Reviewerly/exHarmony huggingface dataset:
Reinforcement learning of motor skills using Policy Search and human corrective advice

Robot learning problems are limited by physical constraints, which make learning successful policies for complex motor skills on real systems unfeasible. Some reinforcement learning methods, like Policy Search, offer stable convergence toward locally optimal solutions, whereas interactive machine learning or learning-from-demonstration methods allow fast transfer of human knowledge to the agents. However, most methods require expert demonstrations. In this work, we propose the use of human corrective advice in the actions domain for learning motor trajectories. Additionally, we combine this human feedback with reward functions in a Policy Search learning scheme. The use of both sources of information speeds up the learning process, since the intuitive knowledge of the human teacher can be easily transferred to the agent, while the Policy Search method with the cost/reward function take over for supervising the process and reducing the influence of occasional wrong human corrections. This interactive approach has been validated for learning movement primitives with simulated arms with several degrees of freedom in reaching via-point movements, and also using real robots in such tasks as “writing characters” and the ball-in-a-cup game. Compared with standard reinforcement learning without human advice, the results show that the proposed method not only converges to higher rewards when learning movement primitives, but also that the learning is sped up by a factor of 4–40 times, depending on the task.
Neural Predictive Monitoring

Neural State Classification (NSC) is a recently proposed method for runtime predictive monitoring of Hybrid Automata (HA) using deep neural networks (DNNs). NSC trains a DNN as an approximate reachability predictor that labels a given HA state x as positive if an unsafe state is reachable from x within a given time bound, and labels x as negative otherwise. NSC predictors have very high accuracy, yet are prone to prediction errors that can negatively impact reliability. To overcome this limitation, we present Neural Predictive Monitoring (NPM), a technique based on NSC and conformal prediction that complements NSC predictions with statistically sound estimates of uncertainty. This yields principled criteria for the rejection of predictions likely to be incorrect, without knowing the true reachability values. We also present an active learning method that significantly reduces both the NSC predictor’s error rate and the percentage of rejected predictions. Our approach is highly efficient, with computation times on the order of milliseconds, and effective, managing in our experimental evaluation to successfully reject almost all incorrect predictions.
Feature Pyramid Networks for Crowd Counting

In this paper, we propose the use of Feature Pyramid Networks (FPN) for Crowd Counting problem. FPN previously has been used for retinanet, the state-of-the-art model for object detection. By using FPN, our proposed crowd counting model achieved a state-of-the-art performance for UCF CC 50 dataset with MAE 136.4 and MSE 223.6. The proposed model is also achieved a state-of-the-art MSE value of 7.6 for ShanghaiTech Part B dataset. The code can be accessed at https: //github.com/wawancenggoro/fpncc.
Development of Escape Room Game using VR Technology

Escape room is one of the media games that can improve the logic of thinking. Puzzles in the escape room traditionally have disadvantages because the type of puzzle that is made requires a lot of material. The purpose of this research is to produce a game with Escape Room as the basic theme with Virtual Reality technology. Virtual Reality technology is used to develop presence in users, attendance is about the intimacy of users with the gaming world. By using Virtual Reality, the puzzle elements that are created can be replaced regularly without the need to change the building’s skeleton. The development method used is a prototype model using Unity game machines. The research method was carried out using a questionnaire for user analysis. The application generated from this research is the Escape Room VR game that can be played on an Android smartphone that is compatible with Samsung Gear VR. The application can be used as an additional means for traditional Escape Room games.
A Method of Data Augmentation for Classifying Road Damage Considering Influence on Classification Accuracy

This paper proposes a method for augmenting learning data of road damage dataset considering the influence of the augmented data on classification accuracy. Data augmentation is a very important task in the field of machine learning because more learning data causes increasing the accuracy of classification accuracy in general. The quality of the augmented data influences the accuracy of the classification. Effective data augmentation method for increasing classification accuracy is needed. The proposed method generates learning data by selecting effective data augmentation methods depending on the class of road damage. The method uses You Only Look Once v3 (YOLOv3) for detection and classification of road damage in an image. It is tuned by data adding the data augmented by the proposed method to the road damage dataset presented to the public. The experimental results show that the proposed method can increase the accuracy efficiently and effectively. The proposed selection of data augmentation methods improves remarkably mean Average Precision (mAP) which is one of the accuracy indices.
Using External Knowledge for Financial Event Prediction Based on Graph Neural Networks

This paper focuses on a novel financial event prediction task that takes a historical event chain as input and predicts what event will happen next. We introduce financial news as supplementary information to solve problems of multiple interpretations of same financial event. Besides, a gated graph neural network based approach is utilized to capture complicated relationships between event graphs for better event prediction. For the evaluation, we build a new dataset consisting of financial events for thousands of Chinese listed companies from 2013 to 2017. Experimental results show the effectiveness of our proposed model.
Toward successful agile requirements change management process in global software development: a client–vendor analysis

Requirements change management (RCM) is an important and complicated phase in the agile for the software development process. The objective of this study was to identify and analyse the factors that can positively affect the agile RCM (ARCM) process in the context of global software development (GSD). To this end, a questionnaire survey was carried out with researcher and practitioner participants working in the field of GSD. From the 56 survey respondents, a total of 20 factors that can positively influence the ARCM activities in GSD were identified. The authors also performed a client–vendor-based analysis to investigate the significance of the considered factors for different organisation types. Additionally, they compared the data collected from researchers and practitioners. The comparison results (rs (20) = 0.076) revealed that there exists a positive correlation between the rankings in each data set. The investigated factors were mapped to ten project management book of knowledge areas, and the mapping results revealed that human resource management is the most significant knowledge area amongst the investigated factors. The findings of this study provide a robust framework to assist GSD firms in implementing ARCM activities.
Recurrent Imputation for Multivariate Time Series with Missing Values

Multivariate time series data are ubiquitous in real-world healthcare systems. It is a common issue that the data contain missing values due to various reasons, such as sensor damage, data corruption, patient dropout. There have been various works on filling the missing values in multivariate time series. Classical imputation methods include KNN-based, Matrix Factorization based, and Expectation-Maximization (EM) based imputation and so on. These methods are developed for general imputation purpose and rarely utilize the temporal relations between observations. Classical statistical time series models such as autoregressive (AR) models and dynamic linear models (DLM) (e.g. [1]) can capture the temporal information, but they are essentially linear and may not be suitable for modern complex large-scale data. ImputeTS [2] employs time dependencies on univariate time series imputation, which ignores feature correlations. Recent works [3, 4] develop the imputation framework that can take advantages of the traditional methods and resolve their drawbacks. Another trend of models is based on recurrent neural network (RNN) [5-10], utilizing RNN to capture temporal dependencies and further considering various aspects of the data characteristics, such as time decay, feature correlation, residual link, and temporal belief gate. In this paper, we propose an RNN-based imputation method for filling the missing values in multivariate time series. RNN is used to capture the temporal information of time series. We use a global RNN and variable-specific RNNs to perform imputation based on historical information, and a fusion gate to combine them. At each timestamp, we use a regression layer to impute the value of a certain variable using other variables, by utilizing the relationship of variables. Bi-directional imputation is adopted to improve the ability of long-term memory and performance of starting timestamps.
Relationship between price and volume in the Bitcoin market

Purpose Bitcoin has recently become the focal point of investors as a digital currency and an alternative payment method. Despite Bitcoin being in the spotlight, a gap in the literature on its price-setting behaviors has been observed. This study aims to contribute to the literature by investigating the relationship between Bitcoin price and volume in the period between January 1, 2012 and April 7, 2018 through a symmetric and asymmetric causality test. Design/methodology/approach Daily price and volume data relevant to Bitcoin traded in the Bitstamp market were obtained from www.bitcoincharts.com . Within the framework of data applicable for analysis, the data set for this study includes a total of 2,286 observations for the period between January 1, 2012 and April 7, 2018. Findings Based on the results of the standard causality test, a causality relationship was determined from price to volume. Based on the results of the asymmetric causality test between positive and negative shocks of variables, a unilateral causality relationship was determined from negative shocks in Bitcoin prices to negative shocks in trading volume as well as from positive shocks in trading volume to positive shocks in prices. Furthermore, it was found that the relationship between Bitcoin price and volume is cointegrated. Practical implications The empirical results can be used by investors and portfolio managers to make trading decisions. Originality/value The contribution of this paper to the literature is that it is the first study on the symmetric and asymmetric causality relationship between Bitcoin price and volume. Moreover, this paper reveals short- and long-term behaviors of Bitcoin using the cointegration test used for determining the long-term relationship between Bitcoin price and volume.
Selection via Proxy: Efficient Data Selection for Deep Learning

Data selection methods, such as active learning and core-set selection, are useful tools for machine learning on large datasets, but they can be prohibitively expensive to apply in deep learning. Unlike in other areas of machine learning, the feature representations that these techniques depend on are learned in deep learning rather than given, requiring substantial training times. In this work, we show that we can greatly improve the computational efficiency of data selection in deep learning by using a small proxy model to perform data selection (e.g., selecting data points to label for active learning). By removing hidden layers from the target model or training for fewer epochs, we create proxies that are an order of magnitude faster to train. Although these small proxy models have higher error rates, we find that they empirically provide useful signal for data selection. We evaluate this “selection via proxy” (SVP) approach on several data selection tasks across five datasets: CIFAR10, CIFAR100, ImageNet, Amazon Review Polarity, and Amazon Review Full. For active learning, applying SVP can give an order of magnitude improvement in data selection runtime (i.e., the time it takes to repeatedly train and select points) without significantly increasing the final error. For core-set selection, proxies that are over 10x faster to train than their larger, more accurate target models can remove up to 50% of the data without harming the final accuracy of the target, making end-to-end training time savings possible.
Tree aggregation for random forest class probability estimation

Abstract In random forest methodology, an overall prediction or estimate is made by aggregating predictions made by individual decision trees. Popular implementations of random forests rely on different methods for aggregating predictions. In this study, we provide an empirical analysis of the performance of aggregation approaches available for classification and regression problems. We show that while the choice of aggregation scheme usually has little impact in regression, it can have a profound effect on probability estimation in classification problems. Our study illustrates the causes of calibration issues that arise from two popular aggregation approaches and highlights the important role that terminal nodesize plays in the aggregation of tree predictions. We show that optimal choices for random forest tuning parameters depend heavily on the manner in which tree predictions are aggregated.
NAS-Bench-102: Extending the Scope of Reproducible Neural Architecture Search

Neural architecture search (NAS) has achieved breakthrough success in a great number of applications in the past few years. It could be time to take a step back and analyze the good and bad aspects in the field of NAS. A variety of algorithms search architectures under different search space. These searched architectures are trained using different setups, e.g., hyper-parameters, data augmentation, regularization. This raises a comparability problem when comparing the performance of various NAS algorithms. NAS-Bench-101 has shown success to alleviate this problem. In this work, we propose an extension to NAS-Bench-101: NAS-Bench-201 with a different search space, results on multiple datasets, and more diagnostic information. NAS-Bench-201 has a fixed search space and provides a unified benchmark for almost any up-to-date NAS algorithms. The design of our search space is inspired from the one used in the most popular cell-based searching algorithms, where a cell is represented as a DAG. Each edge here is associated with an operation selected from a predefined operation set. For it to be applicable for all NAS algorithms, the search space defined in NAS-Bench-201 includes all possible architectures generated by 4 nodes and 5 associated operation options, which results in 15,625 candidates in total. The training log and the performance for each architecture candidate are provided for three datasets. This allows researchers to avoid unnecessary repetitive training for selected candidate and focus solely on the search algorithm itself. The training time saved for every candidate also largely improves the efficiency of many methods. We provide additional diagnostic information such as fine-grained loss and accuracy, which can give inspirations to new designs of NAS algorithms. In further support, we have analyzed it from many aspects and benchmarked 10 recent NAS algorithms.
Tracking of targets of interest using labeled multi-Bernoulli filter with multi-sensor control

This paper proposes a novel multi-sensor control scheme for tracking of targets of interest in the presence of clutter and detection uncertainty, using labeled multi-Bernoulli filters. The focus is on multi-target tracking applications where multiple centrally connected and controllable sensors are used for tracking of particular targets of interest. We derive an efficient analytical approximation for a task-driven objective function for multi-sensor selective control, that can be computed immediately after the prediction step. Compared to the state-of-the-art, drastic reduction in computation is achieved. Numerical experiments, involving challenging multi-sensor control scenarios, demonstrate how the proposed method can lead to significant improvements in the tracking accuracy of the targets of interest, in comparison to the generic non-selective sensor control methods. It is also shown that while our proposed method has comparable performance to the state-of-art selective sensor control method (selective-PEECS) in terms of the mean-square-error (MSE) of tracking for targets of interest, it runs significantly faster than the state-of-art (both selective and non-selective) multi-sensor control algorithms. Indeed, the resulting reduction in computation time is shown to be in the order of tens to hundreds time, depending on the number of sensors to be controlled.
A Wearable Flow-MIMU Device for Monitoring Human Dynamic Motion

For personalized rehabilitation or sports training, it is necessary to monitor dynamic motions and track postures of human body and limbs. Traditional methods using micro inertial sensors usually suffer from drift problems in tracking dynamic motions. In this paper, a wearable flow-MIMU human motion capture device is proposed by incorporating micro flow sensor with micro inertial measurement unit (MIMU). Motion velocity is detected by a micro flow sensor and utilized to figure out the motion acceleration. The gravity accelerations are extracted by eliminating the motion accelerations from the accelerometer outputs. Finally, posture estimation is implemented by using a tailor-designed Kalman-based data fusion of the gyroscope outputs and the extracted gravity accelerations. The flow-MIMU device with wireless communication is designed like a watch to be wearable. Experimental results validate that the motion velocity, acceleration and posture of human limb are determined accurately and free of accumulative error in monitoring of dynamic motions using the proposed method. The wearable flow-MIMU device provides an advantageous monitoring approach for applications of personalized rehabilitation, sports training, intelligent prosthetics, etc.
Introducing Hann windows for reducing edge-effects in patch-based image segmentation

There is a limitation in the size of an image that can be processed using computationally demanding methods such as e.g. Convolutional Neural Networks (CNNs). Furthermore, many networks are designed to work with a pre-determined fixed image size. Some imaging modalities—notably biological and medical—can result in images up to a few gigapixels in size, meaning that they have to be divided into smaller parts, or patches, for processing. However, when performing pixel classification, this may lead to undesirable artefacts, such as edge effects in the final re-combined image. We introduce windowing methods from signal processing to effectively reduce such edge effects. With the assumption that the central part of an image patch often holds richer contextual information than its sides and corners, we reconstruct the prediction by overlapping patches that are being weighted depending on 2-dimensional windows. We compare the results of simple averaging and four different windows: Hann, Bartlett-Hann, Triangular and a recently proposed window by Cui et al., and show that the cosine-based Hann window achieves the best improvement as measured by the Structural Similarity Index (SSIM). We also apply the Dice score to show that classification errors close to patch edges are reduced. The proposed windowing method can be used together with any CNN model for segmentation without any modification and significantly improves network predictions.
Towards Deep Object Detection Techniques for Phoneme Recognition

The use of cutting edge object detection techniques to build an accurate phoneme sequence recognition system for English and Arabic languages is investigated in this study.Recently, numerous techniques have been proposed for object detection in daily life applications using deep learning.In this paper, we propose the use of object detection techniques in speech processing tasks.We selected two state-of-the-art object detectors, namely YOLO and CenterNet, based on a trade-off between detection accuracy and speed.We tackled the problem of phoneme sequence recognition using three systems: the domain transfer learning system (DTS) from image to speech, intra-language transfer leaning system (IaTS) between speech corpora within the same language (English to English), and inter-language transfer learning system (IeTS) between speech corpora from dissimilar languages (English to Arabic).For English phoneme recognition, the Texas Instruments/Massachusetts Institute of Technology (TIMIT) corpus is used to evaluate the performance of the proposed systems.Our IaTS based on the CenterNet detector achieves the best results using the test core set of TIMIT with 15.89% phone error rate (PER).For Arabic phoneme recognition, the best performance, with 7.58% PER, was achieved using the CenterNet.These results show the effectiveness of using object detection techniques in phoneme recognition tasks.Furthermore, based on the findings of this study, speech processing tasks may be treated as object detection tasks.
Observer-Based Interval Type-2 L<sub>2</sub> – L<sub>∞</sub>/H<sub>∞</sub> Mixed Fuzzy Control for Uncertain Nonlinear Systems Under Measurement Outliers

In this article, the observer-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}-\mathcal {L}_{\infty }/ \mathcal {H}_{\infty }$ </tex-math></inline-formula> mixed control issue for uncertain nonlinear plants in the presence of measurement outliers is investigated under interval type-2 (IT2) Takagi–Sugeno (T–S) fuzzy approach. Through using lower and upper membership functions, the uncertainties that exist in nonlinear systems can be captured efficaciously. For the sake of reducing the effect of abrupt abnormal signals that disturb the measurements utilized for the purpose of state estimation, a novel fuzzy observer is designed via utilizing the adaptive saturation of output errors. After that, sufficient conditions are derived to ensure the exponential stability with a mixed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {L}_{2}- \mathcal {L}_{\infty }/\mathcal {H}_{\infty }$ </tex-math></inline-formula> performance level of considered systems on the basis of Lyapunov stability theory. Finally, the usefulness of the new designed control approach is confirmed through two demonstrative simulation examples.