Reviewerly/exHarmony huggingface dataset:
Mesh-based camera pairs selection and occlusion-aware masking for mesh refinement

Many Multi-View-Stereo algorithms extract a 3D mesh model of a scene, after fusing depth maps into a volumetric representation of the space. Due to the limited scalability of such representations, the estimated model does not capture fine details of the scene. Therefore a mesh refinement algorithm is usually applied; it improves the mesh resolution and accuracy by minimizing the photometric error induced by the 3D model into pairs of cameras. The choice of these pairs significantly affects the quality of the refinement and usually relies on sparse 3D points belonging to the surface. Instead, in this paper, to increase the quality of pairs selection, we exploit the 3D model (before the refinement) to compute five metrics: scene coverage, mutual image overlap, image resolution, camera parallax, and a new symmetry term. To improve the refinement robustness, we also propose an explicit method to manage occlusions, which may negatively affect the computation of the photometric error. The proposed method takes into account the depth of the model while computing the similarity measure and its gradient. We quantitatively and qualitatively validated our approach on publicly available datasets against state of the art reconstruction methods.
Finding Possible Precursors for the 2015 Cotopaxi Volcano Eruption Using Unsupervised Machine Learning Techniques

Cotopaxi Volcano showed an increased activity since April 2015 and evolved into its eventual mild eruption in August 2015. In this work we use records from a broadband seismic station located at less than 4 km from the vent that encompass data from April to December of 2015, to detect and study low-frequency seismic events. We applied unsupervised learning schemes to group and identify possible premonitory low-frequency seismic families. To find these families we applied a two-stage process in which the events were first separated by their frequency content by applying the k-means algorithm to the spectral density vector of the signals and then were further separated by their waveform by applying Correntropy and Dynamic Time Warping. As a result, we found a particular family related to the volcano’s state of activity by exploring its time distribution and estimating its events’ locations.
Learning Kernel for Conditional Moment-Matching Discrepancy-Based Image Classification

Conditional maximum mean discrepancy (CMMD) can capture the discrepancy between conditional distributions by drawing support from nonlinear kernel functions; thus, it has been successfully used for pattern classification. However, CMMD does not work well on complex distributions, especially when the kernel function fails to correctly characterize the difference between intraclass similarity and interclass similarity. In this paper, a new kernel learning method is proposed to improve the discrimination performance of CMMD. It can be operated with deep network features iteratively and thus denoted as KLN for abbreviation. The CMMD loss and an autoencoder (AE) are used to learn an injective function. By considering the compound kernel, that is, the injective function with a characteristic kernel, the effectiveness of CMMD for data category description is enhanced. KLN can simultaneously learn a more expressive kernel and label prediction distribution; thus, it can be used to improve the classification performance in both supervised and semisupervised learning scenarios. In particular, the kernel-based similarities are iteratively learned on the deep network features, and the algorithm can be implemented in an end-to-end manner. Extensive experiments are conducted on four benchmark datasets, including MNIST, SVHN, CIFAR-10, and CIFAR-100. The results indicate that KLN achieves the state-of-the-art classification performance.
Improving novelty detection with generative adversarial networks on hand gesture data

We propose a novel way of solving the issue of classification of out-of-vocabulary gestures using Artificial Neural Networks (ANNs) trained in the Generative Adversarial Network (GAN) framework. A generative model augments the data set in an online fashion with new samples and stochastic target vectors, while a discriminative model determines the class of the samples. The approach was evaluated on the UC2017 SG and UC2018 DualMyo data sets. The generative models’ performance was measured with a distance metric between generated and real samples. The discriminative models were evaluated by their accuracy on trained and novel classes. In terms of sample generation quality, the GAN is significantly better than a random distribution (noise) in mean distance, for all classes. In the classification tests, the baseline neural network was not capable of identifying untrained gestures. When the proposed methodology was implemented, we found that there is a trade-off between the detection of trained and untrained gestures, with some trained samples being mistaken as novelty. Nevertheless, a novelty detection accuracy of 95.4% or 90.2% (depending on the data set) was achieved with just 5% loss of accuracy on trained classes.
Squeezed displaced entangled states in the quantum Rabi model

The quantum Rabi model accepts analytical solutions in the so-called degenerate qubit and relativistic regimes with discrete and continuous spectrum, in that order. We show that solutions are the superposition of even and odd displaced number states, in the former, and infinitely squeezed coherent states, in the latter, of the boson field correlated to the internal states of the qubit. We propose a single parameter model that interpolates between these discrete and continuous spectrum regimes to study the spectral statistics for first and second neighbor differences before the so-called spectral collapse. We find two central first neighbor differences that interweave and fluctuate keeping a constant second neighbor separation.
Near-optimal Optimistic Reinforcement Learning using Empirical Bernstein Inequalities

We study model-based reinforcement learning in an unknown finite communicating Markov decision process. We propose a simple algorithm that leverages a variance based confidence interval. We show that the proposed algorithm, UCRL-V, achieves the optimal regret $\tilde{\mathcal{O}}(\sqrt{DSAT})$ up to logarithmic factors, and so our work closes a gap with the lower bound without additional assumptions on the MDP. We perform experiments in a variety of environments that validates the theoretical bounds as well as prove UCRL-V to be better than the state-of-the-art algorithms.
Human resources staffing process and its impact on job involvement: Irbid District Electricity Company as a case study

The study aimed to identify the level of practicing the human resources staffing process and the level of the employees’ job involvement in the Irbid District Electricity Company in Jordan; it also aimed to examine the impact of human resources staffing process on the employees’ job involvement. The study adopted the descriptive, analytical method, since it determines the characteristics of the phenomenon, describes its nature accurately and then determines the quality of the relationship between its variables. The study population included all the individuals in the senior and middle administrative levels by 100 individuals. To achieve the objectives of the study, the researchers prepared a questionnaire to examine the study variables. One hundred questionnaires were distributed to each manager, deputy manager, and department head in the Irbid District Electricity Company out of which the researchers retrieved 97 questionnaires valid for analysis. In order to analyze the data collected, the study relies on the Statistical Package for Social Sciences (SPSS) where the descriptive statistics for all the fields of the study were estimated. The correlation matrix was also used to determine the relationship between variables. Furthermore, multiple regressions were used to determine the impact of the independent variable on the dependent variable. The findings showed that the level of practicing the human resources staffing process and the employees’ job involvement at Irbid District Electricity Company (IDECO) was moderate. The findings of the study showed that the process of human resources staffing was positively associated with the job involvement where the Pearson coefficient was r= .851 and at level of significance of 0.00. The research data have also indicated that the human resources staffing process had a positive impact on the employees’ job involvement. The study recommended that the procedures of staffing in all organizations should focus on achieving the fit between the employee and his job through focusing on choosing employees whose knowledge, skills and abilities correspond to the requirements of vacant jobs and also through implementing different tests and selection interviews, which help choose the person whose traits fit the job requirements. The study has also indicated that the recommended organizations should also pay attention to the job design process where the focus is on designing jobs in a way that creates a challenge, and enables employees feel independence and joy during the job; in this method, the employee can be more involved and can work in an effective way.
A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions

One of the hardest problems in the area of Natural Language Processing and Artificial Intelligence is automatically generating language that is coherent and understandable to humans. Teaching machines how to converse as humans do falls under the broad umbrella of Natural Language Generation. Recent years have seen unprecedented growth in the number of research articles published on this subject in conferences and journals both by academic and industry researchers. There have also been several workshops organized alongside top-tier NLP conferences dedicated specifically to this problem. All this activity makes it hard to clearly define the state of the field and reason about its future directions. In this work, we provide an overview of this important and thriving area, covering traditional approaches, statistical approaches and also approaches that use deep neural networks. We provide a comprehensive review towards building open domain dialogue systems, an important application of natural language generation. We find that, predominantly, the approaches for building dialogue systems use seq2seq or language models architecture. Notably, we identify three important areas of further research towards building more effective dialogue systems: 1) incorporating larger context, including conversation context and world knowledge; 2) adding personae or personality in the NLG system; and 3) overcoming dull and generic responses that affect the quality of system-produced responses. We provide pointers on how to tackle these open problems through the use of cognitive architectures that mimic human language understanding and generation capabilities.
Pretraining Methods for Dialog Context Representation Learning

This paper examines various unsupervised pretraining objectives for learning dialog context representations. Two novel methods of pretraining dialog context encoders are proposed, and a total of four methods are examined. Each pretraining objective is fine-tuned and evaluated on a set of downstream dialog tasks using the MultiWoz dataset and strong performance improvement is observed. Further evaluation shows that our pretraining objectives result in not only better performance, but also better convergence, models that are less data hungry and have better domain generalizability.
Conditional Generative Models are not Robust

Class-conditional generative models are an increasingly popular approach to achieve robust classification. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate robust classification with likelihood-based conditional generative models from a theoretical and practical perspective. Our theoretical result reveals that it is impossible to guarantee detectability of adversarial examples even for near-optimal generative classifiers. Experimentally, we show that naively trained conditional generative models have poor discriminative performance, making them unsuitable for classification. This is related to overlooked issues with training conditional generative models and we show methods to improve performance. Finally, we analyze the robustness of our proposed conditional generative models on MNIST and CIFAR10. While we are able to train robust models for MNIST, robustness completely breaks down on CIFAR10. This lack of robustness is related to various undesirable model properties maximum likelihood fails to penalize. Our results indicate that likelihood may fundamentally be at odds with robust classification on challenging problems.
Learning Interpretable Metric between Graphs

Graph is a standard approach to modeling structured data. Although many machine learning methods depend on the metric of the input objects, defining an appropriate distance function on graph is still a controversial issue. We propose a novel supervised metric learning method for a subgraph-based distance, called interpretable graph metric learning (IGML). IGML optimizes the distance function in such a way that a small number of important subgraphs can be adaptively selected. This optimization is computationally intractable with naive application of existing optimization algorithms. We construct a graph mining based efficient algorithm to deal with this computational difficulty. Important advantages of our method are 1) guarantee of the optimality from the convex formulation, and 2) high interpretability of results. To our knowledge, none of the existing studies provide an interpretable subgraph-based metric in a supervised manner. In our experiments, we empirically verify superior or comparable prediction performance of IGML to other existing graph classification methods which do not have clear interpretability. Further, we demonstrate usefulness of IGML through some illustrative examples of extracted subgraphs and an example of data analysis on the learned metric space.
Classical simulation of linear optics subject to nonuniform losses

We present a comprehensive study of the impact of non-uniform, i.e.\ path-dependent, photonic losses on the computational complexity of linear-optical processes. Our main result states that, if each beam splitter in a network induces some loss probability, non-uniform network designs cannot circumvent the efficient classical simulations based on losses. To achieve our result we obtain new intermediate results that can be of independent interest. First, we show that, for any network of lossy beam-splitters, it is possible to extract a layer of non-uniform losses that depends on the network geometry. We prove that, for every input mode of the network it is possible to commute $s_i$ layers of losses to the input, where $s_i$ is the length of the shortest path connecting the $i$th input to any output. We then extend a recent classical simulation algorithm due to P. Clifford and R. Clifford to allow for arbitrary $n$-photon input Fock states (i.e. to include collision states). Consequently, we identify two types of input states where boson sampling becomes classically simulable: (A) when $n$ input photons occupy a constant number of input modes; (B) when all but $O(\log n)$ photons are concentrated on a single input mode, while an additional $O(\log n)$ modes contain one photon each.
ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero

The AlphaGo, AlphaGo Zero, and AlphaZero series of algorithms are remarkable demonstrations of deep reinforcement learning's capabilities, achieving superhuman performance in the complex game of Go with progressively increasing autonomy. However, many obstacles remain in the understanding of and usability of these promising approaches by the research community. Toward elucidating unresolved mysteries and facilitating future research, we propose ELF OpenGo, an open-source reimplementation of the AlphaZero algorithm. ELF OpenGo is the first open-source Go AI to convincingly demonstrate superhuman performance with a perfect (20:0) record against global top professionals. We apply ELF OpenGo to conduct extensive ablation studies, and to identify and analyze numerous interesting phenomena in both the model training and in the gameplay inference procedures. Our code, models, selfplay datasets, and auxiliary data are publicly available at https://ai.facebook.com/tools/elf-opengo/.
Postselection and counterfactual communication

A recent quantum protocol for counterfactual communication [Y. Aharonov and L. Vaidman, Phys. Rev. A 99, 010103(R), 2019] relies on post-selection to eliminate the weak trace in the transmission channel. We show that the post-selection in this protocol also eliminates the flow of Fisher information from transmitter to receiver. However, we also show that a classical communication protocol with post-selection can be counterfactual. Hence, we argue that post-selection should not be allowed in genuine counterfactual communication. In the quantum counterfactual communication protocol, the probability of discarding an event by post-selection tends to zero with an increasing number of ideal optical components. But the counterfactual violation strength tends to infinity at a faster rate. Consequently, the quantum protocol is not counterfactual proper.
Blockchain and Democracy

In the past few years in many countries people have experienced the erosion of trust in the main pillars of democracy, the voting and election systems. Many authors envisage the blockchain technology as a tool for restoration of trust (Tapscott 2016; Swislow 2016; Shin 2016). Our research is aimed at the potential use of blockchain technology in social systems for enhancing trust and increasing participation. We aim to explore whether the blockchain technology is suitable for voting or elections in large communities and the issues to be addressed for real world applications to leverage democratic rights. Our final conclusion is that there are both theoretical and practical obstacles in the way of such direct applications.
Anomaly detection in Bitcoin market via price return analysis

The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms.
Auto-labelling of Markers in Optical Motion Capture by Permutation Learning

Optical marker-based motion capture is a vital tool in applications such as motion and behavioural analysis, animation, and biomechanics. Labelling, that is, assigning optical markers to the pre-defined positions on the body, is a time consuming and labour intensive post-processing part of current motion capture pipelines. The problem can be considered as a ranking process in which markers shuffled by an unknown permutation matrix are sorted to recover the correct order. In this paper, we present a framework for automatic marker labelling which first estimates a permutation matrix for each individual frame using a differentiable permutation learning model and then utilizes temporal consistency to identify and correct remaining labelling errors. Experiments conducted on the test data show the effectiveness of our framework.
Lossless $(k,n)$ -Threshold Image Secret Sharing Based on the Chinese Remainder Theorem Without Auxiliary Encryption

A typical Chinese remainder theorem (CRT)-based secret sharing (SS) scheme has been proposed by Asmuth and Bloom for several decades, with lower computation complexity compared to Shamir's original polynomial-based SS. But when applied to images, CRT-based image secret sharing (CRTISS) shows many problems, such as lossy recovery, auxiliary encryption, and extra parameters requirement. We analyze the characteristics of images and ISS and propose a k,n)-threshold CRTISS based on the Asmuth and Bloom's scheme by sharing the high 7 bits of a grayscale secret pixel and embedding the least significant bit (LSB) into the random integer. The pixel values of a grayscale image are divided into two parts, which make it possible to share all the secret pixels with no expansion. Our method has the advantages of (k,n) threshold, lossless recovery, and no auxiliary encryption. The parameters requirement is the same as that in the Asmuth and Bloom's original method. Analysis and experiments are provided to validate the effectiveness of the proposed method.
Defuzzification method for ranking fuzzy numbers based on centroids and maximizing and minimizing set

This paper proposes a new method on ranking fuzzy numbers through the process of defuzzification by using maximizing and minimizing set on the triangular fuzzy numbers formed from generalized trapezoidal fuzzy numbers.In this method, a total utility value of each fuzzy number is defined by considering two left and two right utility values along with decision maker's optimism which serves as a criterion for ranking fuzzy numbers and overcomes the limitations of Chen's (1985) [Chen, S. H. (1985).Ranking fuzzy numbers with maximizing set and minimizing set.Fuzzy sets and systems, 17(2), 113-129] ranking method..
Tripartite non-maximally-entangled mixed states as a resource for optimally controlled quantum teleportation fidelity

Three-qubit mixed states are used as a channel for controlled quantum teleportation (CQT) of single-qubit states. The connection between different channel parameters to achieve maximum controlled teleportation fidelity is investigated. We show that for a given multipartite entanglement and mixedness a class of non-maximally-entangled mixed $X$ states ($X$-NMEMS) achieves optimum controlled quantum teleportation fidelity; interestingly a class of maximally entangled mixed $X$ states ($X$-MEMS) fails to do so. This demonstrates, for a given spectrum and mixedness, that $X$-MEMS are not sufficient to attain optimum controlled quantum teleportation fidelity, which is in contradiction with the traditional quantum teleportation of single qubits. In addition, we show that biseparable $X$-NMEMS, for a certain range of mixedness, are useful as a resource to attain high controlled quantum teleportation fidelity, which essentially lowers the requirements of quantum channels for CQT.