Reviewerly/exHarmony huggingface dataset:
Target inductive methods for zero-shot regression

This research arises from the need to predict the amount of air pollutants in meteorological stations. Air pollution depends on the location of the stations (weather conditions and activities in the surroundings). Frequently, the surrounding information is not considered in the learning process. This information is known beforehand in the absence of unobserved weather conditions and remains constant for the same station. Considering the surrounding information as side information facilitates the generalization for predicting pollutants in new stations, leading to a zero-shot regression scenario. Available methods in zero-shot typically lean towards classification, and are not easily extensible to regression. This paper proposes two zero-shot methods for regression. The first method is a similarity based approach that learns models from features and aggregates them using side information. However, potential knowledge of the feature models may be lost in the aggregation. The second method overcomes this drawback by replacing the aggregation procedure and learning the correspondence between side information and feature-induced models, instead. Both proposals are compared with a baseline procedure using artificial datasets, UCI repository communities and crime datasets, and the pollutants. Both approaches outperform the baseline method, but the parameter learning approach manifests its superiority over the similarity based method.
Emotion Analysis of Arabic Tweets: Language Models and Available Resources

One of the most popular social media platforms is Twitter. Emotion analysis and classification of tweets have become a significant research topic recently. The Arabic language faces challenges for emotion classification on Twitter, requiring more preprocessing than other languages. This article provides a practical overview and detailed description of a material that can help in developing an Arabic language model for emotion classification of Arabic tweets. An emotion classification of Arabic tweets using NLP, overall current practical practices, and available resources are highlighted to provide a guideline and overview sight to facilitate future studies. Finally, the article presents some challenges and issues that can be future research directions.
A novel particle filter for extended target tracking with random hypersurface model

In the random hypersurface model for extended target tracking problem, the scaling factor in the measurement equation brings difficulty for existing particle filter to calculate the likelihood in the weighting update stage. In this paper, we firstly simplify the existing approximate likelihood function where the distribution of the scaling factor is approximated by Gaussian one. Then, by directly dealing with the distribution of the scaling factor whose square has uniform distribution, we propose a novel explicit formula of the logarithm of likelihood. Based on this formula, a feasible weighting scheme is obtained and a novel particle filtering algorithm (NPFA) is proposed. Simulation shows that NPFA improves estimation accuracy compared with the existing unscented Kalman filter and particle filter for the tracking problem under discussion.
Bilateral discriminative autoencoder model orienting co-representation learning

Autoencoder is an important representation learning model which has attracted extensive research attention. However, an autoencoder learns latent representation by reducing reconstruction error without emphasis on discrimination, which is vital to downstream machine learning tasks like classification and clustering. Many existing works have improved the discrimination of autoencoders. But as far as we know, there is no work focusing on bilateral discriminative representation learning(i.e. co-representation learning). Our work unlocks the potential of autoencoder on co-representation learning and proposes a bilateral discriminative autoencoder model for co-representation learning(CRBDAE). By utilizing a fuzzy set, the topological relationship between samples and features is represented as fuzzy information. In the bilateral discriminative autoencoder, by means of regularization, fuzzy information is employed to enhance the self-supervised co-representation learning ability. Thus, the corresponding loss function is illustrated. We also inferred the parameters updating method and proposed the model training algorithm. Finally, the availability of the CRBDAE model was demonstrated on 12 datasets and the results proved that the performance of the proposed model meets our expectations.
Toward Structural Learning and Enhanced YOLOv4 Network for Object Detection in Optical Remote Sensing Images

Abstract With the maturity of technological tools such as satellites and airplanes, object detection in optical remote sensing images have been widely used in military and civilian fields. Due to the interference of extensive multi‐scale targets and complex backgrounds, the existing algorithms are still limited, especially for small‐scale targets. To solve this problem, a high‐precision remote sensing detection method based on the advanced YOLOv4 framework is proposed. First, a clustering algorithm that combines knowledge of object scales to generate a priori anchor boxes with a higher matching degree is proposed. The feature extension module is designed to expand the receptive field of the backbone network and capture important contextual information. After that, coordinate attention (CA) is introduced to suppress the extra noise under multi‐scale fusion that is rarely noticed in previous work. In addition, a convolutional idea of self‐learning structure has also been novelly proposed and combined with a high‐resolution network to efficiently enhance the performance of the network under limited computing resources. Finally, detailed experiments are performed on the HRRSD dataset. The experimental results show that the network has a more satisfactory superiority, and it is conducive to the advancement of optical remote sensing technology.
With or without Emoji? Perceptions about Emoji Use in Different Brand-Consumer Communication Contexts

Brands are increasingly using emoji in their computer-mediated communication (CMC). However, research on how consumers perceive such use, and the determinants of those perceptions, is scarce and results may be inconsistent. In a cross-sectional study ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>N</mi> <mo>=</mo> <mn>540</mn> </math> ) we examined how appropriate participants considered to be the use of emoji by brands, across five brand-consumer communication contexts. We additionally examined whether these perceptions were determined by demographic and individual variables (e.g., gender and frequency of emoji use), as well as individual views about emoji use in written CMC. Overall, perceptions toward the use of emoji by brands depended on the context, with participants considering more appropriate for brands to use emoji when publicizing on social media and less appropriate when making callbacks of defective products. Results further showed that such perceptions were more favorable among younger participants and those who used emoji more frequently, but also among those who considered emoji use more useful and formal. These findings contribute to the CMC field by highlighting how perceptions of emoji use by brands are shaped, while also informing how brands can enhance CMC with consumers.
Quantifying correlations via local channels

Quantum channels, in general, will disturb quantum states and will lead to decoherence (i.e., information leakage to the environment). For bipartite systems, local channels often cause more decoherence for global states than for local states due to correlations. Intuitively, the difference between these two kinds of decoherence (i.e., global and local ones) may be regarded as a quantifier characterizing certain aspects of correlations in the global bipartite states. Based on this idea, we introduce a quantifier of correlations (relative to a local channel) as the coherence difference and investigate its basic properties. We probe and quantify correlations relative to various channels including the unitary channels, the twirling channels, the projective measurements, and the weak measurements. In particular, we show that both product states and some natural classical-quantum states can be operationally characterized in terms of local channels: The product states are just those states with vanishing coherence difference relative to the twirling channel induced by the unitary group, whereas these natural classical-quantum states are just those states with vanishing coherence difference relative to the twirling channel induced by the subgroup of the unitary group that does not disturb the local states. We further illustrate the results by various examples.
Breast tumor segmentation in digital mammograms using spiculated regions

Mammogram image segmentation is the process of partitioning mammograms into meaningful and separate areas. However, during the segmentation process, masses are extracted and the spiculated regions of a mass, which contain significant characteristics of the mass margins, are omitted. The present research introduces a new method for segmentation of tumor mammograms that extracts the spiculated regions and the mass core. Generally, the pixels of a spiculated region are located along a line and the pixels of the mass core regions are similar. The proposed method extracts these regions using the differences between a pixel and its adjacent pixels. The proposed method uses three thresholds to delete redundant pixels from the spiculated regions and the mass core. These regions then are merged to form the segmented tumor. The results show that the respective mean of the Dice and Jaccard coefficients for the suggested segmentation method, respectively, are 0.9309 and 0.9024 for MIAS and 0.9557 and 0.9132 for DDSM. Quantitative analysis of the results confirms that the suggested segmentation method is comparable to other techniques and extracts the segmentation of tumor accurately.
Improving lookup and query execution performance in distributed Big Data systems using Cuckoo Filter

Abstract Performance is a critical concern when reading and writing data from billions of records stored in a Big Data warehouse. We introduce two scopes for query performance improvement. One is to improve the performance of lookup queries after data deletion in Big Data systems that use Eventual Consistency. We propose a scheme to improve lookup performance after data deletion by using Cuckoo Filter. Another scope for improvement is to avoid unnecessary network round-trips for querying in remote nodes in a distributed Big Data cluster when it is known that the nodes do not have requested partition of data. We propose a scheme using probabilistic filters that are looked up before querying remote nodes so that queries resulting in no data can be skipped from passing through the network. We evaluate our schemes with Cassandra using real dataset and show that each scheme can improve performance of lookup queries for up to 2 x .
GNNkeras: A Keras-based library for Graph Neural Networks and homogeneous and heterogeneous graph processing

<h2>Abstract</h2> In several areas of science and engineering, data can be naturally represented in graph form, where nodes denote entities and edges stand for relationships between them. Graph Neural Networks (GNNs) are a well-known class of machine learning models for graph processing. In this paper, we present GNNkeras, a library, based on Keras, which allows the implementation of a large subclass of GNNs. GNNkeras is a flexible tool: the implemented models can be used to classify/cluster nodes, edges, or whole graphs. Moreover, GNNkeras can be applied to both homogeneous and heterogeneous graphs, exploiting both inductive and mixed inductive–transductive learning, and can implement a layered version of GNNs, namely the LGNN model.
<i>Here’s What I’ve Learned:</i> Asking Questions that Reveal Reward Learning

Robots can learn from humans by asking questions. In these questions, the robot demonstrates a few different behaviors and asks the human for their favorite. But how should robots choose which questions to ask? Today’s robots optimize for informative questions that actively probe the human’s preferences as efficiently as possible. But while informative questions make sense from the robot’s perspective, human onlookers may find them arbitrary and misleading . For example, consider an assistive robot learning to put away the dishes. Based on your answers to previous questions this robot knows where it should stack each dish; however, the robot is unsure about right height to carry these dishes. A robot optimizing only for informative questions focuses purely on this height: it shows trajectories that carry the plates near or far from the table, regardless of whether or not they stack the dishes correctly. As a result, when we see this question, we mistakenly think that the robot is still confused about where to stack the dishes! In this article, we formalize active preference-based learning from the human’s perspective. We hypothesize that—from the human’s point-of-view —the robot’s questions reveal what the robot has and has not learned. Our insight enables robots to use questions to make their learning process transparent to the human operator. We develop and test a model that robots can leverage to relate the questions they ask to the information these questions reveal. We then introduce a tradeoff between informative and revealing questions that considers both human and robot perspectives: a robot that optimizes for this tradeoff actively gathers information from the human while simultaneously keeping the human up to date with what it has learned. We evaluate our approach across simulations, online surveys, and in-person user studies. We find that robots, which consider the human’s point of view learn just as quickly as state-of-the-art baselines while also communicating what they have learned to the human operator. Videos of our user studies and results are available here: https://youtu.be/tC6y_jHN7Vw.
Gradient-Guided and Multi-Scale Feature Network for Image Super-Resolution

Recently, deep-learning-based image super-resolution methods have made remarkable progress. However, most of these methods do not fully exploit the structural feature of the input image, as well as the intermediate features from the intermediate layers, which hinders the ability of detail recovery. To deal with this issue, we propose a gradient-guided and multi-scale feature network for image super-resolution (GFSR). Specifically, a dual-branch structure network is proposed, including the trunk branch and the gradient one, where the latter is used to extract the gradient feature map as structural prior to guide the image reconstruction process. Then, to absorb features from different layers, two effective multi-scale feature extraction modules, namely residual of residual inception block (RRIB) and residual of residual receptive field block (RRRFB), are proposed and embedded in different network layers. In our RRIB and RRRFB structures, an adaptive weighted residual feature fusion block (RFFB) is investigated to fuse the intermediate features to generate more beneficial representations, and an adaptive channel attention block (ACAB) is introduced to effectively explore the dependencies between channel features to further boost the feature representation capacity. Experimental results on several benchmark datasets demonstrate that our method achieves superior performance against state-of-the-art methods in terms of both subjective visual quality and objective quantitative metrics.
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison

Forensic facial image comparison lacks a methodological standardization and empirical validation. We aim to address this problem by assessing the potential of machine learning to support the human expert in the courtroom. To yield valid evidence in court, decision making systems for facial image comparison should not only be accurate, they should also provide a calibrated confidence measure. This confidence is best conveyed using a score-based likelihood ratio. In this study we compare the performance of different calibrations for such scores. The score, either a distance or a similarity, is converted to a likelihood ratio using three types of calibration following similar techniques as applied in forensic fields such as speaker comparison and DNA matching, but which have not yet been tested in facial image comparison. The calibration types tested are: naive, quality score based on typicality, and feature-based. As transparency is essential in forensics, we focus on state-of-the-art open software and study their power compared to a state-of-the-art commercial system. With the European Network of Forensic Science Institutes (ENFSI) Proficiency tests as benchmark, calibration results on three public databases namely Labeled Faces in the Wild, SC Face and ForenFace show that both quality score and feature based calibration outperform naive calibration. Overall, the commercial system outperforms open software when evaluating these Likelihood Ratios. In general, we conclude that calibration implemented before likelihood ratio estimation is recommended. Furthermore, in terms of performance the commercial system is preferred over open software. As open software is more transparent, more research on open software is urged for.
Physics-informed recurrent neural network for time dynamics in optical resonances

Resonance structures and features are ubiquitous in optical science. However, capturing their time dynamics in real-world scenarios suffers from long data acquisition time and low analysis accuracy due to slow convergence and limited time windows. Here we report a physics-informed recurrent neural network to forecast the time-domain response of optical resonances and infer corresponding resonance frequencies by acquiring a fraction of the sequence as input. The model is trained in a two-step multi-fidelity framework for high-accuracy forecast, using first a large amount of low-fidelity physical-model-generated synthetic data and then a small set of high-fidelity application-specific data. Through simulations and experiments, we demonstrate that the model is applicable to a wide range of resonances, including dielectric metasurfaces, graphene plasmonics and ultra-strongly coupled Landau polaritons, where our model captures small signal features and learns physical quantities. The demonstrated machine-learning algorithm can help to accelerate the exploration of physical phenomena and device design under resonance-enhanced light–matter interaction. Cascaded gated-recurrent-unit networks trained through a physics-informed multi-fidelity approach can accurately forecast long time sequences and capture their dynamics in a wide range of optical resonance structures and features.
Information Gain Based Feature Selection for Improved Textual Sentiment Analysis

Sentiment analysis or opinion mining is the process of mining the emotion from a given text. It is a text mining technique that effectively measures the inclination of public opinions and aids in analysing the subjective information from the given context. Sentiment analysis evaluates the opinion of a sentiment as either positive or negative or neutral. Sentiments are very specific and with respect to the underlying content, it plays a very crucial role in depicting the real-world scenario. Sentiment analysis can be performed at three levels namely document level, sentence level and feature level. This paper proposes a novel Information Gain based Feature Selection algorithm that selects highly correlated features by removing inappropriate content. Using this algorithm, extensive sentimental analysis is performed at the document level, sentence level and feature level. Datasets from Cornell and Kaggle are exploited for experimental purposes. Compared to other baseline classifiers experimental results show that the proposed Information Gain based classifier resulted in an accuracy of 95, 96.3 and 97.4% for document, sentence and feature levels respectively. The proposed method is also tested with higher dimensional datasets namely Movielens 1M, 10M and 25M datasets. Experimental results proved that the proposed method works better even for high dimensional datasets.
Noun‐Phrase Complexity Measures in Chinese and Their Relationship to L2 Chinese Writing Quality: A Comparison with Topic–Comment‐Unit‐Based Measures

Abstract This study proposed a set of measures for assessing noun phrase (NP) complexity in second language (L2) Chinese writing and compared the predictive power of these measures for L2 Chinese writing quality to that of a set of syntactic complexity measures based on the topic–comment unit (TC‐unit). Our data consisted of 101 narratives written by beginning–intermediate, intermediate–advanced, and advanced Korean Chinese‐as‐a‐second‐language (CSL) learners and rated by 2 trained CSL teachers. Results showed that the NP complexity measures explained a substantially larger proportion of variance in holistic writing scores than the TC‐unit‐based measures. Our findings confirmed the validity of the NP complexity measures we proposed and the need to attend to phrasal complexity in assessing L2 Chinese writing quality.
Neural Network-Based Approach for Evaluating College English Teaching Methodology

A fair and scientific method of evaluating collegiate English instruction is currently lacking. Traditional methods are commonly used to evaluate the quality of college English education in terms of the standard statistical analysis evaluation model. As the evaluation of English teaching is a nonlinear issue, the above strategies have achieved some good results but have certain limitations. They are not scientific and objective in the selection of evaluation indicators, or in the setting of evaluation index weights, and there is a certain degree of subjectivity. Artificial neural network (ANN) is widely used in massive fields due to its characteristics of nonlinear processing, adaptive learning, and high fault tolerance. As a kind of neural network, BP neural network has strong nonlinear mapping ability, so it is feasible and scientific to solve the nonlinear relationship of college English teaching evaluation (ETE). Therefore, in this work, we first designed an ETE system index. Then, a strategy of college ETE with BP network is designed, which can carry out high-performance modeling for the designed teaching evaluation index. In order to alleviate the issue of slow convergence speed for BP network and fall into local optimum, this work also combines particle swarm algorithm with BP network to further improve network performance. Massive experiments have proved the reliability and effectiveness of this work.