Reviewerly/exHarmony huggingface dataset:
A New and Fast Bias Correction and Segmentation Model with Application to Human Brain MR Images

Magnetic resonance (MR) images have been used in detecting human brain disease more and more in recent years. The most important problem in the diagnose process is to find accurate regions, which makes image segmentation necessary and important. Though there are kinds of segmentation approaches for MR images, different limits and defects also appear in the practical application for bias fields and complex structures in MR images, such as inaccurate segmentation, over-correction and long iteration time. Considering those shortcomings, this paper presents a new level set model by expressing the image intensity in a continuous form and applying the split Bregman method. Thanks to the two improvements, this model can segment and correct MR images simultaneously and efficiently, and can achieve bias correction results without over-correction. Then the authors applied this model to a large quantity of MR images, and gained many good results. For better observation, quantitative and qualitative comparisons have been done between this model and the multiplicative intrinsic component optimization (MICO) model. The experimental results indicate that this model has performed well for challenging intensity inhomogeneity problems.
Handwritten English Character Recognition and translate English to Devnagari Words

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.
Computational models of shape saliency

Computational models predicting stimulus-driven human visual attention usually incorporate simple visual features, such as intensity, color and orientation. However, saliency of shapes and their contour segments influence attention too. Therefore, we built 30 own shape saliency models based on existing shape representation and matching techniques and compared them with 5 existing saliency methods. Since available fixation datasets were usually recorded on natural scenes where various factors of attention are present, we performed a novel eye-tracking experiment that primarily focuses on shape and contour saliency. Fixations from 47 participants who looked at silhouettes of abstract and realworld objects were used to evaluate the accuracy of proposed saliency models and investigate which shape properties are most attentive. The results showed that visual attention integrates local contour saliency, saliency of global shape features and shape dissimilarities. Fixation data also showed that intensity and orientation contrasts play an important role in shape perception. We found that humans tend to fixate first irregular geometrical shapes and objects whose similarity to a circle is different from other objects.
Nonclassical and nonlocal properties of atom-field systems in the framework of Pöschl-Teller potentials

We study the nonclassical and nonlocal properties for atom-field systems in the context of Pöschl-Teller potential. Using the coherent states for Pöschl-Teller potentials, we propose a Jaynes Cummings model that characterizes the interaction among two-level atoms and fields with this kind of potential. After solving the motion equation, we examine the dynamical change of atomic inversion, entanglement, fidelity, and squeezing phenomenon with respect to initial parameter values. We show how the quantities can be controlled through the proper choice of the potential parameters. Finally, we give the connection among the quantum quantifiers for both whole system and subsystems during the evolution.
Dialog-Based Meaning Derivation Service for Technical Language Domains

In our previous work, we present on how new algorithms can be recognized and learned from human descriptions. In this case, end users are able to extend the given system by their own functionality. During the evaluation, due to language limits on the system, it could interpret only 59 % of user input correctly. In this paper, we provide an approach on the Dialog-based Meaning Derivation Service (DMDS). In case, user input does not match to the system knowledge, DMDS serves various word networks to find relevant synonyms as candidates for the unknown word. DMDS then tries to verify these candidates to the given knowledge base. Finally, matched candidates are presented to the end user by the dialog system for confirmation of a contextual match. The meaning is learned after the user confirmation and is mapped to the given functionality, and can be used afterwards. Therefore, the model developed in this work can be categorized as supervised learning. Finally, both the performance and the quality recorded by input from a user study were examined. However, DMDS improves the correct interpretation of the system from 59 % to 82 %. Our focus is to improve the interaction between humans and machines and enable the end user to instruct programmable devices, without having to learn a programming language.
Deep Learning Technology for Identifying a Person of Interest in Real World

In this paper, we will propose a new embedded system prototype called PubFace, which uses the CNN model trained from scratch on facial celebrity images [1], to identify a “Person of Interest” in public space. This is done by tuning this model on new dataset comprising 5000 real images of 1000 different identity collected from social networks as Facebook and Instagram. After I got permission of collected images persons to use their facial images in this scientific research project. Then, we have investigated some ways for compressing the number of parameters of the resulting model to reduce the memory needed for both storing and performing a forward pass while simultaneously preserving acceptable good accuracy.
Reliability of computer-aided diagnosis tools with multi-center MR datasets: impact of training protocol

Computer-aided diagnosis (CAD) tools using MR images have been largely developed for disease burden quantification, patient diagnosis and follow-up. Newer CAD tools, based on machine learning techniques, often require large and heterogeneous data-sets to provide accurate and generalizable results. Commonly multi-center MR imaging data-sets are used. Typically, collection of these data-sets require adherence to an appropriate experimental protocol in order to assure that findings are due to a pathology and not due to variability in image quality or acquisition parameters across scanners and/or imaging centers. We compared different experimental training protocols used with a representative CAD tool (in this work, designed to identify Alzheimer’s disease (AD) patients from normal control (NC) subjects) using public multi-center data-sets. We examined: 1) subsets of the data-set that were acquired on the same scanner (simulating a single site homogeneous data-set), 2) a traditional cross validation framework (i.e., randomly splitting the data-set into training and testing sets irrespective of centre), and 3) a site-wise cross validation framework, in which training and testing data were differentiated by center using a leave one center out per iteration method. Results achieved with the homogeneous data-set, traditional cross-validation and site-wise cross validation differed (p = 0.0005): 100.0% (i.e., no misclassifications), 99.6% and 97.3% accuracy rates, respectively, even when the same image data-set, features and classifier were used. The lowest accuracy was observed with site-wise cross validation, the only protocol with no site-wise contamination between training and testing samples.
Student difficulties on understanding word problem based on ESD goals

Education for Sustainable Development (ESD) is a government program to achieve sustainability. This study contains math lessons using the word problem that is integrated in ESD. The purpose of this study is to find out student's response about sustainable development. This research uses a qualitative method. Written tests that integrate with issues of ESD objectives consist of several topics in mathematics that address environmental, social and economic issues are given. This test is followed by interview. The subject of the study were 32 students grade VIII in Bandung and 8 of them were interviewed afterward. Data analysis conducted many students who have difficulty in understanding the word problem. meanwhile the limited knowledge of students about the socioeconomic and environmental aspects becomes a challenge for the realization of ESD goals. From the results, we recommend for teacher to offend something happening in the social life, economy and environment in teaching mathematics so that students are more aware of the importance of sustainable development
The Assessment of Environmental Situation in the YaNAO Using Satellite Imagery in GIS-technology

The article addressed the issues of integrated environmental assessment based on the results of chemical analysis and satellite monitoring data. The method for estimating the anthropogenic load and the degree of territories degradation in the areas of oil production using the normalized difference vegetation index (NDVI), characterizing the change in the state of the vegetation cover, has been proposed. The dynamic of vegetation state change in the Yamal-Nenets Autonomous Okrug was calculated using Landsat satellite imagery data on the basis of GIS ArcGIS Desktop and the results were compared with the data of field observations of the environmental situation. A stable correlation between the NDVI change and the increase of pollution level of the territory was revealed. The thematic maps in GIS were created according to results of analysis.
Clustering‐Based Resource Allocation mechanism in Long Term Evolution Advanced networks with auto‐encoder for feature learning

Abstract Clustering‐Based Resource Allocation (CBRA) is a relatively recent approach applied in some communication network technologies that executes resource allocation rules by following patterns discovered in the user and traffic flow information. It is a useful option for classifying traffic and detecting the most suitable Quality‐of‐Service parameters to perform improved resource allocation, especially in mobile systems such as Long Term Evolution Advanced, which are growing in user demand. An open issue in the CBRA approach is the necessity to perform feature engineering manually (feature selection), which may cause loss of relevant system information and consequent deterioration of performance. In this research article, we present a novel CBRA mechanism that implements an auto‐encoder to perform feature learning, thus avoiding feature selection. The auto‐encoder, deployed at the early stages of the CBRA mechanism, transforms the user and traffic flow data into a shortened representation, preventing the curse of dimensionality while capturing the nonlinear correlations of the data. The simulation results indicate better cluster formation and performance improvement for real‐time video applications using the proposed mechanism.
Revisiting Resource Management for Deep Learning Framework

The recent adoption of deep learning for diverse applications has required infrastructures to be scaled horizontally and hybrid configured vertically. As a result, efficient resource management for distributed deep learning (DDL) frameworks is becoming increasingly important. However, existing techniques for scaling DDL applications rely on general-purpose resource managers originally designed for data intensive applications. In contrast, DDL applications present unique challenges for resource management as compared to traditional big data frameworks, such as a different master–slave communication paradigm, deeper ML models that are more computationally and network bounded than I/O, the use of heterogeneous resources (e.g., GPUs, TPUs) and the variable memory requirement. In addition, most DDL frameworks require data scientists to manually configure the task placement and resource assignment to execute DDL models. In this paper, we present Dike, an automatic resource management framework that transparently makes scheduling decisions for placement and resource assignment to DDL workers and parameter servers, based on the unique characteristics of the DDL model (number and type of parameters and neural network layers), node heterogeneity (CPU/GPU ratios), and input dataset. We implemented Dike as a resource manager for DDL jobs in Tensorflow on top of Apache Mesos. We show that Dike significantly outperformed both manual and static assignment of resource offers to Tensorflow tasks, and achieved at least 95% of the optimal throughput for different DDL models such as ResNet and Inception.
Second Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2019)

The second WEPIR 2019 workshop brings together researchers with different backgrounds interested in continuing to explore and advance the evaluation of personalisation in information retrieval. The workshop builds on the first WEPIR workshop held at CHIIR 2018, and will focus on further developing a common understanding of the challenges, requirements and practical limitations of meaningful evaluation of personalisation in information retrieval. In particular, the planned outcome of the workshop is to progress the work from WEPIR 2018 towards the development of concrete proposals for novel and innovative methodologies to support evaluation of personalised information retrieval from both the perspectives of the user experience in interactive search settings, and of user models for personalised information retrieval and their algorithmic incorporation in the search process.
Vehicle Detection, Tracking and Counting on M4 Motorway Pakistan

An immense interest of the researchers in real time vehicle detection, tracking and counting is a need of society for trouble free and safer travelling in cities. Automatic tracking and detection of vehicles is a laborious task in traffic monitoring. The proposed method processes an input video to track and detects the vehicle through its motion and also counts the total number of vehicles on the road. To enhance the process, we use consolidation of different image processing and computer vision techniques. The proposed method of detection and tracking of vehicles on a road has been implemented on hardware raspberry Pi 3B using MATLAB as software for the simulation. The video is captured on the M4 motorway in Pakistan by a camera attached with raspberry pi then it is processed through the proposed algorithm. The Gaussian Mixture Model (GMM) along with optical flow parameters are used to detect vehicles which are in motion. To segment objects from the background vector threshold is used. The filtering process is applied to suppress the noise and then blob analysis is used to identify the vehicles from an input video. The outcomes demonstrate that the proposed framework effectively distinguishes and tracks moving objects in the urban recordings.
Research on Optimization Retrieval Technology of Multimedia Information Management System Based on Fuzzy Control

In order to improve the efficiency of image information retrieval in multimedia information management system, a retrieval technology of multimedia information management system based on fuzzy directivity control is proposed. The retrieval multimedia information management system is decomposed with LGB to realize the image multimedia information feature compression, and the control constraint parameter model of the multimedia information management system is constructed. The fuzzy directivity control method is used to adjust the retrieval category adaptively, and the edge contour feature quantity of the image in the multimedia information management system is extracted. The fuzzy PID control algorithm is used to cluster and fuse the extracted edge contour features of multimedia images, which can realize the accurate retrieval and scheduling of the images in the multimedia information management system. The simulation results show that the proposed algorithm has high precision and zero frame difference in image retrieval in multimedia information management system, which shows good multimedia information management and system control ability.
Application of MCC-based Robust High-degree Cubature Kalman Filter in Integrated Navigation System

As the integrated navigation system is a nonlinear system, in the case of non-gaussian noise, the traditional nonlinear gaussian filtering algorithm has a serious problem of decreasing filtering precision. In this paper, a new robust high-degree Cubature Kalman filtering algorithm is proposed, which takes into account the nonlinearity of the system and non-gaussian noise. The algorithm improves the measurement updating process by using the Maximum correntropy criterion(MCC), and converts the traditional measurement updating problem into the linear regressione quation solving problem. Combines the advantages of Maximum correntropy criterion and Cubature Kalman filter to deal with non-Gaussian and nonlinear systems. The proposed algorithm is applied to the SINS/GPS integrated navigation system, the simulation results show that the proposed algorithm's filtering performance is greatly affected by the kernel width. Under the condition of gaussian mixture noise, the new robust high-degree Cubature Kalman filter based on Maximum correntropy criterion(MCC-HCKF) is more robust and has higher filtering precision than the traditional high-degree Cubature Kalman filter(HCKF).
Texture classification using the rotational-invariant local symmetric tetra pattern

Feature extraction plays a key role in pattern recognition problems. The texture feature is an important feature which helps to describe an image with textural information. A new texture descriptor, the Local Symmetric Tetra Pattern (LSTP), is proposed in this work. This descriptor is developed for the local description of an image. It considers not only the surrounding eight neighbors, but also the eight pixels at the next level to describe the texture efficiently. For every pixel, the maximum edge value, the number of negative sign bits and the number of positive sign bits for each degree of symmetry are computed. Image classification is experimented using the Original Brodatz, Outex and Kylberg Texture Dataset v.1.0 databases. The investigation results are compared with existing method which shows promising achievement of the proposed techniques in terms of their evaluation measures. It is also found that the proposed texture descriptor is rotationally invariant.
Vanishing Point Detection Under Foggy Weather with Edge-Based Approach

Vanishing Point detection is one of the vision-based approaches used for autonomous vehicles and Driver Assistance Systems DAS. It is principally useful for detecting the road needed in vehicle navigation and tracking. Like other methods based on vision, the vanishing point detection approach is deeply sensitive to the presence of bad weather as fog. In this paper, we present an efficient edge-based approach for detecting the vanishing point of road scene under foggy weather based on a combination of an adaptive Canny method for edge detection, and the Hough Transform for straight line extraction. The optimal vanishing point is estimated by applying a k-mean clustering on the candidate points obtained by the straight lines intersection. We tested our approach on 731 real and synthetic images, where the experimental results show that the proposed approach for detecting the vanishing point under foggy weather gives good results.
Multi-choice Virtual Machine Allocation with Time Windows in Cloud Computing

Virtual machine allocation is a core problem in cloud computing. Most cloud computing platforms allow users to submit one requirement, which does not satisfy the diversity of user demands and also reduces the incomes of the platform. We propose a novel model, called multi-choice virtual machine allocation (MCVMA) with time windows, where the users can enter and leave the system at any time and submit multiple requirements. We design an optimal algorithm based on dynamic programming and a heuristic algorithm based on the resource scarcity and density for the MCVMA problem with time windows. We experimentally analyze both algorithms in terms of social welfare, execution time, resource utilization and users served.
Generative Matching Between Heterogeneous Meta-Model' Systems Based on Hybrid Heuristic

Nowadays, designing and building computer systems has become increasingly difficult; this is essentially due to the great number of existing solutions. This article proposes a hybrid heuristic allowing the connection between meta-models of different systems, which will allow the generation between models conforming to these connected meta-models. First, this article presents the architecture of the generative matching approach named generative automatic matching (GAM), then is introduced an important part of this approach, a hybrid heuristic allowing the matching between the meta-models. Finally, the authors conclude by a multiple criteria evaluation of this approach.
Software Encryption and Software Authorization Management Based on Sentinel

In this paper, based on Sentinel encryption tool, according to the important functions and publishing methods that Smart-Color medical image acquisition system needs to protect, combined with software authorization mode, the S-Color Medical Image Acquisition System is encrypted. The steps and methods of encryption are introduced in detail.
An Analysis of the Patterns of Code Switching in the Namibian Parliament

This chapter examines patterns of code switching in the Namibian Parliament as represented in the volumes of parliamentary Hansards from 2015 to 2017. In this chapter, a sample of 10 issues of the Namibian parliamentary Hansards were selected. The selected Hansards constituted a representative purposive sample of the Hansards produced by the Parliament of the Republic of Namibia from 2015 to 2017. The selection of the Hansards to form part of the sample depended on the availability of hard copies of the selected issues. Using purposive sampling was important in choosing the right sample for the study as some Hansards had fewer instances of code switching than others. It was important to select those with high frequencies of code switching.