Reviewerly/exHarmony huggingface dataset:
The Percentile Bootstrap: A Primer With Step-by-Step Instructions in R

The percentile bootstrap is the Swiss Army knife of statistics: It is a nonparametric method based on data-driven simulations. It can be applied to many statistical problems, as a substitute to standard parametric approaches, or in situations for which parametric methods do not exist. In this Tutorial, we cover R code to implement the percentile bootstrap to make inferences about central tendency (e.g., means and trimmed means) and spread in a one-sample example and in an example comparing two independent groups. For each example, we explain how to derive a bootstrap distribution and how to get a confidence interval and a p value from that distribution. We also demonstrate how to run a simulation to assess the behavior of the bootstrap. For some purposes, such as making inferences about the mean, the bootstrap performs poorly. But for other purposes, it is the only known method that works well over a broad range of situations. More broadly, combining the percentile bootstrap with robust estimators (i.e., estimators that are not overly sensitive to outliers) can help users gain a deeper understanding of their data than they would using conventional methods.
Emotional neural networks with universal approximation property for stable direct adaptive nonlinear control systems

Universal approximation, continuity, and differentiability are desirable properties of any computational framework, including those that rise from human cognition and/or are inspired by nature. Emotional machines constitute one such framework, but few studies have addressed their mathematical properties. Here, we propose a Continuous Radial Basis Emotional Neural Network (CRBENN) that benefits from the universal approximation property, continuous output, and simple structure of RBF; while keeping the fast response properties of emotion-based approaches. As such, CRBENN is amenable to a wide array of challenging problems in systems engineering and artificial intelligence. Here, we propose a CRBENN-based direct adaptive robust emotional neuro-control approach (DARENC) for a class of uncertain nonlinear systems. Stability is theoretically established using Lyapunov analysis of the closed-loop system. DARENC is then applied to control two nonlinear systems, and the performance of the controller is numerically compared with several competing fuzzy, neural, and emotional controllers. The simulation results indicate improved tracking performance, better disturbance rejection, and less control effort. Finally, DARENC is implemented on a real-world 3-PSP (spherical–prismatic–spherical) parallel robot in our laboratory. The experimental results show the satisfactory performance of the robot in tracking the desired trajectory with low control effort.
Improving Information Sharing and Collaborative Analysis for Remote GeoSpatial Visualization Using Mixed Reality

Remote collaboration systems allow users at different sites to perform joint tasks, which are required by many real-life applications. For example, environmental pollution is a complex problem requiring many kinds of expertise to fully understand, as pollutants disperse not only locally but also regionally or even globally. This paper presents a remote collaborative visualization system through providing co-presence, information sharing, and collaborative analysis functions based on mixed reality techniques. We start with developing an immersive visualization approach for analyzing multi-attribute and geo-spatial data with intuitive multi-model interactions, simulating co-located collaboration effects. We then go beyond by designing a set of information sharing and collaborative analysis functions to support different users to share and analyze their sensemaking processes collaboratively. We provide example results and usage scenario to demonstrate that our system enables users to perform a variety of immersive and collaborative analytics tasks effectively. Through two small user studies focusing on evaluating our design of information sharing and system usability, the evaluation results confirm the effectiveness of comprehensive sharing among user, data, physical, and interaction spaces for improving remote collaborative analysis experience.
Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing

The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised real-time anomaly detection algorithm based on LSTM-based Auto-Encoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%~85%.
Multi-Rule Based Ensemble Feature Selection Model for Sarcasm Type Detection in Twitter

Sentimental analysis aims at inferring how people express their opinion over any piece of text or topic of interest. This article deals with detection of an implicit form of the sentiment, referred to as sarcasm. Sarcasm conveys the opposite of what people try to convey in order to criticize or ridicule in a humorous way. It plays a vital role in social networks since most of the tweets or posts contain sarcastic nuances. Existing approaches towards the study of sarcasm deals only with the detection of sarcasm. In this paper, in addition to detecting sarcasm from text, an approach has been proposed to identify the type of sarcasm. The main motivation behind determining the types of sarcasm is to identify the level of hurt or the true intent behind the sarcastic text. The proposed work aims to improve upon the existing approaches by incorporating a new perspective which classifies the sarcasm based on the level of harshness employed. The major application of the proposed work would be relating the emotional state of a person to the type of sarcasm exhibited by him/her which could provide major insights about the emotional behavior of a person. An ensemble-based feature selection method has been proposed for identifying the optimal set of features needed to detect sarcasm from tweets. This optimal set of features was employed to detect whether the tweet is sarcastic or not. After detecting sarcastic sentences, a multi-rule based approach has been proposed to determine the type of sarcasm. As an initial attempt, sarcasm has been classified into four types, namely, polite sarcasm, rude sarcasm, raging sarcasm, and deadpan sarcasm. The performance and efficiency of the proposed approach has been experimentally analyzed, and change in mood of a person for each sarcastic type has been modelled. The overall accuracy of the proposed ensemble feature selection algorithm for sarcasm detection is around 92.7%, and the proposed multi-rule approach for sarcastic type identification achieves an accuracy of 95.98%, 96.20%, 99.79%, and 86.61% for polite, rude, raging, and deadpan types of sarcasm, respectively.<?tex id="Q1" staff-cmt="We have changed &#x201C;Multirule-Based&#x201d; to &#x201C;Multi-rule-Based&#x201d; for correctness here and in the other instances. Please confirm."?>
Digitally stimulated Raman passage by deep reinforcement learning

Preparing an arbitrary preselected coherent superposition of quantum states finds widespread application in physics, including initialization of trapped ion and superconductor qubits in quantum computers. Both fractional and integer stimulated Raman adiabatic passage involve smooth Gaussian pulses, designed to grant adiabaticity, so to keep the system in an eigenstate constituted only of the initial and final states. We explore an alternative method for discovering appropriate pulse sequences based on deep reinforcement learning algorithms and by imposing that the control laser can be only either on or off instead of being continuously amplitude-modulated. Despite the adiabatic condition is violated, we obtain fast and flexible solutions for both integer and fractional population transfer. Such method, consisting of a Digital Stimulated Raman Passage (D-STIRaP), proves to be particularly effective when the system is affected by dephasing therefore providing an alternative path towards control of noisy quantum states, like trapped ions and superconductor qubits.
Global-local fusion network for face super-resolution

Face hallucination is a domain-specific super-resolution (SR) algorithm, that generates high-resolution (HR) images from the observed low-resolution (LR) inputs. Recently, deep convolutional neural network (CNN) based SR offers an end-to-end solution for learning the complex relationship between LR and HR images, and achieves superior performance. However, most of them ignore the role of high-frequency (HF) information in image recovering. We design a novel global-local fused network (GLFSR) to refine HF information for recovering fine details of facial images. In contrast to existing methods that often increase the depth of network, we enhance the residual HF information from local to global levels through the networks. The proposed global-local fused network involves four sub-modules: first, reconstruction network, which is used to super-resolve the synthetic HR image from pixel level by reconstruction network, local and global residual enhancement networks, which generate residual information for learning; and fusion module, which is used to generate the final HR image. Experimental results on CAS-PEAL-R1 and CASIA-Webface databases demonstrate that GLFSR is superior to other state-of-the-art deep learning approaches.
Learn#: A Novel incremental learning method for text classification

Deep learning is an effective method for extracting the underlying information in text. However, it performs better on closed datasets and is less effective in real-world scenarios for text classification. As the data is updated and the amount of data increases, the models need to be retrained, in what is often a long training process. Therefore, we propose a novel incremental learning strategy to solve these problems. Our method, called Learn#, includes four components: a Student model, a reinforcement learning (RL) module, a Teacher model, and a discriminator model. The Student models first extract the features from the texts, then the RL module filters the results of multiple Student models. After that, the Teacher model reclassifies the filtered results to obtain the final texts category. To avoid increasing the Student models unlimitedly as the number of samples increases, the discriminator model is used to filter the Student models based on their similarity. The Learn# method has the advantage of a shorter training time than the One-Time model, because it only needs to train a new Student model each time, without changing the existing Student models. Furthermore, it can also obtain feedback during application and tune the models parameters over time. Experiments on different datasets show that our method for text classification outperforms many traditional One-Time methods, reducing training time by nearly 80%.
Anomalous Trajectory Detection and Classification Based on Difference and Intersection Set Distance

Anomaly detection is an important issue in trajectory data mining. Various approaches have been proposed to address this issue. However, most previous studies focus only on outlier detection but rarely on pattern mining of anomalous trajectories. Mining patterns of anomalous trajectories can reveal the underlying mechanisms of these outliers. This paper studies four distinct patterns of anomalous trajectories, and proposes a method to detect and classify them. First, we present the difference and intersection set (DIS) distance metric to evaluate the similarity between any two trajectories. Based on this distance, we design an anomaly score function to quantify the differences between different types of anomalous trajectories and normal trajectories. We further propose an anomalous trajectory detection and classification (ATDC) method to find anomalies in different anomalous patterns. Finally, we evaluate the proposed ATDC method through extensive experiments on real cab trajectory data. The results show that the proposed approach outperforms existing methods by a significant margin.
PSDRNN: An Efficient and Effective HAR Scheme Based on Feature Extraction and Deep Learning

Recently, in this article, due to pervasive usability, smartphone-based human activity recognition (HAR) has witnessed significant development in smart health. Meanwhile, deep recurrent neural network (DRNN) shows a strong ability to automatically extract features from the time-series data, and therefore, DRNN-based HAR schemes have achieved more effective recognition (i.e., recognition accuracy) than those adopting the traditional machine learning. However, the efficiency in training and recognition (in terms of running time) has not fully taken into account, especially for resource-constraint smartphones. To solve the above issue, we propose the PSDRNN and tri-PSDRNN schemes that employ the explicit feature extraction before DRNN. Specifically, considering that the power spectral density (PSD) feature can capture the frequency characteristics and meanwhile retain the successive time characteristics of data gathered from smartphone accelerometer, PSD feature vectors are, respectively, extracted from linear accelerations and triaxle accelerations and explicitly used as the input to the following DRNN classification model. Thorough experiments based on a real dataset demonstrate that the PSDRNN can achieve the comparable effectiveness as the xyz-DRNN (the most accurate DRNN-based HAR scheme only using acceleration data), and the average recognition and training time were reduced by 56% and 80%, respectively. Moreover, tri-PSDRNN advantages over the xyz-DRNN in terms of recognition accuracy, and the running time is still lower than the xyz-DRNN. Besides, our proposed PSDRNN scheme achieved superiority in the recognition of complex transition activities.
Trustless education? A blockchain system for university grades1

Abstract We have implemented a blockchain system based on Ethereum for use by a university to store student grades and to provide a cryptocurrency. Based upon an exploratory, qualitative evaluation we have found several tensions between the concept of a university as an organization and the concept of distributed autonomous organizations (DAOs) in Ethereum. These include tensions in (i) mechanisms of trust, (ii) boundaries of openness and (iii) values in procedures. In this article we outline our implementation and the evaluation process.
Conservative Wasserstein Training for Pose Estimation

This paper targets the task with discrete and periodic class labels (e.g., pose/orientation estimation) in the context of deep learning. The commonly used cross-entropy or regression loss is not well matched to this problem as they ignore the periodic nature of the labels and the class similarity, or assume labels are continuous value. We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining (i.e., using arc length of a circle) or adaptively learning the ground metric. We extend the ground metric as a linear, convex or concave increasing function w.r.t. arc length from an optimization perspective. We also propose to construct the conservative target labels which model the inlier and outlier noises using a wrapped unimodal-uniform mixture distribution. Unlike the one-hot setting, the conservative label makes the computation of Wasserstein distance more challenging. We systematically conclude the practical closed-form solution of Wasserstein distance for pose data with either one-hot or conservative target label. We evaluate our method on head, body, vehicle and 3D object pose benchmarks with exhaustive ablation studies. The Wasserstein loss obtaining superior performance over the current methods, especially using convex mapping function for ground metric, conservative label, and closed-form solution.
Trajectory Tracking of Wheeled Mobile Robots Using Z-Number Based Fuzzy Logic

Being trajectory tracking key for safe mobile robot navigation, Fuzzy Logic (FL) has been useful in tackling uncertainty and imprecision to realize robust and smooth trajectory tracking.In this paper, we present the Z-number based Fuzzy Logic control for trajectory tracking of differential wheeled mobile robots.The unique point of our approach lies in the ability to encode constraint and reliability in multi-input and multi-output rules, whose antecedent universe considers only the instantaneous measurements of distance and the orientation gaps, and whose consequent universe is computed by the interpolative reasoning and the graded mean integration approach.As a consequence, not only our approach avoids the complexity of encoding error gradients, but also is advantageous to model versatile control rules able to cope with missing observations and noisy inducements on actuators.Our experiments using both physics-based simulations and real-world tests based on a Pioneer 3DX robot architecture have elucidated the superior efficacy and the feasibility of the proposed controller regarding accuracy, robustness, and smoothness compared to other well-known related frameworks such as Fuzzy Logic Type 1, Fuzzy Logic Type 2 and Fuzzy Logic with PID.Our results provide unique insights to realize generalizable algorithms aided by FL and Z-number towards robust trajectory tracking.
Providing effective recommendations in discussion groups using a new hybrid recommender system based on implicit ratings and semantic similarity

Discussion groups are one of the most important elements of collaborative learning which utilize recommender systems to improve their performance in several aspects. This type of learning facilitates a comfort communication between users to share their problems and questions and receive the appropriate solutions. Most of recommender systems of discussion groups are based on using collaborative filtering techniques and a few numbers of them use content-based or hybrid filtering. Experimental results of previous works show that using hybrid recommender systems on discussion groups’ databases cause significant improvement in accuracy of recommended posts in comparison with other filtering techniques (Kardan and Ebrahimi, 2013). To improve performance of (Kardan and Ebrahimi, 2013), in this paper, a new recommender system is represented, which includes three parts, namely content-based, collaborative, and hybrid filtering parts. The proposed recommender system uses the tagging features to provide more appropriate recommendations on discussion groups. For this purpose, semantic relevance of tags is extracted using WordNet lexical database and the tags are organized in a hierarchical structure based on their semantic relevance. The hierarchical structure is used for searching relevant posts in content-based filtering part, and the user’s query is extended using related semantic tags. The implicit ratings of the users are calculated in the collaborative filtering part using similarity measures. Finally, the results of these two parts are combined in the hybrid filtering part of the proposed system to recommend the posts of the discussion group which are similar to the query of the active user. Experimental results show higher precision of the proposed system comparing to the former recommender systems.
An Automatic Analog Instrument Reading System Using Computer Vision and Inspection Robot

Since the manual inspection of analog instruments is inefficient, many computer vision-based automatic reading systems have been proposed recently. However, most of them use fixed cameras that are costly due to a large number of used cameras. Although some other systems adopting the pan-tilt-zoom camera and the movable inspection robot can avoid using many cameras, they have to overcome high computational cost in aligning the camera to the tested instrument. Meanwhile, most existing systems are instrument type-dependent and, hence, cannot handle multiple types of instruments simultaneously. In this article, first, based on an inspection robot, an automatic reading system equipped with a pan-tilt-zoom camera is designed for different types of round-shape analog instruments. Then, a fast camera alignment algorithm based on visual servo is proposed, in which YOLOv3 is applied and improved to locate the instrument and guide the camera to iteratively align with the instrument. Finally, a monocular-vision pointer reconstruction algorithm is proposed to accurately read the instrument. Experimental results demonstrated that our proposed system is fast and reliable in the camera-alignment process and is effective in reading different types of analog instruments during the robot-based inspection.
Robust Trajectory Planning for a Multirotor against Disturbance based on Hamilton-Jacobi Reachability Analysis

Ensuring safety in trajectory planning of multirotor systems is an essential element for risk-free operation. Even if the generated trajectory is known to be safe in the planning phase, unknown disturbance during an actual operation can lead to a dangerous situation. This paper proposes safety-guaranteed receding horizon planning against unknown, but bounded, disturbances. We first characterize forward reachable set (FRS) of the system, the set of states after a certain duration considering all possible disturbances, using Hamilton-Jacobi (HJ) reachability analysis. To compute the FRSs in real-time, we conservatively approximate the true FRS and perform ellipsoidal parameterization on the FRSs. Using the FRSs, we can plan a robust trajectory that avoids risky regions and rapidly re-plan the trajectory when the system encounters sudden disturbance. The proposed method is validated through an experiment of avoiding obstacles in a wind.
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining

Single image deraining task is still a very challenging task due to its ill-posed nature in reality. Recently, researchers have tried to fix this issue by training the CNN-based end-to-end models, but they still cannot extract the negative rain streaks from rainy images precisely, which usually leads to an over de-rained or under de-rained result. To handle this issue, this paper proposes a new coarse-to-fine single image deraining framework termed Multi-stream Hybrid Deraining Network (shortly, MH-DerainNet). To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i.e., Residual path and Dense path. The Residual path is used to reuse com-mon features from the previous layers while the Dense path can explore new features. In addition, to concatenate different scaled features, we also apply the idea of multi-stream with shortcuts between cascaded dual path residual dense block based streams. To obtain more distinct derained images, we combine the SSIM loss and perceptual loss to preserve the per-pixel similarity as well as preserving the global structures so that the deraining result is more accurate. Extensive experi-ments on both synthetic and real rainy images demonstrate that our MH-DerainNet can deliver significant improvements over several recent state-of-the-art methods.