Reviewerly/exHarmony huggingface dataset:
Unmanned aerial vehicle design for pressure washing building facades in Lima Metropolitan Area using hydrogen fuel cell

The following paper has the purpose of presenting an innovative solution for building facade cleaning using an unmanned aerial vehicle (UAV) that employs pressure washing technique and hydrogen fuel cell (HFC) technology to enhance flight autonomy. Indeed, the main design stages are both stated and explained within the paper. However, the design is separated in two stages: Conceptual and Preliminary. Thus, it begins with the research statement, state of the art revision and conceptual design proposal. Then, it continues with the development of the propulsion system, which uses a proposed recursive methodology for estimating, selecting, and then confirming all the inter-dependent elements such as motors, propellers, fuel cell and hydrogen amount. As the drone is treated as a mechatronic machine, the following design explores certain domains that help build it, such as: Mechanical, Electrical and Control. The Mechanical domain emphasizes on frame design, material selection, resistance calculations, pressure washing gun recoil analysis, and finite element and aerodynamic simulations. The Electrical domain focuses on component selection and power consumption verification while the last domain proposes flow diagrams and a PID control diagram based on an hexacopter mathematical modelling. Finally, the main flight parameters are simulated on SIMULINK, obtaining appropriate stabilizing and response times according to limitations cited.
Attracting private capital for development: Are poorer countries less efficient?

Abstract According to common measures of private capital mobilization, such as the shares of foreign direct and portfolio investment in GDP, many low-income countries perform poorly in attracting private capital flows. However, these indicators do not account for differences in economic characteristics across countries, and thus cannot provide insights on countries’ performance relative to their potential levels of private capital flows as determined by their domestic economic structure and constraints. This paper addresses this gap by using nonparametric data envelopment analysis (DEA) to empirically calculate the efficient frontier for private capital mobilization as a function of countries’ domestic enabling environment, and then assess each country’s performance relative to that of other countries featuring similar domestic conditions. Our results reveal that many low-income countries, particularly in Africa and South Asia, which do not rank high according to achieved levels of private capital flows, are in fact performing on or very close to the efficient frontier. This indicates that these countries are good performers given the limited resources available to them. Tracking the performance of countries and regions over time, we furthermore find that sub-Saharan Africa experienced the strongest increases in DEA efficiency scores between 2007 and 2018, indicating that various countries in this region were catching up with the frontier.
NPNT: Non-oscillating Process Negation Transformation of mass functions and a negation-based discounting method in information fusion

Yager proposes a Probability Negation Transformation (PNT) based on the maximum entropy distribution. After continuous PNTs, the Probability Mass Function (PMF) reaches the maximum entropy distribution, i.e., an uniform distribution. In this paper, we propose a new negation transformation method based on Belief Evolution Network (BEN) and Disjunctive Combination Rule (DCR), called Non-oscillating Process Negation Transformation (NPNT). As a cognitive negation transformation, NPNT expresses the negation from known to unknown. The Full Causality Probability Transformation (FCPT) is used to connect the PMF and Basic Belief Assignment (BBA) under NPNT. Based on the proposed method, we extend the Shafer’s discounting method from the perspective of DCR, which realizes less commitment under a same discount coefficient.
Sample efficient transfer in reinforcement learning for high variable cost environments with an inaccurate source reward model

Here we propose an algorithm that combines two classic ideas, transfer learning and temporal abstraction, to accelerate learning in high variable cost environments (HVC-envs). In an HVC-env, each sampling of the environment incurs a high cost, thus methods to accelerate learning are sought to reduce the incurred cost. Transfer learning can be useful for such environments by using prior knowledge from a source environment. As only a small number of samples can be collected from an HVC-env due to high sampling cost, learning becomes challenging when the source environment provides inaccurate rewards. To overcome this challenge we propose a simple but effective way of creating useful temporally extended actions from an inaccurate physics guided model (PGM) that acts as the source task. At first we address this issue theoretically by providing performance bounds between two semi-Markov Decision Processes (SMDPs) with different reward functions. Later we develop two benchmark HVC-envs where learning must happen using a small number of real samples (often on the order of ~ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> or 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). Finally we show that it is possible to obtain sequential high rewards in both of these environments using ~ 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> real samples by leveraging knowledge from PGMs with inaccurate reward models.
Aerial Interception of Non-Cooperative Intruder using Model Predictive Control

Autonomous capture of an unknown non-cooperative aerial target is a complex task requiring real-time target localization, trajectory prediction and control. The current work presents an estimation and control system for a quadrotor in order to track and grab a dynamic aerial target. A Kalman filter is used to estimate and predict the target’s pose in real time, which is further used as reference by a Model Predictive Controller for tracking and grabbing the target while chasing it. The efficacy of the proposed controller is demonstrated through repeated trials with varying initial conditions and target maneuvers. The controller is compared with a conventional PD controller. The accuracy of estimation algorithms and the ability of the proposed controller to neutralize the target is demonstrated by means of simulations in gazebo.
Power Management for Noise Aware Path Planning of Hybrid UAVs

Here we consider the problem of path planning for a hybrid fuel Unmanned Aerial Vehicle (UAV), equipped with both a battery and gasoline generator as two sources of energy. Routing the vehicle while considering this setup adds fuel constraints in addition to requiring the power source state to be specified along each edge along the path. We further add noise restrictions to the problem where certain edges (corresponding to certain portions of the path) have the restriction that the gasoline generator cannot be run. A solution to this path planning problem entails both a sequence of nodes, being the plain path the vehicle travels, and a sequence of values specifying the power source along each edge in the path. There is no work in the current literature regarding such a problem, with little at all concerning general planning or routing for hybrid fuel UAVs. Here, hybrid fuel UAV routing problem with noise restrictions is presented. The problem is first formulated as an Mixed Integer Linear Program. Then a dynamic programming algorithm is presented to solve the problem exactly. This algorithm is then compared to a branch-and-cut based method in terms of computational times for a variety of problem sizes. A case study is presented, using a graph which location data from an area surrounding an airport to produce goal nodes and noise restrictions to display the problem itself as well as the performance of the dynamic programming algorithm in a realistic scenario for this problem.
Constraint Management For Quadcopter Drones: Reference Governor-Based Approaches

This paper presents two constraint management strategies based on Reference Governors (RG) to enforce output and state constraints on nonlinear dynamics of quadcopter drones. The first solution, referred to as the nonlinear decoupled reference governor (NL-DRG), is based on the sequential application of three bisection searches and online numerical simulations to find constraint-aware reference commands for each of the pitch, roll, and yaw channels. While NL-DRG performs well, it relies on online simulations and is thus computationally demanding. For applications where a smaller computational footprint is desired, we propose a second solution, which we refer to as the modified reference governor (M-RG). This solution consists of the sequential application of three scalar reference governors based on linear prediction models, each robustified to account for the worst-case linearization error and coupling behavior. The M-RG can guarantee constraint satisfaction for the quadcopter dynamics while maintaining the highly-attractive computational features of scalar reference governors. However, the performance of M-RG may be more conservative than that for NL-DRG, so a trade-off between computation and performance can be made. Finally, a quantitative comparison between all the methods is presented in terms of performance and computation time.
Quasi-Newton Iteration in Deterministic Policy Gradient

This paper presents a model-free approximation for the Hessian of the performance of deterministic policies to use in the context of Reinforcement Learning based on Quasi-Newton steps in the policy parameters. We show that the approximate Hessian converges to the exact Hessian at the optimal policy, and allows for a superlinear convergence in the learning, provided that the policy parametrization is rich. The natural policy gradient method can be interpreted as a particular case of the proposed method. We analytically verify the formulation in a simple linear case and compare the convergence of the proposed method with the natural policy gradient in a nonlinear example.
Cooperative Filtering and Parameter Estimation for Polynomial PDEs using a Mobile Sensor Network

In this paper, a constrained cooperative Kalman filter is developed to estimate field values and gradients along trajectories of mobile robots collecting measurements. We assume the underlying field is generated by a polynomial partial differential equation with unknown time-varying parameters. A long short-term memory (LSTM) based Kalman filter, is applied for the parameter estimation leveraging the updated state estimates from the constrained cooperative Kalman filter. Convergence for the constrained cooperative Kalman filter has been justified. Simulation results in a 2-dimensional field are provided to validate the proposed method.
Guaranteeing Safety via Active-Set Invariance Filters for Multi-Agent Space Systems with Coupled Dynamics

As space operations increase both in number and complexity, there exists a growing need for mission automation with provable safety guarantees. In this paper, we provide a formulation of the vehicle routing problem with safety guarantees for a multi-spacecraft inspection mission, where multiple "deputy" spacecraft inspect a passive "chief" spacecraft. We ensure safety through a pair of optimization-based filters: the explicit and the implicit active set-invariance filter. For each filter, we derive conditions that guarantee each deputy simultaneously satisfies constraints regarding chief collision avoidance, other deputy collision avoidance, a maximum relative distance from the chief, sun avoidance, and a distance-based maximum relative velocity. Numerical simulations demonstrate the computational efficiency of both filters, indicating the practicality of incorporating either one in real-world applications.
QoE-Driven Optimization in 5G O-RAN-Enabled HetNets for Enhanced Video Service Quality

Many innovative applications are projected to be supported by 5G networks across three verticals: enhanced mobile broadband, ultra-reliable low-latency communication, and massive machine-type communication. Given the constraints of the current radio access networks (RANs), accommodating all these applications, considering their quality of service and quality of experience (QoE) requirements is not practical. OpenRAN is a new architecture touted as the most viable next-generation RAN solution. It promotes a software-defined component, the RAN intelligent controller (RIC), which governs and supplies intelligence to optimize radio resource allocation, implement handovers, manage interference, and balance load between cells. RIC has two parts: non-real-time (non-RT) and near-RT. This article introduces a novel QoE enhancement function (QoE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F) xApp to enhance the functionality of a near-RT RIC through providing efficient resource provisioning to users requesting high-resolution video services. It deploys an innovative adaptive genetic algorithm to perform optimal user association along with resource and power allocation in HetNets. Simulation results demonstrate superior QoE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> F xApp performance in terms of VMAF and MoS for two different resolution videos and diverse numbers of users.
Implementation of an Quantum Circuit Simulator using Classical Bits

The quantum computer has attracted much attention since it can reduce running time of many tasks such as factorization and search compared to classical computers. However, the actual quantum computer is difficult to get access, and the classical computer has a problem of calculation time and computation cost grows exponentially with increasing the number of qubits. In this paper, we address an efficient quantum circuit simulator including quantum circuit emulation hardware and its software framework using classical bit. Also, the proposed quantum circuit simulator operates the same as the conventional quantum software framework and shows a fast operation speed.
From computer vision to short text understanding: Applying similar approaches into different disciplines

With the development of IoT and 5G technologies, more and more online resources are presented in trendy multimodal data forms over the Internet. Hence, effectively processing multimodal information is significant to the development of various online applications, including e-Iearning and digital health, to just name a few. However, most AI-driven systems or models can only handle limited forms of information. In this study, we investigate the correlation between natural language processing (NLP) and pattern recognition, trying to apply the mainstream approaches and models used in the computer vision (CV) to the task of NLP. Based on two different Twitter datasets, we propose a convolutional neural network based model to interpret the content of short text with different goals and application backgrounds. The experiments have demonstrated that our proposed model shows fairly competitive performance compared to the mainstream recurrent neural network based NLP models such as bidirectional long short-term memory (Bi-LSTM) and bidirectional gate recurrent unit (Bi-GRU). Moreover, the experimental results also demonstrate that the proposed model can precisely locate the key information in the given text.
Hardware-Friendly Logarithmic Quantization with Mixed-Precision for MobileNetV2

In a variety of computer vision applications, convolutional neural networks (CNNs) have achieved excellent accuracy. However, in order for a CNN to operate on embedded platforms such as mobile devices, hardware resources and power consumption must be reduced. Accordingly, research involving the application of low-precision quantization to lightweight networks, such as MobileNet, has attracted considerable attention. In particular, compared to linear quantization, logarithmic quantization can significantly reduce hardware resources by processing multiplication operations as addition operations when implementing a hardware accelerator. In this study, we propose a novel logarithmic weight quantization considering the characteristics of MobileNetV2, which is known to be notoriously difficult to quantize, and a mixed-precision quantization that minimizes accuracy loss by training the distribution range using the trainable parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> , Experimental results show that the proposed method achieves accuracies greater than 1.47% and 2% on the CIFAR-10 and Tiny-ImageNet datasets, respectively, compared to the general log-scale quantization methods. As a result, the proposed method achieves a significant hardware resource reduction with only a slight degradation in performance when compared to the full precision (i.e., FP32), and achieves an additional power reduction effect of about 48% compared to linear scale quantization at the same precision.
Irrelevant Pixels are Everywhere: Find and Exclude Them for More Efficient Computer Vision

Computer vision is often performed using Convolutional Neural Networks (CNNs). CNNs are compute-intensive and challenging to deploy on power-contrained systems such as mobile and Internet-of-Things (IoT) devices. CNNs are computeintensive because they indiscriminately compute many features on all pixels of the input image. We observe that, given a computer vision task, images often contain pixels that are irrelevant to the task. For example, if the task is looking for cars, pixels in the sky are not very useful. Therefore, we propose that a CNN be modified to only operate on relevant pixels to save computation and energy. We propose a method to study three popular computer vision datasets, finding that 48% of pixels are irrelevant. We also propose the focused convolution to modify a CNN's convolutional layers to reject the pixels that are marked irrelevant. On an embedded device, we observe no loss in accuracy, while inference latency, energy consumption, and multiply-add count are all reduced by about 45%.
Performance of different KNN models in prediction english language readability

Assessing the readability of English, a universal language, is important in terms of meeting readers at different reading levels with texts at their own level. Presenting texts to readers at their own level will help them develop their learning, comprehension and reading capacities. In this study, a data set collected from BBC news was used to predict the readability of the English language. The data set consists of 17724 different sentences. Different k-nearest neighbor (KNN) models were used to predict the readability of English sentences. These models are basic KNN, two different weighted KNN and KNN base random subspace ensembles. KNN base random subspace ensemble has obtained superior results compared to other KNN models. KNN base random subspace ensemble accuracy was 0.9749 and f1-score 0.9692.
A Numerical Approach for Solving the Inversion Problem for n-Trailer Systems

Planning feasible trajectories is a necessity for developing autonomous vehicles. This especially becomes a difficult task when arbitrary trailers are involved. This paper proposes a sequential approach to solve the inversion problem for the so-called n-trailer systems. The idea is to specify the movement of the rearmost axle and convert the vehicle chain into concatenated axle pairs. Starting from the rearmost axle, the hitching angles and their time derivatives can be computed using geometric relations as well as either numerical differentiation or integration depending on the hitching type. The algorithm constructs the vehicle motion trajectories and the required control signals. Additionally, a method for generating the reference paths is provided. The results are validated for a truck with three trailers and different hitching types.
Integrating Coordinate Transformation and Random Sampling Into High-Capacity Reversible Data Hiding in Encrypted Polygonal Models

Reversible data hiding in encrypted media involves using algorithms to perform data encryption to improve the privacy of the original media and hide data for covert communication or access control. This study explores the feasibility of applying coordinate transformation and random sampling to enhance the embedding rate and total embedding capacity of separable reversible data hiding in encrypted polygonal models based on multiple most significant bit (multi-MSB) prediction and Huffman coding. We first transform each vertex coordinate value of the input model into a decimal value between 0 and 1 and then convert the value into numerous binary digits with a user-defined compression threshold. Thereafter, the random sampling concept is used to generate embeddable vertices with only partial neighboring vertices as a reference. Thus, the embedding rate and total embedding capacity can both be increased considerably. The multi-MSB prediction technique is adopted to obtain the embedding capacity of each embeddable vertex, each vertex coordinate value in the proposed study can have a respective embedding length. Finally, the auxiliary information compressed through Huffman coding and an encrypted secret message are embedded in the multi-MSB of each embeddable vertex coordinate value through bit substitution. The experimental results of this study indicate the feasibility of the proposed algorithm.
Enhanced Multiscale Attention Network for Single Image Dehazing

Under severe weather conditions, the quality of the images taken outside is directly affected by floating atmospheric particles. To keep the quality of the images, haze removal methods play a critical role. The most difficult part of haze removal is removing the haze that spreads over the entire image. Many CNN-basedmethods have been proposed to remove the haze, and can be divided into two types. One is to use a multi-scale structure and the other is to stack layers. The former causes image degradation due to the loss of some of the original information in an image and the latter increases computational complexity due to not reducing the resolution. In addition, a large number of parameters is required to secure the expressive power of the model, which leads to a huge amount of memory. To tackle these problems, we tried to 1) downsample the image while saving parameters and maintaining the quality of the generated image, and 2) consider the information in the entire image to remove the haze. For the first problem, we tried to solve this by using a feature extractor that has been used in other tasks, learning to optimize the output image in low-resolution, and preparing kernels with various dilation rates to expand the receptive fields. For the second problem, we use the attention structure to determine which part of the image features should be focused on from the entire feature map. By incorporating such modules, our method achieves better results on both synthetic and real-world images when compared with state-of-the-art methods.