Reviewerly/exHarmony huggingface dataset:
Blockchain Technology for Networked Swarms of Unmanned Aerial Vehicles (UAVs)

Unmanned Aerial Vehicle (UAV) technology is quickly growing with a wide range of current and planned future applications. As the technology grows in usage, the data gathered by UAV systems as well as the UAVs themselves will become bigger targets for cyber-attacks. New cyber security technologies, such as the immutable ledger technology known as blockchain, should therefore be applied to provide a defense against the growing threat of cyber-attacks. This paper explores blockchain technology, first through a general overview of its components and characteristics, and then at what security improvements it can provide to a system. Following this exploration, the application of blockchain to a UAV swarm environment is briefly expanded on. Lastly, one such blockchain framework known as Hyperledger Fabric is explored, that could potentially be applied to a swarm of UAVs to increase its security.
Image Encryption Scheme with Compressed Sensing Based on New Three-Dimensional Chaotic System

In this paper, a new three-dimensional chaotic system is proposed for image encryption. The core of the encryption algorithm is the combination of chaotic system and compressed sensing, which can complete image encryption and compression at the same time. The Lyapunov exponent, bifurcation diagram and complexity of the new three-dimensional chaotic system are analyzed. The performance analysis shows that the chaotic system has two positive Lyapunov exponents and high complexity. In the encryption scheme, a new chaotic system is used as the measurement matrix for compressed sensing, and Arnold is used to scrambling the image further. The proposed method has better reconfiguration ability in the compressible range of the algorithm compared with other methods. The experimental results show that the proposed encryption scheme has good encryption effect and image compression capability.
Learning with Noisy Labels for Sentence-level Sentiment Classification

Deep neural networks (DNNs) can fit (or even over-fit) the training data very well.If a DNN model is trained using data with noisy labels and tested on data with clean labels, the model may perform poorly.This paper studies the problem of learning with noisy labels for sentence-level sentiment classification.We propose a novel DNN model called NETAB (as shorthand for convolutional neural NETworks with AB-networks) to handle noisy labels during training.NETAB consists of two convolutional neural networks, one with a noise transition layer for dealing with the input noisy labels and the other for predicting 'clean' labels.We train the two networks using their respective loss functions in a mutual reinforcement manner.Experimental results demonstrate the effectiveness of the proposed model.
A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages

Parsers are available for only a handful of the world's languages, since they require lots of training data.How far can we get with just a small amount of training data?We systematically compare a set of simple strategies for improving low-resource parsers: data augmentation, which has not been tested before; cross-lingual training; and transliteration.Experimenting on three typologically diverse low-resource languages-North Sámi, Galician, and Kazah-We find that (1) when only the low-resource treebank is available, data augmentation is very helpful; (2) when a related high-resource treebank is available, cross-lingual training is helpful and complements data augmentation; and (3) when the high-resource treebank uses a different writing system, transliteration into a shared orthographic spaces is also very helpful.
Learning Dynamic Context Augmentation for Global Entity Linking

Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space.In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document.DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plugand-enhance module.We explore both supervised and reinforcement learning strategies for learning the DCA model.Extensive experiments 1 show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots

This paper proposes a dually interactive matching network (DIM) for presenting the personalities of dialogue agents in retrievalbased chatbots.This model develops from the interactive matching network (IMN) which models the matching degree between a context composed of multiple utterances and a response candidate.Compared with previous persona fusion approaches which enhance the representation of a context by calculating its similarity with a given persona, the DIM model adopts a dual matching architecture, which performs interactive matching between responses and contexts and between responses and personas respectively for ranking response candidates.Experimental results on PERSONA-CHAT dataset show that the DIM model outperforms its baseline model, i.e., IMN with persona fusion, by a margin of 14.5% and outperforms the current state-of-the-art model by a margin of 27.7% in terms of top-1 accuracy hits@1.
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation

Document-level machine translation (MT) remains challenging due to the difficulty in efficiently using document context for translation.In this paper, we propose a hierarchical model to learn the global context for documentlevel neural machine translation (NMT).This is done through a sentence encoder to capture intra-sentence dependencies and a document encoder to model document-level intersentence consistency and coherence.With this hierarchical architecture, we feedback the extracted global document context to each word in a top-down fashion to distinguish different translations of a word according to its specific surrounding context.In addition, since largescale in-domain document-level parallel corpora are usually unavailable, we use a twostep training strategy to take advantage of a large-scale corpus with out-of-domain parallel sentence pairs and a small-scale corpus with in-domain parallel document pairs to achieve the domain adaptability.Experimental results on several benchmark corpora show that our proposed model can significantly improve document-level translation performance over several strong NMT baselines.
Sequential Convolutional Recurrent Neural Networks for Fast Automatic Modulation Classification

A novel and efficient end-to-end learning model for automatic modulation classification is proposed for wireless spectrum monitoring applications, which automatically learns from the time domain in-phase and quadrature data without requiring the design of hand-crafted expert features. With the intuition of convolutional layers with pooling serving as the role of front-end feature distillation and dimensionality reduction, sequential convolutional recurrent neural networks are developed to take complementary advantage of parallel computing capability of convolutional neural networks and temporal sensitivity of recurrent neural networks. Experimental results demonstrate that the proposed architecture delivers overall superior performance in signal to noise ratio range above -10 dB, and achieves significantly improved classification accuracy from 80% to 92.1% at high signal to noise ratio range, while drastically reduces the average training and prediction time by approximately 74% and 67%, respectively. Response patterns learned by the proposed architecture are visualized to better understand the physics of the model. Furthermore, a comparative study is performed to investigate the impacts of various sequential convolutional recurrent neural network structure settings on classification performance. A representative sequential convolutional recurrent neural network architecture with the two-layer convolutional neural network and subsequent two-layer long short-term memory neural network is developed to suggest the option for fast automatic modulation classification.
Training of Mathematical Disciplines Teachers for Higher Educational Institutions as a Contemporary Problem

The article describes the problem of preparing a modern teacher of mathematical disciplines for a higher technical educational institution in the system of higher pedagogical and classical education.The modern scientific research focusing on training a highly-qualified mathematics teacher has been analyzed.The results of the questionnaire for mathematics teachers at higher technical education institutions and students studying in 014 'Secondary Education (Mathematics)' specialties are presented.The current state of preparedness of master's degree holders to teach mathematicians at higher technical educational institutions is evaluated.One of the ways to solve the problem is to develop the 'For Higher School Mathematics Teacher' educational platform.The concept of developing this online environment and filling its components with content has been highlighted.The rationale for creating a community of practitioners consisting of teachers of higher technical and pedagogical educational institutions, the cooperation of which should contribute to solving the problem, is provided.The possibilities of using the platform for further training of both young and experienced teachers are considered.
Bi-Modality Medical Image Synthesis Using Semi-Supervised Sequential Generative Adversarial Networks

In this paper, we propose a bi-modality medical image synthesis approach based on sequential generative adversarial network (GAN) and semi-supervised learning. Our approach consists of two generative modules that synthesize images of the two modalities in a sequential order. A method for measuring the synthesis complexity is proposed to automatically determine the synthesis order in our sequential GAN. Images of the modality with a lower complexity are synthesized first, and the counterparts with a higher complexity are generated later. Our sequential GAN is trained end-to-end in a semi-supervised manner. In supervised training, the joint distribution of bi-modality images are learned from real paired images of the two modalities by explicitly minimizing the reconstruction losses between the real and synthetic images. To avoid overfitting limited training images, in unsupervised training, the marginal distribution of each modality is learned based on unpaired images by minimizing the Wasserstein distance between the distributions of real and fake images. We comprehensively evaluate the proposed model using two synthesis tasks based on three types of evaluate metrics and user studies. Visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and reasonable visual quality and clinical significance. Code is made publicly available at https://github.com/hustlinyi/Multimodal-Medical-Image-Synthesis.
Evaluating Commonsense in Pre-Trained Language Models

Contextualized representations trained over large raw text data have given remarkable improvements for NLP tasks including question answering and reading comprehension. There have been works showing that syntactic, semantic and word sense knowledge are contained in such representations, which explains why they benefit such tasks. However, relatively little work has been done investigating commonsense knowledge contained in contextualized representations, which is crucial for human question answering and reading comprehension. We study the commonsense ability of GPT, BERT, XLNet, and RoBERTa by testing them on seven challenging benchmarks, finding that language modeling and its variants are effective objectives for promoting models' commonsense ability while bi-directional context and larger training set are bonuses. We additionally find that current models do poorly on tasks require more necessary inference steps. Finally, we test the robustness of models by making dual test cases, which are correlated so that the correct prediction of one sample should lead to correct prediction of the other. Interestingly, the models show confusion on these test cases, which suggests that they learn commonsense at the surface rather than the deep level. We release a test set, named CATs publicly, for future research.
An Imbalanced Big Data Mining Framework for Improving Optimization Algorithms Performance

Big data is an important factor almost in all nowadays technologies, such as, social media, smart cities, and internet of things.Most of standard classifiers tends to be trapped in local optima problem when dealing with such massive datasets.Hence, investigating new techniques for dealing with such massive data sets is required.This paper presents a novel imbalanced big data mining framework for improving optimization algorithms performance by eliminating the local optima problem consists of three main stages.Firstly, the preprocessing stage, which uses the LSH-SMOTE algorithm for solving the class imbalance problem, then it uses the LSH algorithm for hashing the data set instances into buckets.Secondly, the bucket search stage, which uses the GWO for training bidirectional recurrent neural network BRNN and searching for the global optimum in each bucket.Lastly, the final tournament winner stage, which uses the GWO+BRNN for finding the global optimum of the whole data set among all global optimums from all buckets.Our proposed framework LSHGWOBRNN has been tested over 9 data sets one of them is big data set in terms of AUC, MSE, against seven well-known machine-learning algorithms (Naive Bayes, Random Tree, Decision Table , andAdaBoostM1, WOA+MLP, GWO+MLP, and WOA+BRNN), then, we tested our algorithm over four well-known data sets against GWO+MLP, ACO+MLP, GA+MLP, PSO+MLP, PBIL+MLP, and ES+MLP in terms of classification accuracy and MSE.Our experimental results have proved that our proposed framework LSHGWOBRNN has provided high local optima avoidance, and higher accuracy, less complexity and overhead.
Overview of clinical prediction models

Clinical prediction models are becoming more and more popular over recent years, which could be used in not only medicine area but also other areas, such as engineering, mathematics, and computer science (1).
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data

State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this ability, which makes it challenging to find and evaluate improvements. We introduce a novel method to systematically construct such benchmarks by maximizing compound divergence while guaranteeing a small atom divergence between train and test sets, and we quantitatively compare this method to other approaches for creating compositional generalization benchmarks. We present a large and realistic natural language question answering dataset that is constructed according to this method, and we use it to analyze the compositional generalization ability of three machine learning architectures. We find that they fail to generalize compositionally and that there is a surprisingly strong negative correlation between compound divergence and accuracy. We also demonstrate how our method can be used to create new compositionality benchmarks on top of the existing SCAN dataset, which confirms these findings.
Learning-Based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching

Graph similarity computation is one of the core operations in many graph-based applications, such as graph similarity search, graph database analysis, graph clustering, etc. Since computing the exact distance/similarity between two graphs is typically NP-hard, a series of approximate methods have been proposed with a trade-off between accuracy and speed. Recently, several data-driven approaches based on neural networks have been proposed, most of which model the graph-graph similarity as the inner product of their graph-level representations, with different techniques proposed for generating one embedding per graph. However, using one fixed-dimensional embedding per graph may fail to fully capture graphs in varying sizes and link structures—a limitation that is especially problematic for the task of graph similarity computation, where the goal is to find the fine-grained difference between two graphs. In this paper, we address the problem of graph similarity computation from another perspective, by directly matching two sets of node embeddings without the need to use fixed-dimensional vectors to represent whole graphs for their similarity computation. The model, Graph-Sim, achieves the state-of-the-art performance on four real-world graph datasets under six out of eight settings (here we count a specific dataset and metric combination as one setting), compared to existing popular methods for approximate Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) computation.
CatGAN: Category-Aware Generative Adversarial Networks with Hierarchical Evolutionary Learning for Category Text Generation

Generating multiple categories of texts is a challenging task and draws more and more attention. Since generative adversarial nets (GANs) have shown competitive results on general text generation, they are extended for category text generation in some previous works. However, the complicated model structures and learning strategies limit their performance and exacerbate the training instability. This paper proposes a category-aware GAN (CatGAN) which consists of an efficient category-aware model for category text generation and a hierarchical evolutionary learning algorithm for training our model. The category-aware model directly measures the gap between real samples and generated samples on each category, then reducing this gap will guide the model to generate high-quality category samples. The Gumbel-Softmax relaxation further frees our model from complicated learning strategies for updating CatGAN on discrete data. Moreover, only focusing on the sample quality normally leads the mode collapse problem, thus a hierarchical evolutionary learning algorithm is introduced to stabilize the training procedure and obtain the trade-off between quality and diversity while training CatGAN. Experimental results demonstrate that CatGAN outperforms most of the existing state-of-the-art methods.
Deep Atlas Network for Efficient 3D Left Ventricle Segmentation on Echocardiography

We proposed a novel efficient method for 3D left ventricle (LV) segmentation on echocardiography, which is important for cardiac disease diagnosis. The proposed method effectively overcame the 3D echocardiography’s challenges: high dimensional data, complex anatomical environments, and limited annotation data. First, we proposed a deep atlas network, which integrated LV atlas into the deep learning framework to address the 3D LV segmentation problem on echocardiography for the first time, and improved the performance based on limited annotation data. Second, we proposed a novel information consistency constraint to enhance the model’s performance from different levels simultaneously, and finally achieved effective optimization for 3D LV segmentation on complex anatomical environments. Finally, the proposed method was optimized in an end-to-end back propagation manner and it achieved high inference efficiency even with high dimensional data, which satisfies the efficiency requirement of clinical practice. The experiments proved that the proposed method achieved better segmentation results and a higher inference speed compared with state-of-the-art methods. The mean surface distance, mean hausdorff surface distance, and mean dice index were 1.52 mm, 5.6 mm and 0.97 respectively. What’s more, the method is efficient and its inference time is 0.02s. The experimental results proved that the proposed method has a potential clinical application for 3D LV segmentation on echocardiography.
Multi-view feature selection via Nonnegative Structured Graph Learning

Graph-based solutions have achieved state-of-the-art performance on unsupervised multi-view feature selection. However, existing methods generally characterize the sample similarities first by constructing multiple fixed graphs with manually determined parameters, and then perform the feature selection on a composite one. They will suffer from two severe problems: (1) The fixed graphs may be unreliable as the raw multi-view features usually contain adverse noises and cannot accurately capture the intrinsic sample relations. (2) The graph construction and feature selection are separate and independent, the two-step learning may lead to sub-optimal performance. To tackle these problems, in this paper, we propose an effective unsupervised multi-view feature selection method, dubbed as Nonnegative Structured Graph Learning (NSGL). Specifically, we develop a unified learning framework, which directly learns the structured graph from the raw features by imposing a rank constraint, and simultaneously performs adaptive feature selection with exploiting the complementarity of multi-view features. Besides, we introduce the pseudo label learning to extract the discriminative semantic information in unsupervised scenarios and steer the graph learning process. The informative features are finally selected by forcing the feature selection matrix to be sparse in rows with sparse regression. To solve the challenging optimization problem, we first transform the formulated problem into an equivalent one that can be tackled more easily, and then develop an efficient alternate optimization algorithm guaranteed with convergence to calculate the solution iteratively. Extensive experiments on several widely tested benchmarks demonstrate the superiority of NSGL compared with several state-of-the-art approaches.
3D Trajectory Planning Method for UAVs Swarm in Building Emergencies

The development in Multi-Robot Systems (MRS) has become one of the most exploited fields of research in robotics in recent years. This is due to the robustness and versatility they present to effectively undertake a set of tasks autonomously. One of the essential elements for several vehicles, in this case, Unmanned Aerial Vehicles (UAVs), to perform tasks autonomously and cooperatively is trajectory planning, which is necessary to guarantee the safe and collision-free movement of the different vehicles. This document includes the planning of multiple trajectories for a swarm of UAVs based on 3D Probabilistic Roadmaps (PRM). This swarm is capable of reaching different locations of interest in different cases (labeled and unlabeled), supporting of an Emergency Response Team (ERT) in emergencies in urban environments. In addition, an architecture based on Robot Operating System (ROS) is presented to allow the simulation and integration of the methods developed in a UAV swarm. This architecture allows the communications with the MavLink protocol and control via the Pixhawk autopilot, for a quick and easy implementation in real UAVs. The proposed method was validated by experiments simulating building emergences. Finally, the obtained results show that methods based on probability roadmaps create effective solutions in terms of calculation time in the case of scalable systems in different situations along with their integration into a versatile framework such as ROS.