Reviewerly/exHarmony huggingface dataset:
Decrease in water level by rooter technology one-way system on flood-prone land in Sunggal District, North Sumatra, Indonesia

Abstract Rooter system technology is a technology used to accommodate and absorb water into the soil through pipes that are designed like tree roots where the pipe is planted into the ground as deep as two meters with a slope of 45 degrees. Rainwater is collected and absorbed into the soil. Rooter technology system only holds rainwater, it does not waste water. System rooter technology is a well or hole on the ground surface which is made to hold rainwater so that it can seep into the soil. This study aims to control flooding in flood-prone areas using one-way rooter technology with an environmentally friendly concept. This research was conducted from 1 January 2018 to 28 February 2018 by observing directly in the field. Observations carried out included analysis of the study site, the water level at the study site, area of land affected by flooding, length of time, the soil absorbed, water level which did not use one-way rooter system technology. The volume of water that entered the soil was calculated using both rooter system technology and land that does not consume system rooter technology. The results of this study indicated that by using rooter technology a one-way water system is faster to enter the ground and can overcome flooding on the land.
Efficient Sparse to Dense Stereo Matching Technique

Acquiring accurate dense depth maps with low computational complexity is crucial for real-time applications that require 3D reconstruction. The current sensors capable of generating dense maps are expensive and bulky, while compact low-cost sensors can only generate the sparse map measurements reliably. To overcome this predicament, we propose an efficient stereo analysis algorithm that constructs a dense disparity map from the sparse measurements. Our approach generates a dense disparity map with low computational complexity using local methods. The algorithm has much less computation time than the existing dense stereo matching techniques and has a high visual accuracy. Experiments results performed on KITTI and Middlebury datasets show that our algorithm has much less running time while providing accurate disparity maps.
Common Structured Low-Rank Matrix Recovery for Cross-View Classification

Low-rank multi-view subspace learning (LMvSL) has been an essential solution to the problem of cross-view classification. Despite the promising performance on real applications, it still remains challenging to classify objects when there is a large discrepancy between gallery data and probe data. In this paper, we propose a Common Structured Low-rank Matrix Recovery (CSLMR) algorithm to elegantly handle view discrepancy and discriminancy simultaneously. Specifically, our CSLMR incorporates common representation constraint and structured regularization into the fundamental model of LMvSL to learn a discriminant latent subspace. Furthermore, an efficient optimization method is developed and the complexity analysis of CSLMR is presented for completeness. Experimental results on CMU PIE dataset demonstrate the superiority of our CSLMR.
HUMAN BEING IN NEW REALITY: DIMENSIONS OF VIRTUAL ECOSYSTEM

The article reveals the correlation of the notions “virtual reality” and “virtual world” taking into account the existing technological level. Introduction of convergent technologies and virtualization of space promote a human being’s deeper involvement into the virtual world, which results in the formation of “virtual ecosystem” (nature 3.0). Potential risks when following the path of technological development are identified. The author concludes that the vector of civilization development is critically dangerous but not fatal for a future human being.
Near-conformal window assembly for airborne payloads: improved time on-station and optical performance

Conventional windows for airborne payloads are often discontinuous with the aircraft or pod skin. A protruding structure or hollow cavity increases aerodynamic drag, which consumes more fuel and thus reduces the amount of time available on-station. These geometries give rise to turbulent aero-optical effects, which can reduce the payload’s optical performance because it has to see through turbulence. This paper describes a multi-paned or segmented window concept that matches the local topology of the aircraft pod or skin. This approach is suitable for optical payloads having multiple fixed fields-of-view such as staring infrared search and track systems, but not scanning systems. This approach for creating a near-conformal window assembly should be particularly useful for rapid prototyping of windows for airborne optical payloads, providing a nearer-term alternative to monolithic windows that are ground and polished into complex shapes. In this paper, a 14-inch diameter pod faring with three window segments was chosen as a point design for a notional airborne optical payload. Fused silica planar windowpanes were fabricated with matching, mating mitered edges. The panes were chemically bonded directly to each other with a sodium-silicate solution. The bonding process and fixturing are described. The resulting glass bond is strong and minimizes the non-useable seam between panes. This approach increases the clear aperture of each pane compared with windowpanes bonded into individual mechanical bezels. Interferometric measurements of the prototype show no degradation in transmitted wavefront error after silicate bonding.
Improving Keyphrase Extraction Using LL-Ranking

Keyphrases provide a concise representation of the main content of a document and can be effectively used within information retrieval systems. In the paper, we deal with the keyphrase extraction problem when a given number of keyphrases for a text should be extracted. The research is focused on the keyphrase candidates ranking stage. In the domain, the question remains open of whether the keyphrase extraction quality can be improved by putting limits on the number of phrases of different lengths extracted during candidate ranking. We assume that the quality of resulting keyphrases can be enhanced if we introduce \(\underline{L}\)imitations on the number of phrases of specific \(\underline{L}\)engths in the resulting set (LL-ranking strategy). The experiments are performed on the well-known INSPEC dataset of scientific abstracts. The obtained results show that the proposed limitations help to significantly increase the quality of extracted keyphrases in terms of Precision and F1.
Deep Convolutional Neural Networks Based on Image Data Augmentation for Visual Object Recognition

Deep Neural Networks (DNNs) have achieved a great success in machine learning. Among a lot of DNN structures, Deep Convolutional Neural Networks (DCNNs) are currently the main tool in the state-of-the-art variety of classification tasks like visual object recognition and handwriting and speech recognition. Despite wide perspectives, DCNNs have still some challenges to deal with. In previous work, we demonstrated the effectiveness of using some regularization techniques such as the dropout to enhance the performance of DCNNs. However, DCNNs need enough training data or even a class balance within datasets to conduct better results. To resolve this problem, some researchers have evoked different data augmentation approaches. This paper presents an extension of a later study. In this work, we conducted and compared the results of many experiments on CIFAR-10, STL-10 and SVHN using variant techniques of data augmentation combined with regularization techniques. The analysis results show that with the right use of data augmentation approaches, it is possible to achieve good results and outperform the state-of-the-art in this field.
Comparison of Optimization Using Hybrid Genetic Agorithm-Backpropagation and Hybrid Particle Swarm Optimization-Backpropagation for Tide Level Forecasting

Abstract Evolutionary computation or evolutionary algorithm has been used in many areas. For the last ten years, evolutionary computation became a powerful method to solve problems in the real world. Forecasting is a well-known method to determine the direction of the future for better results. Hybridization between the Genetic Algorithm and Particle Swarm Optimization to Backpropagation Neural Network are applied to forecast tide level data. The experiments based on a comparison of these two algorithms prove that Particle Swarm Optimization with Backpropagation Neural Network is exceeding Genetic Algorithm in measuring tide level forecasting.
PECULIARITIES OF EFFICIENT METACOGNITIVE MONITORING OF UNIVERSITY STUDENTS’ LEARNING ACTIVITY

The article is dedicated to the theoretical description of the peculiarities of formation of the skills of efficient metacognitive monitoring of the learning activity of university students.In particular, we highlighted negative impacts of the illusion of knowing in the learning activity of university students such as inadequate self-estimation of the learning results, metacognitive incompetence, inability to estimate actual level of real understanding of students' own knowledge skills and strategies, inability to differentiate the illusory knowledge and not illusory one, etc.We also provided an analysis of importance of effective metacognitive monitoring skills formation of the learning activity of university students.It is approved that such formation is possible because of stimulation of students' learning motivation, formation of adequate self-estimation, development of high levels of reflexivity, development of students' capability of individual estimation of their own activity and its results, development of abilty to provide effective feedback, teaching the ways how to use the processes of metacognitive monitoring with an aim to understand their own attitude towards the learning disciplines and the chosen speciality.Possible perspectives of future investigations of the problem are also described.
An efficient reinforcement learning algorithm for learning deterministic policies in continuous domains

In this paper, we present an improvement to an existing reinforcement learning algorithm that can learn very efficiently deterministic policies in continuous domains. It builds on two recently-proposed techniques. First, it can be seen as a variation of an actor-critic algorithm, called Penalized Neural-Fitted Actor Critic (PeNFAC) [24], which showed excellent experimental performance in the Roboschool environments. Second, it incorporates a better estimate for the value function of the current policy, called V-trace target [3], by allowing the reuse of off-policy data generated by recent previous policies. We experimentally compare two different implementations of V-trace: one based on n-step returns and the other on λ-returns. Finally, we show that our proposed algorithm can outperform several state-of-the-art algorithms (TD3, DDPG, PPO, PeNFAC, NFAC) over three environments of the Roboschool benchmark (Hopper, HalfCheetah, Humanoid).
Parallel Multispectral Image Super-resolution Based on Sparse Representations

Image super-resolution (SR) produces a high-resolution (HR) image using either a single or multiple low-resolution (LR) input image(s) of the same scene. Sparse representation techniques are effectively applied for image SR because of their high reconstruction accuracy. SR reconstruction is accomplished through learning of an overcomplete dictionary and then solving a series of regularization problems applied on each patch extracted from the input image. This paper demonstrates a sparse representation based coupled overcomplete dictionary training and SR procedure for LR multispectral images. The proposed work is also implemented using a multicore parallel processing technique to provide faster reconstruction. Experimental results show superiority of the proposed method over some others.
Point Mapping From 2D Face to 3D Face Using Linear Equation with Gauss Elimination

In the field of computer vision, 3D face making is interesting, but it is also difficult. There are ways to create 3D faces that are done manually using complex software but this also requires special skills, there are also other ways that use sophisticated methods such as artificial intelligent. However, both require a long time and process. In this study a simple method is applied, in the form of 2D face point mapping to 3D face point by utilizing linear equations that are built based on the Gauss elimination method. As a first step, two 2D face models and two 3D face models are correlated. Both are used as template for finding the basic formula, then the area of the two faces is normalized from 2D and 3D models into x and y coordinates so that they have the same area. Thus, each point of a 2D face model will have a correlation with the point of the 3D face model. Each x and y coordinate of the two models is at the same point, plus z-point information obtained from the 3D face model. Next, by slicing the 3D face model in the direction of the y-axis, you get a number of slices image of x and z axes. From each slice, several points are taken with coordination of x and z as a reference to determine the linear equation. With the Gauss elimination method, a linear equation can be built. After obtaining two models of linear equations for each slice derived from two different 3D models, a linear equation can be determined for the slices of the query model. The value obtained by a point in the query model, involving a comparison of colors between two adjacent points, is an interpolation of the two equations obtained. With the linear equation in each slice plane, a triangular face attribute can be built in a neater 3D model. In addition, you can also specify the number of vertices and faces. This is useful for modifying the 3D model. This research results in how to make 3D face models based on 2D face models more quickly, without training data. The results of this study can encourage the discovery of more template models that will increase the accuracy of the results obtained.
Optimal Sampling of Parametric Families: Implications for Machine Learning

It is well known in machine learning that models trained on a training set generated by a probability distribution function perform far worse on test sets generated by a different probability distribution function. In the limit, it is feasible that a continuum of probability distribution functions might have generated the observed test set data; a desirable property of a learned model in that case is its ability to describe most of the probability distribution functions from the continuum equally well. This requirement naturally leads to sampling methods from the continuum of probability distribution functions that lead to the construction of optimal training sets. We study the sequential prediction of Ornstein-Uhlenbeck processes that form a parametric family. We find empirically that a simple deep network trained on optimally constructed training sets using the methods described in this letter can be robust to changes in the test set distribution.
Our robotic future and how we should react

column Our robotic future and how we should react Share on Author: Matt Jones Swansea University Swansea UniversityView Profile Authors Info & Claims InteractionsVolume 26Issue 6November - December 2019 pp 50–55https://doi.org/10.1145/3358910Published:30 October 2019 0citation668DownloadsMetricsTotal Citations0Total Downloads668Last 12 Months442Last 6 weeks44 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Knowledge-Based Solvers for RGT Combinatorial Problems

In this work, we present means for usage of RGT (Reproducible Game Trees) Solvers, that include knowledge acquisition, matching and decision making algorithms for RGT problems. The RGT Solvers are being regularly improved and the current implementation tries to cover the drawbacks of previous versions, improves presentation of classifiers and mental doings, as well as provides enhancements in interface. We also discuss ways of knowledge acquisition for marketing and battlefield problems by RGT Solvers.
An Accurate 1D Camera Calibration Based on Weighted Similar-Invariant Linear Algorithm

In recent years, researchers around the world have been researching and improving the technique of 1D calibration of cameras. The previous work has been primarily focused on reducing the motion constraints of one-dimensional calibration objects, however the accuracy of the existing methods still needs to be improved when random noise is introduces. In order to improve the accuracy of the one-dimensional calibration of the camera, in this paper, we propose a new calibration method by combining a weighted similar invariant linear algorithm and an improved nonlinear optimization algorithm. Specifically, we use the weighted similar invariant linear algorithm to obtain the camera parameters as the initial calibration parameters, and then optimize the parameters by using improved nonlinear algorithm. Finally, in the case of introducing random noise, the results of computer simulations and laboratory experiments show that when the noise level reaches 2 pixels, the parameter error of this method is mostly reduced to 0.2% compared with other methods, which verifies the feasibility of our proposed method.
Geophysical Methods of Identification of Ore Control Structures of Orogenic Gold Deposits in Low-Contrast Terrigenous Rocks by Petrophysical Characteristics

Abstract The paper presents the analysis of characteristics of abnormal geophysical fields of the ore control of the Malo-Tarynskoe orogenic gold deposit in Verkhoyano-Kolymsky folded area, the Northeast of Russia. It is located in terrigenous rocks of the top Triassic, poorly contrast on petrophysical characteristics. Ore bodies of the Malo-Tarynskoe deposit are presented by the mineralized crushing zones with veinlet-disseminated and vein-disseminated mineralization. To identify the features of manifestation the ore control in geophysical fields land geophysical surveys are conducted by the methods of high-precision magnetic exploration and electroinvestigation. Magnetoprospecting works have established the low-amplitude mosaic structure of the magnetic field of the Malo-Tarynskoe deposit. The main magnetorevolting structure is the Samyr granitoid massif which is allocated with a magnetic minimum (-99 nT). Extreme positive values of the magnetic field are connected with hornfelsed ores in the zone of contact metamorphism (170 nT). Various anomalies as low-magnetic, mainly linear ones caused by explosive violations are distinguished (-12 – 20 nT). In the electric field, the abnormal zones have the linear nature of various extent and intensity. Pro-deleting of anomalies changes from sublongitudinal to northwest. The anomalies of low electrical resistance, generally trace zones of crushing and jointing (208-2300 ohm*m). In general, they are grouped in three “regional” abnormal areas of the northwest and North northwest orientation of low electrical resistance and correlated with the situation of the ore control zones of the Malo-Tarynskoe fault (208 – 1600 ohm*m). Sites with frequent alternativeness and alternating electric anomalies indicate lithologic heterogeneity of the environment (2500 – 10000 ohm*m). The study has shown magnetoprospecting to be effective when mapping magmatic formations and explosive violations. They allow delineating intrusive bodies and allocating hornfelsed zones. The electric profiling application by the method of a median gradient solves a wide range of geological tasks. Apart from allocation of the structures controlling gold mineralization in faint terrigenous ores, electro-investigation allows to carry out the lithologic partition of permafrost rocks in the territories blocked by friable deposits of considerable power.
PolSAR Image Edge Detection via Structure Tensor Analysis

The weighted structure tensor (ST) can be usually used on edge detection of multi-channel images, including polarimetric synthetic aperture radar (polSAR) images. The traditional weighting method is average weighting, each channel considered to provide the same edge information. But in fact different channels tend to contain different amount of edge information, so the traditional weighting method cannot be adopted to extract full edge information of the multi-channel images. This letter proposes a novel weighting method for ST, in which the weight of each channel is obtained by the eigenvalue-measured way. Experimental results show that the proposed method outperforms the traditional method on edge detection.
A Fast Online Cascaded Regression Algorithm for Face Alignment

Traditional face alignment based on machine learning usually tracks the localizations of facial landmarks employing a static model trained offline where all of the training data is available in advance. When new training samples arrive, the static model must be retrained from scratch, which is excessively time-consuming and memory-consuming. In many real-time applications, the training data is obtained one by one or batch by batch. It results in that the static model limits its performance on sequential images with extensive variations. Therefore, the most critical and challenging aspect in this field is dynamically updating the tracker's models to enhance predictive and generalization capabilities continuously. In order to address this question, we develop a fast and accurate online learning algorithm for face alignment. Particularly, we incorporate on-line sequential extreme learning machine into a parallel cascaded regression framework, coined incremental cascade regression(ICR). To the best of our knowledge, this is the first incremental cascaded framework with the non-linear regressor. One main advantage of ICR is that the tracker model can be fast updated in an incremental way without the entire retraining process when a new input is incoming. Experimental results demonstrate that the proposed ICR is more accurate and efficient on sequential images compared with the recent state-of-the-art cascade approaches. Furthermore, the incremental learning proposed in this paper can update the trained model in real time.
Improved Collaborative Filtering Algorithm Incorporating User Information and Using Differential Privacy

Collaborative filtering algorithm is one of the most popular recommendation algorithms. There is, however, the risk of privacy leakage when making effective recommendation. Differential privacy is a relatively new privacy protection mechanism in the field, and has been used in recommendation systems. To this end, the existing research still has some disadvantages. Particularly, they do not have satisfactory performance and have difficulty in solving the cold start scenarios. In this paper, we propose an improved differential privacy enabled collaborative filtering algorithm incorporating user information. The algorithm improves similarity calculation, and solves the user cold start problem by making effective use of user information (related attributes). Experiments show that with the same privacy guarantee, the proposed algorithm improves the performance of the recommendation system and indeed solves the problem of cold start to some good extent.