Reviewerly/exHarmony huggingface dataset:
Methods of Graphical Research of Stochastic Transformation Results

The paper discusses graphical research methods of stochastic transformations. The modified methods for detecting anomalies, patterns and deviations in pseudorandom sequences are presented. The method based on filling coordinate and coordinate-free planes is described. The method based on plotting empirical frequencies distribution of zeros and ones in a researched pseudo-random sequence is described. The method based on visualization of stochastic transformation results in form of several graphs is described. Proofs of correctness of the described methods are presented. The results of PRNG research with proposed methods are presented. The recommendations to increase effectiveness of presented investigation methods are formulated.
Implementation of comic on linear program material to increase mathematical understanding for students of XI grade senior high school

The purpose of this study is to determine the results of educational comic implementation that has been developed in linear program material. The data were collected on students of XI grade selected by the purposive sample. This research uses quantitative descriptive approach. Data collection is done through observation of learning, test, and questionnaire of student learning interest after using educational comic. The results showed that educational comic could improve students mathematical understanding ability with N-gain value = 0.48 including high-quality improvement. Students interested in the following learning using educational comic media of 77% with high interest criteria. So educational comic media has been appropriate in its development because it can improve students mathematical understanding ability and student interest in learning.
Automatic data labeling by neural networks for the counting of objects in videos

The paper proposes an efficient method for training a neural network to count moving objects in a video, while another neural network concurrently prepares a labeled dataset for the first one. The detection, tracking, and counting of objects is crucial for effective Intelligence Transportation Systems (ITS), which should reduce congestion and recognize traffic offenders on highways and in urban areas. Creation of labeled data for training a neural network is one of the essential prerequisites for successful application of supervised machine learning. In this paper, the experimental results of the automatic labeling and counting of vehicles under real world conditions are shown. The method shows that by using the Convolutional Neural Network (CNN), the computing power and speed-up time for training a Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) cell for counting moving objects can be decreased.
Features of the Design Operating Unit Inference Engine and Its Implementation on FPGA

Modern information technologies are increasingly focused on the tasks related to the knowledge processing: intelligent Internet search systems and enterprise resource planning, DataMining and smart home systems, recommender systems for various purposes, etc. Such problems are specified by the availability of large arrays of complexly structured symbol information, a high degree of implicit parallelism, weak formalizability, and a low need for complex arithmetic operations. As a result, the solution of such problems using the off-the-shelf microprocessors is extremely inefficient. One of approaches to solving this problem is to develop a specialized hardware device designed for high-performance symbolic data processing. The proposed logical inference engine is focused on the accelerated method of parallel logical inference that has a high degree of parallelism, and dataflow architecture. The article describes the development of the executive processor of the logical inference engine. The emphasize is placed on the unification unit design. A new method of parallel unification of the predicate pairs is presented. The unification unit logic diagrams developed using the Quartus CAD system have been implemented on FPGA by Altera. The comparative analysis of the sequential and parallel unit implementation on the basis of the experiments performed is provided.
Quantum technologies and the National Quantum Initiative

Quantum EngineeringVolume 1, Issue 1 e7 GUEST EDITORIAL Quantum technologies and the National Quantum Initiative Kenneth R. Brown, Corresponding Author Kenneth R. Brown ken.brown@duke.edu orcid.org/0000-0001-7716-1425 Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina Department of Chemistry, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina Department of Physics, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina Kenneth R. Brown, Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham NC 27708, USA Email: ken.brown@duke.eduSearch for more papers by this author Kenneth R. Brown, Corresponding Author Kenneth R. Brown ken.brown@duke.edu orcid.org/0000-0001-7716-1425 Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina Department of Chemistry, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina Department of Physics, Trinity College of Arts and Sciences, Duke University, Durham, North Carolina Kenneth R. Brown, Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham NC 27708, USA Email: ken.brown@duke.eduSearch for more papers by this author First published: 10 March 2019 https://doi.org/10.1002/que2.7Citations: 3Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume1, Issue1March 2019e7 RelatedInformation
Generation of synthetic training data for object detection in piles

Current state-of-the-art object detectors are based on supervised deep learning approaches. These methods require a large amount of annotated training data, which hinders a wider use of these methods in industry. We propose a method for generating synthetic training data for the task of detecting which objects in a pile can be picked up by a robot arm. The method requires few input images, which are used to create annotated images of piles. After training a state-of-theart detector on the synthetic data, we test it on real images. The results show that the model trained in such a way is not a rival to the best object detectors trained on large datasets of real images, but it is good for the specific task of detecting pickable objects in the piles. The main advantage of the proposed training approach is that the existing models can be easily re-trained to work with piles of different objects by personnel who do not specialize in machine learning.
General models for estimating daily diffuse solar radiation in China: Diffuse fraction and diffuse coefficient models

Empirical models proposed to estimate the daily diffuse solar radiation (Hd) in different areas worldwide, are ordinarily site-dependent. Thus, a timely scientific question would be if an application of such an empirical model to other stations can occur, without any Hd records. To estimate the daily Hd of these stations in different areas without Hd, it is critical to establish the general models of the estimation of regional daily Hd. Therefore, daily meteorological data, including daily Hd, daily global solar radiation Hd, daily sunshine duration, daily average temperature and daily average relative humidity, were collected and analyzed in this paper. Considering different data conditions of stations (with and without H, five diffuse fraction and three diffuse coefficient models were proposed and calibrated. Through the analysis of statistical indicators, the most efficient diffuse fraction and diffuse coefficient models were identified. According to the climatic classification, the meteorological data were collected and gathered. Then, the gathered data were used to determine the generalized constants of general models based on the best models. The results demonstrated that the general models proposed in this paper presented satisfactory accuracies and could represent the general models for the daily Hd estimation of stations without Hd records in China.
Fast Approximate Score Computation on Large-Scale Distributed Data for Learning Multinomial Bayesian Networks

In this article, we focus on the problem of learning a Bayesian network over distributed data stored in a commodity cluster. Specifically, we address the challenge of computing the scoring function over distributed data in an efficient and scalable manner, which is a fundamental task during learning. While exact score computation can be done using the MapReduce-style computation, our goal is to compute approximate scores much faster with probabilistic error bounds and in a scalable manner. We propose a novel approach, which is designed to achieve the following: (a) decentralized score computation using the principle of gossiping; (b) lower resource consumption via a probabilistic approach for maintaining scores using the properties of a Markov chain; and (c) effective distribution of tasks during score computation (on large datasets) by synergistically combining well-known hashing techniques. We conduct theoretical analysis of our approach in terms of convergence speed of the statistics required for score computation, and memory and network bandwidth consumption. We also discuss how our approach is capable of efficiently recomputing scores when new data are available. We conducted a comprehensive evaluation of our approach and compared with the MapReduce-style computation using datasets of different characteristics on a 16-node cluster. When the MapReduce-style computation provided exact statistics for score computation, it was nearly 10 times slower than our approach. Although it ran faster on randomly sampled datasets than on the entire datasets, it performed worse than our approach in terms of accuracy. Our approach achieved high accuracy (below 6% average relative error) in estimating the statistics for approximate score computation on all the tested datasets. In conclusion, it provides a feasible tradeoff between computation time and accuracy for fast approximate score computation on large-scale distributed data.
Association Rules Mining Method of Big Data for E-Learning Recommendation Engine

Today, recommender systems are increasingly used due to its success in several areas such as e-commerce, tourism, social networks, and e-learning. Indeed, most of the computing environment for human learning, especially the online learning platforms have a very large number of learners’ profiles, thousands of courses, and various educational resources. However, students often face many challenges, such as the absence of a real solution of recommendation to orientate them to take more appropriate learning materials. In this article, we develop a recommendation engine for the e-learning platform in order to help learners to easily find the most proper pedagogical resources without any search effort. It aims to discover relationships between student’s courses activities through the association rules mining method. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules among objects in the transaction database. Then, we use the extracted rules to find the list of suitable courses according to the learner’s behaviors and preferences. Next, we implement our system using Apriori algorithm and R, which is efficient big data analysis language and environment, on data collected from ESTenligne [ESTenLigne project is supported by the EST Network of Morocco and the Eomed association ( http://www.eomed.org )] platform database of High school of Technology of Fez. Finally, the experimental results prove the effectiveness and reliability of the proposed system to increase the quality of student’s decision, guide them during the learning process and provide targeted online learning courses to meet the needs of the learners.
Bringing Vision to the Blind: From Coarse to Fine, One Dollar at a Time

While deep learning has achieved great success in building vision applications for mainstream users, there is relatively less work for the blind and visually impaired to have a personal, on-device visual assistant for their daily life. Unlike mainstream applications, vision system for the blind must be robust, reliable and safe-to-use. In this paper, we propose a fine-grained currency recognizer based on CONGAS, which significantly surpasses other popular local features by a large margin. In addition, we introduce an effective and light-weight coarse classifier that gates the fine-grained recognizer on resource-constrained mobile devices. The coarse-to-fine approach is orchestrated to provide an extensible mobile-vision architecture, that demonstrates how the benefits of coordinating deep learning and local feature based methods can help in resolving a challenging problem for the blind and visually impaired. The proposed system runs in real-time with ~150ms latency on a Pixel device, and achieved 98% precision and 97% recall on a challenging evaluation set.
Online Handwritten Signature Verification System Based on Neural Network Classification

This paper discusses the biometric personal characteristics appliance as an identifying information for granting remote access to information resources. The authors investigated the possibility to apply handwriting dynamics as a characteristic that can be changed in case of its compromise. They proposed their own method of digital data pre-processing based on an algorithm of viewing a straight line as a curved one. The authors analyzed the proposed algorithm for the possibility of applying it to set an online verification. They received positive results. The proposed algorithm allows us to form a feature vector of an info system user of the given dimensionality. The authors suggest using artificial neural network as a classifier with different the most well-known algorithms of their training. The authors propose to use the statistical processing of training set samples with the correlation analysis method of the data, which enters the neural network.
Discontinuous model recovery anti-windup for image based visual servoing

A discontinuous model recovery anti-windup technique is proposed for a double integrator with the output measurement corrupted by a time-varying scaling factor. This challenging measurement configuration arises in vision-based control, where the position of an object moving in the three-dimensional space is determined using two-dimensional images. We prove uniform global asymptotic stability of the closed loop by applying an invariance principle argument to the regularized dynamics. The effectiveness of the proposed approach is illustrated on a visual aircraft landing simulation example.
GAN-based data augmentation for visual finger spelling recognition

In this work we extend WGAN-GP in order to achieve better generation of synthesized images for finger spelling classification. The main difference between the ordinary WGAN-GP and the proposed algorithm is that in the training we employ both training samples and training labels. These training labels are fed to the generator, that generates the synthetic images using both the randomized latent input and the input label. In ordinary WGAN-GP, latent input variables are usually sampled from an unconditional prior. In the proposed algorithm the latent input vector is a concatenation of random part, the class labels and additional variables that are drawn from Gaussian distributions representing hand poses or gesture attributes. The JSL dataset for Hiragana sign recognition has been balanced using the rendered samples on the basis of a 3D hand model as well as the extended WGAN-GP.
A fast algorithm for local feature selection in data classification

Abstract Typical feature selection methods select a global feature subset that is applied over all regions of the sample space. In localized feature selection (LFS), each region of the sample space is associated with its own optimized feature subset. This allows the feature subset to adapt to local variations in the sample space. Feature subsets are selected such that within a localized region, within‐class distances are minimized and between‐class distances are maximized. LFS outperforms global feature selection methods. LFS is solved using a randomized rounding approach when weights of regions are fixed. Randomized rounding is a too time‐consuming algorithm. In this paper, we show that LFS has a closed‐form solution when weights of regions are fixed. Using this closed‐form solution can decrease the runtime of solving LFS substantially. Experimental results on real datasets confirm that the classification error rate of our proposed method and the randomized rounding‐based method are the same; the runtime of our proposed method is much better than that of the randomized rounding‐based method; and the classification error rate of our proposed method and the randomized rounding‐based method outperforms the state‐of‐the‐art feature selection methods.
Spatiotemporal visual-semantic embedding network for zero-shot action recognition

Zero-shot learning (ZSL) has recently attracted increasing attention in visual tasks like action recognition. We propose a spatiotemporal visual-semantic embedding network (STVSEM) for zero-shot action recognition. First, given the fact that two-stream architecture based action recognition algorithms have achieved excellent results in recent years, the module is assembled to our designed network by simultaneously using the spatial features (e.g., RGB appearance) and optical flow in time domain as visual features to significantly improve the visual expression capability. Then, in order to slightly alleviate the problem of semantic loss that typically occurs in the case of using embedding-based ZSL methods, an autoencoder is introduced to get a better semantic representation and complement semantic relationship information for unseen classes by seen classes. Last but not least, a joint embedding mechanism that explores and exploits the relationships of the visual data and semantic information in an intermediate space is employed to ameliorate the gap between vision and semantics. The experimental results on Charades and UCF101 datasets indicate that the proposed method outperforms the state-of-the-art methods in accuracy, which further demonstrates the effectiveness of our method.
Three‐dimensional‐based landmark tracker employing a superpixels method for neuroscience, biomechanics, and biology studies

Abstract Examining locomotion has improved our basic understanding of motor control and aided in treating motor impairment. Mice and rats are premier models of human disease and increasingly the model systems of choice for basic neuroscience. High frame rates (250 Hz) are needed to quantify the kinematics of these running rodents. Manual tracking, especially for multiple markers, becomes time‐consuming and impossible for large sample sizes. Therefore, the need for automatic segmentation of these markers has grown in recent years. Here, we address this need by presenting a method to segment the markers using the simple linear iterative clustering superpixel method. The two‐dimensional coordinates on the image plane are projected to a three‐dimensional (3D) domain using direct linear transform and a 3D Kalman filter has been used to predict the position of markers based on the speed and position of markers from the previous frames. A probabilistic function is used to find the best match from among superpixels. The method is evaluated for different difficulties for tracking of the markers, and it achieves 95% correct labeling of markers. Finally, the method was used to analyze data from an ongoing study seeking to improve recovery from spinal cord injury in rats
Cost Minimization Through Load Balancing and Effective Resource Utilization in Cloud-Based Web Services

With the growth of internet-based applications and the explosion of consumers, cloud-based web service applications have become more common and the importance of minimizing the cost, increasing the interactivity, and management and efficient use of resources has become high. Existing methods like fixed cost per month no longer satisfy the application maintenance costs of the modern app developers. In this article, the authors propose an enhanced model for improving efficiency; maximize availability and minimizing the cost of cloud-based web applications. The authors have conducted experiments on grid dataset and analyzed the results using several algorithms on the load balancer with the multilevel optimized shortest remaining time scheduling method. The analysis clearly proves that applying a “pay as you” go mechanism will substantially reduce the cost and will improve the efficiency which resources are utilized. The results clearly suggest improvements in cost minimization and effective utilization of resources leading to effective utilization of services.
Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

Astroturfing is a phenomenon in which sponsors of fake messages or reviews are masked because their intentions are not genuine. Astroturfing reviews are intentionally made to influence people to take decisions in favour of or against a target service or product or organization. The tourism sector being one of the sectors that is flourishing and witnessing unprecedented growth is affected by the activities of astroturfers. Astroturfing reviews can cause many problems to tourists who make decisions based on available online reviews. However, authentic and genuine reviews help people make informed decisions. In this paper a Latent Dirichlet Allocation (LDA) based Group Topic-Author model is proposed for efficient discovery of social astroturfing groups within the tourism domain. An algorithm named Astroturfing Group Topic Detection (AGTD) is defined for the implementation of the proposed model. The experimental results of this study revealed the utility of the proposed system for the discovery of social astroturfing groups within the tourism domain.
Characterizing generics are material inference tickets: a proof-theoretic analysis*

An adequate semantics for generic sentences must stake out positions across a range of contested territory in philosophy and linguistics. For this reason the study of generic sentences is a venue for investigating different frameworks for understanding human rationality as manifested in linguistic phenomena such as quantification, classification of individuals under kinds, defeasible reasoning, and intensionality. Despite the wide variety of semantic theories developed for generic sentences, to date these theories have been almost universally model-theoretic and representational. This essay outlines a range of proof-theoretic analyses for characterizing generics. Particular attention is given to an expressivist proof-theory that can be traced to 1) work on logical syntax that Carnap undertook prior to his turn toward truth-conditional model theory in the late 1930s, and 2) research on sequent calculi and natural deduction systems that originate in work from Gentzen and Prawitz.1