Reviewerly/exHarmony huggingface dataset:
Number Partitioning With Grover’s Algorithm in Central Spin Systems

Numerous conceptually important quantum algorithms rely on a blackbox device known as an oracle, which is typically difficult to construct without knowing the answer to the problem that the algorithm is intended to solve. A notable example is Grover's search algorithm. Here we propose a Grover search for solutions to a class of NP-complete decision problems known as subset sum problems, including the special case of number partitioning. Each problem instance is encoded in the couplings of a set of qubits to a central spin or boson, which enables a realization of the oracle without knowledge of the solution. The algorithm provides a quantum speedup across a known phase transition in the computational complexity of the partition problem, and we identify signatures of the phase transition in the simulated performance. Whereas the naive implementation of our algorithm requires a spectral resolution that scales exponentially with system size for NP-complete problems, we also present a recursive algorithm that enables scalability. We propose and analyze implementation schemes with cold atoms, including Rydberg-atom and cavity-QED platforms.6 MoreReceived 11 September 2020Revised 23 February 2021Accepted 23 March 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020319Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAtoms, ions, & molecules in cavitiesCavity quantum electrodynamicsQuantum algorithmsQuantum computationQuantum entanglementQuantum gatesQuantum information with atoms & lightQuantum opticsPhysical SystemsRydberg atoms & moleculesTechniquesComputational complexityQuantum InformationAtomic, Molecular & OpticalGeneral Physics
A new analytical framework for missing data imputation and classification with uncertainty: Missing data imputation and heart failure readmission prediction

Background The wide adoption of electronic health records (EHR) system has provided vast opportunities to advance health care services. However, the prevalence of missing values in EHR system poses a great challenge on data analysis to support clinical decision-making. The objective of this study is to develop a new methodological framework that can address the missing data challenge and provide a reliable tool to predict the hospital readmission among Heart Failure patients. Methods We used Gaussian Process Latent Variable Model (GPLVM) to impute the missing values. Specifically, a lower dimensional embedding was learned from a small complete dataset and then used to impute the missing values in the incomplete dataset. The GPLVM-based missing data imputation can provide both the mean estimate and the uncertainty associated with the mean estimate. To incorporate the uncertainty in prediction, a constrained support vector machine (cSVM) was developed to obtain robust predictions. We first sampled multiple datasets from the distributions of input uncertainty and trained a support vector machine for each dataset. Then an optimal classifier was identified by selecting the support vectors that maximize the separation margin of a newly sampled dataset and minimize the similarity with the pre-trained support vectors. Results The proposed model was derived and validated using Physionet MIMIC-III clinical database. The GPLVM imputation provided normalized mean absolute errors of 0.11 and 0.12 respectively when 20% and 30% of instances contained missing values, and the confidence bounds of the estimations captures 97% of the true values. The cSVM model provided an average Area Under Curve of 0.68, which improves the prediction accuracy by 7% as compared to some existing classifiers. Conclusions The proposed method provides accurate imputation of missing values and has a better prediction performance as compared to existing models that can only deal with deterministic inputs.
The Effect of Marketing Knowledge Management on Bank Performance Through Fintech Innovations: A Survey Study of Jordanian Commercial Banks

Aim/Purpose: This study aimed to examine the effect of marketing knowledge management (MKM) on bank performance via the mediating role of the Fintech innovation in Jordanian commercial banks. Background: An extensive number of studies found a significant relationship between Marketing knowledge management and bank performance (e.g., Akroush &amp; Al-Mohammad, 2010; Hou &amp; Chien 2010; Rezaee &amp; Jafari, 2015; Veismoradi et al., 2013). However, there remains a lack of clarity regarding the relationship between marketing knowledge management (MKM) and bank performance (BP). Furthermore, the linkage between MKM and BP is not straightforward but, instead, includes a more complicated relationship. Therefore, it is argued that managing marketing knowledge management assets and capabilities can enhance performance via the role of financial innovation as a mediating factor on commercial banks; to date, however, there is no empirical evidence. Methodology: Based on a literature review, knowledge-based theory, and financial innovation theory, an integrated conceptual framework has been developed to guide the study. A quantitative approach was used, and the data was collected from 336 managers and employees in all 13 Jordanian commercial banks using online and in hand instruments. Structural equation modeling (SEM) was used to analyze and verify the study variables. Contribution: This article contributes to theory by filling a gap in the literature regarding the role of marketing knowledge management assets and capabilities in commercial banks operating in a developing country like Jordan. It empirically examined and validated the role of Fintech innovation as mediators between marketing knowledge management and bank performance Findings: The main findings revealed that marketing knowledge management had a significant favorable influence on bank performance. Fintech innovation acted as partial mediators in this relationship. Recommendations for Practitioners: Commercial banks should be fully aware of the importance of knowledge management practices to enhance their financial innovation and bank performance. They should also consider promoting a culture of practicing knowledge management processes among their managers and employees by motivating and training to promote innovations. Recommendation for Researchers: The result endorsed Fintech innovation’s mediating effect on the relationship between the independent variable, marketing knowledge management (assets and capabilities), and the dependent variable bank performance, which was not addressed before; thus, it needs further validation. Future Research: The current designed research model can be applied and assessed further in other sectors, including banking and industrial sectors across developed and developing countries. It would also be of interest to introduce other variables in the study model that can act as consequences of MKM capabilities, such as financial and non-financial performance measures
Visual tracking of mobile robots with both velocity and acceleration saturation constraints

In this paper, an adaptive visual tracking controller is proposed for the nonholonomic mobile robot with an onboard monocular camera, wherein both the velocity and acceleration saturation constraints can be guaranteed by properly selecting the control parameters during the visual servoing process. Specifically, the virtual velocity-level controller is firstly designed based on the first-order filter, by which the bounds of the virtual velocity signals can be estimated. Then, according to the finite-time control theory, the actual acceleration-level controller is developed to track the virtual velocity input in a finite time. For the unknown distance between the plane of feature points and the origin of the global frame due to the lack of the image depth, an adaptive updating law is designed and also used for the parameter identification. The global asymptotical stability of the closed-loop system is proven using Lyapunov techniques with Barbalat’s lemma. The calculations for the upper bounds of velocity and acceleration signals are given. In addition, some tips for the control gains tuning in practical experiments are summarized. Experimental results are provided to validate the effectiveness of the proposed controller.
Visual Sentiment Analysis With Active Learning

Visual Sentiment Analysis (VSA) has attracted wide attention since more and more people are willing to express their emotion and opinions via visual contents on social media.Meanwhile, extensive datasets drive the rapid development of deep neural networks for this task.However, the annotation of large-scale datasets is very expensive and time consuming.In this paper, we propose a novel active learning framework, which uses few labeled training samples to achieve an effective sentiment analysis model.First, we attach a new branch to the traditional Convolution Neural Network (CNN), which is named "texture module".The affective vector will be obtained by computing inner products of feature maps from different convolutional blocks in this branch.We will utilize this vector to distinguish affective images.Second, the query strategy is formed by the classification scores from both the traditional CNN and the texture module.Then, we can use samples obtained by utilizing the query strategy to train our model.Extensive experiments on four public affective datasets show that our approach uses few labeled training samples to achieve promising results for VSA.
Spatial Presence, Performance, and Behavior between Real, Remote, and Virtual Immersive Environments

Spatial presence encompasses the user's ability to experience a sense of “being there”. While particular attention was given to assess spatial presence in real and virtual environments, few have been interested in measuring it in telepresence situations. To bridge this gap, the present work introduces a study that compares the execution of a task in three conditions: a real physical environment, a remote environment via a telepresence system, and a virtual simulation of the real environment. Following a within-subject design, 27 participants performed a navigation task consisting in following a route while avoiding obstacles. Spatial presence and five related factors (affordance, enjoyment, attention allocation, reality, and cybersickness) were evaluated using a presence questionnaire. In addition, performance measures were gathered regarding environment recollection and task execution. The evaluation also included a behavioral metric measured by obstacle avoidance distance extracted from participants' trajectories. Results indicated a higher presence in the real environment, along with the best performance measures. No difference was found in spatial presence between the remote and the virtual conditions, although a higher degree of affordance and enjoyment was attributed to the virtual environment, and a higher degree of reality was attributed to the remote environment. The number of collisions was found to be lower in the remote condition compared to the virtual condition. Similarly, the avoidance distance was also bigger (and almost similar) in the real and the remote environments compared to the virtual environment indicating a greater caution of participants. These cues highlight that the behavior of participants in the remote condition was closer to their behavior in the real situation than it was in the virtual condition. Furthermore, positive correlations were found between the reality factor and two of the three performance measures, as well as with the behavioral metric. This suggests that the degree of physical existence of the space in which participants operate can influence their performance and behavior.
Adaptive Wiener Filter and Natural Noise to Eliminate Adversarial Perturbation

Deep neural network has been widely used in pattern recognition and speech processing, but its vulnerability to adversarial attacks also proverbially demonstrated. These attacks perform unstructured pixel-wise perturbation to fool the classifier, which does not affect the human visual system. The role of adversarial examples in the information security field has received increased attention across a number of disciplines in recent years. An alternative approach is “like cures like”. In this paper, we propose to utilize common noise and adaptive wiener filtering to mitigate the perturbation. Our method includes two operations: noise addition, which adds natural noise to input adversarial examples, and adaptive wiener filtering, which denoising the images in the previous step. Based on the study of the distribution of attacks, adding natural noise has an impact on adversarial examples to a certain extent and then they can be removed through adaptive wiener filter, which is an optimal estimator for the local variance of the image. The proposed improved adaptive wiener filter can automatically select the optimal window size between the given multiple alternative windows based on the features of different images. Based on lots of experiments, the result demonstrates that the proposed method is capable of defending against adversarial attacks, such as FGSM (Fast Gradient Sign Method), C&amp;W, Deepfool, and JSMA (Jacobian-based Saliency Map Attack). By compared experiments, our method outperforms or is comparable to state-of-the-art methods.
A Blockchain-Based Public Auditing Scheme for Cloud Storage Environment without Trusted Auditors

In the cloud storage applications, the cloud service provider (CSP) may delete or damage the user’s data. In order to avoid the responsibility, CSP will not actively inform the users after the data damage, which brings the loss to the user. Therefore, increasing research focuses on the public auditing technology recently. However, most of the current auditing schemes rely on the trusted third public auditor (TPA). Although the TPA brings the advantages of fairness and efficiency, it cannot get rid of the possibility of malicious auditors, because there is no fully trusted third party in the real world. As an emerging technology, blockchain technology can effectively solve the trust problem among multiple individuals, which is suitable to solve the security bottleneck in the TPA-based public auditing scheme. This paper proposed a public auditing scheme with the blockchain technology to resist the malicious auditors. In addition, through the experimental analysis, we demonstrate that our scheme is feasible and efficient.
Mathematical Word Problem Generation from Commonsense Knowledge Graph and Equations

There is an increasing interest in the use of mathematical word problem (MWP) generation in educational assessment.Different from standard natural question generation, MWP generation needs to maintain the underlying mathematical operations between quantities and variables, while at the same time ensuring the relevance between the output and the given topic.To address above problem, we develop an end-to-end neural model to generate diverse MWPs in real-world scenarios from commonsense knowledge graph and equations.The proposed model (1) learns both representations from edge-enhanced Levi graphs of symbolic equations and commonsense knowledge; (2) automatically fuses equation and commonsense knowledge information via a self-planning module when generating the MWPs.Experiments on an educational gold-standard set and a large-scale generated MWP set show that our approach is superior on the MWP generation task, and it outperforms the SOTA models in terms of both automatic evaluation metrics, i.e., BLEU-4, ROUGE-L, Self-BLEU, and human evaluation metrics, i.e., equation relevance, topic relevance, and language coherence.To encourage reproducible results, we make our code and MWP dataset public available at https:// github.com/tal-ai/MaKE_EMNLP2021.
XED: A Multilingual Dataset for Sentiment Analysis and Emotion Detection

We introduce XED, a multilingual fine-grained emotion dataset.The dataset consists of humanannotated Finnish (25k) and English sentences (30k), as well as projected annotations for 30 additional languages, providing new resources for many low-resource languages.We use Plutchik's core emotions to annotate the dataset with the addition of neutral to create a multilabel multiclass dataset.The dataset is carefully evaluated using language-specific BERT models and SVMs to show that XED performs on par with other similar datasets and is therefore a useful tool for sentiment analysis and emotion detection.
Clinical prediction models to predict the risk of multiple binary outcomes: a comparison of approaches

Abstract Clinical prediction models (CPMs) can predict clinically relevant outcomes or events. Typically, prognostic CPMs are derived to predict the risk of a single future outcome. However, there are many medical applications where two or more outcomes are of interest, meaning this should be more widely reflected in CPMs so they can accurately estimate the joint risk of multiple outcomes simultaneously. A potentially naïve approach to multi‐outcome risk prediction is to derive a CPM for each outcome separately, then multiply the predicted risks. This approach is only valid if the outcomes are conditionally independent given the covariates, and it fails to exploit the potential relationships between the outcomes. This paper outlines several approaches that could be used to develop CPMs for multiple binary outcomes. We consider four methods, ranging in complexity and conditional independence assumptions: namely, probabilistic classifier chain, multinomial logistic regression, multivariate logistic regression, and a Bayesian probit model. These are compared with methods that rely on conditional independence: separate univariate CPMs and stacked regression. Employing a simulation study and real‐world example, we illustrate that CPMs for joint risk prediction of multiple outcomes should only be derived using methods that model the residual correlation between outcomes. In such a situation, our results suggest that probabilistic classification chains, multinomial logistic regression or the Bayesian probit model are all appropriate choices. We call into question the development of CPMs for each outcome in isolation when multiple correlated or structurally related outcomes are of interest and recommend more multivariate approaches to risk prediction.
Digital Education and Artistic-Visual Learning in Flexible University Environments: Research Analysis

The constant development of digital technologies has allowed living in a digital environment based on connections, also transforming the context of the educational process. Experiences show that digital technologies have influenced the way of learning and, consequently, the way of teaching. Learning in the digital age is a complex process since it is a multifaceted and diverse action. The aim of this research is to identify global trends in digital education and its link with the learning of artistic and visual education in higher education settings, during the period 2000–2019. For this, bibliometric techniques have been applied to 1291 documents, obtaining results from the scientific activity of the main authors, research institutions, and countries that promote this topic. The data show increasing relevance, particularly in the last three years. The main subject area is the social sciences. The study has detected the lines of research that are related to the Internet, education, visuals, computer programs, learning, digital media literacy, and educational technology. This work contributes to the academic, scientific, and institutional debate to enhance decision-making based on existing information.
Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics

This paper contributes towards the development of motion tracking algorithms for time-critical applications, proposing an infrastructure for dynamically solving the inverse kinematics of highly articulate systems such as humans. The method presented is model-based, it makes use of velocity correction and differential kinematics integration in order to compute the system configuration. The convergence of the model towards the measurements is proved using Lyapunov analysis. An experimental scenario, where the motion of a human subject is tracked in static and dynamic configurations, is used to validate the inverse kinematics method performance on human and humanoid models. Moreover, the method is tested on a human-humanoid retargeting scenario, verifying the usability of the computed solution in real-time robotics applications. Our approach is evaluated both in terms of accuracy and computational load, and compared to iterative optimization algorithms.
AR-LabOr: Design and Assessment of an Augmented Reality Application for Lab Orientation

Lab orientation is a vital part of learning for new students entering the university, as it provides the students with all the necessary and important information about the lab. The current orientation is manual, tedious, suffers from logistical constraints, lacks engagement, and provides no way to assess that outcomes have been achieved. This is also supported by the results of a student survey which revealed students’ dissatisfaction with current process of orientation. This study presents the design and development of a sample augmented reality mobile application, AR-LabOr, for the lab orientation that helps students in a quick and easy adaptation to the lab environment by familiarizing them with the lab equipment, staff, and safety rules in a fun and interactive manner. This application makes use of marker-less augmented reality technology and a blend of multimedia information such as sound, text, images, and videos that are superimposed on real-world contents. An experiment with 56 students showed that they found the novel method of orientation using the application more engaging than the traditional instructor-led method. Students also found the application to be more supportive, motivating, and that it helped them in better understanding the lab equipment.
Intra-Class Variation Reduction of Speaker Representation in Disentanglement Framework

In this paper, we propose an effective training strategy to ex-tract robust speaker representations from a speech signal. Oneof the key challenges in speaker recognition tasks is to learnlatent representations or embeddings containing solely speakercharacteristic information in order to be robust in terms of intra-speaker variations. By modifying the network architecture togenerate both speaker-related and speaker-unrelated representa-tions, we exploit a learning criterion which minimizes the mu-tual information between these disentangled embeddings. Wealso introduce an identity change loss criterion which utilizes areconstruction error to different utterances spoken by the samespeaker. Since the proposed criteria reduce the variation ofspeaker characteristics caused by changes in background envi-ronment or spoken content, the resulting embeddings of eachspeaker become more consistent. The effectiveness of the pro-posed method is demonstrated through two tasks; disentangle-ment performance, and improvement of speaker recognition ac-curacy compared to the baseline model on a benchmark dataset,VoxCeleb1. Ablation studies also show the impact of each cri-terion on overall performance.