Reviewerly/exHarmony huggingface dataset:
Rule-Based Spanish Multiple Question Reformulation and their Classification using a Convolutional Neuronal Network

Question reformulation allows the creation of different forms of the same question in order to identify the best answer. However, when aspects suchas length and complexity increase, the reformulation process becomes more complicated, consequently also the recovery of the corresponding information. In this research, a method for the reformulation of multiple questions in Spanish is presented, as part of the pre-processing stage in a question-answer system. The lexical category of each word, Named Entities and Multi-Word Terms, were used to reformulate multiple questions into new individual questions, and then a Convolutional Neural Network was used to classify them, allowing to find or build adequate answers to improve the quality of the results, which is fundamental in QAsystems. A dataset with multiple questions was also created to evaluate our reformulation method, since it was not possible to find any. On the other hand, for the evaluation of the question classification model, we used the TREC, Simple Questions, Web Questions, Wiki Movies and Curated TREC datasets, translated into Spanish. Both tasks achieved promising results for further work.
A Theorem on Power Superposition in Resistive Networks

The superposition theorem, a particular case of the superposition principle, states that in a linear circuit with several voltage and current sources, the current and voltage for any element of the circuit is the algebraic sum of the currents and voltages produced by each source acting independently. The superposition theorem is not applicable to power, because it is a non-linear quantity. Therefore, the total power dissipated in a resistor must be calculated using the total current through (or the total voltage across) it. The theorem proposed and proved in this brief states that in a linear network consisting of resistors and independent voltage and current sources, the total power dissipated in the resistors of the network is the sum of the power supplied simultaneously by the voltage sources with the current sources replaced by open circuit, and the power supplied simultaneously by the current sources when the voltage sources are replaced by short-circuit. This means that the power is superimposed. The theorem can be used to simplify the power analysis of resistive networks.
Terrorism, innovation and venture capital

Purpose This paper aims to examine the relationship between terrorism and innovation and the moderating role of venture capital. Design/methodology/approach The paper has used panel data from 140 countries covering the period of 2007–2016 and has analysed the data by using generalised method of moments instrumental variables (GMM-IV) estimation method to control for unobserved endogeneity among the variables. Findings The authors find that terrorism has negative impact on innovation. Interesting results emerge when we separated the developed countries from others. The results show that the impact of terrorism on innovation is lower in developed countries. This is due to the fact that strong institutional settings in developed countries make the investors confident by providing support and incentives. Better institutional settings in developed countries also help to reduce uncertainty, which maximise innovation and minimise terrorism risk. The authors also find that venture capital positively moderates the terrorism and innovation relationship. This implies that by providing sufficient fund for technological development, venture capital may help to reduce terrorism risk. Practical implications These results may guide the policy makers to find a business solution instead of lengthy political solution to mitigate terrorism risk in emerging countries. Overall, this paper will provide the basis for improving the counter-terrorism approaches from an innovation perspective. Originality/value The paper has used terrorism and venture capital data from 140 countries and finds interesting results that may help the policy makers to reduce the effect and intensity of terrorism in emerging countries.
SISTEM INFORMASI ADMINISTRASI KEUANGAN SEKOLAH BERBASIS WEB (STUDI KASUS: SMK AL-KAHFI)

Al-Kahfi Vocational School is a place where writers conduct research. At this time, the school's financial administration management system is still done manually, causing a lot of bookkeeping, repeated writing of data, and delays in making reports. The solution to the problem that is happening at Al-Kahf Vocational School is the application of information technology, namely building an information system that can help manage the school's financial administration system more efficiently. So from that the purpose of this study is the creation of a Web-Based School Financial Administration Information System at Al-Kahf Vocational School. This system was developed using the PHP programming language Hypertext Preprocessor (PHP) and MySql as a database. The existence of an information system built is expected to be able to help financial officers in the processing of school financial administration data so as to produce a well-organized school financial administration information system.
A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.
Origins of the Time, Mass, Energy, Wave Function, Electric Charges, and Magnetic Monopoles

Origins of the time, mass, electric charges and magnetic monopoles are explained. The energies, electric charges and magnetic charges of the particles are defined as E = cDtDV and |q| = cDt = E/DV, and |qm| = c2Dt, respectively, for the 3-D quantized spaces warped along the time axis direction of ct in the 4-D Euclidean space. The energy (or mass) and charges (or electric charges and magnetic charges) are the vectors along the time axis of ct in the 4-D Euclidean space. This new concept is closely connected to the wave function of the quantum mechanics. The electric charges, magnetic charges and energies have the property of the space direction independence. Magnetic monopoles (charges) are the force carrying bosons with the inside electric field time loop. Electric monopoles are the elementary fermions. Photon space fluctuations are explained with the quantized magnetic charges.
Online Apprenticeship Learning

In Apprenticeship Learning (AL), we are given a Markov Decision Process (MDP) without access to the cost function. Instead, we observe trajectories sampled by an expert that acts according to some policy. The goal is to find a policy that matches the expert's performance on some predefined set of cost functions. We introduce an online variant of AL (Online Apprenticeship Learning; OAL), where the agent is expected to perform comparably to the expert while interacting with the environment. We show that the OAL problem can be effectively solved by combining two mirror descent based no-regret algorithms: one for policy optimization and another for learning the worst case cost. By employing optimistic exploration, we derive a convergent algorithm with O(sqrt(K)) regret, where K is the number of interactions with the MDP, and an additional linear error term that depends on the amount of expert trajectories available. Importantly, our algorithm avoids the need to solve an MDP at each iteration, making it more practical compared to prior AL methods. Finally, we implement a deep variant of our algorithm which shares some similarities to GAIL, but where the discriminator is replaced with the costs learned by OAL. Our simulations suggest that OAL performs well in high dimensional control problems.
Addressing Extreme Imbalance for Detecting Medications Mentioned in Twitter User Timelines

Tweets mentioning medications are valuable for efforts in digital epidemiology to supplement traditional methods of monitoring public health. A major obstacle, however, is to differentiate them from the large majority of tweets on other topics posted in a user’s timeline: solving the infamous ‘needle in a haystack’ problem. While deep learning models have significantly improved classification, their performance and inference processing time remain low on extremely imbalanced corpora where the tweets of interest are less than 1% of all tweets. In this study, we empirically evaluate under-sampling, fine-tuning, and filtering heuristics to train such classifiers. Using a corpus of 212 Twitter timelines (181,607 tweets with only 0.2% tweets mentioning a medication), our results show that combining these heuristics is necessary to impact the classifier’s performance. In our intrinsic evaluation, a classifier based on a lexicon and a BERT-base neural network achieved a 0.838 F1-score, a score similar to the score achieved by the best classifier on this task during the #SMM4H’20 competition, but it processed the corpus 28 times faster - a positive result, since processing speed is still a roadblock to deploying classifiers on large cohorts of Twitter users needed for pharmacovigilance. In our extrinsic evaluation, our classifier helped a labeler to extract the spans of medications more accurately and achieved a 0.76 Strict F1-score. To the best of our knowledge, this is the first evaluation of medications extraction from Twitter timelines and it establishes the first benchmark for future studies.
QUANTUM ANNEALING OPTIMIZATION OF A HEURISTIC SURROGATE MODEL FOR PWR FUEL LOADING

An efficient fuel arrangement must be generated by PWR operators every 6–18 months. This complex problem has been extensively researched with two broad approaches, heuristic and stochastic methods, becoming accepted. This initial study qualitatively introduces the concept of encoding full-core PWR fuel loading patterns in a form suitable for quantum annealing. The concepts of adiabatic quantum computers and quantum annealing are introduced, and a surrogate model encoding of a set of heuristics for loading pattern design produced in a form suitable for use in present-day quantum annealers. The simulated results show significant similarity to benchmark loading patterns.
Analyzing Phonetic Data with Generalized Additive Mixed Models

This chapter provides a tutorial on how to analyze phonetic data with Generalized Additive Mixed Models (GAMs). It presents analyses for three example datasets, all of which address aspects of the phonetics of Taiwan Mandarin. The first dataset contains response time data from an auditory priming experiment. For the second dataset, the chapter focuses on the realizations of Mandarin high-level tones by different speaker groups in Taiwan. The third dataset addresses the ongoing merging of two sets of sibilants in Taiwan Mandarin. GAMs can model time-varying response variables. The linear mixed model can allow the intercept and slope of regression lines for individual subjects to vary, but it cannot handle the case in which functional relations are not linear but instead wiggly. In phonetics, a lot of features that speech clinician measure change over time. These include pitch, intensity and formants. Time is a numeric variable ranging from one to ten in normalized time.
Digital maturity as a critical element development of the media industry

The article defines that the social functioning of communication technologies forms the media environment (media space) of a digital society. Organizations that are at high levels of digital maturity are significantly more likely than organizations with lower maturity to achieve high net income and annual revenue growth. Based on the analysis, ten key competencies that are critical for the management of the media industry are identified.
DIGITAL TRAINING MODELS

Татаринов Константин Анатольевич
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations

Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks. In this paper, we provide the first integral study and analysis for feed-forward neural networks in terms of the robustness in pairwise class margin and its generalization behavior under weight perturbation. We further design a new theory-driven loss function for training generalizable and robust neural networks against weight perturbations. Empirical experiments are conducted to validate our theoretical analysis. Our results offer fundamental insights for characterizing the generalization and robustness of neural networks against weight perturbations.
Synthesize and Segment: Towards Improved Catheter Segmentation via Adversarial Augmentation

Automatic catheter and guidewire segmentation plays an important role in robot-assisted interventions that are guided by fluoroscopy. Existing learning based methods addressing the task of segmentation or tracking are often limited by the scarcity of annotated samples and difficulty in data collection. In the case of deep learning based methods, the demand for large amounts of labeled data further impedes successful application. We propose a synthesize and segment approach with plug in possibilities for segmentation to address this. We show that an adversarially learned image-to-image translation network can synthesize catheters in X-ray fluoroscopy enabling data augmentation in order to alleviate a low data regime. To make realistic synthesized images, we train the translation network via a perceptual loss coupled with similarity constraints. Then existing segmentation networks are used to learn accurate localization of catheters in a semi-supervised setting with the generated images. The empirical results on collected medical datasets show the value of our approach with significant improvements over existing translation baseline methods.
Super Resolution Generative Adversarial Network Based Image Augmentation for Scene Classification of Remote Sensing Images

High spatial resolution remote sensing image (RSI) scene classification, aimed at automatically labelling images with the given semantic categories, has been a hot issue. As it's difficult for RSI to quickly obtain a large number of training samples from a specific area. Traditional scene classification researches were mainly using deep learning models to transfer natural images to RSI. Considering the differences between natural images and RSI, we trained several Super Resolution GAN models by using different resolution RSI data from Google earth image. This paper proposed a novel SRGAN-CNN framework. Through transferring the data with scene classification dataset to obtain high resolution fake RSI. The experimental results demonstrate that the proposed framework can enhance transfer effect and help improve the accuracy of scene classification using low resolution RSI.
WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras

The wavelet transform is a powerful tool for performing multiscale analysis and it is a key subroutine in countless applications, from image processing to astronomy. Recently, it has extended its range of users to include the ever growing machine learning community. For a wavelet library to be efficiently adopted in this context, it needs to provide transformations which can be integrated seamlessly in already existing machine learning workflows and neural networks, being able to leverage the same libraries and run on the same hardware (e.g., CPU vs GPU) as the rest of the machine learning pipeline, without impacting training and evaluation performance. In this paper we present WaveTF, a wavelet library available as a Keras layer, which leverages TensorFlow to exploit GPU parallelism and can be used to enrich already existing machine learning workflows. To demonstrate its efficiency we compare its raw performance against other alternative libraries and finally measure the overhead it causes to the learning process when it is integrated in an already existing Convolutional Neural Network.
On the Impact of Device and Behavioral Heterogeneity in Federated Learning

Federated learning (FL) is becoming a popular paradigm for collaborative learning over distributed, private datasets owned by non-trusting entities. FL has seen successful deployment in production environments, and it has been adopted in services such as virtual keyboards, auto-completion, item recommendation, and several IoT applications. However, FL comes with the challenge of performing training over largely heterogeneous datasets, devices, and networks that are out of the control of the centralized FL server. Motivated by this inherent setting, we make a first step towards characterizing the impact of device and behavioral heterogeneity on the trained model. We conduct an extensive empirical study spanning close to 1.5K unique configurations on five popular FL benchmarks. Our analysis shows that these sources of heterogeneity have a major impact on both model performance and fairness, thus sheds light on the importance of considering heterogeneity in FL system design.
Identifying and Repairing Catastrophic Errors in Galaxy Properties Using Dimensionality Reduction

Our understanding of galaxy evolution is derived from large surveys designed to maximize efficiency by only observing the minimum amount needed to infer properties for a typical galaxy. However, for a few percent of galaxies in every survey, these observations are insufficient and derived properties can be catastrophically wrong. Further, it is currently difficult or impossible to determine which objects have failed, so that these contaminate every study of galaxy properties. We develop a novel method to identify these objects by combining the astronomical codes which infer galaxy properties with the dimensionality reduction algorithm t-SNE, which groups similar objects to determine which inferred properties are out of place. This method provides an improvement for the COSMOS catalog, which already uses existing techniques for catastrophic error removal, and therefore should improve the quality of large catalogs and any studies which are sensitive to large redshift errors.
Locality of three-qubit Greenberger-Horne-Zeilinger-symmetric states

The hierarchy of nonlocality and entanglement in multipartite systems is one of the fundamental problems in quantum physics. We study this topic in three-qubit systems considering the entanglement classification of stochastic local operations and classical communication (SLOCC). The equivalence under SLOCC divides threequbit states into separable, biseparable, W, and Greenberger-Horne-Zeilinger (GHZ) classes. The W and GHZ are two subclasses of genuine tripartite entanglement.We adopt the family of GHZ-symmetric states as a research subject, which share the symmetries of the GHZ state and have a complete characterization of SLOCC classes. In the biseparable region (with bipartite entanglement), there exist GHZ-symmetric states that are found to be fully local. In addition, there are bilocal states in both theW and GHZ classes. That is, neither of the subclasses of genuine tripartite entanglement can ensure genuinely tripartite nonlocality.
Asymptotics with remainder term for moments of the total cycle number of random <i>A</i>-permutation

Abstract Dedicated to the memory of Alexander Ivanovich Pavlov . We consider the set of n -permutations with cycle lengths belonging to some fixed set A of natural numbers (so-called A -permutations). Let random permutation τ n be uniformly distributed on this set. For some class of sets A we find the asymptotics with remainder term for moments of total cycle number of τ n .
Joint artifact correction and super-resolution of image slicing and mapping system via a convolutional neural network

As the key component of the image mapping spectrometer, the image mapper introduces complex image degradation in the reconstructed images, including low spatial resolution and intensity artifacts. In this paper, we propose a novel image processing method based on the convolutional neural network to perform artifact correction and super-resolution (SR) simultaneously. The proposed joint network contains two branches to handle the artifact correction task and SR task in parallel. The artifact correction module is designed to remove the artifacts in the image and the SR module is used to improve the spatial resolution. An attention fusion module is constructed to combine the features extracted by the artifact correction and SR modules. The fused features are used to reconstruct an artifact-free high-resolution image. We present extensive simulation results to demonstrate that the proposed joint method outperforms state-of-the-art methods and can be generalized to other image mapper designs. We also provide experimental results to prove the efficiency of the joint network.
A Joint Mapping and Synthesis Approach for Multiview Facial Expression Recognition

This paper presents a novel approach to address pose-invariant face frontalization aiming Multiview Facial Expression Recognition (MFER). Particularly, the proposed approach is a hybrid method, including both synthesizing and mapping techniques. The key idea is to use mapped reconstructive coefficients of each arbitrary viewpoints and the frontal bases where the mapping functions are provided by learning between frontal and non-frontal faces’ coefficients. We also exploit sparse coding for synthesizing the frontalized faces, even with large poses. For evaluation, two qualitative and quantitative assessments are used along with an application of multiview facial expression recognition as a case study. The results show that our approach is efficient in terms of frontalizing non-frontal faces. Moreover, its validation on two popular datasets, BU3DFE and Multi-PIE, with various assessments contexts reveals its efficiency and stability on head pose variation, especially on large poses.
Research on cloud forging resource service selection optimization based on genetic algorithm

Abstract As a concrete manifestation of “manufacturing as service”, cloud forging is a cross-integrated product of advanced manufacturing, information and emerging Internet of Things technology. To make full use of the manufacturing resources of cloud forging, it is of great importance to optimize the resource selection before scheduling. This research proposes a cloud forging resource service optimization strategy based on genetic algorithm. Taking into account the characteristics of cloud forging resource service optimization, sharing cost, interaction time, and service quality are selected as the three preferred indexes of cloud forging resource service, and these three indexes are quantified. The genetic algorithm is used to solve the cloud forging resource service optimization model, and the cloud forging resource service with the best performance is selected. Finally, an example is used to verify the effectiveness of the proposed method.