Reviewerly/exHarmony huggingface dataset:
Automatic brain extraction from 3D fetal MR image with deep learning-based multi-step framework

Brain extraction is a fundamental prerequisite step in neuroimage analysis for fetus. Due to surrounding maternal tissues and unpredictable movement, brain extraction from fetal Magnetic Resonance (MR) images is a challenging task. In this paper, we propose a novel deep learning-based multi-step framework for brain extraction from 3D fetal MR images. In the first step, a global localization network is applied to estimate probability maps for brain candidates. Connected-component labeling algorithm is applied to eliminate small erroneous components and accurately locate the candidate brain area. In the second step, a local refinement network is implemented in the brain candidate area to obtain fine-grained probability maps. Final extraction results are derived by a fusion network with the two cascaded probability maps obtained from previous two steps. Experimental results demonstrate that our proposed method has superior performance compared with existing deep learning-based methods.
Modified Perceptual Cycle Generative Adversarial Network-Based Image Enhancement for Improving Accuracy of Low Light Image Segmentation

In recent years, the importance of the semantic segmentation field has been increasingly emphasized because autonomous vehicle and artificial intelligence (AI)-based robot technology are being researched extensively; and methods for accurately recognizing objects are required.Previous state-ofthe-art segmentation methods have been proven to be effective for databases obtained during daytime.However, in extremely low light or nighttime environments, the shape and color information of objects are very small or disappear due to an insufficient amount of external light, which makes it difficult to train the segmentation network and significantly degrades performance.In our previous work, segmentation performance in a low light environment was improved using the enhancement-based segmentation method.However, low light images could not be restored precisely and segmentation performance improvement was limited because only per-pixel loss functions were used when training the enhancement network.To overcome these drawbacks, we propose a low light image segmentation method based on a modified perceptual cycle generative adversarial network (CycleGAN).Perceptual image enhancement was performed using our network, which significantly improved segmentation performance.Unlike the existing perceptual loss, the Euclidean distance of the feature maps extracted from the pretrained segmentation network was used.In our experiments, we used low light databases generated from two famous road scene open databases, which are Cambridge-driving Labeled Video Database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI), and confirmed that our proposed method shows better segmentation performance in extremely low light environments than the existing state-of-the art methods.
Lost in Back-Translation: Emotion Preservation in Neural Machine Translation

Machine translation provides powerful methods to convert text between languages, and is therefore a technology enabling a multilingual world.An important part of communication, however, takes place at the non-propositional level (e.g., politeness, formality, emotions), and it is far from clear whether current MT methods properly translate this information.This paper investigates the specific hypothesis that the non-propositional level of emotions is at least partially lost in MT.We carry out a number of experiments in a back-translation setup and establish that (1) emotions are indeed partially lost during translation; (2) this tendency can be reversed almost completely with a simple re-ranking approach informed by an emotion classifier, taking advantage of diversity in the n-best list; (3) the re-ranking approach can also be applied to change emotions, obtaining a model for emotion style transfer.An in-depth qualitative analysis reveals that there are recurring linguistic changes through which emotions are toned down or amplified, such as change of modality.
Heterogeneous Recycle Generation for Chinese Grammatical Error Correction

Most recent works in the field of grammatical error correction (GEC) rely on neural machine translation-based models. Although these models boast impressive performance, they require a massive amount of data to properly train. Furthermore, NMT-based systems treat GEC purely as a translation task and overlook the editing aspect of it. In this work we propose a heterogeneous approach to Chinese GEC, composed of a NMT-based model, a sequence editing model, and a spell checker. Our methodology not only achieves a new state-of-the-art performance for Chinese GEC, but also does so without relying on data augmentation or GEC-specific architecture changes. We further experiment with all possible configurations of our system with respect to model composition order and number of rounds of correction. A detailed analysis of each model and their contributions to the correction process is performed by adapting the ERRANT scorer to be able to score Chinese sentences.
Enhancing three variants of harmony search algorithm for continuous optimization problems

Meta-heuristic algorithms are well-known optimization methods, for solving real-world optimization problems. Harmony search (HS) is a recognized meta-heuristic algorithm with an efficient exploration process. But the HS has a slow convergence rate, which causes the algorithm to have a weak exploitation process in finding the global optima. Different variants of HS introduced in the literature to enhance the algorithm and fix its problems, but in most cases, the algorithm still has a slow convergence rate. Meanwhile, opposition-based learning (OBL), is an effective technique used to improve the performance of different optimization algorithms, including HS. In this work, we adopted a new improved version of OBL, to improve three variants of Harmony Search, by increasing the convergence rate speed of these variants and improving overall performance. The new OBL version named improved opposition-based learning (IOBL), and it is different from the original OBL by adopting randomness to increase the solution's diversity. To evaluate the hybrid algorithms, we run it on benchmark functions to compare the obtained results with its original versions. The obtained results show that the new hybrid algorithms more efficient compared to the original versions of HS. A convergence rate graph is also used to show the overall performance of the new algorithms.
BERGAMOT-LATTE Submissions for the WMT20 Quality Estimation Shared Task

This paper presents our submission to the WMT2020 Shared Task on Quality Estimation (QE). We participate in Task and Task 2 focusing on sentence-level prediction. We explore (a) a black-box approach to QE based on pre-trained representations; and (b) glass-box approaches that leverage various indicators that can be extracted from the neural MT systems. In addition to training a feature-based regression model using glass-box quality indicators, we also test whether they can be used to predict MT quality directly with no supervision. We assess our systems in a multi-lingual setting and show that both types of approaches generalise well across languages. Our black-box QE models tied for the winning submission in four out of seven language pairs inTask 1, thus demonstrating very strong performance. The glass-box approaches also performed competitively, representing a light-weight alternative to the neural-based models.
Faint trace of a particle in a noisy Vaidman three-path interferometer

Abstract We study weak traces of particle passing Vaidman’s nested Mach–Zehnder interferometer. We investigate an effect of decoherence caused by an environment coupled to internal degree of freedom (a spin) of a travelling particle. We consider two models: pure decoherence leading to exact results and weak coupling Davies approximation allowing to include dissipative effects. We show that potentially anomalous discontinuity of particle paths survives an effect of decoherence unless it affects internal part of the nested interferometer.
Incorporating Temporal Information in Entailment Graph Mining

We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities.We focus on the sports domain in which the same pairs of teams play on different occasions, with different outcomes.We present an unsupervised model that aims to learn entailments such as win/lose → play, while avoiding the pitfall of learning non-entailments such as win → lose.We evaluate our model on a manually constructed dataset, showing that incorporating time intervals and applying a temporal window around them, are effective strategies.
Ariadne: A common-sense thread for enabling provable safety in air mobility systems with unreliable components

Commercial air travel is the safest transportation modality available to humanity today. It has achieved this enviable status by deploying thousands of professionals, including pilots, cabin crew, mechanics, dispatchers, and air traffic controllers to operate air vehicles, bringing them and their passengers safely from origin to destination while managing dangerous weather, other traffic and system failures for decades. The air transportation has been undergoing undeniable and continuous progress and modernization since its inception.We introduceAriadne, a thread to accelerate the productivity gains achieved by air traffic services providers around the globe. Ariadne is an engineered version of the common sense practice of always keeping a "Plan B", and possibly "plans C, D, E, and F" against unexpected events when any decision is made by pilots, air traffic controllers, dispatchers, and any other safety-critical actor of the air transportation system. The name "Ariadne" was chosen to honor the mythical character Ariadne, daughter of Minos the king of Crete, who conceived the"Plan B" mechanism that would allow her lover to exit Daedalus’ Labyrinth after killing the Minotaur. Ariadne and its informal definition as "Plan B engineering" offer surprising opportunities and properties, including not only provable operations safety with unproven components, but also a thread that can inherently be scaled up and quickly adapt to new air traffic scenarios, including the transition to free flight and accommodation of unmanned aviation. It also supports existing operations and therefore it does not conflict with current air traffic control practices.Modern computational capabilities and powerful AI algorithms make its implementation increasingly feasible to address more aspects of air traffic management. Ariadne can be readily generalized to other contexts, such as Robotics and any safety-critical cyber-physical system.
Limited-view binary tomography reconstruction assisted by shape centroid

In this paper, the binary tomographic reconstruction problem for very limited projection data availability is considered. Being this inverse problem highly ill-posed, we propose a new reconstruction model that uses a shape centroid-based regularization term, i.e., we assume that the center of gravity of the object of interest is known, at least approximately, in advance. Motivation for this regularization is found in the close connection between the projection data and the object centroid, as we will show. Experimental evaluation underpins that reasonable results can be obtained from practically minimal amount of projection data, gathered from just one projection direction.
A Unified Model for Arabizi Detection and Transliteration using Sequence-to-Sequence Models

While online Arabic is primarily written using the Arabic script, a Roman-script variety called Arabizi is often seen on social media. Although this representation captures the phonology of the language, it is not a one-to-one mapping with the Arabic script version. This issue is exacerbated by the fact that Arabizi on social media is Dialectal Arabic which does not have a standard orthography. Furthermore, Arabizi tends to include a lot of code mixing between Arabic and English (or French). To map Arabizi text to Arabic script in the context of complete utterances, previously published efforts have split Arabizi detection and Arabic script target in two separate tasks. In this paper, we present the first effort on a unified model for Arabizi detection and transliteration into a code-mixed output with consistent Arabic spelling conventions, using a sequence-to-sequence deep learning model. Our best system achieves 80.6% word accuracy and 58.7% BLEU on a blind test set.
The Effectiveness of Mobile Blended Problem Based Learning on Mathematical Problem Solving

&lt;p&gt;This study aimed to examine the effectiveness of mobile blended compared to conventional problem-based learning on mathematical problem-solving skills and look for students' responses about the use of online learning. The study was conducted using a pretest-posttest quasi-experimental research design supported with descriptive qualitative responses involving 188 students taken by applying cluster random sampling as subjects of research distributed from vocational high schools in &lt;em&gt;Bojonegoro&lt;/em&gt; City, East Java, Indonesia. Research findings revealed that mathematical problem-solving skills for students treated with mobile blended problem-based learning were higher than those who have been treated with purely problem-based learning. In addition, students have responded positively to the usage of mobile blended problem-based learning that was applicable and acceptable for effectively improving mathematical problem-solving skills.&lt;/p&gt;
Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy.
The ADAPT System Description for the WMT20 News Translation Task

This paper describes the ADAPT Centre’s submissions to the WMT20 News translation shared task for English-to-Tamil and Tamil-to-English. We present our machine translation (MT) systems that were built using the state-of-the-art neural MT (NMT) model, Transformer. We applied various strategies in order to improve our baseline MT systems, e.g. onolin- gual sentence selection for creating synthetic training data, mining monolingual sentences for adapting our MT systems to the task, hyperparameters search for Transformer in lowresource scenarios. Our experiments show that adding the aforementioned techniques to the baseline yields an excellent performance in the English-to-Tamil and Tamil-to-English translation tasks.
Local Convergence Analysis of Gradient Descent Ascent with Finite Timescale Separation

We study the role that a finite timescale separation parameter τ has on gradient descent-ascent in non-convex, non-concave zero-sum games where the learning rate of player 1 is denoted by γ1 and the learning rate of player 2 is defined to be γ2=τγ1. We show there exists a finite timescale separation parameter τ∗ such that x∗ is a stable critical point of gradient descent-ascent for all τ∈(τ∗,∞) if and only if it is a strict local minmax equilibrium. Moreover, we provide an explicit construction for computing τ∗ along with corresponding convergence rates. The convergence results we present are complemented by a non-convergence result: given a critical point x∗ that is not a strict local minmax equilibrium, there exists a finite timescale separation τ0 such that x∗ is unstable for all τ∈(τ0,∞). Finally, we extend the results to gradient penalty regularization methods for generative adversarial networks and empirically demonstrate on CIFAR-10 and CelebA the significant impact timescale separation has on training performance.
An Interactive Multi-Agent System for Game Design

This paper presents a novel approach to procedural generation of game maps for multi-player, competitive video games. A multi-agent evolutionary system is employed to place streets, buildings and other items, resulting in a playable video game map. The system utilises computational agents that act in conjunction with the human designer to produce maps that exhibit desirable characteristics. This paper compares the impact that the additional agents have in terms of the quality of candidate solutions. The results indicate that the use of the agents produces higher quality solutions in comparison to a traditional interactive genetic algorithm.
A Meta-Heuristic Automatic CNN Architecture Design Approach Based on Ensemble Learning

Convolutional Neural Networks (CNNs) models achieve a dominant performance on immense domains. There are CNNs that come in numerous topologies of different sizes. This field's challenge is to design the right CNN architecture for a specific problem to attain high results with low computing resources for training this architecture. Our proposed automatic design approach is concerned with finding a solution to this challenge. The suggested framework is based on the genetic algorithm that works on evolving a population of CNN models to find the best-fitted architecture. Unlike the co-related work, our proposed method is concerned with generating lightweight architectures that are characterized by a low number of parameters with preserving high validation accuracy based on an ensemble learning technique. This framework is designed to work on limited computing resources to be feasible for deployment in a variety of environments. Four popular benchmark image datasets are used to validate the proposed framework, and it is compared to the peer competitors work based on various methods in terms of validation accuracy, the number of model' parameters, the number of used Graphical Processing Units (GPUs), and GPU days was taken to complete the process. Our experimental results showed mainly superiority in terms of GPU days with relatively high validation accuracy and a low number of parameters of the discovered model.
Shear measurement bias

We present a new shear calibration method based on machine learning. The method estimates the individual shear responses of the objects from the combination of several measured properties on the images using supervised learning. The supervised learning uses the true individual shear responses obtained from copies of the image simulations with different shear values. On simulated GREAT3 data, we obtain a residual bias after the calibration compatible with 0 and beyond Euclid requirements for a signal-to-noise ratio &gt; 20 within ∼15 CPU hours of training using only ∼10 5 objects. This efficient machine-learning approach can use a smaller data set because the method avoids the contribution from shape noise. The low dimensionality of the input data also leads to simple neural network architectures. We compare it to the recently described method Metacalibration, which shows similar performances. The different methods and systematics suggest that the two methods are very good complementary methods. Our method can therefore be applied without much effort to any survey such as Euclid or the Vera C. Rubin Observatory, with fewer than a million images to simulate to learn the calibration function.
Global solar radiation prediction model with random forest algorithm

Global solar radiation estimation is crucial for regional climate assessment and crop growth. Therefore, studies on the prediction of solar radiation are emerging. With the availability of the public data on solar radiation, computerized models have been developed as well. These predictive models play significant role in determining the potentials of regions suitable for renewable energy generation required by engineering and agricultural activities. Herein a computerized model has been presented for estimating global solar radiation. The model utilizes random forest algorithm and reached predictive value of 93.9%.
Gradient Projection Memory for Continual Learning

The ability to learn continually without forgetting the past tasks is a desired attribute for artificial learning systems. Existing approaches to enable such learning in artificial neural networks usually rely on network growth, importance based weight update or replay of old data from the memory. In contrast, we propose a novel approach where a neural network learns new tasks by taking gradient steps in the orthogonal direction to the gradient subspaces deemed important for past tasks. We find the bases of these subspaces by analyzing network representations (activations) after learning each task with Singular Value Decomposition (SVD) in a single shot manner and store them in the memory as Gradient Projection Memory (GPM). With qualitative and quantitative analyses, we show that such orthogonal gradient decent induces minimum to no interference with the past tasks, thereby mitigating forgetting. We evaluate our algorithm on diverse image classification datasets with short and long sequences of tasks and report better or on-par performance compared to the state-of-the-art approaches.
Preprocessing of Text for Emotion Detection and Sentiment Analysis of Hindi Movie Reviews

Analyzing Sentiment and Detecting Emotion from text is a relatively challenging task. We tackle the complication of Recognizing Emotion from text, it is the unexcelled way to transmit feel and emotion. However, text-based emotion recognition systems need more attraction of researcher it is progressing slowly when collated with face or voice based systems. In this paper we are presenting about data set used, Preprocessing of Data, Feature Extraction, Approaches used.