Reviewerly/exHarmony huggingface dataset:
Projector distortion correction in 3D shape measurement using a structured-light system by deep neural networks

In a structured-light system, lens distortion of the camera and projector is the main source of 3D measurement error. In this Letter, a new approach, to the best of our knowledge, of using deep neural networks to address this problem is proposed. The neural network consists of one input layer, five densely connected hidden layers, and one output layer. A ceramic plate with flatness less than 0.005 mm is used to acquire the training, validation, and test data sets for the network. It is shown that the measurement accuracy can be enhanced to 0.0165 mm in the RMS value by this technique, which is an improvement of 93.52%. It is also verified that the constructed neural network is with satisfactory repeatability.
Feedback and Mobile Instant Messaging: Using WhatsApp as a Feedback Tool in EFL

Mobile assisted language learning (MALL) has developed and gained importance over the years in foreign language learning settings.However, mobile phones are still perceived as distracting devices in most of the schools.Within the field of second and foreign language learning, one of the most difficult things for school teachers is to give feedback to students when using a mobile device.In the present paper we present a case study research, which will investigate the best form of giving feedback via WhatsApp to students in primary education.Mobile instant messaging (MIM) has been used to practice vocabulary as well as the speaking and listening skills, but the study focuses on the feedback that the students have received.30 primary (volunteer) students were selected to participate in the 3-week study and were divided into 3 groups, according to their age (low, middle and upper primary).A mixed approach method was employed to analyze the data (quantitative and qualitative).Results indicated that the most popular form of feedback among young students were smileys and images and not written feedback, and the study reveals that a set of varied images, even to send corrective feedback was an engaging element for the young students.Upper-primary students considered the written feedback more interesting, since they received more language-related details with this type of feedback than with a simple image.
A Novel Model for Imbalanced Data Classification

Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.
Visual Dialogue State Tracking for Question Generation

GuessWhat?! is a visual dialogue task between a guesser and an oracle. The guesser aims to locate an object supposed by the oracle oneself in an image by asking a sequence of Yes/No questions. Asking proper questions with the progress of dialogue is vital for achieving successful final guess. As a result, the progress of dialogue should be properly represented and tracked. Previous models for question generation pay less attention on the representation and tracking of dialogue states, and therefore are prone to asking low quality questions such as repeated questions. This paper proposes visual dialogue state tracking (VDST) based method for question generation. A visual dialogue state is defined as the distribution on objects in the image as well as representations of objects. Representations of objects are updated with the change of the distribution on objects. An object-difference based attention is used to decode new question. The distribution on objects is updated by comparing the question-answer pair and objects. Experimental results on GuessWhat?! dataset show that our model significantly outperforms existing methods and achieves new state-of-the-art performance. It is also noticeable that our model reduces the rate of repeated questions from more than 50% to 21.9% compared with previous state-of-the-art methods.
RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning

This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Different from offline music generation and harmonization, online music accompaniment requires the algorithm to respond to human input and generate the machine counterpart in a sequential order. We cast this as a reinforcement learning problem, where the generation agent learns a policy to generate a musical note (action) based on previously generated context (state). The key of this algorithm is the well-functioning reward model. Instead of defining it using music composition rules, we learn this model from monophonic and polyphonic training data. This model considers the compatibility of the machine-generated note with both the machine-generated context and the human-generated context. Experiments show that this algorithm is able to respond to the human part and generate a melodic, harmonic and diverse machine part. Subjective evaluations on preferences show that the proposed algorithm generates music pieces of higher quality than the baseline method.
3D reconstruction toolbox for behavior tracked with multiple cameras

Markerless tracking is a crucial experimental requirement for behavioral studies conducted in many species in different environments.A recently developed toolbox called DeepLabCut (DLC) (Mathis et al. (2018)) leverages Artificial Neural Network (ANN) based computer vision to make precise markerless tracking possible for scientific experiments.DLC uses a deep convolutional neural network, ResNet (He, Zhang, Ren, & Sun (2016)) pre-trained on ImageNet database (Deng et al. ( 2009)) and adapts it to make it applicable for behavioral tracking tasks.To track complex behaviors such as grasping with object interaction in 3D, experimental setups with multiple cameras are required.Such systems can largely benefit from a robust and easy to use camera calibration and 3D reconstruction toolbox.The current version of DLC allows 3D reconstruction of features tracked in 2D from pairs of cameras only (Nath, Mathis, Chen, Bethge, & Mathis (2019)) and is not sufficient when behavior is tracked with more than 2 cameras.Furthermore, for noisy 2D tracking conditions, such as low light or low resolution, the accuracy of tracked 3D coordinates can be improved by evaluating data from more than two cameras.
Graph Fourier Transform: A Stable Approximation

In Graph Signal Processing (GSP), data dependencies are represented by a graph whose nodes label the data and the edges capture dependencies among nodes. The graph is represented by a weighted adjacency matrix $A$ that, in GSP, generalizes the Discrete Signal Processing (DSP) shift operator $z^{-1}$. The (right) eigenvectors of the shift $A$ (graph spectral components) diagonalize $A$ and lead to a graph Fourier basis $F$ that provides a graph spectral representation of the graph signal. The inverse of the (matrix of the) graph Fourier basis $F$ is the Graph Fourier transform (GFT), $F^{-1}$. Often, including in real world examples, this diagonalization is numerically unstable. This paper develops an approach to compute an accurate approximation to $F$ and $F^{-1}$, while insuring their numerical stability, by means of solving a non convex optimization problem. To address the non-convexity, we propose an algorithm, the stable graph Fourier basis algorithm (SGFA) that we prove to exponentially increase the accuracy of the approximating $F$ per iteration. Likewise, we can apply SGFA to $A^H$ and, hence, approximate the stable left eigenvectors for the graph shift $A$ and directly compute the GFT. We evaluate empirically the quality of SGFA by applying it to graph shifts $A$ drawn from two real world problems, the 2004 US political blogs graph and the Manhattan road map, carrying out a comprehensive study on tradeoffs between different SGFA parameters. We also confirm our conclusions by applying SGFA on very sparse and very dense directed Erd\H os-R\'enyi graphs.
Real-time audio detection and regeneration of moving sound source based on optical flow algorithm of laser speckle images

Sound detection with optical means is an appealing research topic. In this manuscript, we proposed a laser microphone system allowing simultaneous detection and regeneration of the audio signal by observing the movement of secondary speckle patterns. In the proposed system, optical flow method, along with some denoising algorithms are employed to obtain the motion information of the speckle sequence with high speed. Owing to this, audio signal can be regenerated in real time with simple optical setup even the sound source is moving. Experiments have been conducted and the results show that the proposed system can restore high quality audio signal in real time under various conditions.
Revisiting Boustrophedon Coverage Path Planning as a Generalized Traveling Salesman Problem

In this paper, we present a path planner for low-altitude terrain coverage in known environments with unmanned rotary-wing micro aerial vehicles (MAVs). Airborne systems can assist humanitarian demining by surveying suspected hazardous areas (SHAs) with cameras, ground-penetrating synthetic aperture radar (GPSAR), and metal detectors. Most available coverage planner implementations for MAVs do not consider obstacles and thus cannot be deployed in obstructed environments. We describe an open source framework to perform coverage planning in polygon flight corridors with obstacles. Our planner extends boustrophedon coverage planning by optimizing over different sweep combinations to find the optimal sweep path, and considers obstacles during transition flights between cells. We evaluate the path planner on 320 synthetic maps and show that it is able to solve realistic planning instances fast enough to run in the field. The planner achieves 14% lower path costs than a conventional coverage planner. We validate the planner on a real platform where we show low-altitude coverage over a sloped terrain with trees.
SWEclat: a frequent itemset mining algorithm over streaming data using Spark Streaming

Abstract Finding frequent itemsets in a continuous streaming data is an important data mining task which is widely used in network monitoring, Internet of Things data analysis and so on. In the era of big data, it is necessary to develop a distributed frequent itemset mining algorithm to meet the needs of massive streaming data processing. Apache Spark is a unified analytic engine for massive data processing which has been successfully used in many data mining fields. In this paper, we propose a distributed algorithm for mining frequent itemsets over massive streaming data named SWEclat. The algorithm uses sliding window to process streaming data and uses vertical data structure to store the dataset in the sliding window. This algorithm is implemented by Apache Spark and uses Spark RDD to store streaming data and dataset in vertical data format, so as to divide these RDDs into partitions for distributed processing. Experimental results show that SWEclat algorithm has good acceleration, parallel scalability and load balancing.
shapr: An R-package for explaining machine learning models with dependence-aware Shapley values

A common task within machine learning is to train a model to predict an unknown outcome (response variable) based on a set of known input variables/features.When using such models for real life applications, it is often crucial to understand why a certain set of features lead to a specific prediction.Most machine learning models are, however, complicated and hard to understand, so that they are often viewed as "black-boxes", that produce some output from some input.Shapley values (Shapley, 1953) is a concept from cooperative game theory used to distribute fairly a joint payoff among the cooperating players.Štrumbelj & Kononenko (2010) and later Lundberg & Lee (2017) proposed to use the Shapley value framework to explain predictions by distributing the prediction value on the input features.Established methods and implementations for explaining predictions with Shapley values like Shapley Sampling Values (Štrumbelj & Kononenko, 2014), SHAP/Kernel SHAP (Lundberg & Lee, 2017), and to some extent TreeSHAP/TreeExplainer (Lundberg et al., 2020;Lundberg, Erion, & Lee, 2018), assume that the features are independent when approximating the Shapley values.The R-package shapr, however, implements the methodology proposed by Aas, Jullum, & Løland (2019), where predictions are explained while accounting for the dependence between the features, resulting in significantly more accurate approximations to the Shapley values.
A Novel Method for Twitter Sentiment Analysis Based on Attentional-Graph Neural Network

Twitter sentiment analysis is an effective tool for various Twitter-based analysis tasks. However, there is still no neural-network-based research which takes both the tweet-text information and user-connection information into account. To this end, we propose the Attentional-graph Neural Network based Twitter Sentiment Analyzer (AGN-TSA), a Twitter sentiment analyzer based on attentional-graph neural networks. AGN-TSA fuses the tweet-text information and the user-connection information through a three-layered neural structure, which includes a word-embedding layer, a user-embedding layer and an attentional graph network layer. For the training of AGN-TSA, dedicated loss functions are designed for the structural controllability of AGN-TSA network. Experiments based on real-world dataset concerning the 2016 presidential election of America exhibit that AGN-TSA is superior under multiple metrics over several prevailing methods, with a performance boost of over 5%. The empirical settings of parameters are given based on extensive rotation experiments.
Knowledge Discovery in Smart City Digital Twins

Despite the abundance of available urban data and the potential for reaching enhanced capabilities in the decision-making and management of city infrastructure, current data-driven approaches to knowledge discovery from city data often lack the capacity for collective data exploitation.Loosely defined data interpretation components, or disciplinary isolated interpretations of specific datasets make it easy to overlook necessary domain expertise, often resulting in speculative decision-making.Smart City Digital Twins are designed to overcome this barrier by integrating a more holistic analytics and visualization approach into the real-time knowledge discovery process from heterogeneous city data.Here, we present a spatiotemporal knowledge discovery framework for the collective exploitation of city data in smart city digital twins that incorporates both social and sensor data, and enables insights from human cognition.This is an initial step towards leveraging heterogeneous city data for digital twin-based decision-making.
Integral Knowledge Distillation for Multi-Person Pose Estimation

Both accuracy and efficiency are of equal importance to the human pose estimation. Most of the existing methods simply pursue excellent performance, sacrificing massive computing resources and memory. Out of this consideration, we present a novel compact and lightweight framework to train more efficient estimators using knowledge distillation. Three distillation mechanisms are proposed in our method from different perspectives, including logit distillation, feature distillation and structure distillation. Concretely, the logit distillation regards the output of teacher model as soft target to stimulate the student model. The feature distillation distills the high-level features of the teacher model to assist the student. Unlike the above strategies, the structure distillation considers the problem in a global view, aiming at ensuring the student prediction contains quite abundant structure knowledge like the teacher. We empirically demonstrate the effectiveness and efficiency of our methods on two multi-person pose estimation datasets (COCO and MPII). Specifically, our model can achieve competitive performance with the most state-of-the-art methods and consume only 35% model parameters and GFLOPs of our baseline (SimpleBaseline-ResNet-50) on the COCO dataset.
Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?

The increasing amount of publicly available research data provides the opportunity to link and integrate data in order to create and prove novel hypotheses, to repeat experiments or to compare recent data to data collected at a different time or place. However, recent studies have shown that retrieving relevant data for data reuse is a time-consuming task in daily research practice. In this study, we explore what hampers dataset retrieval in biodiversity research, a field that produces a large amount of heterogeneous data. In particular, we focus on scholarly search interests and metadata, the primary source of data in a dataset retrieval system. We show that existing metadata currently poorly reflect information needs and therefore are the biggest obstacle in retrieving relevant data. Our findings indicate that for data seekers in the biodiversity domain environments, materials and chemicals, species, biological and chemical processes, locations, data parameters and data types are important information categories. These interests are well covered in metadata elements of domain-specific standards. However, instead of utilizing these standards, large data repositories tend to use metadata standards with domain-independent metadata fields that cover search interests only to some extent. A second problem are arbitrary keywords utilized in descriptive fields such as title, description or subject. Keywords support scholars in a full text search only if the provided terms syntactically match or their semantic relationship to terms used in a user query is known.
Layout2image: Image Generation from Layout

Despite significant recent progress on generative models, controlled generation of images depicting multiple and complex object layouts is still a difficult problem. Among the core challenges are the diversity of appearance a given object may possess and, as a result, exponential set of images consistent with a specified layout. To address these challenges, we propose a novel approach for layout-based image generation; we call it Layout2Im. Given the coarse spatial layout (bounding boxes + object categories), our model can generate a set of realistic images which have the correct objects in the desired locations. The representation of each object is disentangled into a specified/certain part (category) and an unspecified/uncertain part (appearance). The category is encoded using a word embedding and the appearance is distilled into a low-dimensional vector sampled from a normal distribution. Individual object representations are composed together using convolutional LSTM, to obtain an encoding of the complete layout, and then decoded to an image. Several loss terms are introduced to encourage accurate and diverse image generation. The proposed Layout2Im model significantly outperforms the previous state-of-the-art, boosting the best reported inception score by 24.66% and 28.57% on the very challenging COCO-Stuff and Visual Genome datasets, respectively. Extensive experiments also demonstrate our model's ability to generate complex and diverse images with many objects.
Hybrid Fuzzy Clustering Method Based on FCM and Enhanced Logarithmical PSO (ELPSO)

Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect.
Neighborhood search methods with Moth Optimization algorithm as a wrapper method for feature selection problems

Feature selection methods are used to select a subset of features from data, therefore only the useful information can be mined from the samples to get better accuracy and improves the computational efficiency of the learning model. Moth-flam Optimization (MFO) algorithm is a population-based approach, that simulates the behavior of real moth in nature, one drawback of the MFO algorithm is that the solutions move toward the best solution, and it easily can be stuck in local optima as we investigated in this paper, therefore, we proposed a MFO Algorithm combined with a neighborhood search method for feature selection problems, in order to avoid the MFO algorithm getting trapped in a local optima, and helps in avoiding the premature convergence, the neighborhood search method is applied after a predefined number of unimproved iterations (the number of tries fail to improve the current solution). As a result, the proposed algorithm shows good performance when compared with the original MFO algorithm and with state-of-the-art approaches.