Reviewerly/exHarmony huggingface dataset:
Transform Domain Based Medical Image Super-resolution via Deep Multi-scale Network

This paper proposes a new medical image super-resolution (SR) network, namely deep multi-scale network (DMSN), in the uniform discrete curvelet transform (UDCT) domain. DMSN is made up of a set of cascaded multi-scale fushion (MSF) blocks. In each MSF block, we use convolution kernels of different sizes to adaptively detect the local multi-scale feature, and then local residual learning (LRL) is used to learn effective feature from preceding MSF block and current multi-scale features. After obtaining multi-scale features of different MSF block, we use global feature fusion (GFF) to jointly and adaptively learn global hierarchical features in a holistic manner. Finally, compared with other prediction methods in spatial domain, we applied DMSN in UDCT domain, which enables a better representation of global topological structure and local texture detail of HR images. DM-SN shows superior performance over other state-of-the-art medical image SR methods.
Intelligent Human–UAV Interaction System with Joint Cross-Validation over Action–Gesture Recognition and Scene Understanding

We propose an intelligent human–unmanned aerial vehicle (UAV) interaction system, in which, instead of using the conventional remote controller, the UAV flight actions are controlled by a deep learning-based action–gesture joint detection system. The Resnet-based scene-understanding algorithm is introduced into the proposed system to enable the UAV to adjust its flight strategy automatically, according to the flying conditions. Meanwhile, both the deep learning-based action detection and multi-feature cascade gesture recognition methods are employed by a cross-validation process to create the corresponding flight action. The effectiveness and efficiency of the proposed system are confirmed by its application to controlling the flight action of a real flying UAV for more than 3 h.
Word Sense Disambiguation: Survey Study

The process of identifying the correct sense of a given word in a particular sentence is called Word Sense Disambiguation (WSD).It is complex problem because it involves drawing knowledge from various sources.Significant amount of effort has been put into resolving this problem in machine learning since its inception but the toil is still ongoing.Many techniques were used in WSD and implemented on different corpora for almost all languages.In this paper, WSD algorithms were classified to three categories as Knowledge-based, supervised and unsupervised techniques.Each category will be studied in details with explanation of almost all the algorithms in each category.Hence work examples for each method were taken with the used language, the used corpora and other factors.The benefits and drawback of each method were recorded.Some of these techniques have limitations in some situations, therefore this work will helps the researchers in the field of natural language processing to select the suitable algorithms to solve their particular problem in WSD.The novelty of the work can be seen in the comparison of the used works and the used algorithms.From this work, it was concluded that (i) some methods give high accuracy for language but low for other, (ii) the size of the used data set affects the performance of the used algorithm, (iii) some of these approaches can be run fastly but with limitation of the accuracy and (iv) most of these approaches are implemented for many languages successfully.
VARYS: an agnostic model-driven monitoring-as-a-service framework for the cloud

Cloud systems are large scalable distributed systems that must be carefully monitored to timely detect problems and anomalies. While a number of cloud monitoring frameworks are available, only a few solutions address the problem of adaptively and dynamically selecting the indicators that must be collected, based on the actual needs of the operator. Unfortunately, these solutions are either limited to infrastructure-level indicators or technology-specific, for instance, they are designed to work with OpenStack but not with other cloud platforms. This paper presents the VARYS monitoring framework, a technology-agnostic Monitoring-as-a-Service solution that can address KPI monitoring at all levels of the Cloud stack, including the application-level. Operators use VARYS to indicate their monitoring goals declaratively, letting the framework to perform all the operations necessary to achieve a requested monitoring configuration automatically. Interestingly, the VARYS architecture is general and extendable, and can thus be used to support increasingly more platforms and probing technologies.
Improve Language Modelling for Code Completion through Learning General Token Repetition of Source Code

In last few years, to solve the problem of code completion, using a language model such as LSTM to learn code token sequences is the state-of-art method.However, tokens in source code are more repetitive than words in natural languages.For example, once a variable is declared in a program, it may be used many times.Other elements such as generic types in templates also occur repeatedly.It is important to capture token repetition of code.For example, if usage patterns of variables are not captured, there is little chance for a model trained on one project to predict the name of an unseen variable in another project correctly.Capturing token repetition of source code is challenging because not only the repeated token but also the place at where the repetition should happen must be both decided at the same time.Hence, we propose a novel deep neural model named REP to capture the general token repetition of source code.The repetitions of code tokens are modeled as edges connecting between repeated tokens on a graph.The REP model is essentially a deep neural graph generation model.The experiments indicate that the proposed model outperforms stateof-arts in code completion.
Real Time 3D Magnetic Field Visualization Based on Augmented Reality

In physics teaching, electromagnetism is one of the most difficult concepts for students to understand. This paper proposes a real time visualization method for 3-D magnetic field based on the augmented reality technology, which can not only visualize magnetic flux lines in real time, but also simulates the approximate sparse distribution of magnetic flux lines in space. An application utilizing this method is also presented. It permits leaners to freely and interactively move the magnets in 3-D space and to observe the magnetic flux lines in real time. As a result, the proposed method visualizes the invisible factors in 3-D magnetic field, with which students will have real-life reference when studying electromagnetic.
Data set for automatic detection of online misogynistic speech

The data set is composed of 2285 definitions posted on the Urban Dictionary platform from 1999 to May 2016. The data was classified as misogynistic and non-misogynistic by three independent researchers with domain knowledge. The data set is available in public repository in a table containing two columns: the text-based definition from Urban Dictionary and its respective classification (1 for misogynistic and 0 for non-misogynistic).
A Differential Evolution Flower Pollination Algorithm with Dynamic Switch Probability

For the shortcomings of the basic flower pollination algorithm, this paper proposes a differential evolution flower pollination algorithm with dynamic switch probability based on the Weibull distribution. This new algorithm improved the convergence rate and precision. The switch probability is improved by Weibull distribution function combined with the number of iterations. It can balance the relationship between the global pollination and the local pollination to improve the overall optimization performance of the algorithm. Random mutation operator is merged into the global pollination process to increase diversity of the population, enhance the ability of the algorithm&apos;s global search and avoid premature convergence. In the process of local pollination, directed mutation and crossover operation of the differential evolution are incorporated, it makes the individual flower position update with the memory function, which can choose the direction of variation reasonably. The use of cross-operation can avoid new solutions crossing the boundary. Convergence rate is improved and the algorithm can approach the global optimal solution continuously. Theoretical analysis proved the convergence and time complexity of the improved algorithm. The simulation results based on the function optimization problem show that the improved algorithm has better performance of optimization, faster convergence speed and higher convergence accuracy.
Neural Network Control Design for an Unmanned Aerial Vehicle with a Suspended Payload

Unmanned aerial vehicles (UAVs) demonstrate excellent manoeuvrability in cluttered environments, which makes them a suitable platform as a data collection and parcel delivering system. In this work, the attitude and position control challenges for a drone with a package connected by a wire is analysed. During the delivering task, it is very difficult to eliminate the external unpredictable disturbances. A robust neural network-based backstepping sliding mode control method is designed, which is capable of monitoring the drone’s flight path and desired attitude with a suspended cable attached. The convergence of the position and attitude errors together with the Lyapunov function are employed to attest to the robustness of the nonlinear transportation platform. The proposed control system is tested with a simulation and in an outdoor environment. The simulation and open field test results for the UAV transportation platform verify the controllers’ reliability.
Advanced CNN Based Motion Compensation Fractional Interpolation

Fractional-sample precision motion compensation has been widely adopted in a series of video coding standards to further improve compression efficiency. Usually, signal decomposition based interpolation filters are used to generate fractional samples from integer pixels. However, the coefficients of these finite impluse response filters may not be suitable for varied video contents and coding conditions because of the assumption when designing these filters. In this paper, we regard the fractional interpolation process as an image generation task, which utilizes the real interger position samples at the reference block to predict and generate fractional samples that are much closer to current coding block. We use the con-volutional neural netwok (CNN) as the generator. Moreover, to make the best of CNN's powerful nonlinear learning ability, instead of inputting the reference block directly, we separately input the corresponding prediction and residual parts of reference block. The proposed dual-input CNN-based interpolation scheme has been incorporated into the HEVC framework and experimental results demonstrate our approach achieves average 0.9% bitrate reduction.
Effectivity of Augmented Reality as Media for History Learning

Augmented Reality (AR) as a learning media has a very great potential to help students learn history, including Indonesian ancient history. AR gave the opportunity for students to learn heritages from Indonesian’s ancient history, i.e. from Singhasari period, by scanning a marker that would show a 3D model in their smartphone screen. This research focussing on the effectivity of AR as media for history learning by comparing students’ score in a pre-test – post-test design. 30 students from 10th grade at a vocational high school involved in this quasi-experiment. Overall, the AR is effective as history learning media in improving students’ score.
Computerized Training for Neuroplasticity and Cognitive Improvement

The research area of brain plasticity studies indicates that individuals can train and improve their cognitive abilities throughout life. In addition, more and more computerized training tools are presented in recent studies. The purpose of this study is to represent studies of the last decade in the field of cognitive training with the use of Information and Communication Tech-nologies, to record the cognitive improvement techniques used, as well as to evaluate the effectiveness of these intervention programs. As indicated by the literature review, computer-based tools, mobile training apps and video games could be used in intervention studies for cognitive improve-ment. In addition, cognitive training techniques seem to be significantly ef-fective for the cognitive improvement of healthy or cognitive impaired in-dividuals.
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching

Sentence matching is a key issue in natural language inference and paraphrase identification.Despite the recent progress on multi-layered neural network with cross sentence attention, one sentence learns attention to the intermediate representations of another sentence, which are propagated from preceding layers and therefore are uncertain and unstable for matching, particularly at the risk of error propagation.In this paper, we present an original semantics-oriented attention and deep fusion network (OSOA-DFN) for sentence matching.Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target.The multiple attention layers allow one sentence to repeatedly read the important information of another sentence for better matching.We then additionally design deep fusion to propagate the attention information at each matching layer.At last, we introduce a self-attention mechanism to capture global context to enhance attention-aware representation within each sentence.Experiment results on three sentence matching benchmark datasets SNLI, SciTail and Quora show that OSOA-DFN has the ability to model sentence matching more precisely.
Recognizing Fall Actions from Videos Using Reconstruction Error of Variational Autoencoder

In this paper, we propose a method for detecting fall actions using a variational auto-encoder (VAE) with 3D-convolutional residual blocks. The VAE learns a distribution of Activity of Daily Life (ADL) data, and the reconstruction error is used to detect fall actions. The proposed method is a kind of unsupervised learning method with weakly labeled data for solving the problem of imbalance between the amount of fall data and that of ADL data. Furthermore, we propose extracting a human region from an entire image using skeleton information and aligning motions using the same joint point so that the neural network can focus on learning human motions, which can enhance the accuracy of fall detection. The results of experiments showed that our method achieved a competitive level of accuracy and better generalization ability compared to supervised learning with well-labeled data.
Multi-Source Transformer for Kazakh-Russian-English Neural Machine Translation

We describe the neural machine translation (NMT) system developed at the National Research Council of Canada (NRC) for the Kazakh-English news translation task of the Fourth Conference on Machine Translation (WMT19).Our submission is a multi-source NMT system taking both the original Kazakh sentence and its Russian translation as input for translating into English.
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons

Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work.With the belief that modeling hierarchical structure is an essential complementary between SANs and RNNs, we propose to further enhance the strength of hybrid models with an advanced variant of RNNs -Ordered Neurons LSTM (ON-LSTM, Shen et al., 2019), which introduces a syntax-oriented inductive bias to perform tree-like composition.Experimental results on the benchmark machine translation task show that the proposed approach outperforms both individual architectures and a standard hybrid model.Further analyses on targeted linguistic evaluation and logical inference tasks demonstrate that the proposed approach indeed benefits from a better modeling of hierarchical structure.
HARE: a Flexible Highlighting Annotator for Ranking and Exploration

Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains.We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning.We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features.We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools.
Answer-guided and Semantic Coherent Question Generation in Open-domain Conversation

Generating intriguing question is a key step towards building human-like open-domain chatbots.Although some recent works have focused on this task, compared with questions raised by humans, significant gaps remain in maintaining semantic coherence with the posts, which may result in generating dull or deviated questions.We observe that the answer has strong semantic coherence to its question and post, which can be used to guide question generation.Thus, we devise two methods to further enhance semantic coherence between post and question under the guidance of answer.First, the coherence score between generated question and answer is used as the reward function in a reinforcement learning framework, to encourage the cases that are consistent with the answer in semantic.Second, we incorporate adversarial training to explicitly control question generation in the direction of question-answer coherence.The extensive experiments show that our two methods outperform state-of-the-art baseline algorithms with large margins in raising semantic coherent questions.
Synchronously Generating Two Languages with Interactive Decoding

In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively.Specifically, the generation of one language relies on not only previously generated outputs by itself, but also the outputs predicted in the other language.Experimental results on IWSLT and WMT datasets demonstrate that our method can obtain significant improvements over both conventional Neural Machine Translation (NMT) model and multilingual NMT model.
Towards Understanding the Importance of Shortcut Connections in Residual Networks

Residual Network (ResNet) is undoubtedly a milestone in deep learning. ResNet is equipped with shortcut connections between layers, and exhibits efficient training using simple first order algorithms. Despite of the great empirical success, the reason behind is far from being well understood. In this paper, we study a two-layer non-overlapping convolutional ResNet. Training such a network requires solving a non-convex optimization problem with a spurious local optimum. We show, however, that gradient descent combined with proper normalization, avoids being trapped by the spurious local optimum, and converges to a global optimum in polynomial time, when the weight of the first layer is initialized at 0, and that of the second layer is initialized arbitrarily in a ball. Numerical experiments are provided to support our theory.
Pre-Joined Semantic Indexing Graph for QoS-Aware Service Composition

Cloud computing users may obtain great flexibility with low cost by outsourcing their data and services to the cloud. Services are composed together as a solution when no individual services meet the goal. Unfortunately, searching an optimal composition requires significant time due to the high number of available services in the cloud. In this paper, we develop an approach that solves the composition problem with a graph database. This approach uses the shortest bidirectional breadth-first and Dijkstra algorithms to find solutions with either fewest services or the optimal QoS values. Firstly, we preprocess service compositions and store them as paths in a directed bipartite graph in graph database. Compared with using a relational database, the join operator can be avoided and the search speed increases. Secondly, this approach utilize existing resources and can be easily migrated to a cloud database. Correctness and feasibility of this system are verified with experimental results, which shows this system leads to good performance in finding users' satisfying solutions.
Noisy Channel for Low Resource Grammatical Error Correction

This paper describes our contribution to the low-resource track of the BEA 2019 shared task on Grammatical Error Correction (GEC).Our approach to GEC builds on the theory of the noisy channel by combining a channel model and language model.We generate confusion sets from the Wikipedia edit history and use the frequencies of edits to estimate the channel model.Additionally, we use two pretrained language models: 1) Google's BERT model, which we fine-tune for specific error types and 2) OpenAI's GPT-2 model, utilizing that it can operate with previous sentences as context.Furthermore, we search for the optimal combinations of corrections using beam search.
Deep tensor fusion network for multimodal ground-based cloud classification in weather station networks

Accurate multimodal ground-based cloud classification in weather station networks is a challenging task, because the existing methods fuse cloud visual data and multimodal data at the vector level resulting in the spatial information loss. In this work, we propose a method named deep tensor fusion network (DTFN) for multimodal ground-based cloud classification in weather station networks, which could learn completed cloud information by fusing heterogeneous features at the tensor level in a unified framework. The DTFN is composed of the visual tensor subnetwork (VTN) and the multimodal tensor subnetwork (MTN). The VTN transforms cloud images into cloud visual tensors using a deep network and therefore the spatial information of ground-based cloud images can be maintained. Meanwhile, the MTN is designed as a couple of deconvolutional layers in order to transform the multimodal data into multimodal tensors and ensure the multimodal tensors to be mathematically compatible with cloud visual tensors. Furthermore, to fuse cloud visual tensor and multimodal tensor, we propose the tensor fusion layer to exploit the high-order correlations between them. The DTFN is evaluated on MGCD and exceeds the state-of-the-art methods, which validates its effectiveness for multimodal ground-based cloud classification in weather station networks.
Structured Graph Learning Via Laplacian Spectral Constraints

Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. But structured graph learning from observed samples is an NP-hard combinatorial problem. In this paper, we first show, for a set of important graph families it is possible to convert the combinatorial constraints of structure into eigenvalue constraints of the graph Laplacian matrix. Then we introduce a unified graph learning framework lying at the integration of the spectral properties of the Laplacian matrix with Gaussian graphical modeling, which is capable of learning structures of a large class of graph families. The proposed algorithms are provably convergent and practically amenable for big-data specific tasks. Extensive numerical experiments with both synthetic and real datasets demonstrate the effectiveness of the proposed methods. An R package containing codes for all the experimental results is submitted as a supplementary file.