Reviewerly/exHarmony huggingface dataset:
Dependence of remote sensing accuracy of global horizontal irradiance at different scales on satellite sampling frequency

The emergence of new-generation geostationary satellites (such as Himawari-8 or FenYun-4) provides us an opportunity to obtain more accurate global horizontal irradiance (GHI) data. In this study, instantaneous GHI estimates (with a spatio-temporal resolution of 10 min and 5 km) are produced using the cloud products from the new-generation geostationary satellite Himawari-8 with a physically based algorithm. The hourly, daily, and monthly GHI estimates are aggregated using “snapshots” (or instantaneous estimates) at two different sampling frequencies (i.e., 1 h and 10 min), and validated against surface radiometry measurements collected in China. The root mean square errors (RMSE) for hourly, daily and monthly GHI estimates calculated using 1-h snapshots were 106.6, 27.9, and 17.7 W m−2, respectively, whereas those for hourly, daily and, monthly GHI calculated using 10-min snapshots decreased to 94.0, 24.0, and 16.6 W m−2, respectively. This result demonstrates that the accuracies of hourly, daily, and monthly GHI estimates are improved by increasing the frequency of satellite observations from 1 h (frequency of the previous-generation geostationary satellites) to 10 min (frequency of the new-generation geostationary satellites).
Flexibly-Structured Model for Task-Oriented Dialogues

This paper proposes a novel end-to-end architecture for task-oriented dialogue systems.It is based on a simple and practical yet very effective sequence-to-sequence approach, where language understanding and state tracking tasks are modeled jointly with a structured copy-augmented sequential decoder and a multi-label decoder for each slot.The policy engine and language generation tasks are modeled jointly following that.The copyaugmented sequential decoder deals with new or unknown values in the conversation, while the multi-label decoder combined with the sequential decoder ensures the explicit assignment of values to slots.On the generation part, slot binary classifiers are used to improve performance.This architecture is scalable to real-world scenarios and is shown through an empirical evaluation to achieve state-of-the-art performance on both the Cambridge Restaurant dataset and the Stanford in-car assistant dataset 1 .
Fuzzy Ordered $c$-Means Clustering and Least Angle Regression for Fuzzy Rule-Based Classifier: Study for Imbalanced Data

This article introduces a new classifier design method that is based on a modification of the traditional fuzzy clustering. First, a new fuzzy ordered c-means clustering is proposed. This method can be considered as a generalization of the concept of the conditional fuzzy clustering by introducing ordering and weighting distances from data to cluster prototypes. As a result, a more local impact of data on created groups and increased repulsive force between group prototypes are obtained. The proposed method provides a better representation of the data classes, in particular for classes with small cardinality in the training set (imbalanced data). A special initialization of the prototypes is also introduced. Next, the proposed clustering method is used to construct the premises of if-then rules of a fuzzy classifier. The conclusions of the rules are obtained by the least angle regression algorithm, which selects only those rules, that maximize the generalization ability of a classifier. Each if-then rule is represented in easily interpretable Mamdani-Assilian form. Finally, an extensive experimental analysis on 89 benchmark balanced and imbalanced datasets is performed to demonstrate the validity of the introduced classifier. Its competitiveness to state-of-the-art classifiers, with respect to both performance and interpretability, is shown as well.
Multi-task Learning for Neonatal Brain Segmentation Using 3D Dense-Unet with Dense Attention Guided by Geodesic Distance

The deep convolutional neural network has achieved outstanding performance on neonatal brain MRI tissue segmentation. However, it may fail to produce reasonable results on unseen datasets that have different imaging appearance distributions with the training data. The main reason is that deep learning models tend to have a good fitting to the training dataset, but do not lead to a good generalization on the unseen datasets. To address this problem, we propose a multi-task learning method, which simultaneously learns both tissue segmentation and geodesic distance regression to regularize a shared encoder network. Furthermore, a dense attention gate is explored to force the network to learn rich contextual information. By using three neonatal brain datasets with different imaging protocols from different scanners, our experimental results demonstrate superior performance of our proposed method over the existing deep learning-based methods on the unseen datasets.
Trajectory Tracking and Speed Control of Cleaning Vehicle Based on Improved Pure Pursuit Algorithm

This paper proposes a trajectory tracking algorithm based on two fuzzy controllers to adjust the running speed and foresight distance in pure pursuit method simultaneously. The tracking performance of cleaning vehicle can be improved significantly. The outputs of each fuzzy controller are the inputs of another fuzzy controller, so it is credible to ensure the robustness of the entire system and stability of the vehicle by reasonably controlling and adjusting parameters according to the interaction among the real-time parameters. The effectiveness of the proposed algorithm is illustrated by simulation and field experiments on different trails. The simulation results show that this algorithm can track the trajectories of continuous as well as discontinuous curvature more effectively compared with the traditional pure pursuit algorithm. The field experiment indicates this algorithm has small distance errors when tracking the reference path and its average distance error can meet the actual requirement of cleaning vehicle.
Macro Action Selection with Deep Reinforcement Learning in StarCraft

StarCraft (SC) is one of the most popular and successful Real Time Strategy (RTS) games. In recent years, SC is also widely accepted as a challenging testbed for AI research because of its enormous state space, partially observed information, multi-agent collaboration, and so on. With the help of annual AIIDE and CIG competitions, a growing number of SC bots are proposed and continuously improved. However, a large gap remains between the top-level bot and the professional human player. One vital reason is that current SC bots mainly rely on predefined rules to select macro actions during their games. These rules are not scalable and efficient enough to cope with the enormous yet partially observed state space in the game. In this paper, we propose a deep reinforcement learning (DRL) framework to improve the selection of macro actions. Our framework is based on the combination of the Ape-X DQN and the Long-Short-Term-Memory (LSTM). We use this framework to build our bot, named as LastOrder. Our evaluation, based on training against all bots from the AIIDE 2017 StarCraft AI competition set, shows that LastOrder achieves an 83% winning rate, outperforming 26 bots in total 28 entrants.
Privacy-preserving content-based image retrieval for mobile computing

Abstract With the increasing popularity of mobile cloud computing, image outsourcing provides a cost-effective way to support images management for smart mobile devices. However, users default that the cloud server is not trustworthy, secure image retrieval has attracted considerable interests recently to address users’ privacy concerns. Due to the limited resources such as computing power, battery lifetime, and storage capacity, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as feature extraction and index building. In this paper, we propose a framework that supports cloud server side local-feature (scale-invariant feature transform, SIFT) extraction, index building, and image similarity scoring. We further propose a multi-index for SIFT descriptors, a secure bucket identifier computation protocol, and a secure image similarity computation protocol. In our framework, mobile users only need to encrypt and upload their images to the cloud. The later provides privacy-preserving image retrieval services without the mobile users extra interaction. Our framework greatly reduces computation overhead on the mobile user side. The security analysis and experimental evaluations demonstrate the efficiency and the scalability of our framework.
Application of trilateration and Kalman filtering algorithms to track dynamic brain deformation using sonomicrometry

Trilateration and Kalman filtering algorithms have been used in many tracking applications ranging from Global Positioning Systems to biomechanics experiments to track rigid body motion and position. Recently, a technique called sonomicrometry was used to measure the deformation of human brain tissue during an injurious impact by measuring distances between sensor pairs embedded in the parenchyma and skull. The array of measured distances were used to calculate the position of each sensor at high sampling rates to quantify brain dynamic deformation. However, non-linear trilateration and Kalman filtering algorithms, which are traditionally used to track object positions, have not been investigated for the unique application of high-rate, small displacement, and dynamic deformation data collected using sonomicrometry. The objective of this study was to compare eight trilateration and Kalman filtering algorithms to determine the most suitable method for sonomicrometry trilateration. The algorithms were tested using experimental brain deformation sonomicrometry data in which random measurement errors were intentionally introduced to evaluate the effect and robustness on tracking dynamic position. The results showed that linear least squares trilateration methods performed poorly compared to the non-linear methods. Maximum Likelihood Estimate and Kalman filtering were the best performing algorithms. The Kalman filtering method was the most suitable for tracking dynamic brain deformation using sonomicrometry because it provided an accurate estimation of dynamic position and the estimated position was insensitive to the chosen initial parameters. The algorithms and error analysis can be extended to a variety of positioning applications using sonomicrometry or similar high-rate dynamic deformation tracking.
Software review: the GPTIPS platform

GPTIPS is a widely used genetic programming software that was developed in Matlab. The most recent version of this software, GPTIPS 2.0, provides a symbolic multi-gene regression for data analysis, in addition to traditional evolutionary algorithms. We briefly explain the GPTIPS methodology and describe its main features, including its weaknesses and strengths, and give examples of GPTIPS applications.
Pretrained Ensemble Learning for Fine-Grained Propaganda Detection

In this paper, we describe our team's effort on the fine-grained propaganda detection on sentence level classification (SLC) task of NLP4IF 2019 workshop co-located with the EMNLP-IJCNLP 2019 conference.Our top performing system results come from applying ensemble average on three pretrained models to make their predictions.The first two models use the uncased and cased versions of Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018) while the third model uses Universal Sentence Encoder (USE) (Cer et al., 2018).Out of 26 participating teams, our system is ranked in the first place with 68.8312 F1-score on the development dataset and in the sixth place with 61.3870 F1-score on the testing dataset.
Quantum codes from a class of constacyclic codes over finite commutative rings

Let [Formula: see text] be an odd prime, and [Formula: see text] be an integer such that [Formula: see text]. Using pairwise orthogonal idempotents [Formula: see text] of the ring [Formula: see text], with [Formula: see text], [Formula: see text] is decomposed as [Formula: see text], which contains the ring [Formula: see text] as a subring. It is shown that, for [Formula: see text], [Formula: see text], and it is invertible if and only if [Formula: see text] and [Formula: see text] are units of [Formula: see text]. In such cases, we study [Formula: see text]-constacyclic codes over [Formula: see text]. We present a direct sum decomposition of [Formula: see text]-constacyclic codes and their duals, which provides their corresponding generators. Necessary and sufficient conditions for a [Formula: see text]-constacyclic code to contain its dual are obtained. As an application, many new quantum codes over [Formula: see text], with better parameters than existing ones, are constructed from cyclic and negacyclic codes over [Formula: see text].
A Deep Analysis on Aspect based Sentiment Text Classification Approaches

Now-a-days, people often express their opinions as reviews, comments, feedback in various social networking sites, business organizations.Feedbacks that are given by the end users have a great impact for the evolution of new version of product or service.For business invested in customers, analyzing each piece of feedback by hand can be overwhelming and similarly for an organization to rate an employee regarding his/her performance based on usual quantitative feedback system is a challenging task.Sentiment analysis, developed within this context can be helpful to solve such issues at early stage and provide guidance in improving their sales and productivity.Moreover, reviews written in natural language are mostly unstructured and needs huge time for processing.As the data is available in large size, it's impossible to process and analyze the information manually.In order to solve this issue, many machine Learning techniques and Deep Learning models are being proposed for automatic learning, extraction and analysis.As the technology advances businesses, organizations, social media and e-commerce sites can benefit from these in-depth insights and customer satisfaction can be analyzed.Sentiment analysis is an excellent source to perform fine-grained analysis like feature-based sentiment analysis and it can be used to identify different aspects expressed at either document or sentence level.This paper highlights the insights of extracting the most important aspects from the opinions expressed in the input text using various machine learning techniques.
Experimental observation of conditional past-future correlations

Conditional past-future correlations measure the lack of statistical independence between past and future system measurement outcomes when conditioned to a given state at a present time. Quantum non-Markovian memory effects are present whenever this correlation is not null. Conditional past-future correlations can also be used to detect initial system-environment correlated states. In this work, we provide theoretical and experimental evidence for these general properties. We build an optical setup that implements the dynamics of a qubit interacting with a dephasing spin bath. Both finite and infinite bath-size limits are observed. Confirmation of theoretical predictions for conditional past-future correlations is obtained. This work provides experimental support for quantum memory indicators based solely on outcomes of explicit system measurement processes.
Slang Detection and Identification

The prevalence of informal language such as slang presents challenges for natural language systems, particularly in the automatic discovery of flexible word usages.Previous work has explored slang in terms of dictionary construction, sentiment analysis, word formation, and interpretation, but scarce research has attempted the basic problem of slang detection and identification.We examine the extent to which deep learning methods support automatic detection and identification of slang from natural sentences using a combination of bidirectional recurrent neural networks, conditional random field, and multilayer perceptron.We test these models based on a comprehensive set of linguistic features in sentencelevel detection and token-level identification of slang.We found that a prominent feature of slang is the surprising use of words across syntactic categories or syntactic shift (e.g., verb→noun).Our best models detect the presence of slang at the sentence level with an F1-score of 0.80 and identify its exact position at the token level with an F1-Score of 0.50.
Graph2Seq: Fusion Embedding Learning for Knowledge Graph Completion

Knowledge Graph (KG) usually contains billions of facts about the real world, where a fact is represented as a triplet in the form of (head entity, relation, tail entity).KG is a complex network and consists of numerous nodes (entities) and edges (relations).Given that most KGs are noisy and far from being complete, KG analysis and completion methods are becoming more and more important.Knowledge graph embedding (KGE) aims to embed entities and relations in a low dimensional and continuous vector space, which is proven to be a quite efficient and effective method in knowledge graph completion tasks.KGE models devise various kinds of score functions to evaluate each fact in KG, which assign high points for true facts and low points for invalid ones.In a KG of the real world, some nodes may have hundreds of links with other nodes.There is a wealth of information around an entity, and the surrounding information (i.e., the sub-graph structure information) of one entity can make a significant contribution to predicting new facts.However, many previous works including, translational approaches such as Trans(E, H, R, and D), factorization approaches such as DistMult, ComplEx, and other deep learning approaches such as NTN, ConvE, concentrate on rating each fact in an isolated and separated way and lack a specially designed mechanism to learn the sub-graph structure information of the entity in KG.To conquer this challenge, we leverage the information fusion mechanism (Graph2Seq) used in graph neural network which is specially designed for graph-structured data, to learn fusion embeddings for entities in KG.And a novel fusion embedding learning KGE model (referred as G2SKGE) which aims to learn the sub-graph structure information of the entity in KG is proposed.With empirical experiments on four benchmark datasets, our proposed model achieves promising results and outperforms the state-of-the-art models.
Multi-Document Summarization with Determinantal Point Processes and Contextualized Representations

Emerged as one of the best performing techniques for extractive summarization, determinantal point processes select the most probable set of sentences to form a summary according to a probability measure defined by modeling sentence prominence and pairwise repulsion.Traditionally, these aspects are modelled using shallow and linguistically informed features, but the rise of deep contextualized representations raises an interesting question of whether, and to what extent, contextualized representations can be used to improve DPP modeling.Our findings suggest that, despite the success of deep representations, it remains necessary to combine them with surface indicators for effective identification of summary sentences.
On the grammatical source of adjective ordering preferences

Scontras et al. (2017) present experimental evidence demonstrating that the best predictor of adjective ordering preferences in the English noun phrase is the subjectivity of the property named by any given adjective: less subjective adjectives are preferred linearly closer to the nouns they modify. The current work builds on this empirical finding by proposing that the reason subjectivity predicts adjective ordering preferences has to do with the hierarchical structure of nominal modification. Adjectives that are linearly closer to the modified noun are often structurally closer, composing with the noun before adjectives that are farther away. Pressures from successful reference resolution dictate that less subjective, more useful adjectives contribute their meaning to the resulting nominal earlier, in an attempt to more effectively limit the reference search space. EARLY ACCESS
English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning

With the widespread use of Machine Translation (MT) techniques, attempt to minimize communication gap among people from diverse linguistic backgrounds.We have participated in Workshop on Asian Translation 2019 (WAT2019) multi-modal translation task.There are three types of submission track namely, multi-modal translation, Hindionly image captioning and text-only translation for English to Hindi translation.The main challenge is to provide a precise MT output.The multi-modal concept incorporates textual and visual features in the translation task.In this work, multi-modal translation track relies on pre-trained convolutional neural networks (CNN) with Visual Geometry Group having 19 layered (VGG19) to extract image features and attention-based Neural Machine Translation (NMT) system for translation.The merge-model of recurrent neural network (RNN) and CNN is used for the Hindi-only image captioning.The text-only translation track is based on the transformer model of the NMT system.The official results evaluated at WAT2019 translation task, which shows that our multi-modal NMT system achieved Bilingual Evaluation Understudy (BLEU) score 20.37, Rank-based Intuitive Bilingual Evaluation Score (RIBES) 0.642838, Adequacy-Fluency Metrics (AMFM) score 0.668260 for challenge test data and BLEU score 40.55,RIBES 0.760080, AMFM score 0.770860 for evaluation test data in English to Hindi multimodal translation respectively.
Automatic Wireless Signal Classification in Multimedia Internet of Things: An Adaptive Boosting Enabled Approach

Provision of multimedia contents over the Internet of things (IoT) presents significant challenges to wireless networks owing to nodes diversity. Therefore, efficient utilization of resources is required to meet the growing diversity in user's behavior and wireless services that mark the future of wireless communication. Automatic classification of wireless signals based on their modulation schemes is a crucial technology that enables wireless transceivers to utilize the resources efficiently. Traditional feature-based approaches lack generalization, and versatility by relying on single classifier predictions. In this paper, two adaptive boosting (Adaboost) based wireless signal classifiers called SigmaBoost and KNNAdaboost are proposed. Adaboost generates an optimal prediction rule by combining the prediction of many weak component classifiers (CCs). However, sometimes it overfits on real-world scenarios with noisy data. That makes the choice of CC vital for its success in classifying wireless signals. Therefore, two well-known classifiers support vector machine (SVM) and k-nearest neighbor (KNN) are used as CCs in proposed schemes respectively. In SigmaBoost an adaptive decrementing mechanism is introduced, which decreases the value of Gaussian radial function (RBF) of SVM kernel as boosting progresses. It not only ensures the generation of weak RBFSVM component classifiers but also improves diversity across the ensemble. Signal spectral features and higher-order cumulants are used as input features. The experimental results demonstrate significant gain in the performance of SigmaBoost, as compared to KNNAdaboost and other single classifier-based approaches. Hence, SigmaBoost can be used in multimedia-enabled IoT for quick discrimination of wireless signals to ensure better radio spectrum management.