Reviewerly/exHarmony huggingface dataset:
Asynchronous Methods for Model-Based Reinforcement Learning.

Significant progress has been made in the area of model-based reinforcement learning. State-of-the-art algorithms are now able to match the asymptotic performance of model-free methods while being significantly more data efficient. However, this success has come at a price: state-of-the-art model-based methods require significant computation interleaved with data collection, resulting in run times that take days, even if the amount of agent interaction might be just hours or even minutes. When considering the goal of learning in real-time on real robots, this means these state-of-the-art model-based algorithms still remain impractical. In this work, we propose an asynchronous framework for model-based reinforcement learning methods that brings down the run time of these algorithms to be just the data collection time. We evaluate our asynchronous framework on a range of standard MuJoCo benchmarks. We also evaluate our asynchronous framework on three real-world robotic manipulation tasks. We show how asynchronous learning not only speeds up learning w.r.t wall-clock time through parallelization, but also further reduces the sample complexity of model-based approaches by means of improving the exploration and by means of effectively avoiding the policy overfitting to the deficiencies of learned dynamics models.
Psychological characteristics of educational stress of modern students

The article presents the results of the empirical research of psychological characteristics of stress reactions that occur during educational activities of modern Ukrainian students. The analysis was performed on a group of 600 students (including 316 university students, 284 students of vocational schools; 283 men and 317 women) aged 15-45 years. Common and distinctive characteristics as for importance, levels, forms, dynamics and stress management techniques are determined for students of universities and vocational secondary schools. The conclusions are made about the need of organization of specifically targeted psychological work with students having high manifestations of educational and examination stress.
Macro-Action-Based Deep Multi-Agent Reinforcement Learning

In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations. Macro-Action Decentralized Partially Observable Markov Decision Processes (MacDec-POMDPs) provide a general framework for asynchronous decision making under uncertainty in fully cooperative multi-agent tasks. However, multi-agent deep reinforcement learning methods have only been developed for (synchronous) primitive-action problems. This paper proposes two Deep Q-Network (DQN) based methods for learning decentralized and centralized macro-action-value functions with novel macro-action trajectory replay buffers introduced for each case. Evaluations on benchmark problems and a larger domain demonstrate the advantage of learning with macro-actions over primitive-actions and the scalability of our approaches.
A framework to manage reluctance to bad news reporting on software projects in state universities in Zimbabwe

Failure of IT projects has risen to become an eyesore for most organisations. This is often attributed to the ‘mum effect’- an individual’s reluctance to report the exact position of troubled software projects. With the increasing digitalization of operations by most State Universities in Zimbabwe, the mum effect has the potential to rise to exponential proportions. Guided by the Design Science approach data was gathered from an intensity sample of 15 participants comprising 7 software project team members, 3 ICT project managers, and 5 users of the system drawn from three Zimbabwean state universities. The data collection was done using key informant interviews and focus group discussions. Data were analysed using thematic analysis and NVivo (version 11) software was used to store, organise, and code data transcripts. Factors suppressing Bad News Reporting were grouped into three broad categories which included organisational factors, personal factors, and situational factors. The findings suggest the need to incorporate these multi-level factors in the design and implementation of software projects if they are to be efficacious. This study recommends the establishment of clear channels of communication to manage bad news reporting and creating formal structures that function outside the traditional organisational hierarchy to convey information regarding anomalies. This study contributes to practice by providing appropriate interventions based on empirical evidence based on the centrality of software project team members’ insights, experiences as well as practices.
Coaching & Enfermagem: Uma Análise Qualitativa da Literatura

Coaching is an interactive and interpersonal process that offers personal and professional support through the acquisition of knowledge, skills and fundamental actions for professional practice. Objective: to analyze in scientific production the use of coaching in nursing development. Method: Literature review, supported by exploratory research in databases: EBSCO | Proquest | RCAAP. 13 articles selected, submitted to the thematic analysis. Results: coaching emerges as a strategy applied by nurses in different areas: education/training, management, care and as the subject of research studies. Three coaching types were identified in the articles: peer, health and interprofessional. Conclusions: in Nursing it can be mobilized in different areas, offering an opportunity and a challenge for nurses|coaches, for their clients (patients|peers|other professionals), promoting quality improvement at individual level, level of teaching and health institutions.
Smart Feature Fusion and Model for Human Detection

Extraction of discriminate and accurate features is challenging to precise the statistical data on monitoring people. It still remains an active research due to various variations as inter class and intra class, lighting challenge, static and dynamic occlusion. To tackle this variation and occlusion issue, this paper proposes to combine the differential gradient and statistical Tamura features with joint histogram. In addition, these extracted smart features use actually to detect people by using the gradient feature descriptor and a statistical feature detector. The model fusion of human detection creates by combining two models result (Grammar model and Poselet model) with the adaptive threshold weighted non-maximum suppression algorithm. The system presents a powerful fusion insight to capture the stronger occlusion parts and several variations of the foreground people. To compare the performance with the state of the arts, the public Pascal VOC 2007 Dataset is used. The outperformed result of this work proofs our concern.
Edugame Application as Vocabulary Learning Media for Deaf Children

Reading activity has tremendous impacts and benefits in sharpening and increasing one's intelligence.Each individual is entitled to the opportunity to be able to read, both those who are normal and those who have disabilities, such as the one with hearing impairment or deaf.Hearing impediment often causes a setback or even loss of speech because the sufferer has a deficiency or loss of hearing ability either partial or total.The problem encountered by the deaf person breeds the idea of developing learning applications for people with disabilities especially children.This research aims to make it easier for children to understand and foster vocabulary acquisition as well as to develop children's motivation in the learning process as this learning media is designed with an attractive and interactive interface.Edugame application which emphasized an android-based visual form was established as a support to the development of children's speech and reading skills.The development method used in designing this application is the prototyping model which consists of the needs analysis, prototype design, prototype evaluation, program writing/coding, program testing, program evaluation, and implementation.Edugame as vocabulary learning media for deaf children promotes an interesting and interactive learning process to improve children's understanding of words in learning vocabulary.The results of the black box testing, pre-test and post-test, paired sample t-test, SUS, and Aiken V revealed that all the results were valid, so it could be concluded that there were significant differences (real) before and after the use of the application in the learning process.
Linguistic Summaries of Graph Databases in Customer Relationship Management (CRM)

The paper concentrates on data models that differ from the traditional relational one by Codd (1970). In particular, we are interested in processing graph databases (graph datasets) without any pre-configured structure, in which graph nodes may represent different objects and graph edges – relations between them. In this approach, the linguistic summarization methods for graph datasets are introduced, and differences for these methods with respect to traditional relational approach are shown, commented and improved in comparison to the preceding proposition (Strobin, Niewiadomski, 2016). The novelty of the paper is mostly the new form for summaries: Multi-Subject linguistic summaries of graph databases, previously introduced for relational databases (Superson, 2018).
Suspense in Short Stories is Predicted By Uncertainty Reduction over Neural Story Representation.

Suspense is a crucial ingredient of narrative fiction, engaging readers and making stories compelling. While there is a vast theoretical literature on suspense, it is computationally not well understood. We compare two ways for modelling suspense: surprise, a backward-looking measure of how unexpected the current state is given the story so far; and uncertainty reduction, a forward-looking measure of how unexpected the continuation of the story is. Both can be computed either directly over story representations or over their probability distributions. We propose a hierarchical language model that encodes stories and computes surprise and uncertainty reduction. Evaluating against short stories annotated with human suspense judgements, we find that uncertainty reduction over representations is the best predictor, resulting in near human accuracy. We also show that uncertainty reduction can be used to predict suspenseful events in movie synopses.
On Learning Language-Invariant Representations for Universal Machine Translation

The goal of universal machine translation is to learn to translate between any pair of languages, given a corpus of paired translated documents for \emph{a small subset} of all pairs of languages. Despite impressive empirical results and an increasing interest in massively multilingual models, theoretical analysis on translation errors made by such universal machine translation models is only nascent. In this paper, we formally prove certain impossibilities of this endeavour in general, as well as prove positive results in the presence of additional (but natural) structure of data. For the former, we derive a lower bound on the translation error in the many-to-many translation setting, which shows that any algorithm aiming to learn shared sentence representations among multiple language pairs has to make a large translation error on at least one of the translation tasks, if no assumption on the structure of the languages is made. For the latter, we show that if the paired documents in the corpus follow a natural \emph{encoder-decoder} generative process, we can expect a natural notion of ``generalization'': a linear number of language pairs, rather than quadratic, suffices to learn a good representation. Our theory also explains what kinds of connection graphs between pairs of languages are better suited: ones with longer paths result in worse sample complexity in terms of the total number of documents per language pair needed. We believe our theoretical insights and implications contribute to the future algorithmic design of universal machine translation.
Shoppers Stop: Leveraging Social Media for Employer Branding

Shoppers Stop had a significant presence on social media platforms—LinkedIn, Facebook, Twitter and YouTube. However, a large number of Shoppers Stop’s customers posted complaints about deficiency in customer service on these social media platforms, mainly on Facebook. The complaints and ratings were also seen by other stakeholders such as potential employees who form a perception about the company’s employer brand based on these ratings. While Shoppers Stop promptly responded to disgruntled customers on Facebook to redress their complaints, Shoppers Stop was uncertain about the possible adverse impact of these customer reviews on the company’s employer brand. Further, Shoppers Stop wanted to leverage its presence on various social media platforms to promote and manage its employer brand.
Use The Positive Selection Algorithm and The Negative Selection Algorithm in The English Character Recognition

In this paper, I use some artificial intelligence techniques algorithms, which are the algorithms of the artificial immune system (positive selection algorithm and negative selection algorithm).Through these two algorithms, it is distinguished between the original and distorted English letters.Through the results of the patterns, which were obtained after the implementation of these two algorithms, I observed that the percentage of discrimination was 85% for the positive selection algorithm, and 80% for the negative selection algorithm.This discrimination was applied after the images had been divided into blocks, and then the properties of the images had been extracted by adopting the method of seven undependable momentums in order to gain patterns which are close to the original pattern of the introduced English letter.
Content Noise Detection Model Using Deep Learning in Web Forums

Spam posts in web forum discussions cause user inconvenience and lower the value of the web forum as an open source of user opinion. In this regard, as the importance of a web post is evaluated in terms of the number of involved authors, noise distorts the analysis results by adding unnecessary data to the opinion analysis. Here, in this work, an automatic detection model for spam posts in web forums using both conventional machine learning and deep learning is proposed. To automatically differentiate between normal posts and spam, evaluators were asked to recognize spam posts in advance. To construct the machine learning-based model, text features from posted content using text mining techniques from the perspective of linguistics were extracted, and supervised learning was performed to distinguish content noise from normal posts. For the deep learning model, raw text including and excluding special characters was utilized. A comparison analysis on deep neural networks using the two different recurrent neural network (RNN) models of the simple RNN and long short-term memory (LSTM) network was also performed. Furthermore, the proposed model was applied to two web forums. The experimental results indicate that the deep learning model affords significant improvements over the accuracy of conventional machine learning associated with text features. The accuracy of the proposed model using LSTM reaches 98.56%, and the precision and recall of the noise class reach 99% and 99.53%, respectively.
Uneven Image Dehazing by Heterogeneous Twin Network

Removing the haze in an image is a huge challenge due to the difficulty of accurate hazy image modeling. Although the atmospheric scattering model (ASM) is widely used to describe the formation of hazy images, it is hard to deal with the uneven haze image, once the ASM is restricted with the assumption that the atmosphere distributed homogeneously. This paper analyzes the haze imaging mechanism, then proposes an image-to-image architecture to handle the uneven image dehazing, in which a heterogeneous twin network (HT-Net) with two parallel sub-networks are constructed to establish the high dimensional nonlinear mapping model between the hazy and clean images. Consequently, the inhomogeneous haze is removed by the symmetric U-shape network with encoder-decoder structure, meanwhile, the other enhancement network extracts the high-frequency feature from the hazy image to compensate the edge and texture of the object. The effectiveness is validated by the experiments based on three real haze image datasets which depict the same visual content recorded in haze-free and hazy conditions, under the same illumination parameters. One professional uneven haze image dataset is found in the real environment and covers 190 types of scene and 21975 uneven images accordingly. This dataset includes thin, heavy, and uneven haze images. The other two benchmark datasets are I-HAZE and O-HAZE, respectively including 35 pairs of indoor real haze and haze-free (ground-truth) images and 45 different outdoor scenes. Extensive experimental results demonstrate that the proposed method in this paper can remove the haze and achieve superior performance over the other mentioned methods.
Настройка и обучение многослойного персептрона для задачи выделения дорожного покрытия на космических снимках города

The paper considers the neural network model application for a multi-layer perceptron to identifying road surface region problems on the urban environment satellite images.Government agencies and enterprises involved in regulating transport flows currently need to solve this problem, as well as to update geographical data and maps of transport infrastructure.In existing works on this topic, there are automatic and semi-automatic methods.Approaches that involve a person's partial involvement in their work are semi-automatic.The operator can set thresholds, setting parameters, mark regions for detection, and do many other operations.Automatic methods work without human involvement and therefore faster and cheaper than semiautomatic ones.
Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression and Semi-Supervised Domain Adaptation

A novel algorithm to estimate instance-level future motion (FM) in a single image is proposed in this paper. First, the FM of an instance is defined with its direction, speed, and action classes. Then, a deep neural network, called FM-Net, is developed to determine the FM of the instance. More specifically, the multi-context pooling layer is proposed to exploit both object and global context features, and the cyclic ordinal regression scheme is developed using binary classifiers for effective FM classification. Also, the proposed FM-Net is trained in a semi-supervised domain adaptation setting to obtain reliable FM estimation results, even when a source domain in the training process and a target domain in the inference process are different. Extensive experimental results demonstrate that the proposed algorithm provides remarkable performance and thus can be used effectively for computer vision applications, including single object tracking, multiple object tracking, and crowd analysis. Furthermore, the FM dataset, collected from diverse sources and annotated manually, is released as a benchmark for single-image FM estimation.
Testable hypotheses by Isaac Newton on particle physics

Three hundred years ago, Isaac Newton published a number of hypotheses on the structure of matter, which were ahead of their time by some two centuries. Speculations were made by Newton that may now be interpreted as precursors to fundamental elements of quantum mechanics and quantum field theory. General features of the layered structure of matter that is now known to exist in the form of nucleons, nuclei, atoms, molecules, and macromolecules were successfully predicted, and hypotheses on self-similarity, simplicity, and purpose were made. In this essay, Newton’s hypotheses are examined in the light of current understanding of matter at the subnucleonic scale. It is found that his hypotheses of self-similarity, simplicity, and purpose raise questions for the quarks and gluons of the current Standard Model (SM), but that various precursors to the SM are more compliant. Experimental tests of the precursors using the Large Hadron Collider and the proposed Large Hadron Electron Collider at CERN are described that could resolve the situation. In addition, it is suggested that Newton’s hypotheses could serve as the basis for the formulation of one or more “postulates of particle physics” comparable to the postulates on which Einstein based his theories of relativity a century ago.
Formal Modelling of FHIR Based, Medical Data Exchange Using Algebraic Petri Nets.

Enabling interoperability is a challenging task in medical data exchange and is often addressed by the use of versatile communication standards like HL7 FHIR. Although daily routine and scientific experiences show its suitability, the use comes with major risks regarding safety and security of data. To overcome these problems, we present an approach to enable a formal verification of medical communication use cases. We identified the Neonatal Screening as a practical example in which two process participants (physician and screening laboratory) are involved. We analyzed the FHIR specification as well as identified necessary resources in that context and formally modeled them as algebraic specifications. By that, we were able to represent the participants' behavior and data flow with help of Algebraic Petri Nets. This strategy allows to formally verify the correctness of a system by specified requirements regarding data safety and data security.
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization

Bayesian optimization (BO) is a sample-efficient global optimization algorithm for black-box functions which are expensive to evaluate. Existing literature on model based optimization in conditional parameter spaces are usually built on trees. In this work, we generalize the additive assumption to tree-structured functions and propose an additive tree-structured covariance function, showing improved sample-efficiency, wider applicability and greater flexibility. Furthermore, by incorporating the structure information of parameter spaces and the additive assumption in the BO loop, we develop a parallel algorithm to optimize the acquisition function and this optimization can be performed in a low dimensional space. We demonstrate our method on an optimization benchmark function, as well as on a neural network model compression problem, and experimental results show our approach significantly outperforms the current state of the art for conditional parameter optimization including SMAC, TPE and Jenatton et al. (2017).
Correcting lateral chromatic aberration using orthogonal fringe patterns

In this paper, we analyzed the distribution of lateral chromatic aberration (LCA) and proposed a high-accuracy and flexible LCA correction method by using orthogonal fringe patterns displayed on a liquid crystal display (LCD) screen. In the proposed method, red, green, and blue orthogonal fringe patterns are consecutively displayed on the LCD screen. These fringe patterns are captured by a color camera from the front viewpoint for calculating the absolute phase maps. Then a pixel deviation matrix (PDM) of the LCA is determined by comparing the obtained phase maps of three channels. Finally, the LCA among the red, green, and blue channels of the color camera is corrected through the obtained PDM and the interpolation method in an application stage. Both simulated and real experiments verified the validity of the proposed method.