Reviewerly/exHarmony huggingface dataset:
Genetic Deep Reinforcement Learning for Mapless Navigation

We consider Deep Reinforcement Learning (DRL) approaches to devise mapless navigation strategies for mobile platforms. We propose a Genetic Deep Reinforcement Learning (GDRL) method that combines Genetic Algorithms (GA) with discrete and continuous action space DRL approaches. The goal of GDRL is to reduce the sensitivity of DRL approaches to their hyper-parameter tuning and to provide robust exploration strategies. We evaluate GDRL in combination with Rainbow and Proximal Policy Optimization (PPO) in two navigation scenarios: i) a wheeled robot avoiding obstacles in an indoor environment and ii) a water drone that must reach a predefined location in presence of waves. Our empirical evaluation demonstrates that GDRL outperforms state-of-the-art DRL and GA methods as well as a previous hybrid approach.
Information in Infinite Ensembles of Infinitely-Wide Neural Networks

In this preliminary work, we study the generalization properties of infinite ensembles of infinitely-wide neural networks. Amazingly, this model family admits tractable calculations for many information-theoretic quantities. We report analytical and empirical investigations in the search for signals that correlate with generalization.
Conceptualization of Major Stakeholders in Emergency Management of Childcare Facilities

ABSTRACT This study aims to conceptualize the key stakeholders in emergency management of childcare facilities for applicable nations by referring to the Korean case. Qualitative content analysis is the main methodology used. Unique features are gathered from the Korean literature on childcare facilities and then summarized; however, universal principles from the English language literature on international childcare facilities are emphasized. The analysis of five major stakeholders in Korean childcare facilities, namely, governments, community personnel, parents, childcare providers, and children, shows that their current efforts are directed only at general safety management. Multi-hazard management or an integrated approach in terms of social work is thus provided as an alternative for not only Korea but also other nations.
Learning to Cooperate: Application of Deep Reinforcement Learning for Online AGV Path Finding

Multi-agent path finding (MAPF), naturally exists in applications like picking-up and dropping-off parcels by automated guided vehicles (AGVs) in the warehouse. Existing algorithms, like conflict-based search (CBS), windowed hierarchical cooperative A* (WHCA), and other A* variants, are widely used to find the shortest paths in different manners. However, in real-world environments, MAPF cases are dynamically generated and need to be solved in real time. In this work, a decentralized multi-agent reinforcement learning (MARL) framework with multi-step ahead tree search (MATS) strategy is proposed to make efficient decisions. Through performing experiments on a 30*30 grid world and a real-world warehouse case, our proposed MARL policy is proved to be capable of: 1) scaling to a large number of agents in real-world environment with online response time within acceptable levels; 2) outperforming existing algorithms with shorter path length and solution time, as the number of agents increases.
Prediction Focused Topic Models via Feature Selection

Supervised topic models are often sought to balance prediction quality and interpretability. However, when models are (inevitably) misspecified, standard approaches rarely deliver on both. We introduce a novel approach, the prediction-focused topic model, that uses the supervisory signal to retain only vocabulary terms that improve, or at least do not hinder, prediction performance. By removing terms with irrelevant signal, the topic model is able to learn task-relevant, coherent topics. We demonstrate on several data sets that compared to existing approaches, prediction-focused topic models learn much more coherent topics while maintaining competitive predictions.
Wasserstein Style Transfer

We propose Gaussian optimal transport for Image style transfer in an Encoder/Decoder framework. Optimal transport for Gaussian measures has closed forms Monge mappings from source to target distributions. Moreover interpolates between a content and a style image can be seen as geodesics in the Wasserstein Geometry. Using this insight, we show how to mix different target styles , using Wasserstein barycenter of Gaussian measures. Since Gaussians are closed under Wasserstein barycenter, this allows us a simple style transfer and style mixing and interpolation. Moreover we show how mixing different styles can be achieved using other geodesic metrics between gaussians such as the Fisher Rao metric, while the transport of the content to the new interpolate style is still performed with Gaussian OT maps. Our simple methodology allows to generate new stylized content interpolating between many artistic styles. The metric used in the interpolation results in different stylizations.
HMMs for Anomaly Detection in Autonomous Robots

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. In this paper, we propose an approach for detecting anomalous behaviors in autonomous robots starting from data collected during their routine operations. The main idea is to model the nominal (expected) behavior of a robot system using Hidden Markov Models (HMMs) and to evaluate how far the observed behavior is from the nominal one using variants of the Hellinger distance adopted for our purposes. We present a method for online anomaly detection that computes the Hellinger distance between the probability distribution of observations made in a sliding window and the corresponding nominal emission probability distribution. We also present a method for offline anomaly detection that computes a variant of the Hellinger distance between two HMMs representing nominal and observed behaviors. The use of the Hellinger distance positively impacts on both detection performance and interpretability of detected anomalies, as shown by results of experiments performed in two real-world application domains, namely, water monitoring with aquatic drones and socially assistive robots for elders living at home. In particular, our approach improves by 6% the area under the ROC curve of standard online anomaly detection methods. The capabilities of our offline method to discriminate anomalous behaviors in real-world applications are statistically proved.
Deep Learning Methods for Mitosis Detection in Breast Cancer Histopathological Images: A Comprehensive Review

In breast cancer histology, there are three important features for tumor grading, where the proliferation score presents a key component. The mitotic count strategy is among the used methods to predict this score. However, this task is tedious and time consuming for pathologists. To simplify their work, there is a recognized need for computer-aided diagnostic systems (CADs). Several attempts have been made to automate the mitosis detection based on both machine and deep learning (DL) methods. This study aims to provide the readers with a medical knowledge on mitosis detection and DL methods, review and compare the relevant literature on DL methods for mitosis detection on H&E histopathological images, and finally discuss the remaining challenges and some of the perspectives.
Stochastic Recursive Variance-Reduced Cubic Regularization Methods.

Stochastic Variance-Reduced Cubic regularization (SVRC) algorithms have received increasing attention due to its improved gradient/Hessian complexities (i.e., number of queries to stochastic gradient/Hessian oracles) to find local minima for nonconvex finite-sum optimization. However, it is unclear whether existing SVRC algorithms can be further improved. Moreover, the semi-stochastic Hessian estimator adopted in existing SVRC algorithms prevents the use of Hessian-vector product-based fast cubic subproblem solvers, which makes SVRC algorithms computationally intractable for high-dimensional problems. In this paper, we first present a Stochastic Recursive Variance-Reduced Cubic regularization method (SRVRC) using a recursively updated semi-stochastic gradient and Hessian estimators. It enjoys improved gradient and Hessian complexities to find an $(\epsilon, \sqrt{\epsilon})$-approximate local minimum, and outperforms the state-of-the-art SVRC algorithms. Built upon SRVRC, we further propose a Hessian-free SRVRC algorithm, namely SRVRC$_{\text{free}}$, which only requires stochastic gradient and Hessian-vector product computations, and achieves $\tilde O(dn\epsilon^{-2} \land d\epsilon^{-3})$ runtime complexity, where $n$ is the number of component functions in the finite-sum structure, $d$ is the problem dimension, and $\epsilon$ is the optimization precision. This outperforms the best-known runtime complexity $\tilde O(d\epsilon^{-3.5})$ achieved by stochastic cubic regularization algorithm proposed in Tripuraneni et al. 2018.
On the issue of assessing and Increasing the effect of training foreign citizens in Russian universities on the economic development of Russia and its Arctic regions

In an effort to comprehensively cover the issue indicated in the title of the article, the author, first, traces the dynamics of the number of persons in question, including students on a contract basis, and identifies factors that affect this dynamics. Secondly, he comments on the direct and indirect assessments of the effects of training of educational migrants available in the literature. Thirdly, he gives examples of the presence of such effects for the economies of countries leading in the export of educational services. Fourth, it reveals the specifics of the Arctic subsystem of the Russian higher education system in comparison with similar subsystems of other countries that include the territories of the Far North. Fifth, it constructively and critically analyzes measures to stimulate the export of University services. Sixth, it sets out a number of considerations for improving the information and statistical support for solving the problems under consideration.
The development of android-based science learning media on human eyes topic

Abstract Either field or literature studies recommended the need of learning media on human eyes topic which present learning objectives, materials, evaluations, and assessments. Also, the learning media should be able to support independent, interactive, and fun learnings. Learning media is developed through the steps of research and development. The developed product was an android application named “Human Eye Optics.” The product characteristics were: providing menus: learning objectives, materials, simulations, exercises, and evaluations; each menu has sub-menus such as eye parts, viewing process, and eye disorders; having five interactive and communicative simulation submenus; materials are described attractively through the use of full-color text and images; the teacher can use it as a learning device since it has covered learning objectives, materials, quizzes, exercises, and evaluations with assessment; the application could be referred to as an independent and repeatable learning source; and usable at anywhere and anytime. The product feasibility was categorized as “excellent” with a score of 4,60. The same thing went with the practicality which belonged to the “excellent” category with a score of 4,51. The effectivity achieved “high” with an n-gain of 0,76 while the average student activity was 89,02% and declared to be “very good.”.
An Evaluation Methodology of Named Entities Recognition in Spanish Language: ECU 911 Case Study

The importance of the gathered information in Integrated Security Services as ECU911 in Ecuador is evidenced in terms of its quality and availability in order to perform decision-making tasks. It is a priority to avoid the loss of relevant information such as event address, places references, names, etc. In this context it is present Named Entity Recognition (NER) analysis for discovering information into informal texts. Unlike structured corpus and labeled for NER analysis like CONLL2002 or ANCORA, informal texts generated from emergency call dialogues have a very wide linguistic variety; in addition, there is a strong tending to lose important information in their processing. A relevant aspect to considerate is the identification of texts that denotes entities such as the physical address where emergency events occurred. This study aims to extract the locations in which an emergency event has been issued. A set of experiments was performed with NER models based on Convolutional Neural Network (CNN). The performance of models was evaluated according to parameters such as training dataset size, dropout rate, location dictionary, and denoting location. An experimentation methodology was proposed, with it follows the next steps: i) Data preprocessing, ii) Dataset labeling, iii) Model structuring, and iv) Model evaluating. Results revealed that the performance of a model improves when having more training data, an adequate dropout rate to control overfitting problems, and a combination of a dictionary of locations and replacing words denoting entities.
Distributed Power Grid Fault Diagnosis Based on Naive Bayesian Network and D-S Evidence Theory

Abstract On the basis of Bayesian network, evidence theory is introduced, a kind of distributed power network fault diagnosis method based on Naive Bayesian network and D-S evidence theory is proposed. Firstly, the fault area is determined by the real-time connection method, the fault region is segmented by the butterfly segmentation method. Secondly, according to the historical fault samples, the decision table is established, and knowledge reduction based on Rough Set Theory. The naive Bayesian network model is constructed according the best combination, and the probability of each node is trained. Finally, D-S evidence fusion is performed on the fault component diagnosis information in the overlap between the subnets. The simulation results show that the proposed method can reduce the complexity of modeling and improve the fault tolerance of the system in the condition of incomplete information, and has good diagnosis results.
Educating and Evaluating in Times of Coronavirus: the Situation in Spain

As a result of the state of alarm, decreed due to the COVID-19 pandemic in Spain, all educational centers were closed and the educational system was reorganized so that 10 million students could continue their education remotely This research, which is exploratory in nature, has investigated the vision that the families and, above all, the students themselves have of the educational measures that have been taken to deal with this period 3400 Spaniards answered a survey, whose answers were analyzed, following mainly quantitative techniques (descriptive statistical analysis,) but also qualitative ones (content analysis ) Five fundamental ideas were identified from the results: (1) the digital divide adds to and amplifies the social divide, increasing inequality in times of crisis;(2) technologies, which have been an essential ally during the crisis, are not an alternative to the face-to-face educational relationship, but are an increasingly enriching tool to support the teaching-learning process;(3) it is necessary to rethink the current encyclopedic curriculum in order to discern what is dispensable from what is indispensable and to prioritize relevant content;(4) evaluation is not about grading, it is about understanding how the learning process has taken place in order to help each student to keep moving forward, and (5) it is necessary to  look after people  and give priority to their overall well-being, so that  no one is left behind
A Criterion for Deciding the Number of Clusters in a Dataset Based on Data Depth

Clustering is a key method in unsupervised learning with various applications in data mining, pattern recognition and intelligent information processing. However, the number of groups to be formed, usually notated as [Formula: see text] is a vital parameter for most of the existing clustering algorithms as their clustering results depend heavily on this parameter. The problem of finding the optimal [Formula: see text] value is very challenging. This paper proposes a novel idea for finding the correct number of groups in a dataset based on data depth. The idea is to avoid the traditional process of running the clustering algorithm over a dataset for [Formula: see text] times and further, finding the [Formula: see text] value for a dataset without setting any specific search range for [Formula: see text] parameter. We experiment with different indices, namely CH, KL, Silhouette, Gap, CSP and the proposed method on different real and synthetic datasets to estimate the correct number of groups in a dataset. The experimental results on real and synthetic datasets indicate good performance of the proposed method.
Model free control of a 2DOF robotic arm using video feedback

In this paper we present our approach on solving the positioning problem for the Tool Center Point (TCP) of a 2 Degrees of Freedom (DOF) robotic arm similar to the way a human solves positioning tasks. The presented method relies on visual feedback and angular positions of the elements and eliminates from the equation the need to know the elements lengths, contrary to classical robotics where positioning is done through the direct and inverse kinematics method. The paper presents hardware, software and the general architecture of the system and also the proposed positioning algorithm. The scope of this research is to expand the area where robotics can be used, toward unstructured environments, where with help of vision systems, robots can successfully complete tasks.
Application of Visual Saliency in the Background Image Cutting for Layout Design

In many poster designs, an image usually will be used as a back-ground image, and text and picture will be carried out on the background image later. For intelligent layout design, cropping a suitable background image should be the first problem to be solved. In this paper, through eye movement experiments, ground truth saliency maps of the posters are obtained. Then, the characteristics of the saliency maps of background images are summarized. The characteristics are mainly the rules of the location and size of the salient areas in the background image. The research found that the salient areas of the poster background images are more concentrated in the upper and middle of the poster image, and they are distributed in an inverted triangle. These rules can cut a more suitable background image for typesetting.
Towards secure and network state aware bitrate adaptation at IoT edge

Abstract Video streaming is critical in IoT systems, enabling a variety of applications such as traffic monitoring and health caring. Traditional adaptive bitrate streaming (ABR) algorithms mainly focus on improving Internet video streaming quality where network conditions are relatively stable. These approaches, however, suffer from performance degradation at IoT edge. In IoT systems, the wireless channels are prone to interference and malicious attacks, which significantly impacts Quality-of-Experience (QoE) for video streaming applications. In this paper, we propose a secure and network-state-aware solution, SASA, to address these challenges. We first study the buffer-level constraint when increasing bitrate. We then analyze the impact of throughput overestimation in bitrate decisions. Based on these results, SASA is designed to consist of both an offline and an online phase. In the offline phase, SASA precomputes the best configurations of ABR algorithms under various network conditions. In the online phase, SASA adopts an online Bayesian changepoint detection method to detect network changes and apply precomputed configurations to make bitrate decisions. We implement SASA and evaluate its performance using 429 real network traces. We show that the SASA outperforms state-of-the-art ABR algorithms such as RobustMPC and Oboe in the IoT environment through extensive experiments.
Finite mixture models are typically inconsistent for the number of components

Scientists and engineers are often interested in learning the number of subpopulations (or components) present in a data set. Practitioners commonly use a Dirichlet process mixture model (DPMM) for this purpose; in particular, they count the number of clusters---i.e. components containing at least one data point---in the DPMM posterior. But Miller and Harrison (2013) warn that the DPMM cluster-count posterior is severely inconsistent for the number of latent components when the data are truly generated from a finite mixture; that is, the cluster-count posterior probability on the true generating number of components goes to zero in the limit of infinite data. A potential alternative is to use a finite mixture model (FMM) with a prior on the number of components. Past work has shown the resulting FMM component-count posterior is consistent. But existing results crucially depend on the assumption that the component likelihoods are perfectly specified. In practice, this assumption is unrealistic, and empirical evidence (Miller and Dunson, 2019) suggests that the FMM posterior on the number of components is sensitive to the likelihood choice. In this paper, we add rigor to data-analysis folk wisdom by proving that under even the slightest model misspecification, the FMM posterior on the number of components is ultraseverely inconsistent: for any finite $k \in \mathbb{N}$, the posterior probability that the number of components is $k$ converges to 0 in the limit of infinite data. We illustrate practical consequences of our theory on simulated and real data sets.
On the stability of the statistical classifier of states of human body systems to distortions in degenerate cases

The methodology and the results of the analysis of the properties of the classifier developed by the authors are described, which are intended for assessing the state of human health according to the results of laboratory analysis of peripheral blood. It is based on the non-linear method of statistical classification, using the polynomial-regression approach and having probabilistic estimates. A study is made of the stability of the method to distortion and of its extrapolation capabilities. Signs that are of constant importance for one of the health classes are considered.
Multi-Task Reinforcement Learning as a Hidden-Parameter Block MDP

Multi-task reinforcement learning is a rich paradigm where information from previously seen environments can be leveraged for better performance and improved sample-efficiency in new environments. In this work, we leverage ideas of common structure underlying a family of Markov decision processes (MDPs) to improve performance in the few-shot regime. We use assumptions of structure from Hidden-Parameter MDPs and Block MDPs to propose a new framework, HiP-BMDP, and approach for learning a common representation and universal dynamics model. To this end, we provide transfer and generalization bounds based on task and state similarity, along with sample complexity bounds that depend on the aggregate number of samples across tasks, rather than the number of tasks, a significant improvement over prior work. To demonstrate the efficacy of the proposed method, we empirically compare and show improvements against other multi-task and meta-reinforcement learning baselines.