Reviewerly/exHarmony huggingface dataset:
Generative and discriminative training of Boltzmann machine through quantum annealing

A hybrid quantum-classical method for learning Boltzmann machines (BM) for a generative and discriminative task is presented. BM are undirected graphs with a network of visible and hidden nodes where the former is used as the reading site. In contrast, the latter is used to manipulate visible states' probability. In Generative BM, the samples of visible data imitate the probability distribution of a given data set. In contrast, the visible sites of discriminative BM are treated as Input/Output (I/O) reading sites where the conditional probability of output state is optimized for a given set of input states. The cost function for learning BM is defined as a weighted sum of Kullback-Leibler (KL) divergence and Negative conditional Log-likelihood (NCLL), adjusted using a hyper-parameter. Here, the KL Divergence is the cost for generative learning, and NCLL is the cost for discriminative learning. A Stochastic Newton-Raphson optimization scheme is presented. The gradients and the Hessians are approximated using direct samples of BM obtained through quantum annealing. Quantum annealers are hardware representing the physics of the Ising model that operates on low but finite temperatures. This temperature affects the probability distribution of the BM; however, its value is unknown. Previous efforts have focused on estimating this unknown temperature through regression of theoretical Boltzmann energies of sampled states with the probability of states sampled by the actual hardware. These approaches assume that the control parameter change does not affect the system temperature; however, this is usually untrue. Instead of using energies, the probability distribution of samples is employed to estimate the optimal parameter set, ensuring that the optimal set can be obtained from a single set of samples. The KL divergence and NCLL are optimized for the system temperature, and the result is used to rescale the control parameter set. The performance of this approach, as tested against the theoretically expected distributions, shows promising results for Boltzmann training on quantum annealers.
Camera distance helps 3D hand pose estimated from a single RGB image

Most existing methods for RGB hand pose estimation use root-relative 3D coordinates for supervision. However, such supervision neglects the distance between the camera and the object (i.e., the hand). The camera distance is especially important under a perspective camera, which controls the depth-dependent scaling of the perspective projection. As a result, the same hand pose, with different camera distances can be projected into different 2D shapes by the same perspective camera. Neglecting such important information results in ambiguities in recovering 3D poses from 2D images. In this article, we propose a camera projection learning module (CPLM) that uses the scale factor contained in the camera distance to associate 3D hand pose with 2D UV coordinates, which facilities to further optimize the accuracy of the estimated hand joints. Specifically, following the previous work, we use a two-stage RGB-to-2D and 2D-to-3D method to estimate 3D hand pose and embed a graph convolutional network in the second stage to leverage the information contained in the complex non-Euclidean structure of 2D hand joints. Experimental results demonstrate that our proposed method surpasses state-of-the-art methods on the benchmark dataset RHD and obtains competitive results on the STB and D+O datasets.
Development of a Virtual Reality-Based System for Simulating Welding Processes

Arc welding processes, such as shielded metal arc welding (SMAW), metal inert gas (MIG), and tungsten inert gas (TIG), play an important role in industrial applications. To improve the efficiency of the exploitation of traditional welding systems, new technologies have been used. Virtual reality technology is one of them. The virtual reality (VR)-based welding system enables to increase the frequency of practice to help learners obtain welding experience to avoid errors that occur during actual welding processes. This paper presents a VR-based system for simulating three welding processes: SMAW, MIG, and TIG. The developed system includes hardware components and VR software installed on a computer. The change in the physical devices, such as moving the welding torch and the distance from the welding torch to the plates to generate the weld bead, will update in real time and appear on the virtual environment. The functionality of the developed system for simulating the welding processes, such as in the real welding environment, was tested successfully. For implementing the system, welding speed and the distance from the welding torch to the plates are important process parameters, which determine the weld size or the weld formation. In this research, the ranges of the welding speed are 70 ÷ 120 mm/min; 240 ÷ 460 mm/min; and 250 ÷ 450 mm/min for the SMAW, TIG, and MIG processes, respectively. These values were tested experimentally. The distance from the welding torch to the plates to display the weld joint is 1.5 ÷ 5 mm. Outside of this range, no weld joint is formed. The welding widths are 4.4 ÷ 12.9 mm, 7.1 ÷ 12.4 mm, and 7.4 ÷ 11.3 mm for the SMAW, TIG, and MIG processes, respectively.
Analyzing Accurate Egocentric Distance Estimates of University Students in Virtual Environments with a Desktop Display and Gear VR Display

The perception of distances is crucial in both the real world and virtual environments. However, distances can be incorrectly estimated in the latter one, and they can be affected by technological and human factors. We created a virtual environment to take a step toward understanding this phenomenon. We assessed the egocentric distance estimation skills of 239 university students at 10 various distances between 25 cm and 160 cm at 15 cm intervals. A desktop display was used by 157 students, while the Gear VR display was used by 72 students. The effects of the following factors were analyzed: gender, height, dominant arm, previous VR experience, gaming hours per week, whether the participants wore glasses, their field of study, and display device. Logistic regression analysis was performed to assess their influences on the probabilities of accurate distance estimates, while linear regression analysis was conducted to examine their effects on estimation times. The results show that except for the factors of whether the participants wore glasses and their field of study, the probabilities of accurate distance estimates can be affected along with estimation times themselves.
MIDOM—A DICOM-Based Medical Image Communication System

Despite the existing medical infrastructure being limited in terms of interoperability, the amount of medical multimedia transferred over the network and shared through various channels increases rapidly. In search of consultations with colleagues, medical professionals with the consent of their patients, usually exchange medical multimedia, mainly in the form of images, by using standard instant messaging services which utilize lossy compression algorithms. That consultation paradigm can easily lead to losses in image representation that can be misinterpreted and lead to the wrong diagnosis. This paper presents MIDOM—Medical Imaging and Diagnostics on the Move, a DICOM-based medical image communication system enhanced with a couple of variants of our previously developed custom lossless Classification and Blending Predictor Coder (CBPC) compression method. The system generally exploits the idea that end devices used by the general population and medical professionals alike are satisfactorily performant and energy-efficient, up to a point to support custom and complex compression methods successfully. The system has been implemented and appropriately integrated with Orthanc, a lightweight DICOM server, and a medical images storing PACS server. We benchmarked the system thoroughly with five real-world anonymized medical image sets in terms of compression ratios and latency reduction, aiming to simulate scenarios in which the availability of the medical services might be hardly reachable or in other ways limited. The results clearly show that our system enhanced with the compression methods in the question pays off in nearly every testing scenario by lowering the network latency to at least 60% of the latency required to send raw and uncompressed image sets and 25% in the best-case, while maintaining the perfect reconstruction of medical images and, thus, providing a more suitable environment for healthcare applications.
Citation analysis of computer systems papers

Citation analysis is used extensively in the bibliometrics literature to assess the impact of individual works, researchers, institutions, and even entire fields of study. In this article, we analyze citations in one large and influential field within computer science, namely computer systems. Using citation data from a cross-sectional sample of 2,088 papers in 50 systems conferences from 2017, we examine four research areas of investigation: overall distribution of systems citations; their evolution over time; the differences between databases (Google Scholar and Scopus), and; the characteristics of self-citations in the field. On citation distribution, we find that overall, systems papers were well cited, with the most cited subfields and conference areas within systems being security, databases, and computer architecture. Only 1.5% of papers remain uncited after five years, while 12.8% accrued at least 100 citations. For the second area, we find that most papers achieved their first citation within a year from publication, and the median citation count continued to grow at an almost linear rate over five years, with only a few papers peaking before that. We also find that early citations could be linked to papers with a freely available preprint, or may be primarily composed of self-citations. For the third area, it appears that the choice of citation database makes little difference in relative citation comparisons, despite marked differences in absolute counts. On the fourth area, we find that the ratio of self-citations to total citations starts relatively high for most papers but appears to stabilize by 12-18 months, at which point highly cited papers revert to predominately external citations. Past self-citation count (taken from each paper's reference list) appears to bear little if any relationship with the future self-citation count of each paper. The primary practical implication of these results is that the impact of systems papers, as measured in citations, tends to be high relative to comparable studies of other fields and that it takes at least five years to stabilize. A secondary implication is that at least for this field, Google Scholar appears to be a reliable source of citation data for relative comparisons.
BERT-PG: a two-branch associative feature gated filtering network for aspect sentiment classification

Aspect sentiment classification is an important branch of sentiment classification that has gained increasing attention recently. Existing aspect sentiment classification methods typically use different network branches to encode context and aspect words separately, and then use an attention mechanism to capture their associations. This attention-based approach cannot completely ignore the contexts unrelated to the current aspect words, which brings noise interference. In this paper, a gated filtering network based on BERT is suggested as a solution to this issue. We employ BERT to encode the text semantics of contexts and sentence pairs consisting of context and aspect words respectively, and to extract lexical features as well as associative features of context and aspect words. Based on this, we designed a gating module that, unlike the attention mechanism, uses association features to precisely filter irrelevant contexts. Additionally, because the BERT network parameters are so big, there is a tendency to over-fitting during training. To effectively combat this problem, we developed a loss function with a threshold. We carried out extensive experiments using three benchmark datasets to verify the performance of our proposed model. The experimental results show that the method improves the accuracy by 0.5%, 1.39% and 2.57% on the Laptop, Restaurant and Twitter datasets respectively, and 1.564%, 2.36% and 4.144% on Macro-F1 respectively, compared to the recent RA-CNN (BERT), proving that our method is effective in improving the presentation of aspect sentiment classification in comparison to other cutting-edge sentiment classification methods.
If blockchain is the solution, robot security is the problem

Robotics systems of all types are revolutionizing a wide variety of industries—transportation, manufacturing, and even healthcare—and yet, many essential ingredients for robotics systems in the real world are not technologically ready for deployment. Currently, robots lack the protocols and standards required to be safe and secure outside factories. In an attempt to close this gap, recent research has demonstrated the security benefits of combining robotics systems with blockchain-based and related technologies (e.g., smart contracts, zero-knowledge proofs, Merkle trees). In this perspective article, I argue that blockchain-based robotics is starting to provide innovative solutions (e.g., secure data sharing, consensus mechanisms, and new interaction methods) to urgent problems of robot security. I list the most important takeaways so far from this emerging field of research that I helped establish together with a growing community. I close the article by discussing the implications of the security challenges that the robotics research community is facing, and possible ways for us to move forward.
Skull stripping on multimodal brain MRI scans using thresholding and morphology

This paper introduces a novel thresholding and morphology-based skull stripping method for different MRI modalities. The proposed method is designed in a way which is easy to use and generates satisfactory results with minimal parameter adjustments. The method is evaluated on three different benchmark datasets and compared with nine state-of-the-art skull stripping methods. The experimental results suggest that the proposed method generates comparable results to some of the best methods in literature. However, unlike many other methods, it works well on different types of MRI scans. Moreover, this method generates the skull mask along with the brain mask that can be used to study various skull pathologies.
Chiral Smith–Purcell Radiation Light Source

Abstract Smith–Purcell radiation (SPR) with broadly tunable spectra has attracted attention in light emission, and this free‐electron‐driven source shows great potential in illumination fields, such as terahertz sources, ultra‐compact light sources, free‐electron lasers, etc. However, it remains difficult to control the polarization properties of SPR and realize a free‐electron chiral light source. In this work, the chiral SPR with the maximum chirality (&gt;40%) is acquired in a periodic stacked nanosquare light‐well, and the radiation chirality is manipulated by variations of the designed electron beam excitation positions. Furthermore, this work reveals that the chiroptical effect originates in the superposition of SPRs with different phases and polarization, which are derived from adjacent sidewalls. This work may deepen the comprehension of electron‐matter interaction and facilitate the development of compact electron‐driven chirality‐tunable source‐on‐chip, highlighting potential applications in photonic integration and binary data processing.
Multistage approach for trajectory optimization for a wheeled inverted pendulum passing under an obstacle

Abstract A robotic system constructed as a wheeled inverted pendulum (WIP) serves as an impressive demonstrator, since this kind of system is inherently nonlinear, unstable, and nonminimum phase. These properties may pose several difficulties, when it comes to control and trajectory planning. This paper shows a method for deriving a highly dynamic trajectory compliant with the system dynamics by means of solving an optimal control problem (OCP) using multiple shooting. The assumed task includes that the WIP should pass a height-restricting barrier. This can be achieved by leaning back or forth, in order to reduce the overall height of the WIP. The constraints inherent to the definition of this trajectory are nonconvex due to the shape of the robot. The constraint functions have a local minimum in an infeasible region. This can cause problems when the initial guess is within this infeasible region. To overcome this, a multistage approach is proposed for this special OCP to evade the infeasible local minimum. After solving four stages of subsequent optimization problems, the optimal trajectory is obtained and can be used as feedforward for the real system.
&lt;p&gt;Black Market Exchange Rate Movement in Nigeria: Does Cryptocurrency Matters?&lt;/p&gt;

The movement of exchange rates generally had demonstrated unpredictable patterns over time as a traditional mode of payment, particularly the black or parallel exchange market that is already fueled by demand pressure. However, cryptocurrency and the parallel or black exchange market are global phenomenon traded outside the government strict regulations in Nigeria, hence, their economic implications and understanding by many persons, banks, policy-makers, governments, and companies remain a priori unclear. Therefore, this study investigates the impacts of cryptocurrency on black or parallel exchange rate market movement in Nigeria from Jan, 2021 to April, 2023 using the autoregressive distributive lag (ARDL) regression analysis and Granger causality test to affirm the hypothesis that, cryptocurrency do not have significant impacts on black market exchange rate movement in Nigeria. The result of the ARDL shows that cryptocurrencies trading are core determinants of the black or parallel market exchange rate movement in Nigeria during the study period. Therefore, we concluded that cryptocurrency has a negative and significant impact on black market exchange rate movement in Nigeria from Jan. 2021 to April, 2023 and recommend that the government as a matter of urgency regulate cryptocurrency in order to curb the excesses that come with it just like in Japan, China, and Australia, since Nigeria still engages foreign currency controls as monetary policy tools, so as to improve on consumers’ confidence on the domestic currency.
Virtual reality and character education: Learning opportunities and risks

ABSTRACTABSTRACTVirtual reality (VR) is an increasingly popular artificially mediated, immersive experience that is accessed through a headset. Recent research is starting to provide evidence about the positive impact that virtual reality technology can have on teaching and learning. This research, to date, has been primarily undertaken in higher education and in disciplines related to the health sciences. In this article, the possibilities, and problematics of using virtual reality in taught character education approaches are explored. The article proposes that VR can bring four learning opportunities for character educators that would not be possible in the traditional classroom. These expansive, immersive, embodied and autonomous learning opportunities can be harnessed to educate six components of virtue. In the article, the risks of VR use for character education are also investigated. The aim of the article is to get ahead of the EdTech developers and provide theoretical, conceptual and practical insights that will help ensure that the promise of VR to enhance character education can be realised.KEYWORDS: Virtual realitycharactervirtue ethicseducationlearning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsTom HarrisonTom Harrison is the Deputy Pro Vice Chancellor for Education Innovation at the University of Birmingham. He is also the Deputy Director of the Jubilee Centre for Character and Virtues. Tom is a HEA Principal Fellow and National Teaching Fellow and Secretary and Trustee for the Society for Educational Studies (SES). Tom Harrison’s specialist interests are innovation in education, character education, graduate attributes and digital-wisdom. Tom researches, publishes and gives presentations in the UK and Internationally on these topics as well as developing resources and training programmes for schools, voluntary sector and other organisations. His most recent book, Thrive – how to cultivate character so your children can flourish online, won the IFFD Global Award.
Trinocular camera self-calibration based on spatio-temporal multi-layer optimization

In stereo vision systems with dynamically rotating cameras, the accuracy of camera self-calibration method is reduced due to the interference of space noise and mismatched features. To address this issue, a new self-calibration method for trinocular camera is proposed. Firstly, to obtain the uniformly distributed and high-quality matched feature points required for initial calibration of camera pose, according to the mapping relationship between the three-view feature matching points, a three-view grid feature support estimator is defined, and a three-view ring matching method with double-layer feature closed-loop verification is designed. Then, according to the projection relationship of spatial feature points, a new heterogeneous cross-projection optimization function based on closed-loop features is established, achieving accurate calibration of trinocular camera system. Comparison experiments of multiple scenes verify the effectiveness of the method, particularly in the low-textured scenes, where the average Sampson error ranged between 1.37e-11 and 0.061 pixel. Furthermore, the proposed method achieves higher calibration accuracy than the comparative method, which can improve the robustness of dynamic rotating cameras under spatial noise conditions.
A social mechanism for healthcare consulting recommendation

People suffering from mental health difficulty need someone’s listening and advice, but there are not enough human resources to provide support. In this research, we aim to propose a social mechanism for recommending healthcare knowledge over social networks to resolve the issue of lacking consulters and encourage people to take the initiative to seek for online help. By analysing users’ posts, background, preferences, similarities and relationships, the proposed mechanism can provide proper posts and consulters to facilitate experience-sharing and consulting help from the crowd. With reliable referrals support, people are more comfortable speaking their minds and expressing their emotions, and thus they receive better guidance.