Reviewerly/exHarmony huggingface dataset:
Progressive Multi-Granularity Training for Non-Autoregressive Translation

Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence.However, recent studies show that NAT is weak at learning high-mode of knowledge such as one-to-many translations.We argue that modes can be divided into various granularities which can be learned from easy to hard.In this study, we empirically show that NAT models are prone to learn fine-grained lower-mode knowledge, such as words and phrases, compared with sentences.Based on this observation, we propose progressive multigranularity training for NAT.More specifically, to make the most of the training data, we break down the sentence-level examples into three types, i.e. words, phrases, sentences, and with the training goes, we progressively increase the granularities.Experiments on Romanian-English, English-German, Chinese-English and Japanese-English demonstrate that our approach improves the phrase translation accuracy and model reordering ability, therefore resulting in better translation quality against strong NAT baselines.Also, we show that more deterministic fine-grained knowledge can further enhance performance.
Learning Heuristic Selection with Dynamic Algorithm Configuration

A key challenge in satisficing planning is to use multiple heuristics within one heuristic search. An aggregation of multiple heuristic estimates, for example by taking the maximum, has the disadvantage that bad estimates of a single heuristic can negatively affect the whole search. Since the performance of a heuristic varies from instance to instance, approaches such as algorithm selection can be successfully applied. In addition, alternating between multiple heuristics during the search makes it possible to use all heuristics equally and improve performance. However, all these approaches ignore the internal search dynamics of a planning system, which can help to select the most useful heuristics for the current expansion step. We show that dynamic algorithm configuration can be used for dynamic heuristic selection which takes into account the internal search dynamics of a planning system. Furthermore, we prove that this approach generalizes over existing approaches and that it can exponentially improve the performance of the heuristic search. To learn dynamic heuristic selection, we propose an approach based on reinforcement learning and show empirically that domain-wise learned policies, which take the internal search dynamics of a planning system into account, can exceed existing approaches.
On the Generation of Medical Dialogs for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors.Because of the shortage of medical professionals, many people cannot receive online consultations timely.To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations.We collected two dialog datasets -CovidDialog -(in English and Chinese respectively) containing conversations between doctors and patients about COVID-19.While the largest of their kind, these two datasets are still relatively small compared with generaldomain dialog datasets.Training complex dialog generation models on small datasets bears high risk of overfitting.To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task.Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach.We perform both human evaluation and automatic evaluation of dialogs generated by our method.Results show that the generated responses are promising in being doctorlike, relevant to conversation history, clinically informative and correct.
Automatic Text Simplification for Social Good: Progress and Challenges

Since the late 1990s, automatic text simplification (ATS) was promoted as a natural language processing (NLP) task with great potential to make texts more accessible to people with various reading or cognitive disabilities, and enable their better social inclusion.Large multidisciplinary projects showed promising steps in that direction.Since 2010, the field started attracting more attention but at the cost of major shifts in system architecture, target audience, and evaluation strategies.Somewhere along the way, the focus has shifted from ATS for social good towards building complex endto-end neural architectures that are not aimed at any particular target population.This study presents the trajectory of ATS for social good, the main issues in current ATS trends, and the ways forward that could bring the field back to its initial goals.
High-speed and accurate 3D shape measurement using DIC-assisted phase matching and triple-scanning

• A robust digital image correlation assisted phase matching method is proposed. • Triple scanning using stereo structured-light system model achieves higher accuracy. • Efficient correlation and refinement strategies are developed in the pipeline of data processing to speed up the algorithm. Structured-light (SL) three-dimensional (3D) shape measurements technique has been extensively studied and applied in various fields, but the real-time capability of SL system is limited by the number of projected SL patterns. This paper presents a digital image correlation (DIC) assisted phase matching and triple-scanning method for high-speed and accurate 3D shape measurements based on the combined projection of three-step phase-shifting patterns and one speckle pattern. Benefiting from the triple-view geometric constraints and DIC technique, stereo correspondence and absolute fringe order of each valid pixel in the left wrapped phase map can be explored without requiring spatial phase unwrapping process or any priori information, the initial disparity map and absolute phase map can be generated. Subsequently, an efficient disparity refinement scheme based on connectivity area segmentation and neighbor disparity check is applied to the initial disparity map and absolute phase map to compensate the pixels with wrong disparities. Several efficient strategies are developed in the pipeline of data processing to speed up the algorithm. To improve the measurement accuracy, stereo SL model is proposed to make adequate use of triple-view information to calculate 3D coordinates using the disparity map and absolute phase map, and the measurement error has been reduced by 33% compared with the conventional methods. The experimental results demonstrate that the proposed method can perform high-speed and accurate 3D shape measurements for arbitrary surfaces and dynamic scenes.
Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence

The COVID-19 pandemic resulted in a significant increase in the workload for the emergency systems and healthcare providers all around the world. The emergency systems are dealing with large number of patients in various stages of deteriorating conditions which require significant medical expertise for accurate and rapid diagnosis and treatment. This issue will become more prominent in places with lack of medical experts and state-of-the-art clinical equipment, especially in developing countries. The machine intelligence aided medical diagnosis systems can provide rapid, dependable, autonomous, and low-cost solutions for medical diagnosis in emergency conditions. In this paper, a privacy-preserving computer-aided diagnosis (CAD) framework, called Decentralized deep Emergency response Intelligence (D-EI), which provides secure machine learning based medical diagnosis on the cloud is proposed. The proposed framework provides a blockchain based decentralized machine learning solution to aid the health providers with medical diagnosis in emergency conditions. The D-EI uses blockchain smart contracts to train the CAD machine learning models using all the data on the medical cloud while preserving the privacy of patients' records. Using the proposed framework, the data of each patient helps to increase the overall accuracy of the CAD model by balancing the diagnosis datasets with minority classes and special cases. As a case study, the D-EI is demonstrated as a solution for COVID-19 diagnosis. The D-EI framework can help in pandemic management by providing rapid and accurate diagnosis in overwhelming medical workload conditions.
PLM: Partial Label Masking for Imbalanced Multi-label Classification

Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative samples for each class skews network output probabilities further from ground-truth distributions. We propose a method, Partial Label Masking (PLM), which utilizes this ratio during training. By stochastically masking labels during loss computation, the method balances this ratio for each class, leading to improved recall on minority classes and improved precision on frequent classes. The ratio is estimated adaptively based on the network’s performance by minimizing the KL divergence between predicted and ground-truth distributions. Whereas most existing approaches addressing data imbalance are mainly focused on single-label classification and do not generalize well to the multi-label case, this work proposes a general approach to solve the long-tail data imbalance issue for multi-label classification. PLM is versatile: it can be applied to most objective functions and it can be used alongside other strategies for class imbalance. Our method achieves strong performance when compared to existing methods on both multi-label (MultiMNIST and MSCOCO) and single-label (imbalanced CIFAR-10 and CIFAR-100) image classification datasets.
Dataset for Automatic Summarization of Russian News

Automatic text summarization has been studied in a variety of domains and languages. However, this does not hold for the Russian language. To overcome this issue, we present Gazeta, the first dataset for summarization of Russian news. We describe the properties of this dataset and benchmark several extractive and abstractive models. We demonstrate that the dataset is a valid task for methods of text summarization for Russian. Additionally, we prove the pretrained mBART model to be useful for Russian text summarization.
GENERATIVE ADVERSARIAL NETWORKS TO GENERALISE URBAN AREAS IN TOPOGRAPHIC MAPS

Abstract. This article presents how a generative adversarial network (GAN) can be employed to produce a generalised map that combines several cartographic themes in the dense context of urban areas. We use as input detailed buildings, roads, and rivers from topographic datasets produced by the French national mapping agency (IGN), and we expect as output of the GAN a legible map of these elements at a target scale of 1:50,000. This level of detail requires to reduce the amount of information while preserving patterns; covering dense inner cities block by a unique polygon is also necessary because these blocks cannot be represented with enlarged individual buildings. The target map has a style similar to the topographic map produced by IGN. This experiment succeeded in producing image tiles that look like legible maps. It also highlights the impact of data and representation choices on the quality of predicted images, and the challenge of learning geographic relationships.
Using design based research to shift perspectives: a model for sustainable professional development for the innovative use of digital tools

Emerging digital technologies offer a transformative potential to redefine learning tasks and many examples of this potential are now available. The scaling of the innovative pedagogies emerging from the research into widespread and sustainable practice, however, remains problematic. This paper addresses the issue of scaling by using Design Based Research (DBR), also known as Educational Design Research, within teacher professional development to reposition teachers’ thinking about the place of digital tools in their teaching. Using a project seeking to support the use of new digital technologies to develop children’s spatial reasoning as a ‘worked example’, this paper highlights how the bringing together of the knowledge of educational research and knowledge of teaching practice in DBR can provide a catalyst for epistemic change. The paper will argue that DBR positions the knowledge and practice objects of both research and teaching as ‘epistemic’ or ‘not yet known’ objects and, therefore, the legitimate focus of experimentation and reflection.
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks

We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.
Oblivious RAM with Worst-Case Logarithmic Overhead

We present the first Oblivious RAM (ORAM) construction that for N memory blocks supports accesses with worst-case $$O(\log N)$$ overhead for any block size $$\varOmega (\log N)$$ while requiring a client memory of only a constant number of memory blocks. We rely on the existence of one-way functions and guarantee computational security. Our result closes a long line of research on fundamental feasibility results for ORAM constructions as logarithmic overhead is necessary. The previous best logarithmic overhead construction only guarantees it in an amortized sense, i.e., logarithmic overhead is achieved only for long enough access sequences, where some of the individual accesses incur $$\varTheta (N)$$ overhead. The previously best ORAM in terms of worst-case overhead achieves $$O(\log ^2 N/\log \log N)$$ overhead. Technically, we design a novel de-amortization framework for modern ORAM constructions that use the “shuffled inputs” assumption. Our framework significantly departs from all previous de-amortization frameworks, originating from Ostrovsky and Shoup (STOC ’97), that seem to be fundamentally too weak to be applied on modern ORAM constructions.
Optimizing Transaction Schedules on Universal Quantum Computers via Code Generation for Grover’s Search Algorithm

Quantum computers are known to be efficient for solving combinatorial problems like finding optimal schedules for processing transactions in parallel without blocking. We show how Grover’s search algorithm for quantum computers can be applied for finding an optimal transaction schedule via generating code from the problem instance. We compare our approach with existing approaches for traditional computers and quantum annealers in terms of preprocessing, runtime, space and code length complexity. Furthermore, we show by experiments the expected number of optimal solutions of this problem as well as suboptimal ones. With the help of an estimator of the number of solutions, we further speed up our optimizer for optimal and suboptimal transaction schedules.
TEACH: Attention-Aware Deep Cross-Modal Hashing

Hashing methods for cross-modal retrieval have recently been widely investigated due to the explosive growth of multimedia data. Generally, real-world data is imperfect and has more or less redundancy, making cross-modal retrieval task challenging. However, most existing cross-modal hashing methods fail to deal with the redundancy, leading to unsatisfactory performance on such data. In this paper, to address this issue, we propose a novel cross-modal hashing method, namely aTtEntion-Aware deep Cross-modal Hashing (TEACH). It could perform feature learning and hash-code learning simultaneously. Besides, with designed attention modules for different modalities, one for each, TEACH can effectively highlight the useful information of data while suppressing the redundant information. Extensive experiments on benchmark datasets demonstrate that our method outperforms some state-of-the-art hashing methods in cross-modal retrieval tasks.
TUN-Det: A Novel Network for Thyroid Ultrasound Nodule Detection

This paper presents a novel one-stage detection model, TUN-Det, for thyroid nodule detection from ultrasound scans. The main contributions are (i) introducing Residual U-blocks (RSU) to build the backbone of our TUN-Det, and (ii) a newly designed multi-head architecture comprised of three parallel RSU variants to replace the plain convolution layers of both the classification and regression heads. Residual blocks enable each stage of the backbone to extract both local and global features, which plays an important role in detection of nodules with different sizes and appearances. The multi-head design embeds the ensemble strategy into one end-to-end module to improve the accuracy and robustness by fusing multiple outputs generated by diversified sub-modules. Experimental results conducted on 1268 thyroid nodules from 700 patients, show that our newly proposed RSU backbone and the multi-head architecture for classification and regression heads greatly improve the detection accuracy against the baseline model. Our TUN-Det also achieves very competitive results against the state-of-the-art models on overall Average Precision (AP) metric and outperforms them in terms of \(AP_{35}\) and \(AP_{50}\), which indicates its promising performance in clinical applications. The code is available at: https://github.com/Medo-ai/TUN-Det.
Towards Ontologically Explainable Classifiers

In order to meet the explainability requirement of AI using Deep Learning (DL), this paper explores the contributions and feasibility of a process designed to create ontologically explainable classifiers while using domain ontologies. The approach is illustrated with the help of the Pizzas ontology that is used to create a synthetic image classifier that is able to provide visual explanations concerning a selection of ontological features. The approach is implemented by completing a DL model with ontological tensors that are generated from the ontology expressed in Description Logic.
New Quantum and LCD Codes over Finite Fields of Even Characteristic

For an integer m ≥ 1, we study cyclic codes of length l over a commutative non-chain ring F2m + uF2m , where u2 = u . With a new Gray map and Euclidean dual-containing cyclic codes, we provide many new and superior codes to the best-known quantum error-correcting codes. Also, we characterise LCD codes of length l with respect to their generator polynomials and prove that F2m − image of an LCD code of length l is an LCD code of length 2l . Finally, we provide several optimal LCD codes from the Gray images of LCD codes over F2m + uF2m .&#x0D;
Sentiment classification for employees reviews using regression vector- stochastic gradient descent classifier (RV-SGDC)

The satisfaction of employees is very important for any organization to make sufficient progress in production and to achieve its goals. Organizations try to keep their employees satisfied by making their policies according to employees' demands which help to create a good environment for the collective. For this reason, it is beneficial for organizations to perform staff satisfaction surveys to be analyzed, allowing them to gauge the levels of satisfaction among employees. Sentiment analysis is an approach that can assist in this regard as it categorizes sentiments of reviews into positive and negative results. In this study, we perform experiments for the world's big six companies and classify their employees' reviews based on their sentiments. For this, we proposed an approach using lexicon-based and machine learning based techniques. Firstly, we extracted the sentiments of employees from text reviews and labeled the dataset as positive and negative using TextBlob. Then we proposed a hybrid/voting model named Regression Vector-Stochastic Gradient Descent Classifier (RV-SGDC) for sentiment classification. RV-SGDC is a combination of logistic regression, support vector machines, and stochastic gradient descent. We combined these models under a majority voting criteria. We also used other machine learning models in the performance comparison of RV-SGDC. Further, three feature extraction techniques: term frequency-inverse document frequency (TF-IDF), bag of words, and global vectors are used to train learning models. We evaluated the performance of all models in terms of accuracy, precision, recall, and F1 score. The results revealed that RV-SGDC outperforms with a 0.97 accuracy score using the TF-IDF feature due to its hybrid architecture.
Back to the Past – Analysing Backporting Practices in Package Dependency Networks

The practice of backporting aims to bring the benefits of a bug or vulnerability fix from a higher to a lower release of a software package. When such a package adheres to semantic versioning, backports can be recognised as new releases in a lower major train. This is particularly useful in case a substantial number of software packages continues to depend on that lower major train. In this article, we study the backporting practices in four popular package distributions, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Cargo</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">npm</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Packagist</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RubyGems</i> . We observe that many dependent packages could benefit from backports provided by their dependencies. In particular, we find that a majority of security vulnerabilities affect more than one major train but are only fixed in the highest one, letting thousands of dependent packages exposed to the vulnerability. Despite that, we find that backporting updates is quite infrequent, and mostly practised by long-lived and more active packages for a variety of reasons.
MSTC<sup>∗</sup>:Multi-robot Coverage Path Planning under Physical Constrain

For large-scale tasks, coverage path planning (CPP) can benefit greatly from multiple robots. In this paper, we present an efficient algorithm MSTC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∗</sup> for multi-robot coverage path planning (mCPP) based on spiral spanning tree coverage (Spiral-STC). Our algorithm incorporates strict physical constraints like terrain traversability and material load capacity. We compare our algorithm against the state-of-the-art in mCPP for regular grid maps and real field terrains in simulation environments. The experimental results show that our method significantly outperforms existing spiral-STC based mCPP methods. Our algorithm can find a set of well-balanced workload distributions for all robots and therefore, achieve the overall minimum time to complete the coverage.