Reviewerly/exHarmony huggingface dataset:
Automatic Classification for Ground Targets under Complex Background Based on Bag of Words Model

Abstract In order to improve the performance of the automatic classifier for ground targets under complex background, a new classifier which was based on bag of words model and support vector machine (SVM) was build. Firstly, the keypoints of a target image were extracted into vectors by using Scale-Invariant Feature Transform algorithm, and vectors which were extracted from all trained images were merged into large vectors. Secondly, the large vectors were clustered into small vectors which included K vectors by using K -means clustering algorithm. To analyze how different values of K affected the classifying results, K was set to several different numbers. Thirdly, vectors which were extracted from all trained images were classified, and K -dimensional numerical vectors were obtained. Finally, the classifier was build with SVM algorithm, and the K -dimensional vectors were as input features of the classifier. The training images were randomly chosen from 90% of images dataset which included missile launch vehicles and tanks, and the rest 10% were as the testing images. The trained classifier is tested, and the f1-score value is 0.79. The results show that the new classifier can perform well.
Research and Application of Judgement Technology of Typical Medium and Low-Voltage Faults Based on Big Data Technology

In order to find the disconnection of medium and low-voltage lines timely and effectively, based on the big data of power supply established by the special work of marketing, distribution and regulation data sharing and integration, this paper constructs a research and adjustment model for typical medium and low-voltage faults from the perspective of electrical value (voltage, current, unbalance rate) in the low-voltage side of the distribution transformer. It analyzes the logical relationship of corresponding electrical characteristic values after faults such as line disconnection, single-phase loss and single-phase grounding of the distribution transformer when the 10 kV distribution transformer operates under different load factors, winding connection group, three-phase unbalance rate and system voltage, and applies fault warning model in the daily operation and maintenance of the distribution network, to improve the quality of active repair service, shorten the fault finding and users’ power outage time, and improve the reliability of power supply.
SAR and Optical Remote Sensing Image Registration Based on an Improved Point Feature

This paper proposes a simple and stable point feature-based registration method for synthetic aperture radar (SAR) and optical remote sensing images. First, we extend Harris detector’s response function from linear item to quadratic form and build a new weight function by combining spatial and intensity information of pixels, which enable the location of corners more precisely. Next, we create a structural feature descriptor using both the amplitude and orientation of corners to provide more distinctive local image features. Finally, we set up the correspondence based on the generated point features, and map all pixels in the sensed image to the reference. Experimental results demonstrate that the improved detector can achieve better detection performance compared with conventional Harris corner detector. In addition, registration with SAR and optical images demonstrate the efficiency and accuracy of the proposed approach.
Embedded Mission Decision-Making based on Dynamic Decision Networks in SoPC Platform

This paper tackles a Bayesian Decision Making approach for unmanned aerial vehicle (UAV) mission that allows UAV to quickly react to unexpected events under dynamic environments.From online observations and the mission statement, the proposed approach is designed by means of Dynamic Bayesian Networks (DBN) arising from the safety or performance failures analysis.After proposing a DBN model, a probabilistic approach based on Multiple-Criteria Decision-Making (MCDM) is then applied to find the best configuration reaching a balance between performance and energy consumption, thus decide which tasks will be implemented as SW and which as HW execution units, regarding the mission requirement.The proposal UAV mission decision-making is three-pronged, providing: (1) real time image pre-processing of sensor observations; (2) temporal and probabilistic approach based on Bayesian Networks to continuously update the mission plan during the flight; and (3) low-power hardware and software implementations for online and real time embedded Decision Making using Xilinx System on Programmable Chip (SoPC) platform.The proposed approach is then validated with a practical case UAV mission planning using the proposed dynamic decision-maker implemented on embedded system based on a hybrid device.
A Novel Particle Swarm Optimization Algorithm Using Orthogonal Directions

The particle swarm optimization (PSO) algorithm has been widely used in the optimization area when the problem to be optimized is non-convex and the gradient function is difficult to obtain. In this paper, a novel particle swarm optimization (NPSO) algorithm is proposed. Different from the standard PSO, in the NPSO algorithm the trajectories of the particles are limited to a set of orthogonal directions, one of which is pointed to the best position in the population. Each particle maintains its own orthogonal directions, which are updated in several iterations. The convergence property of the NPSO algorithm is analyzed with the help of the dynamic system theory. Numerical experiments on benchmark functions show that the NPSO algorithm can locate the global optimum fast and accurately and is superior to the standard PSO algorithm.
Investigation Of Compression Technique applied for Automatic Modulation Classification Deep Neuron Network learning

The aim of this paper is to investigate efficiency of usage information theory methodes to train DNN for Automatic modulation Classification. Compression mechanism, based on Information Bottleneck method (IB), applied for training dataset, lets to retreive minimum sufficient information from input data and improve performance of the model. In the field of deep learning-based wireless communication, dataset RML2016 have been used to train model. Current research applies information theory principe, in the field of Automatic modulation Classification and investigates impact for resulting model accuracy and time for learning and prediction process. CNN model was taken for research.
Application of Stemming Methods to Development a Module of a Post-processing of Recognized Speech in Intelligent Automated System for Dialogue and Decision-Making in Real Time

In this article, we consider the idea of developing an intelligent and customizable automated system for maintaining text and voice dialogs with the user in real time. This system can be used for almost any subject area, for example, to create an automated robot - the operator of the call center or smart chat bots, assistants and so on. In this paper the designed flexible architecture of the proposed system is presented. System has a lot of independed submodules. Those modules working as interacting microservises and uses several layouts of speech recognition including decision support submodule, thirt-party speech recognition systems and postprocessing subsystem. This work in details presents module of recognized text post-processingon on the example of the Russian and English languages dictionary models. Proposed submodule also uses several processing steps including application of different Stemming methods, use of stoplists of words or another lexical structures, applying stochastic keyword ranking using weights table and other.
Quantum-Inspired Weighting Approach to Correlation Diversified Passive Portfolio Strategy

In this paper, we develop a passive strategy improving index investing, which we call Correlation Diversified Portfolio strategy. The proposed method adjusts weight vector of original index based on the permutation of assets composing the original index. We seek the permutation of assets such that assets with strong correlation to many other assets should be placed in the central part of permutation. By reducing the weights of assets placed in the central part of permutation, we can construct portfolios which are more diversified and have better risk-return characteristics than original index. To examine the usefulness and computational feasibility of the proposed method, we apply it to three major indices of US and Japan, and we provide numerical experiments. The numerical experiments show that portfolios constructed by the proposed method can achieve higher return with lower volatility than the original indices, while their behaviors are still similar to those of the original indices.
Parameter Free Clustering Algorithm Based on Density and Natural Nearest Neighbor

The purpose of clustering algorithm is to explore the correlation of a large number of unlabeled data, so as to find valuable information in chaotic data. There are many kinds of clustering algorithms, but most of them need one or more parameters to be selected based on experience, and the selection of parameters will directly affect the accuracy of clustering algorithm, for example, DBSCAN needs domain radius and number of radius points; k-means algorithm needs to know the number of clusters in advance, k-nearest neighbor algorithm needs to be selected. Choose the appropriate number of neighbors, etc. In order to realize parametric clustering, we adopt the concept of natural nearest neighbor, and let data points discover neighbors independently by iteration. To make up for the problem that the selected K value may not be appropriate, in the process of clustering, we proposes a method of similarity between clusters, which is used to modify the misclassified clusters. Finally, the similarity of observation points is proposed. To distinguish boundary points from outliers. By comparing with DBSCAN, BIRCH and K-MEANS, proved that our algorithm can achieve good performance than other algorithms.
CRD-SentEnse

Micro-blogging and comments on social media include valuable information about people's emotions and opinions towards products, political and social topics and so forth. Unfortunately, due to the large volume of data, is infeasible to label all these comments and reviews. Additionally, having this data labelled manually by human experts is very expensive, time-consuming and applicable only for small amounts of data. As a result, a more scalable solution is needed. Cross-domain sentiment analysis addresses the problem of training a model for classifying a text with respect to its sentiment polarity as a negative, positive (and/ or neutral), using data from one domain (source domain), then the same model is tested using data from a different unlabeled domain (target domain). Cross-domain sentiment analysis is still an open research issue, as the classification performance is still not as good as in the in-domain sentiment analysis, even though proposed approaches have improved significantly. In this paper, we propose a framework for cross-domain sentiment analysis that uses the chi-square test with the data in the source domain. Firstly, we eliminate domain-related words from the source domain that do not bear transferable knowledge to the target domain. Secondly, the chi-square test is utilized for finding the important words regarding the sentiment polarity. Subsequently, we develop a second model that drops the nouns both from source and target domains and we use TFIDF weights for finding the important words in both domains. Finally, we use a stacking ensemble model that combines the two above proposed models for enhancing the performance of the proposed framework.
Applications of Generalized Difference Method for Hypothesis Generation to Social Big Data in Concept and Real Spaces

Analytic methodology as to generation of integrated hypotheses is necessary for applications involving different sources of social big data. In this paper, first, we introduce an abstract data model for integrating data management and data mining by using mathematical concepts of families, collections of sets to facilitate reproducibility and accountability required for social big data applications. Next, we describe generalized difference methods as a methodology for generating integrated hypotheses. Finally, we validate our proposal by applying them to three use cases involving data in concept and real spaces by using our data model as their description guided by generalized difference methods.
Conception of Ontology for Computer Methods of Paintings Analysis

This paper describes the procedure of developing the “OntArt” ontology for the subject area “Attribution and restoration of works of easel oil painting”. Special attention is paid to the domain of computer methods application. The basis of the work is a previously developed thesaurus of this domain. The conceptual shell of the original thesaurus descriptors is the standard CIDOC CRM framework, which binds domain concepts to abstract philosophical categories. In order to connect particular concepts with abstract categories, it is proposed to include 21 intermediate-level conceptual classes in the CIDOC CRM structure. Thus, we have built a formalized structure of concepts for the area of attribution and restoration of painting, ranging from the highest philosophical abstractions to specialized methods of computer analysis of picture images. This structure includes about 1300 terms in Russian. The English part has about 500 terms and is replenished as the interlanguage equivalence of the terms is agreed. In the part of vocabulary of computer methods, we have a full bilingual ontology with one-to-one correspondence of the Russian and English versions. The current version of the ontology is implemented as a TiddlyWiki software module and is publicly available.
A Side-Channel Attack Using Order 8 Rational Points against Curve25519 on an 8-Bit Microcontroller

Among the increasing evolution of IoT devices, practical applications need reliable secure protocols to communicate with each other. A major issue for modern cryptosystems is an implementation of secure and trustworthy mechanisms to rely on. A side-channel attack against these cryptosystems may overturn the guarantee of security against conventional cyber-attacks. Elliptic curve cryptography is public-key cryptography based on elliptic curves, and one of the well-known curves is Curve25519 which is used for TLS protocols as a recommended curve. This curve is mainly implemented on limited resource devices such as microcontrollers. However, this curve poses a weakness for low-order points during a Diffie-Hellman key exchange is employed. This research demonstrates possible exploitation of a threat of order 8 rational points of Curve25519 and shows results of the side-channel attacks using order 8 rational points on an embedded system. The results indicate the order 8 rational points might be applied to key extraction as attacker sides.
Application of Deep-RL with Sample-Efficient Method in Mini-games of StarCraft II

Recently, a key challenge of deep reinforcement learning (Deep-RL) is to handle a large amount of samples and learning time in domains with large state and action space. To remedy these problems, we focus on improving the sample efficiency of Deep-RL. We incorporate SIL into the state-of-the-art algorithm IMPALA in learning mini-games of StarCraft II, which has been a challenge to Deep-RL. Our results show that our agents achieve better performance with faster and more stable learning than those trained by the plain IMPALA on two mini-games of StarCraft II.
Image De-noising Method Using Median Type Filter, Fuzzy Logic and Genetic Algorithm

The goal of de-noising the medical images is to get rid of the distortions occurred in the noisy medical images. A new methodology is proposed to overcome the impulse noise affected mammogram images by using hybrid filter (HF), fuzzy logic (FL) and genetic algorithm (GA). The above said method is implemented in three steps: The primary step includes denoising of noisy mammogram images using median filter and adaptive fuzzy median filter respectively. The intermediate step intends to compute the difference vector using the above two filters and it is then given to a fuzzy logic-based system. The system utilizes triangular membership function to generate the fuzzy rules from the computed difference vector value. The last step makes use of genetic algorithm to select the optimal rule. Peak signal to noise ratio (PSNR) value is needed to be found for each population. For obtaining the best fitness value, the new population is formed repeatedly with the help of genetic operator. The performance of the method is measured by calculating the PSNR value. The proposed implementation is tested over mammogram medical images taken from Mammogram Image Analysis Society (MIAS) database. The experimental results are compared with different exiting methods.
Evaluating the Performance of Various Types of Neural Network by Varying Number of Epochs for Identifying Human Emotion

Epochs represents the number of times a network gets trained with a particular database. In this scenario, when we go for more number of epochs there is a possibility that neural network will memorize those values like a human brain, which should not be the case for an efficient target achievement. Here the emotions identified are based on varying number of epochs by using three types of network they are FFNN (Feedforward neural Network), BRRNN (Bayesian Regularized Recurrent Neural Network) and ANFIS (Adaptive Neuro Fuzzy Inference System) and with the help of these networks six basic emotions has been identified they are happy, sad, angry, fear, disgust and surprise. The network which achieves maximum accuracy with less number of epochs has been analysed based on the performance evaluation of Error Histogram, Error plot, Confusion Matrix, Regression Plot, Mean Absolute Error, Accuracy, Sensitivity, Specificity, Precision, Target Vs Output Plot.
Ontology-based 3D Relief Models Retrieval

To improve the precision and efficiency in 3D models management in digital relief editing, this paper proposes a 3D relief models retrieval method based on ontology semantic analysis. Firstly, according to the specific field(Chinese classical story) of relief art, we classified the models, and established the corresponding ontology for these models. Secondly, after analyzing the relationship among these models, we constructed the association between the ontologies and the rule-based semantic reasoning. Then, we used the similarity calculation based on synonyms to solved the common problem if there is no results when search a resemble word. Finally, we developed a relief models retrieval system, which can retrieve 3D models accurately. The experimental results have shown that the proposed method can effectively assist the digital relief editing when retrieving the 3D models, enhance the relativities among the relief models and improve the retrieval efficiency.
Sufficient Simple in-Camera Imaging Processing Pipeline for RAW-Images and sRGB Images

In this paper, we focus on the problem of modeling in-camera imaging processing pipeline. The key idea of the proposed pipeline is to combine several consecutive modules into only two-stage modules for each camera setting, namely, linear transformation module and non-linear transformation module. The major benefit of the pipeline is simple to reproduce the color such that the camera can obtain consistent robust result across changes in the scene illumination changes or camera-to-camera transfer in any image processing algorithms. To evaluate the performance of the proposed pipeline, the pipeline is verified with two different use cases, i.e. (1) reconstruction of RAW image from sRGB image compared to the ground-truth RAW image with various camera brands/models and different camera parameter settings, and (2) camera-to-camera transfer compared to the ground-truth and accommodating Photoshop image by expert (classical method). The quantitative results in term of RMSEs are mostly less than 3% (e.g., 0.0075 out of the intensity range [0, 1]) which is insignificantly different when comparing with the conventional method. This pipeline presents a simpler than the classical and conventional pipeline. Not only simple pipeline but also less computation is presented.
Low Complexity Short Baseline Localization Algorithm Based on Taylor Expansion

Traditional short baseline positioning algorithm suffers from low positioning accuracy and exhaustive search algorithm has high complexity. This paper addresses the problem by introducing a low complexity underwater acoustic short baseline positioning algorithm. First, the positioning principle of short baseline system is studied. After that ingredients that affect estimate accuracy are analyzed, and then the modified algorithm is proposed: based on the exhaustive idea, the performance evaluation function is established first; then it is simplified by Taylor expansion formula; finally, the estimated position is achieved by minimizing it. In the proposed algorithm, linear operations such as multiplication and addition are used instead of square root calculation, thus it can reduce the computational complexity. Furthermore, fixed-point calculation can be realized using the ranging information between the underwater autonomous vehicle (AUV) and the beacon. Simulation results show the effectiveness of the proposed algorithm.
Comparison of Three-Dimensional Scanning Devices

There are now a variety of methods and devices for deriving 3D information, e.g. binocular camera (stereo) systems and methods of the class "shape from X". In addition, there are modern recording technologies that simultaneously measure gray scale values and distance (range cameras, TOF cameras). Many of these methods are available through low-cost sensor systems that are simple and fast to use. We propose a simple experimental setup and a processing chain in order to evaluate the quality of the data recorded by these systems. In an experiment with students we demonstrate the usability of this approach and introduce results for selected low-cost sensors.
Generation of Pixel Level Segmentation Masks for Detected Human Objects in a Scene with Deep Learning

Convolutional networks have gained a lot of importance in the field of computer vision. Various convolutional architectures have unleashed the possibilities to achieve pixel level precision. We present a method to generate highly accurate segmentation masks for human objects. Beginning from a RGB channel image containing more than one human objects, the method uses Single Shot Multibox Detector (SSD) to detect, localize and separate all the object instances. These are then fed into the segmentation network which is a Fully Convolutional Network. The network separates the background from the object, creating a segmentation mask. Experiments were performed on UT Interaction dataset obtaining a mean intersection over union score of 0.9132 and mean pixel level accuracy of 91.89% for segmentation mask.