Reviewerly/exHarmony huggingface dataset:
Bayesian Domain Randomization for Sim-to-Real Transfer.

When learning policies for robot control, the real-world data required is typically prohibitively expensive to acquire, so learning in simulation is a popular strategy. Unfortunately, such polices are often not transferable to the real world due to a mismatch between the simulation and reality, called 'reality gap'. Domain randomization methods tackle this problem by randomizing the physics simulator (source domain) according to a distribution over domain parameters during training in order to obtain more robust policies that are able to overcome the reality gap. Most domain randomization approaches sample the domain parameters from a fixed distribution. This solution is suboptimal in the context of sim-to-real transferability, since it yields policies that have been trained without explicitly optimizing for the reward on the real system (target domain). Additionally, a fixed distribution assumes there is prior knowledge about the uncertainty over the domain parameters. Thus, we propose Bayesian Domain Randomization (BayRn), a black box sim-to-real algorithm that solves tasks efficiently by adapting the domain parameter distribution during learning by sampling the real-world target domain. BayRn utilizes Bayesian optimization to search the space of source domain distribution parameters which produce a policy that maximizes the real-word objective, allowing for adaptive distributions during policy optimization. We experimentally validate the proposed approach by comparing against two baseline methods on a nonlinear under-actuated swing-up task. Our results show that BayRn is capable to perform direct sim-to-real transfer, while significantly reducing the required prior knowledge.
Semantic Scene Completion from a Single 360-Degree Image and Depth Map

We present a method for Semantic Scene Completion (SSC) of complete indoor scenes from a single 360◦ RGB image and corresponding depth map using a Deep Convolution Neural Network that takes advantage of existing datasets of synthetic and real RGB-D images for training. Recent works on SSC only perform occupancy prediction of small regions of the room covered by the field-of-view of the sensor in use, which implies the need of multiple images to cover the whole scene, being an inappropriate method for dynamic scenes. Our approach uses only a single 360◦ image with its corresponding depth map to infer the occupancy and semantic labels of the whole room. Using one single image is important to allow predictions with no previous knowledge of the scene and enable extension to dynamic scene applications. We evaluated our method on two 360◦ image datasets: a high-quality 360◦ RGB-D dataset gathered with a Matterport sensor and low-quality 360◦ RGB-D images generated with a pair of commercial 360◦ cameras and stereo matching. The experiments showed that the proposed pipeline performs SSC not only with Matterport cameras but also with more affordable 360◦ cameras, which adds a great number of potential applications, including immersive spatial audio reproduction, augmented reality, assistive computing and robotics. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
An Action Research on Metacognitive Reading Strategies Instruction to Improve Reading Comprehension

One of the most important skills that students need to master is reading, yet the dismal results of Malaysian students on international reading assessments suggest that they still lack the necessary skills for reading. An action research was conducted to investigate the effects of instruction in metacognitive reading strategies to aid students’ reading comprehension. Chosen based on convenience sampling, 25 participants in this study were 13-year-olds from a secondary school in Kuala Lumpur. The Metacognitive Awareness of Reading Strategies – Revised (MARSI-R) was the main instrument used to identify the needs of the students and to keep track of their progress. The mean for the overall items and three subscales was calculated to compare the results before and after the interventions. Like previous studies carried out on reading strategies instruction, there was an increase in participants’ awareness of metacognitive reading strategies following the training. While the results were limited to this particular school and could not be generalised to all secondary schools in Kuala Lumpur, English language teachers in secondary schools could consider integrating metacognitive reading instruction into their reading classrooms. Future studies could investigate the metacognitive reading strategies that good and poor readers use. Researchers could also look into developing a handbook of materials for training in such strategies.
Single Image De-Raining via Improved Generative Adversarial Nets

Capturing images under rainy days degrades image visual quality and affects analysis tasks, such as object detection and classification. Therefore, image de-raining has attracted a lot of attention in recent years. In this paper, an improved generative adversarial network for single image de-raining is proposed. According to the principles of divide-and-conquer, we divide an image de-raining task into rain locating, rain removing, and detail refining sub-tasks. A multi-stream DenseNet, termed as Rain Estimation Network, is proposed to estimate the rain location map. A Generative Adversarial Network is proposed to remove the rain streaks. A Refinement Network is proposed to refine the details. These three models accomplish rain locating, rain removing, and detail refining sub-tasks, respectively. Experiments on two synthetic datasets and real world images demonstrate that the proposed method outperforms state-of-the-art de-raining studies in both objective and subjective measurements.
Dietitians and Nutritionists Behaviour on Social Media: A Scoping Literature Review

At its present state, Social Media (SM) is an important stage to promote user participation, acting as an open space for the discussion of a multitude of fields, one of which being health.Professionals, like Registered Nutritionists and Dietitians (RNDs), whose access to media was traditionally more restricted, are also more engaged in this new context, creating a new scenario.To better understand how is this group of professionals using social media to communicate with their audiences is the main objective of this study.To approach this topic, a mapping was conducted, followed by a presentation of the summary of the evidence discovered: RDNs demographic and professional profile; their most used social media tools; the reasons why they use social media; their common behaviours and attitudes, as well as a review of the gaps and shortcomings in the literature.A literature review, using a structured approach was also conducted.2877 works were screened, but only 8 were associated with answers.Of these 8, there were 2 studies that partially presented a quantitative analysis.Results show lacks in consolidated studies that can be used to support the creation of knowledge in this field.This lead to conclude that research about social media usage by nutritionist, at present, remains in a nascent stage and requires further studies.
Tactic Learning and Proving for the Coq Proof Assistant

We present a system that utilizes machine learning for tactic proof search in the Coq Proof Assistant. In a similar vein as the TacticToe project for HOL4, our system predicts appropriate tactics and finds proofs in the form of tactic scripts. To do this, it learns from previous tactic scripts and how they are applied to proof states. The performance of the system is evaluated on the Coq Standard Library. Currently, our predictor can identify the correct tactic to be applied to a proof state 23.4% of the time. Our proof searcher can fully automatically prove 39.3% of the lemmas. When combined with the CoqHammer system, the two systems together prove 56.7% of the library’s lemmas.
A New Pseudo-Spectral Method Using the Discrete Cosine Transform

The pseudo-spectral (PS) method on the basis of the Fourier transform is a numerical method for estimating derivatives. Generally, the discrete Fourier transform (DFT) is used when implementing the PS method. However, when the values on both sides of the sequences differ significantly, oscillatory approximations around both sides appear due to the periodicity resulting from the DFT. To address this problem, we propose a new PS method based on symmetric extension. We mathematically derive the proposed method using the discrete cosine transform (DCT) in the forward transform from the relation between DFT and DCT. DCT allows a sequence to function as a symmetrically extended sequence and estimates derivatives in the transformed domain. The superior performance of the proposed method is demonstrated through image interpolation. Potential applications of the proposed method are numerical simulations using the Fourier based PS method in many fields such as fluid dynamics, meteorology, and geophysics.
Proof-of-principle demonstration of measurement-device-independent quantum key distribution based on intrinsically stable polarization-modulated units

The experimental demonstration of measurement-device-independent quantum key distribution (MDI-QKD) has been widely demonstrated. Thus far, several experimental groups have implemented polarization encoding MDI-QKD but with manual polarization controllers, or polarization modulators that make circular polarization states unstable. Here, we apply an intrinsically stable polarization-modulated unit (PMU) to MDI-QKD so that Alice and Bob can modulate four BB84 polarization states, all of which can be kept stable from even the harsh environment. Moreover, our PMU can provide two operational polarization encoding modes suitable to different application scenarios. A proof-of-principle demonstration of MDI-QKD based on our PMU is implemented with an interference visibility of 46.6%, an average quantum bit error rate of 1.49% for the Z basis and the secure key rate of 4.25 × 10 −6 bits per pulse. The proposed study is helpful for building polarization encoding MDI-QKD systems with better stability.
Effective quantum tunneling from a semiclassical momentous approach

We study the quantum tunnel effect through a potential barrier employing a semiclassical formulation of quantum mechanics based on expectation values of configuration variables and quantum dispersions as dynamical variables. The evolution of the system is given in terms of a dynamical system for which we are able to determine effective trajectories for individual particles, in a total resemblance of the Bohmian description of quantum mechanics. We obtain a type of semiclassical confinement for particles in a similar way as with the quantum potential, and also determine a semiclassical transmission coefficient for the tunneling process.
Helping the Ineloquent Farmers: Finding Experts for Questions With Limited Text in Agricultural Q&amp;A Communities

Nowadays, hundreds of thousands of farmers in China seek online in agricultural Q&A communities, such as Farm-Doctor, for agricultural advice. As in many other Q&A communities, the key design issue is to find experts to provide timely and suitable answers. State-of-the-art approaches often rely on extracting topics from the question texts, however, the major challenge here is that questions in agricultural Q&A communities often contain limited textual information. To solve this problem, in this article, we conduct an extensive measurement on Farm-Doctor, which consists of over 690 thousand questions and over 3 million answers, and we model Farm-Doctor as a heterogeneous information network that incorporates rich side information. We propose a novel approach based on graph neural network to accurately recommend for each question the users that are highly likely to answer it. With an average income of fewer than 6 dollars a day, our method helps these less eloquent farmers with their cultivation and hopefully provides a way to improve their lives.
Towards Realistic Byzantine-Robust Federated Learning

Federated Learning (FL) is a distributed machine learning paradigm where data is decentralized among clients who collaboratively train a model in a computation process coordinated by a central server. By assigning a weight to each client based on the proportion of data instances it possesses, the rate of convergence to an accurate joint model can be greatly accelerated. Some previous works studied FL in a Byzantine setting, where a fraction of the clients may send the server arbitrary or even malicious information regarding their model. However, these works either ignore the issue of data unbalancedness altogether or assume that client weights are known to the server a priori, whereas, in practice, it is likely that weights will be reported to the server by the clients themselves and therefore cannot be relied upon. We address this issue for the first time by proposing a practical weight-truncation-based preprocessing method and demonstrating empirically that it is able to strike a good balance between model quality and Byzantine-robustness. We also establish analytically that our method can be applied to a randomly-selected sample of client weights.
Space of Functions Computed by Deep-Layered Machines

We study the space of functions computed by random-layered machines, including deep neural networks and Boolean circuits. Investigating the distribution of Boolean functions computed on the recurrent and layer-dependent architectures, we find that it is the same in both models. Depending on the initial conditions and computing elements used, we characterize the space of functions computed at the large depth limit and show that the macroscopic entropy of Boolean functions is either monotonically increasing or decreasing with the growing depth.
In-Depth Performance Analysis of SMOTE-Based Oversampling Algorithms in Binary Classification

In the field of machine learning, the problem of class imbalance considerably impairs the performance of classification algorithms. Various techniques have been proposed that seek to mitigate classifier bias with respect to the majority class, with simple oversampling approaches being one of the most effective. Their main representative is the well-known SMOTE algorithm, which introduces a synthetic instances creation mechanism as an interpolation procedure between minority instances. To date, an abundance of SMOTE-based extensions that intend to improve the original algorithm has been proposed. This paper aims to compare the performance of several such extensions. In addition to comparing the overall performance, the impact of the selected oversamplers on the per-class performance is also evaluated. Finally, this paper tries to interpret the obtained performance results with respect to the internal procedures of oversampling algorithms. Some interesting findings have been made in this regard.
Study of PSO and Firefly algorithm based Feed-forward neural network training algorithms

Over the past few years, engineered neural network training algorithms based on nature-inspired algorithms have demonstrated their efficacy in proving their dominance over many traditional algorithms. Using the population-based approach has significantly increased the accuracy of the neural artificial network and converged towards higher accuracy. These approaches are well known to solve various problems in engineering and science. This paper discusses the algorithm and flowchart of the entire NI-algorithm hybridization process and the standard training algorithm for the neural network. The firefly and PSO optimization algorithms are considered in this work for analysis purpose. Such algorithms have different parameters and are therefore also discussed how to correct the parameters for that algorithm. UCI machine learning data set is finally tested for the efficiency of these algorithms. To find the best, the comparative analysis is provided on the results of the above-mentioned algorithms.
Obstacle avoidance and navigation utilizing reinforcement learning with reward shaping

In this paper, we investigate the obstacle avoidance and navigation problem in the robotic control area. For solving such a problem, we propose revised Deep Deterministic Policy Gradient (DDPG) and Proximal Policy Optimization algorithms with an improved reward shaping technique. We compare the performances between the original DDPG and PPO with the revised version of both on simulations with a real mobile robot and demonstrate that the proposed algorithms achieve better results.
The evaluation of university’s impact on «human resource potential» of alumni

This paper considers university educational environment to be an important factor in the development of students' organizational and managerial competence. The authors discuss the approaches to the educational environment of the university, analyze its parameters and characteristics and study the connection between the developmental educational environment and a high level of managerial competence attained by technical university graduates. The authors rely on the environmental approach which allows them to assess the impact of the educational environment on students' organizational and managerial competence taking into account the analysis of empirical data obtained by monitoring methods of sociological research. This method also made it possible to improve self-organization and self-management skills of students and to achieve a high level of resilience both on the environmental and personal level.
Recognition Aid for Visually Challenged to Make Out Indoor Environment

The applications that are capable of identifying the tangible products and the movement of those product has attained a predominant position in the field of robotics. The complexity in identifying the changes going around is very high on the indoor environments that are too messy. Segregating things and sorting objects in such messy environment becomes even more tedious and challenging for the people with visual disorders. To subdue these issues and enable the blind and the visually challenged to be aware of the changes or the tangible objects they come across in the indoor environment, the proffered method in the paper devise a recognition aid that is empowered with the deep learning neural networks. The usual conventional-CNN is refurbished by upgrading the components to achieve a better accuracy in recognizing. The images based on different scenes of the indoor environment gathered under different circumstance where used as training and the testing dataset for the proffered model and the accuracy and the recognition rate for the training as well as testing was examined.
Distributed Target Tracking in Challenging Environments Using Multiple Asynchronous Bearing-Only Sensors

In the multiple asynchronous bearing-only (BO) sensors tracking system, there usually exist two main challenges: (1) the presence of clutter measurements and the target misdetection due to imperfect sensing; (2) the out-of-sequence (OOS) arrival of locally transmitted information due to diverse sensor sampling interval or internal processing time or uncertain communication delay. This paper simultaneously addresses the two problems by proposing a novel distributed tracking architecture consisting of the local tracking and central fusion. To get rid of the kinematic state unobservability problem in local tracking for a single BO sensor scenario, we propose a novel local integrated probabilistic data association (LIPDA) method for target measurement state tracking. The proposed approach enables eliminating most of the clutter measurement disturbance with increased target measurement accuracy. In the central tracking, the fusion center uses the proposed distributed IPDA-forward prediction fusion and decorrelation (DIPDA-FPFD) approach to sequentially fuse the OOS information transmitted by each BO sensor. The track management is carried out at local sensor level and also at the fusion center by using the recursively calculated probability of target existence as a track quality measure. The efficiency of the proposed methodology was validated by intensive numerical experiments.
Local algebraic effect theories

Algebraic effects are computational effects that can be described with a set of basic operations and equations between them. As many interesting effect handlers do not respect these equations, most approaches assume a trivial theory, sacrificing both reasoning power and safety. We present an alternative approach where the type system tracks equations that are observed in subparts of the program, yielding a sound and flexible logic, and paving a way for practical optimizations and reasoning tools.
Longitudinal Evaluation of Open-source Software Maintainability

We present a longitudinal study on the long-term evolution of maintainability in open-source software. Quality assessment remains at the forefront of both software research and practice, with many models and assessment methodologies proposed and used over time. Some of them helped create and shape standards such as ISO 9126 and 25010, which are well established today. Both describe software quality in terms of characteristics such as reliability, security or maintainability. An important body of research exists linking these characteristics with software metrics, and proposing ways to automate quality assessment by aggregating software metric values into higher-level quality models. We employ the Maintainability Index, technical debt ratio and a maintainability model based on the ARiSA Compendium. Our study covers the entire 18 year development history and all released versions for three complex, open-source applications. We determine the maintainability for each version using the proposed models, we compare obtained results and use manual source code examination to put them into context. We examine the common development patterns of the target applications and study the relation between refactoring and maintainability. Finally, we study the strengths and weaknesses of each maintainability model using manual source code examination as the baseline.
Portalware: A Smartphone-Wearable Dual-Display System for Expanding the Free-Hand Interaction Region in Augmented Reality

Free-hand manipulation in smartphone augmented reality (AR) enables users to directly interact with virtual contents using their hands. However, human hands can ergonomically move in a broader range than a smartphone's field of view (FOV) can capture, requiring users to be aware of the limited usable interaction and viewing regions at all times. We present Portalware, a smartphone-wearable dual-display system that expands the usable interaction region for free-hand manipulation and enables users to receive visual feedback outside the smartphone's view. The wearable is a lightweight, low-cost display that shares the same AR environment in real-time with the smartphone.This setup empowers AR applications such as mid-air drawing and object manipulation by providing a 180-degree horizontal interaction region in front of the user. Other potential applications include wearing the smartphone like a pendant while using Portalware to continue interacting with AR objects. Without having to hold their phone up with their hand, users can benefit from resting their arms as needed. Finally, we discuss usability explorations, potential interactions, and future plans for empirical studies.