lack of data. To improve on the status-quo we have taken a recognizability database and performed a subjective quality evaluation on a subset of the images. This gives us a new database with both subjective recognizability and quality information and allows to delve into the relation between low quality and recognizability. We analyze the relationship between quality and recognizability as well as the predictive quality of state of the art visual quality indices. We show that the visual quality indices are poor indicators for the estimation of recognizability. Furthermore, we show that they must be a poor fit because of the disparity between two distinct perceptual tasks: quality and recognizability.
Research on the Station Layout Method of Ground-Based Pseudolite Positioning System Based on NSGA-II Algorithm

Given the problem that the existing method of station distributing the pseudosatellite system cannot ensure both its coverage and position in a situation of signal occlusion, it proposed a new stationary layout method with an elite strategy for a ground-based pseudosatellite positioning system based on the elite strategy of the nondominant genetic rankings (NSGA-II). The geometrical design of the pseudosatellite system is calculated by visual domain analysis and precision factors for the signal coverage age and base station. To optimize the algorithm, the NSGA-II algorithm is used. An earth pseudosatellite positioning system method of stationary distribution is obtained that simultaneously optimizes signal coverage and positioning accuracy. The algorithm is better distributed and has a certain superintendence compared with the traditional genetic algorithm.
Short-Term Solar Irradiance Forecasting Model Based on Bidirectional Long Short-Term Memory Deep Learning

Recurrent neural networks (RNNs) are the most effective technology to study and analyze the future performance of solar irradiance. Bidirectional RNNs (BRNNs) provide the key benefit of manipulating the data with two different hidden layers in two opposite directions and can feed back to the same layer of output. In this approach, the output layers can simultaneously receive information from the past (backward layers) and the future (forward layers). A bidirectional long short-term memory (BI-LSTM) model was developed and employed to predict solar irradiance values for the next 169 hours based on hourly historical data (01-01-1985 to 16-09-2020) from Tabuk city. The findings specifically demonstrate that in terms of classification and considerations, the BI-LSTM model has promising performance with notable accuracy. In addition, the model is capable of coping with the selected size of sequential data. The prediction accuracy and performance of the BI-LSTM model were highly enhanced when external data such as wind speed and temperature were provided.
Dual Script E2E Framework for Multilingual and Code-Switching ASR

India is home to multiple languages, and training automatic speech recognition (ASR) systems for languages is challenging. Over time, each language has adopted words from other languages, such as English, leading to code-mixing. Most Indian languages also have their own unique scripts, which poses a major limitation in training multilingual and code-switching ASR systems. Inspired by results in text-to-speech synthesis, in this work, we use an in-house rule-based phoneme-level common label set (CLS) representation to train multilingual and code-switching ASR for Indian languages. We propose two end-to-end (E2E) ASR systems. In the first system, the E2E model is trained on the CLS representation, and we use a novel data-driven back-end to recover the native language script. In the second system, we propose a modification to the E2E model, wherein the CLS representation and the native language characters are used simultaneously for training. We show our results on the multilingual and code-switching tasks of the Indic ASR Challenge 2021. Our best results achieve 6% and 5% improvement (approx) in word error rate over the baseline system for the multilingual and code-switching tasks, respectively, on the challenge development data.
Evaluation of Teaching Effect in Higher Educational Institutions and Identification of Its Influencing Factors

For higher educational institutions, teaching effect evaluation is necessary for improving teaching quality and reforming teaching mode. The evaluation has a long-lasting effect on the growth of students, and a close correlation with the immediate interests of every teacher. This paper evaluates the teaching effect and its influencing factors for the undergraduate education in ordinary colleges. It was found that the teaching effect is mainly affected by factors in four dimensions: teacher, student, teaching method, and teaching environment. Then, the authors reformed the teaching method, and tested its performance. The results show that the reformed teaching method improved the professional English ability of students more significantly than the ordinary teaching method. Therefore, higher educational schools are suggested to establish a set of scientific, reasonable, and practical evaluation methods for teaching effect, reflecting the teaching requirements, policies, faculty, and capital investment. The research findings lay the foundation for the long-term healthy development of higher education.