Reviewerly/exHarmony huggingface dataset:
Mixed reality using illumination-aware gradient mixing in surgical telepresence: enhanced multi-layer visualization

Surgical telepresence using augmented perception has been applied, but mixed reality is still being researched and is only theoretical. The aim of this work is to propose a solution to improve the visualization in the final merged video by producing globally consistent videos when the intensity of illumination in the input source and target video varies. The proposed system uses an enhanced multi-layer visualization with illumination-aware gradient mixing using Illumination Aware Video Composition algorithm. Particle Swarm Optimization Algorithm is used to find the best sample pair from foreground and background region and image pixel correlation to estimate the alpha matte. Particle Swarm Optimization algorithm helps to get the original colour and depth of the unknown pixel in the unknown region. Our results showed improved accuracy caused by reducing the Mean squared Error for selecting the best sample pair for unknown region in 10 each sample for bowel, jaw and breast. The amount of this reduction is 16.48% from the state of art system. As a result, the visibility accuracy is improved from 89.4 to 97.7% which helped to clear the hand vision even in the difference of light. Illumination effect and alpha pixel correlation improves the visualization accuracy and produces a globally consistent composition results and maintains the temporal coherency when compositing two videos with high and inverse illumination effect. In addition, this paper provides a solution for selecting the best sampling pair for the unknown region to obtain the original colour and depth.
Understanding Collections of Related Datasets Using Dependent MMD Coresets

Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets. Representative points selected by a maximum mean discrepancy (MMD) coreset can provide interpretable summaries of a single dataset, but are not easily compared across datasets. In this paper, we introduce dependent MMD coresets, a data summarization method for collections of datasets that facilitates comparison of distributions. We show that dependent MMD coresets are useful for understanding multiple related datasets and understanding model generalization between such datasets.
BLOCKCHAIN TECHNOLOGY IN AGRO-INDUSTRIAL SUPPLY CHAIN: REVIEW

This paper provides a review of the application of blockchain technology in industrial supply chains in general. It aims to investigate how current blockchain technology is and its applications and to look for research loopholes that have not been done before. The theory of several research papers that have been published in journals to date, along with several reports from the grayish literature as a means of streamlining judgments about growing blockchain-based supply chains is included in this review. The research step is to identify papers based on the year of publication so that a theory-based understanding of blockchain technology in the Agro-Industrial Supply Chain ecosystem, between 2007 and 2019. Blockchain technology in the agro-industry supply chain described in the framework in 2018 is 6.57%, while the implication for the case study is 9.11% and identified in the hypothesis with a questionnaire regarding blockchain recognition and exploration so that 4.5% of the blockchain model simulation. Based on the graph, there are 52% of blockchain research articles on the agro-industrial supply chain will be followed up by researchers, thereby increasing the value chain in agro-industry more. Research that discusses the value (value) in the supply chain that occurs from upstream to downstream in the agro-industrial supply chain ecosystem is very little, only around 2%. Excellence blockchain can identify the stakeholders that exist in the supply chain in each ecosystem from upstream to downstream processes so that it can transform a rigid and uncertain supply chain system into an efficient performance supply chain with transparency and trust between stakeholders so that integration, collaboration, and accuracy accurate data information.
MEDIRL: Predicting the Visual Attention of Drivers via Maximum Entropy Deep Inverse Reinforcement Learning

Inspired by human visual attention, we introduce a Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework for modeling the visual attention allocation of drivers in imminent rear-end collisions. MEDIRL is composed of visual, driving, and attention modules. Given a front-view driving video and corresponding eye fixations from humans, the visual and driving modules extract generic and driving-specific visual features, respectively. Finally, the attention module learns the intrinsic task-sensitive reward functions induced by eye fixation policies recorded from attentive drivers. MEDIRL uses the learned policies to predict visual attention allocation of drivers. We also introduce EyeCar, a new driver visual attention dataset during accident-prone situations. We conduct comprehensive experiments and show that MEDIRL outperforms previous state-of-the-art methods on driving task-related visual attention allocation on the following large-scale driving attention benchmark datasets: DR(eye)VE, BDD-A, and DADA-2000. The code and dataset are provided for reproducibility.
Design and Implementation of Shortest Path Line Follower Autonomous Rover Using Decision Making Algorithms

For an autonomous rover, navigating through an unknown environment achieving the target location is the key feature of the rover. Also, path finding is the main problem in the navigation of autonomous vehicles. This is generally done via a dynamic control paradigm, which requires local translation from defined states to actions. Reinforcement learning can be used to develop a control strategy that has learning capabilities in an uncertain environment. The result analysis shows that an autonomous vehicle that is trained by reinforcement can roam freely in a useful or “reasonably” manner, and therefore its training process is increased significantly.
Emphasis Control for Parallel Neural TTS

Recent parallel neural text-to-speech (TTS) synthesis methods are able to generate speech with high fidelity while maintaining high performance. However, these systems often lack control over the output prosody, thus restricting the semantic information conveyable for a given text. This paper proposes a hierarchical parallel neural TTS system for prosodic emphasis control by learning a latent space that directly corresponds to a change in emphasis. Three candidate features for the latent space are compared: 1) Variance of pitch and duration within words in a sentence, 2) Wavelet-based feature computed from pitch, energy, and duration, and 3) Learned combination of the two aforementioned approaches. At inference time, word-level prosodic emphasis is achieved by increasing the feature values of the latent space for the given words. Experiments show that all the proposed methods are able to achieve the perception of increased emphasis with little loss in overall quality. Moreover, emphasized utterances were preferred in a pairwise comparison test over the non-emphasized utterances, indicating promise for real-world applications.
An unsupervised feature learning for quantum-classical convolutional network with applications to fault detection

Combining the advantages of quantum computing and neural networks, quantum neural networks (QNNs) have gained considerable attention recently. However, because of the lack of quantum resource, it is costly to train QNNs. In this work, we presented a simple unsupervised method for quantum-classical convolutional networks to learn a hierarchy of quantum feature extractors. Each level of the resulting feature extractors consist of multiple quanvolution filters, followed by a pooling layer. The main contribution of the proposed approach is to use the K-means clustering to maximize the difference of quantum properties in quantum circuit ansatz. One experiment on the bearing fault detection task shows the effectiveness of the proposed method.
Attribute Augmented Network Embedding Based on Generative Adversarial Nets

Network embedding is to learn low-dimensional representations of nodes while preserving necessary information for network analysis tasks. Though representations preserving both structure and attribute features have achieved in many real-world applications, learning these representations for networks with attribute information is difficult due to the heterogeneity between structure and attribute information. Many existing methods have been proposed to preserve explicit proximities between nodes, with optimization limited to node pairs with large structure and attribute proximities, which may lead to overfitting. To address the above problems, we adopt an attribute augmented network to represent attribute and structure information in a unified framework. Specifically, we study the problem of attribute augmented network embedding that exploits the strength of generative adversarial nets (ANGANs) in capturing the latent distribution of data to learn robust and informative representations of nodes. The ANGAN method obtains the low-dimensional representations of nodes through adversarial learning between the generative and discriminative models. The generative model approximates the underlying connectivity and attributes distributions of nodes by using the distributions generated from the learned representations. It is implemented by utilizing the properties of the attribute augmented network to improve the traditional Skip-gram model. The discriminative model is designed as a binary classifier to distinguish the truly connected node pairs from the generated ones. The pre-training algorithm and the teacher forcing approach are adopted to improve training efficiency and stability. Empirical results show that ANGAN generally outperforms state-of-the-art methods in various real-world applications, which demonstrates the effectiveness and generality of our method.
TX-ESP and BC-ESP: A Collaborative Educational Seismology Project in Texas and New England

There is growing concern that the quality of science education today is not adequate to provide students with the level of scientific literacy they will need to be effective citizens in the 21st century. To address this concern, Educational Seismology Projects (ESPs), such as the Texas Educational Seismic Project (TX-ESP) and the Boston College (BC) Educational Seismology Project (BC-ESP) promote inquiry-based learning and investigative methods in Earth science classrooms, at public events, and in after-school activities. TX-ESP is a 501c3 Non-Profit Public Charity which focuses on educational seismology outreach and collaborative research with BC-ESP. BC-ESP is operated by BC’s Weston Observatory, a research and science education center of its Department of Earth and Environmental Sciences. Our common goal is to enhance science education by offering opportunities for students, teachers, and the public to be directly involved with research scientists. Through our educational partnerships, we make a difference in the lives of citizens and students of all ages and in differing socio-economic circumstances by engaging in hands-on learning opportunities and teaching science as it is actually practiced. The science of seismology forms an excellent foundation for this endeavor because: (1) it is an interdisciplinary science that requires integration of many STEM concepts, and (2) it teaches how the natural environment impacts our everyday lives. A particular advantage of this collaboration is the locations of our ESPs. BC-ESP is located far from plate boundaries, where students don’t tend to record as many earthquakes as they do at TX-ESP, which is much closer to plate boundaries and thus records more earthquakes, with stronger signals, than BC-ESP. When BC-ESP students see the TX-ESP recordings (on their own web-interface), they often find more subtle signals of the same earthquakes on their New England seismograms. This provides teachable moments for students at both locations to investigate seismic wave propagation and attenuation, as well as differences in seismicity between intraplate and plate boundary environments. Also, TX-ESP students leverage a high-quality seismology curriculum through BC-ESP’s research experience and its successful, long-term ESP presence in New England.
Deflectometry based on light-field imaging

This work presents how deflectometry can be coupled with a light-field camera to better characterize and quantify the depth of anomalies on specular surfaces. In our previous work,1 we proposed a new scanning scheme for the detection and 3D reconstruction of defects on reflective objects. However, the quality of the reconstruction was strongly dependent on the object-camera distance which was required as an external input parameter. In this paper, we propose a new approach that integrates an estimation of this distance into our system by replacing the standard camera with a light-field camera.
MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

Human motion prediction is a challenging task due to the stochasticity and aperiodicity of future poses. Recently, graph convolutional network has been proven to be very effective to learn dynamic relations among pose joints, which is helpful for pose prediction. On the other hand, one can abstract a human pose recursively to obtain a set of poses at multiple scales. With the increase of the abstraction level, the motion of the pose becomes more stable, which benefits pose prediction too. In this paper, we propose a novel Multi-Scale Residual Graph Convolution Network (MSR-GCN) for human pose prediction task in the manner of end-to-end. The GCNs are used to extract features from fine to coarse scale and then from coarse to fine scale. The extracted features at each scale are then combined and decoded to obtain the residuals between the input and target poses. Intermediate supervisions are imposed on all the predicted poses, which enforces the network to learn more representative features. Our proposed approach is evaluated on two standard benchmark datasets, i.e., the Human3.6M dataset and the CMU Mocap dataset. Experimental results demonstrate that our method outperforms the state-of-the-art approaches. Code and pre-trained models are available at this https URL.
Dual Contrastive Loss and Attention for GANs

Generative Adversarial Networks (GANs) produce impressive results on unconditional image generation when powered with large-scale image datasets. Yet generated images are still easy to spot especially on datasets with high variance (e.g. bedroom, church). In this paper, we propose various improvements to further push the boundaries in image generation. Specifically, we propose a novel dual contrastive loss and show that, with this loss, discriminator learns more generalized and distinguishable representations to incentivize generation. In addition, we revisit attention and extensively experiment with different attention blocks in the generator. We find attention to be still an important module for successful image generation even though it was not used in the recent state-of-the-art models. Lastly, we study different attention architectures in the discriminator, and propose a reference attention mechanism. By combining the strengths of these remedies, we improve the compelling state-of-the-art Frechet Inception Distance (FID) by at least 17.5% on several benchmark datasets. We obtain even more significant improvements on compositional synthetic scenes (up to 47.5% in FID).
Complex system of quality control of specialists’ training at HEI: project organization and functioning

The article aims to form the special features of using pedagogical technologies of designing and functioning of the complex system of quality control of specialists' training in HEIs. The methodology of the article is built on the statistical analysis of the results of the OECD TALIS (2018) survey of HEIs teachers in the Czech Republic, which allow us to determine the pedagogical technologies of the functioning of a comprehensive system of quality control of training specialists in HEIs. The sample included 6,039 HEIs teachers, of whom 72.4% were women and 27.6% were men. The results show differences between the countries (the Czech Republic, Poland, and Hungary) in the level of innovativeness of HEIs teaching and the level of involvement of students in the development of innovations.
Predicting Jordanian Job Satisfaction Using Artificial Neural Network and Decision Tree

Measuring job satisfaction of the employees is a big concern for the prestigious organizations in order to maintain high productivity from its employees, because if an employee suddenly left the job that may cause a loss to the company because it’s not easy to find new qualified employees. Finding or making an employee qualified will consume the organizations’ time and money. This paper presents two techniques in order to predict the Jordanian job satisfaction: Artificial Neural Network and J48 Decision Tree, using an online questionnaire, the dataset was gathered, specific questions were answered and specific attributes which related to the study were taken to make the prediction using the Weka tool.
Comparison of popular Generative Adversarial Network flavours for fluvial reservoir modelling

Summary This paper presents a commparative study of popular generative adversarial network flavours for fluvial reservoir modelling. Fluvial reservoir contains complex sedimentary architecture and facies heterogeneity. To better represent the real complexity of fluvial reservoir, we used a process-based model, FLUMY, to generated a low NTG fluvial dataset. Low NTG systems have less amalgamation and more complex sand-body connectivity. We simplify this dataset by reducing the number of facies. Thus, the training dataset for testing different GANs is composed of three-facies process-based 2D realizations. Candidates in this study include DCGAN, WGAN, WGAN-gp and PatchGAN. Visual comparison of the GANs generations found that some GANs have difficulties in learning certain key geolgocial features from this training dataset. PatchGAN outperforms in the aspect of channel geometry and facies placement. However, there are still some geologically unrealistic featues remaning. For example, the 'closed channel' pattern. Future efforts in GAN-based modelling would benefit from tackling complex geological systems.
A Deep Learning Approach to Classifying Tyres Using Sidewall Images

End of Life Tyres (ELTs) pose a potential health and environmental risk when dumped in illegal stockpiles. For recycling to be considered feasible, a profitable business opportunity needs to be created. One method of making the recycling process of tyres more profitable is by understanding the compounds found within each tyre. This study aims at classifying these tyres in order to achieve this knowledge. Four identifying features of tyres were investigated, namely: the Department of Transport (DOT) code, tread pattern, tyre size and the logo. The tyre logo was identified to be the classifying element for this research as it was the highest position on a derived hierarchy of identifiers for tyres.The metrics obtained as outputs from training and testing the architectures were the accuracy, precision, recall and F1-score. These metrics were compared in conjunction with the confusion matrix produced from testing. K-fold cross-validation technique was adopted to ensure that variance was accounted.The results identified that one convolutional neural network model, MobileNet, was particularly well suited for the context of classifying logos on tyre sidewalls. The MobileNet architecture had the highest performance metrics for both training from scratch (96.7% accuracy) and transferred learning (98.8% accuracy).
Nonlinear waveguides for integrated quantum light source

Squeezed quantum states of light are essential tools for sensing, quantum computing and secure communication tasks, such as quantum key distribution [1] - [4] . For practical applications, integrated devices combining quantum light sources with additional active and passive components are attractive since they reduce bulky tabletop optical experiments into a single and highly compact chip, eventually leading to lower fabrication costs and the potential for large scale production with improved reproducibility and stability.
Photoneutralization of charges in GaAs quantum dot based entangled photon emitters

Semiconductor-based emitters of pairwise photonic entanglement are a promising constituent of photonic quantum technologies. They are known for the ability to generate discrete photonic states on-demand with low multiphoton emission, near-unity entanglement fidelity, and high single photon indistinguishability. However, quantum dots typically suffer from luminescence blinking, lowering the efficiency of the source and hampering their scalable application in quantum networks. In this paper, we investigate and adjust the intermittence of the neutral exciton emission in a GaAs/AlGaAs quantum dot under two-photon resonant excitation of the neutral biexciton. We investigate the spectral and quantum optical response of the quantum dot emission to an additional wavelength tunable gate laser, revealing blinking caused by the intrinsic Coulomb blockade due to charge capture processes. Our finding demonstrates that the emission quenching can be actively suppressed by controlling the balance of free electrons and holes in the vicinity of the quantum dot and thereby significantly increasing the quantum efficiency by $30%$.
SemLinkWS: Collaboration Social Network of Web Services to Aid Service Discovery

Service-oriented computing (SOC) has come into view as a new computing paradigm that makes systems development and business process management more flexible and agile. It has recourse to services functioning as a fundamental compound in order to assist the construction of rapid and easy composition of distributed applications in heterogeneous environments. The rapid development of web service technologies has dramatically increased the number of available web services including those available on social networks. Thus, many studies have been carried out in the discovery mechanisms. The majority of proposed approaches in the literature for web services discovery based on the description of web services themselves and neglect their interaction with each other. Through the overlapping between social computing (illustrated by social networks) and service oriented computing (illustrated by web services), social web services are produced that are different from regular web services. In fact, they are a member of a social network and have an idea about other services in the same network. The first purpose of this paper is to justify the need of the use of an extension of WSDL-S to facilitate web service discovery, and the second purpose is to build a social network of web services to take advantage of the interactions that occur between services. However, the effort of developing social web services can partly undermine the utility of their implementation.