Reviewerly/exHarmony huggingface dataset:
Feature selection based on feature curve of subclass problem

Feature selection is a key step to improve classification performance. Feature selection methods are divided into three types: filters, wrappers and embedded. Generally speaking, the filter methods use one score to judge the comprehensive classification ability of features for all classes. The higher the score is, the stronger the classification ability is. However, studies in many literature have indicated that only by selecting features with high scores often cannot achieve good effect. Therefore, this paper introduces a new feature selection method based on feature curve of subclass problem (Information Gain Regression Curve Feature Selection, referred to as IGRCFS) to find the features with high discriminal ability for each class, and then obtain the optimal feature subset. In order to verify the validity of the IGRCFS method, five kinds of existing feature selection methods are compared on eight datasets. The results demonstrate that the proposed method IGRCFS is effective.
Frequency-multiplexed Photonic Reservoir Computing

We present a coherent wavelength division multiplexed reservoir computer based on intra-cavity phase modulation. We report preliminary results on a signal classification task using a small number (7) of frequency sidebands.
Age of Great Chinese Dragon: Supercomputer Centers and High Performance Computing

Author describes Chinese supercomputer centers and networks.There are currently five National Supercomputing Centers in China, which are established in Tianjin, Shenzhen, Shanghai, Jinan, and Chang-Sha.These cities were selected as pilot ones for conducting an experiment on the development of the cloud computing services market.The directions of evolutionary and innovative development of exaflops supercomputers are highlighted in Pacific region.The evolutionary approach is the simplest and allows you to quickly get the result, but the created supercomputer of this type will be effective only when solving a narrow class of problems and have low energy efficiency.An innovative approach involves basic research and the development of innovative technologies, which is much more complicated and requires more time.Innovative technologies for the development of exaflops supercomputers, due to the stringent requirements of energy efficiency and productivity efficiency, have much in common with technologies for creating highly efficient on-board and embedded systems.These technologies are called exascale, they should provide the ability to create single-board on-board supercomputers of the teraflops level and single-rack supercomputers of the petaflops level of performance.The main problems of creating exaflops (exascale) systems: increasing the overall performance of the system by three orders of magnitude while weakening the influence of Moore's law on the performance of an individual processor core; minimization of energy losses and performance losses associated with data access, information transfer at all levels of the supercomputer hierarchy and data storage.
Multidivisible Online/Offline Cryptography and Its Application to Signcryptions

We introduce a general concept of multidivisible online/offline (MDO) cryptography , which covers the previous works including online/offline cryptographic schemes, divisible online/offline signatures, incrementally executable signcryptions, and multidivisible online/offline encryptions. We then present the notion of multidivisible online/offline signcryptions (MDOSCs) as novel application of MDO cryptography. We define several security notions for MDOSCs and show implications and separations between these security notions. We also present a generic construction of MDOSC that achieves the strongest security notions with regard to confidentiality and unforgeability. Using MDOSC schemes, the computationally restricted and/or bandwidth-restricted devices can transmit messages in both confidential and authenticated way with low computational overhead and/or low-bandwidth network.
The site selection of heat islands and its adaptation to satellite imagery based on the TOPSIS model in Mashhad

The site selection of heat islands and its adaptation to satellite imagery based on the TOPSIS model in Mashhad
Bridging Symbolic Computation and Economics: A Dynamic and Interactive Tool to Analyze the Price Elasticity of Supply

It is not possible to achieve the objectives and skills of a program in economics, at the secondary and undergraduate levels, without resorting to graphic illustrations. In this way, the use of educational software has been increasingly recognized as a useful tool to promote students’ motivation to deal with, and understand, new economic concepts. Current digital technology allows students to work with a large number and variety of graphics in an interactive way, complementing the theoretical results and the so often used paper and pencil calculations. The computer algebra system Mathematica is a very powerful software that allows the implementation of many interactive visual applications. Thanks to the symbolic and numerical capabilities of Mathematica, these applications allow the user to interact with the graphical and analytical information in real time. However, Mathematica is a commercially distributed application which makes it difficult for teachers and students to access. The main goal of this paper is to present a new dynamic and interactive tool, created with Mathematica and available in the Computable Document Format. This format allows anyone with a computer to use, at no cost, the PES(Linear)-Tool, even without an active Wolfram Mathematica license. The PES(Linear)-Tool can be used as an active learning tool to promote better student activity and engagement in the learning process, among students enrolled in socio-economic programs. This tool is very intuitive to use which makes it suitable for less experienced users.
Camera Motion Estimation and optimization Approach

A camera motion estimation method based on feature matching for low-power processing platform is proposed in this paper. The proposed method is more effective than the previous methods that utilizes epipolar constraint, and provides a better time efficiency than epipolar constraint based method. Experimental results indicate the proposal saves 10% of the time consumption than previous method. In addition, this paper constructs a least squares model of camera re-projection error. On this basis, Levenburg-Marquart gradient descent method is employed to minimize the re-projection error. The experimental results show that the employed optimization method improves the accuracy of camera motion estimation about 10%.
PYCV: a PLUMED 2 Module Enabling the Rapid Prototyping of Collective Variables in Python

Collective variables (CVs) are functions of the coordinates of particles in a molecular system.The choice of CV is crucial to capture relevant degrees of freedom of the model being simulated (Barducci, Bonomi, & Parrinello, 2011).This is especially important when employing biased sampling techniques such as umbrella sampling or metadynamics (Laio & Parrinello, 2002;Torrie & Valleau, 1977), which apply generalized forces to CVs to enhance the sampling of events otherwise not observable by direct simulation.CVs may be simple geometrical observables (distances, angles, torsions, etc.), but often they are more complex functions designed to capture structural determinants, such as tertiary and quaternary structure of proteins, experimental observables, crystal symmetries, etc. (
Converging mobile edge computing and wireless access for virtual reality

Most Virtual Reality (VR) headsets come with cable connections to their computing units. This is inconvenient for deployment in immersive interactive environments. A wireless VR solution is a promising approach for improving the user experience. The Mobile Edge Computing (MEC) provides high bandwidth and low latency, which enhances VR wireless solution for supporting high definition and low latency applications. In this paper, we propose a VR wireless solution based on MEC to meet the requirement of future VR applications with high resolution display and low latency. Compared to existing wireless VR solutions, our solution is superior in improving the video quality and enhancing the quality of the user's experience.
Attention-Guided Decoder in Dilated Residual Network for Accurate Aortic Valve Segmentation in 3D CT Scans

Automatic aortic valve segmentation in cardiac CT scans is of high significance for surgeons’ diagnosis on aortic valve disease and planning of aortic valve-sparing surgery. However, the very fast flapping speed, ambiguous shapes and extremely thin structures of the aortic valve lead to great difficulties in developing automatic segmentation algorithms. In this paper, we proposed an end-to-end deep learning method to address the problem of segmentation of the aortic valve from cardiac CT scans. Our method uses 3D voxel-wise dilated residual network (DRN) as backbone network and we equip it with novel attention-guided decoder modules to suppress non-valve artifacts and noise and pay attention on the fine leaflets in order to acquire accurate valve segmentation results. We conducted qualitative and quantitative analysis to compare with state-of-the-art (SOTA) 3D medical image segmentation models. Experiment results corroborate that the proposed method has very high competence.
ASSESSMENTS 0F THE APPLICATION OF NURSING PROCESS IN SURGICAL WARDS

The nursing process is a problem-solving framework that enables the nursing staff to plan their care for a patents and clients as an individual or group basis .This requires nurses to be accountable for the care that they prescribe with care deliver and to keep clear and accurate records of the discussions .Today, one's ability to use the nursing process is governed by the standards for pre-registration for any nursing education as outline. The present study aimed at utilize the nursing process in Mosul teaching hospitalsThe initial sample consisted of (42) nurses whose were selected randomly from surgical and emergency units at Mosul teaching Hospitals .the period of data collection for this study was carried out from 20-1-2019 until 20-2-2019 . in Ibn-Sina and AL-Jamhuori teaching hospitals.The results shows a highly significant of all assessment for applications of nursing process at p. value (0.05)In conclusions of this study The nursing process it's very important to enhancing the quality of care especially at surgical field and The highly significant number for all elements of nursing process.Furthermore, the nursing staff cant not be able to make any management without nursing process.Thus the researcher put some recommendations for the current study such as ;the nursing process case sheet should be putting in patient case folder , to evaluate the patient and his health status systematically, the nursing staff and health care team must be educating to how could they using nursing process and updating, surgical wards should be formulate a time table for all clients to guide them about their care as a schedules .The head of nursing staff as a adviser must be enrolled with conferences and educations centers to discuss the implementation of nursing process .
A Gallery-Guided Graph Architecture for Sequential Impurity Detection

Ambiguous appearance discrimination plays an important role in the impurity detection task. Among the majority of deep learning models, images from every sequence are processed separately instead of being considered collectively. Therefore, the outputs of these models given a single region proposal might not be accurate. In this paper, a gallery-guided graph architecture is proposed and integrated to overcome such limitations. Specifically, region proposals are firstly generated using a two-stream fusion network; then their feature embeddings are extracted from a convolutional neural network by reducing intra-class variations while increasing inter-class ones. Secondly, a graph representing clusters among different training sequences updates relationships between region proposals in the test sequence. Finally, the features of the graph are classified by a graph convolutional neural network. Different from those learned weights in conventional common object detectors, region features from all the training sequences are explicitly integrated into a gallery-guided graph architecture. Extensive experiments on IML-DET dataset demonstrate that our proposed method can obtain competitive performances compared with previous state-of-the-art object detection approaches transferred into this task.
Horizontal line detection using genetic algorithm

In mobile robots and autonomous vehicles, the information of depth and camera’s orientation are necessary for the applications of positioning and navigation. In this paper, we propose an appearance-assumption-based method for the detection of horizontal line (HL) or vanishing line (VL) on the image observed with a single omnidirectional camera. HL can provide the important information about depth and camera’s orientation for the single view estimation. The detection of HL is assumed as an optimization problem. The optimization of the line is adopted by the genetic algorithm (GA). We implement the real-time detection method of HL using GA. Therefore, the proposed method is evaluated on the multiple videos with the various environment. Experimental results demonstrate the efficiency of the proposed method.
Ethnicity, Inequality and the Tragedy of African Development: A Comparative Case Study of Nigeria and the South Sudan

This paper provides theoretical insights on the behaviour of ethnicity in the developmental experience of two African countries, namely, Nigeria and South Sudan, which have been regarded as failed states not due to their heterogenic configurations but due to the adverse effects of ethnicity and inequality.This paper reinforces the argument that political and social instability are the major causes of African underdevelopment.It posits that ethnicity encapsulates these factors using the cases of Nigeria and South Sudan to offer germane examples of the hydra-headed effects of ethnicity.Since independence, the surge of inequality in Africa has been driven not by the revolution in technology but by ethnicity and its concomitants.Africa is also the continent with the highest rate of poverty, illiteracy, and infant mortality.As palliatives, several development and international financial aid programmes have tried to address the issues of poverty and underdevelopment in Africa, Asia and Latin America.While these programs yielded results in some parts of the world, they were almost a failure in Africa.Indeed, ethnicity remains a source of conflict used by nationalist and political entrepreneurs to promote their ambitions at the expense of Africa's development.This paper established the relationship between ethnicity and development and its dimension on inequalities.It argued that ethnicity and inequality are complex social constructs, that have continually retarded Africa's development.
Using Machine Learning for Decreasing State Uncertainty in Planning

We present a novel approach for decreasing state uncertainty in planning prior to solving the planning problem. This is done by making predictions about the state based on currently known information, using machine learning techniques. For domains where uncertainty is high, we define an active learning process for identifying which information, once sensed, will best improve the accuracy of predictions.&#x0D; We demonstrate that an agent is able to solve problems with uncertainties in the state with less planning effort compared to standard planning techniques. Moreover, agents can solve problems for which they could not find valid plans without using predictions. Experimental results also demonstrate that using our active learning process for identifying information to be sensed leads to gathering information that improves the prediction process.
Neural-Network Based Adaptive Proxemics-Costmap for Human-Aware Autonomous Robot Navigation

Over the years, with the emergence of robots into the manufacturing industry, the manufacturing efficiency have increased.The continuous advancement of robot technologies in the field of navigation including localization, path planning, costmap design, obstacle sensing and tracking have drove revolutions to the industry.In the recent effort of transforming the industry towards Industry 4.0 [1], robot navigation remains a crucial part for a robot to co-exist with human co-workers.Humans prefer to maintain a certain proxemic distance [2] between themselves and robots for safety Abstract: In the revolution of Industry 4.0, autonomous robot navigation plays a vital role in ensuring intelligent cooperation with human workers to increase manufacturing efficiency.Human prefers to maintain a proxemic distance with other subjects for safety and comfort purposes, where the human personal-space can be represented by a costmap.Current proxemic costmaps perform well in defining the proxemic boundary to maintain the human-robot proxemic distance.However, these approaches generate static costmaps that are not adaptive towards different human states (linear position, angular position and velocity).This problem impacts the robot navigation efficiency, reduces human safety and comfort as the autonomous robot failed to prioritize avoiding certain humans over the other.To overcome this drawback, this paper proposed a neural-network based adaptive proxemic-costmap, named as NNPC, that can generate different sized personal-spaces at different human state encounters.The proposed proxemic-costmap was developed by learning a neural-network model using real human state data.A total of three human scenarios were used for data collection.The data were collected by tracking the humans in video recordings.After the model was trained, the proposed NNPC costmap was evaluated against two other state-of-art proxemic costmaps in five simulated human scenarios with various human states.Results show that NNPC outperformed the compared costmaps by ensuring human-aware robot manoeuvres that have higher robot efficiency and increased human safety and comfort.
A Feasibility Study of a Wearable Real-Time Notification System for Self-Awareness of Body-Rocking Behavior

Wearable sensors have been shown to be effective for promoting self-awareness, wellness and re-education. In this work, we perform a preliminary study analyzing the real-time detection and annotation of body-rocking behavior in individuals, which is a type of Stereotypical Motor Movement (SMM). We develop a platform for real-time annotation and detection using wireless inertial sensors and an embedded device. The annotations are analyzed in order to study the duration and frequency of the behavior, and they are corrected offline in order to better understand any offsets in the real-time annotation procedure. Finally, we show the feasibility of a real-time feedback system based on a proof of concept algorithm and the necessary computation resources to execute it.
Creating Visual Vocabularies for The Retrieval And Classification of Histopathology Images

State-of-the-art technologies in the fields of computer vision and machine learning led the automatic recognition of malignant structures in histopathology images. More than often, such structures are reported to be found in glands, where different morphological characteristics indicate the existence of a variety of adenocarcinomas, including prostate, breast, lung and colon cancer. Classification of images containing glandular representations in different cancer types can be performed in the whole image by the utilization of a combination of local and global features. The proposed methodology involves the exploitation of a notion utilized often in text mining called Bag of Words and employed in the service of Computer Vision with the name of Bag of Visual Words (BOVW) for the development of a retrieval and classification system for pathology images. The paper discusses the technical details of implementation, the enhancement of the BOVW technique, while some initial results using public datasets are presented.
New Methods for Morphological Erythrocytes Classification

Specialists may gauge the severity of sickle cell disease crisis by quantifying the number of abnormal-looking and sickle-shaped erythrocytes in blood smears. State-of-the-art integral geometry-based descriptors for automatic classification of erythrocytes as normal cells, sickle cells or cells with other deformations have achieved excellent results. Unfortunately, they are computationally expensive, requiring powerful desktop computers and a great deal of memory to run. We propose two new integral geometry-based descriptors for the shape of erythrocytes. Like state-of-the-art techniques, the overall sensitivity of our solutions is above 94%. Nevertheless, our descriptors are designed to avoid a great amount of computation in comparison to similar solutions and to present a lower memory footprint. Our descriptors offer a high specificity of normal cells and a high sensitivity of deformed cells, making them a good alternative in clinical applications.
Diffraction-grating-based insitu displacement, tilt, and strain measurements on high-speed composite rotors

Polymer composite rotors offer promising perspectives in high-speed applications such as turbomachinery. However, failure modeling is a challenge due to the material’s anisotropy and heterogeneity, which makes high-speed in situ deformation measurements necessary. The challenge is to maintain precision and accuracy in the environment of fast rigid-body movement. A diffraction-grating-based sensor is used for spatio-temporally resolved displacement, tilt, and strain measurements at surface velocities up to 260 m/s with statistical strain uncertainties down to 16µϵ. As a line camera is used, vibrations in the kHz range are measurable in principle. Due to sensor calibration and the use of a novel scan-correlation analysis approach, the rigid-body-movement-induced uncertainties are reduced significantly. The measurement of strain fluctuations on a rotating composite disc show that the crack propagation can be tracked spatially resolved and as a function of the rotational speed, which makes an in situ quantification of the damage state of the rotor possible.
Fall-down event detection for elderly based on motion history images and deep learning

The goal of this research is to apply the state-of-the-art deep learning approach to human fall-down event detection based on Motion History Images (MHI) from multiple color video sequences captured at different viewing angles. MHI is derived by detecting and combining temporal 2D human contours from surveillance cameras. A human action can then be represented by several continuous MHI images. We then use deep learning approach (CNN + LSTM architectures) to recognize the fall-down behavior from MHI sequences. Our method is capable of not only recognizing the actions of walking, standing, falling down, but also rising after falling down to avoid excessive false alarms. The accuracy of classification into the above 4 short-term actions is capable of achieving 97.66%. We also compare the performances of deep learning architectures that use simple CNN or CNN+LSTM, one or two-stage training, and single or two cameras. Our contributions lie on two aspects: (1) improving the performance on human action recognition based on MHIs and a combination of CNN+LSTM architecture, (2) preventing the false alarm of falling-down events that actually need no help.