Reviewerly/exHarmony huggingface dataset:
Halftone based face recognition using SVM

We propose a face recognition method based on halftone binary image using SVM classifier. In this method, a training set and a testing set of halftone images are created from a face database of gray images. Then, features are extracted from halftone images and a multi-class SVM model is created. To extract features from a halftone image, the image is divided into non-overlapping regions of equal size. Each region is processed to give a feature value corresponding to the region. This reduces the size of feature vector depending on the size of region considered for a feature. Four different types of features can be generated depending on how the processing of the pixels in each region is done to generate a feature. Recognition rate is computed for each of the four different types of features. Three different types of features give comparatively higher recognition rate for different window sizes. The method has been tested on AT&T face database using different feature types and window sizes. In one of feature types, it gives recognition rate of 95% which much higher than recognition rate 91.25% when using with HoG features on the same face database.
Fault value analysis of on board ATP system using big data

Abstract RFM module is widely used for the classification of customer value. The classification results based on big data are often used as portrait of customer loyalty and customer value evaluation. In this paper, RFM model is introduced innovatively to analyse the fault of on board ATP (Automatic Train Protection) system. Through the fault value classification, it can effectively predict the impact of a certain type of fault on operation. For one thing, the operation departments and equipment providers can pre-process the corresponding failures according to the evaluation results, so as to reduce operational losses. For another thing, it can be used as the basis for dealing with big data of ATP faults.
Electronic Medical Record of University Hospital Based on Deep Learning

Abstract Because the traditional paper medical record has the disadvantages of illegible handwriting and being difficult to save, we design the electronic medical record system for the university hospital. Additionally, the neural network model is used to realize auxiliary diagnosis and help doctors make decisions, which can provide effective reference for preventive health care, health education, and medical services in school hospitals. The system is putting into use in our university and has effectively improved the work efficiency of our school hospital.
Oracle RAC performance Comparison on Physical Servers and VMware VSAN Servers

Abstract The physical performance of the Oracle RAC hardware architecture directly affects Oracle’s output performance. In this paper, we perform performance tests on three Oracle RAC hardware architectures, and the test data is simulated using real teaching management system data on campus. The simulation results show that the two new oracle RAC hardware architectures are respectively occupied by the TPM and IOPS indicators, and the overall is stronger than the traditional external storage architecture.
Object movement highlighting technique using a deep-learning based object detector for effective UAV control

This paper propose a method that highlights the objects, which are moving in real-time, using a deep-learning based detector. The method first detects the objects in real-time, using the state-of-art deep learning object detector named “YOLOv3”. After that, for effective Unmanned Aerial Vehicles (UAV) control, the objects, which were detected, will be highlighted by the image difference function. In this part, the most important job is to know which object has moved and how much it has moved. Our study, focus on these two parts. Using the Bounding box size sort, the object bounding boxes will be sorted from small size to big size boxes and with the Pixel winning function, the object boxes will take the pixels that belong to them. At last, with each object's pixel value the percentage of the object's movement will be calculated.
An efficient FCN based neural network for image semantic segmentation

Image segmentation has always been a key research issue in the field of computer vision. Image segmentation networks that use deep learning methods require a large number of finely labeled samples, which is difficult to obtain. In this paper, we combine the focal loss function with the fully convolutional networks to improve network performance. And we collected and built a dataset contents 1500 samples with complex background. We trained the improved network with the dataset to achieve 81.55% in mean average precision and 76.13% in mean intersection over union.
Research on Electrical Automation Control System and Design Based on Geographic Information Technology

Abstract As the pace of power plant construction accelerates, higher requirements are placed on the application performance of the control system. From the current development status of the current electrical control system, it reflects the characteristics of multi-function, miniaturization and intelligence, and provides an important guarantee for the reliable operation of the electrical system. This paper analyzes and studies GIS in geographic information technology and power system application. The structure and function of electric power GIS system are designed. According to the structure and particularity of distribution network, a new distribution network database model is established. The overall planning and design of the network information database are carried out. The design idea of electric automation control system is discussed, and the development trend of electric automation control system in the future is prospected.
Image Denoising Using Deep CGAN With Bi-Skip Connections

With the rapid development of neural networks, many deep learning-based image processing tasks have shown outstanding performance. In this paper, we describe a unified deep learning-based approach for image image denoising. The proposed method is composed of deep convolutional neural and conditional generative adversarial networks. For the discriminator network, we present a new network architecture with bi-skip connections to address hard training and details losing issues. In the generative network, a objective optimization is derived to solve the problem of common conditions being non-identical. Through extensive experiments on image denoising task on both qualitative and quantitative criteria, we demonstrate that our proposed method performs favorably against current state-of-the-art approaches.
Complexity Assessment of Battlefield Electromagnetic Environment

The battlefield environment in modern warfare, especially the electromagnetic environment, is increasingly complex. By quantitatively evaluating and classifying the complexity of the battlefield electromagnetic environment, and considering the usual training situation, the commander can judge the performance of the equipment in that environment and know what to do. The paper uses three basic parameters of spectrum occupancy rate, time occupancy rate, and spatial coverage rate, and uses analytical methods to establish the formula to measure the complexity of the electromagnetic environment. Through case analysis, it is proved that this evaluation method is scientific and feasible. The paper has important significance for studying combat problem in complex electromagnetic environment.
A CLASS OF IRREDUCIBLE POLYNOMIALS ASSOCIATED WITH PRIME DIVISORS OF VALUES OF CYCLOTOMIC POLYNOMIALS

We prove that for every sufficiently large integer , the polynomial is irreducible over the rationals, where the coefficient of for is the reciprocal of the decimal number consisting of digits which are each 1 . Similar results following from the same techniques are discussed.
Pengembangan Buku Ajar IPS Terpadu Berbasis 3 Dimensi di SMPN 3 Pasiran - Lumajang dengan Pendekatan Kontekstual Learning

The involvement of teaching materials for the ongoing teaching and learning activities in schools is very important, especially teaching materials that have appeal and are arranged creatively for use by students. This attraction will encourage students to be more enthusiastic about learning. The motivations of students are inseparable from the role of educators in choosing teaching materials that are appropriate to the needs of their students. Educators also need to develop self-creativity to be able to create creative teaching materials. Teaching materials in the form of teaching books are generally in the form of textbooks and student worksheets so that the tendency of students to be lazy and bored in learning appears. Therefore we need teaching materials that have more appeal so students can easily understand. One of the creative teaching materials that can be developed is a three-dimensional textbook that can be used for students entering the age of children to adolescents. This textbook is intended to motivate students to be more active in reading so that they are able to link the examples presented in the book into real conditions in the surrounding environment.
Image Segmentation of LBF Model with Variable Coefficient Regularized Area Term

In this paper, an improved LBF model based on local regional information is proposed for image segmentation. The basic idea is to add the regularized area term to the energy function of the LBF model and establish a variable coefficient with adaptive capability composed of image regional information. Compared with the LBF model, the proposed model increases the driving force of the evolution curve, making the result better when dealing with the images with weak boundaries and intensity inhomogeneity. At the same time, it effectively solves the problem that the LBF model is sensitive to the initial position and size of the evolution curve. This model is used to segment medical images with complex topological structure and intensity inhomogeneity. Experimental results show that regardless of the initial curve of any position or any size, it has little influence on the segmentation result; moreover, the localization of deep-depressed image boundaries is more accurate, so we get the conclusion that the new model has corresponding improvements in segmentation accuracy and robustness.
A Systematic Evaluation of Multi-Sensor Array Configurations for SLAM Tracking with Agile Movements

Accurate tracking of a user in a marker-less environment can be difficult, even more so when agile head or hand movements are expected. When relying on feature detection as part of a SLAM algorithm the issue arises that a large rotational delta causes previously tracked features to become lost. One approach to overcome this problem is with multiple sensors increasing the horizontal field of view. In this paper, we perform a systematic evaluation of tracking accuracy by recording several agile movements and providing different camera configurations to evaluate against. We begin with four sensors in a square configuration and test the resulting output from a chosen SLAM algorithm. We then systematically remove a camera from the feed covering all permutations to determine the level of accuracy and tracking loss. We cover some of the lessons learned in this preliminary experiment and how it may guide researchers in tracking extremely agile movements.
Human Tracking Using Wigner Distribution and Rule-Based System in RGB Video

In recent times, human tracking plays a crucial role in several applications like surveillance, free biometry, realistic world etc. In this research work, we suggest a new method to track the objects like humans using the motion obtained from color images. This algorithm does not use the object characteristics which is tracked and hence it resembles human eyes that uses the process of tracking in all the available images in RGB. Spatial and temporal association of motions are considered for motion association, which is the proposed plan of action to decrease the undesired selection process. Furthermore, for different images the Wigner distribution has been used which is less dependent on the fluctuation in threshold frame and thus reduces the untrue object detections. The results acquired with this algorithm is identical and consistent which in turn provides the reduction in computational complexity of this algorithm.
Masked RCNN+ fast boosted tree classifier

The most intriguing advancements brought by deep learning and neural networks is in the field of computer vision. We associate any problem that has an image or camera input to encompass problems within computer vision. Self-driving cars, MRI analysis, Mars exploration rovers, facial recognition systems, object detection and augmented reality are just a few breakthroughs in the field. In this paper we will take a look at ways to improve pedestrian detection by using Convolutional Neural Network (CNN) along with Region Proposal Network (RPN), Masked Region Based Convolutional Neural Network (RCNN) and Fast Boosted Tree Classifier. Our approach effectively recognizes objects in a picture while at the same time generating an excellent segmentation mask for each instance. The technique, called Mask R-CNN, expands Faster R-CNN by including a branch for predicting an item mask in parallel with the current branch for bouncing box recognitionn.
Fourth International Workshop on Software Product Line Teaching (SPLTea 2019)

Education has a key role to play for disseminating the constantly growing body of Software Product Line (SPL) knowledge. In a sense, every researcher in SPL should think about how to teach SPL. This workshop aims to explore and explain the current status and ongoing work on teaching SPLs at universities, colleges, and in industry (e.g., by consultants). This fourth edition will continue the effort made at SPLTea'14, SPLTea'15 and SPLTea'18. In particular we seek to better understand how to build a curriculum for teaching SPLs - a central issue as reported in surveys and as informally discussed at SPLTea'18. We expect several lightning talks that report on traditional questions like: what is the targeted audience? What is the place in the curriculum? What is the material (slides, tools, books, etc) used? As there is hardly a one-size-fits-all curriculum, the workshop aims to collectively identify commonality and variability when building SPL curriculums. As a concrete outcome, we expect to elaborate a variability model of SPL teaching that could be actuated to derive custom curriculum in various contexts.
Semantic Concept Detection for Multilabel Unbalanced Dataset Using Global Features

Digital evolution in capturing video, advances in compression technology and internet leads to availability of large videos on the web. There is growing need for efficiently retrieving relevant videos. Semantic Concept detection assigns multiple labels to segmented shots or entire video which facilitates many applications like multimedia indexing and retrieval. This paper presents the semantic concept detector architecture for unbalanced dataset which assigns multiple labels with probability to input video. The proposed architecture uses visual features extracted on global scale. The unbalanced dataset problem is handled by partitioning dataset into segments further evaluating classifiers on these dataset. Feature fusion and decision fusion is evaluated using machine learning algorithm for all segments. Performance of the concept detection architecture for above fusion methods are reported with Mean Average Precision. The proposed method for multilabel concept detection is evaluated on TRECVID 2007 dataset and performance is better than existing early and late fusion.
Research and Implementation of BDaaS Cloud Platform for Security Industry

with the sharp growth of security data and equipment resources in the security industry, the traditional management mode caused such problems as low utilization rate, poor flexibility, weak scheduling ability, insufficient scalability and serious waste. And at present, few researchers in the security industry use the idea of big data to analyze and process security data. In this paper, based on the idea of cloud computing and big data, the demand analysis of security industry data cloud management was completed, and by using cloud computing virtualization technology, the security industry big data platform was demonstrated and designed, realized the integration of resources and data within the security industry. The data application and processing cluster of security industry could be constructed by deploying computing nodes quickly, and computing resources could be allocated on demand to reduce redundant deployment of security management and waste of resources. The functions of security incident alarm management, security patrol management, security resource management, decision support, data operation and maintenance were realized. Through functional testing and performance testing, it was proved that the BDaaS cloud platform for security industry had greatly improved the data storage capacity, stability, security data processing efficiency and operation response speed of the original security management platform.
Detection and Classification of Facial Features Through the Use of Convolutional Neural Networks (CNN) in Alzheimer Patients

In recent years, the widespread use of artificial neural networks in the field of image processing has been of vital relevance to research. The main objective of this research work is to present an effective and efficient method for the detection of eyes, nose and lips in images that include faces of Alzheimer’s patients. The methods to be used are based on the extraction of deep features from a well-designed convolutional neural network (CNN). The result focuses on the processing and detection of facial features of people with and without Alzheimer’s disease.
Can Transparent Virtual Objects Be Represented Realistically on OST-HMDs?

In mixed reality using video see-through displays, various optical effects can be simulated for representing a transparent object. However, consumer-available OST-HMDs can not reproduce some of their effects correctly because of its mechanism. There has been little discussion concerning to what extent such transparent objects can be represented realistically. In this demonstration, we will show how effective each for combination of existing methods is.
CACV-tree

Sentence similarity modeling plays an important role in Natural Language Processing (NLP) tasks, and thus has received much attention. In recent years, due to the success of word embedding, the neural network method has achieved sentence embedding, obtaining attractive performance. Nevertheless, most of them focused on learning semantic information and modeling it as a continuous vector, while the syntactic information of sentences has not been fully exploited. On the other hand, prior works have shown the benefits of structured trees that include syntactic information, while few methods in this branch utilized the advantages of sentence compression. This paper makes the first attempt to absorb their advantages by merging these techniques in a unified structure, dubbed as CACV-tree (Compression Attention Constituency Vector-tree). The experimental results, based on 14 widely used datasets, demonstrate that our model is effective and competitive, compared against state-of-the-art models.
Geo-Referencing System

At this point, you have learnt about the basic concept of the database in the GIS environmentEnvironment . The aim of this chapter is to understand our Earth’s reference system and how we can manage our database in the real coordinate system to locate Earth’s features. After reading this chapter you should be able to understand the following:
Adaptive fractional-order differentiation filter guided by feature asymmetry for feature-preserving ultrasound despeckling

In ultrasound despeckling, it is essential to remove speckle noise with satisfactory feature preservation for better diagnosis and analysis in many applications. This paper proposes an adaptive fractional-order differentiation filter guided by feature asymmetry for feature-preserving ultrasound despeckling. Since fractional anisotropic diffusion (FAD) filter performs well in smooth regions while fractional total variation (FTV) filter works better near features, our framework combines the FAD filter and FTV filter to maintain their advantages. Moreover, the feature significance calculated by using feature asymmetry is integrated into the diffusion coefficient of the FAD filter to protect low contrast features. Finally, rather than adopting one fixed fractional order, the proposed filter adaptively assigns fractional order on the basis of the feature significance to further preserve features. Experiments on synthetic and clinic ultrasound images demonstrate that the proposed filter performs better in both speckle reduction and feature preservation compared with other state-of-the-art ultrasound speckle reduction filters.
Water Quality Modeling and Prediction Method Based on Sparse Recurrent Neural Network

It is an important prerequisite for scientific management and maintenance of water resources to accurately predict all kinds of indices that affect water quality. This paper proposed a method of forecasting water quality index and rank based on sparse recurrent neural network (SRNN). The learning algorithm of the network is designed based on the principle of minimum mean square recursive error. A prediction model for predicting water quality index and rank is constructed by using the neural network. The validity of the model is verified by predicting the water quality parameters and water quality rank of a river in Zhejiang Province.
Evaluation of Crosshair-Aided Gyroscope Gamepad Controller

With an ever-increasing number of technological tools and gadgets in our life, people have become familiar with multiple kinds of user interaction interfaces and devices. Day in, day out people interact with various devices that follow different user interaction paradigms, such as when they are using a mobile phone, smart TV or a gaming console. Although these devices have various forms among themselves, the interaction method used to control them can make a significant difference in usability. Gaming consoles have become a huge area of the computer entertainment business in years. With every new generation of gaming consoles, the technologies behind it improve dramatically. Most of the time, the improvements are about the graphics and interaction devices. It can be clearly said that even though the graphics have improved in the last two generations of gaming consoles, the interaction paradigms and approaches were not up to the users’ expectations. This is especially the case for a first-person shooter and real-time strategy (RTS) games. Accordingly, bearing in mind the above as a motivation, the aim of this work is to develop a prototype game controller that will improve the usability and gameplay experience of the first-person shooter games.