Reviewerly/exHarmony huggingface dataset:
An Efficient Method of Histological Cell Image Detection Based on Spatial Information Convolution Neural Network

As an important research direction in the field of medical images, histopathological cell image detection has been widely used in computer-aided diagnosis, biological research fields. With the rise of deep learning, neural network is applied to medical image analysis, which can realize the automatic detection and classification of histological cell images. In order to solve the problem that the output of the existing neural network is affected by spatial information factors in its topological domain, on the basis of the traditional convolution neural network. Combined with the spatial position information, an improved convolution neural network model for histological cell image detection is proposed. Taking the traditional convolution neural network as the carrier, the convolution neural network model based on spatial information is constructed, which makes the model has the ability to fuse spatial information and eigenvector. Histopathological cell images were preprocessed by color deconvolution. Finally, a model verification experiment based on colorectal cancer image dataset is designed. The model proposed in this paper shows better performance than the state-of-the-art methods in four different categories (more than 20000 experimental images): the experimental accuracy is 75.8%, and the recall rate is 82.3%. F1 reached 80.1%.
Analysis of the Problems of Ensuring Information Security in the Terms of the Contemporary Society

The article analyzes the domestic and foreign experience of information security in today's society.The peculiarities of the research are analyzing the process of information security from the point of two sociological paradigms: structural-functional and interpretive.The process of informatization in the society is one of the key factors of its development.This article analyzes the establishment of an information society.The article analyzes the process of informatization, which is to facilitate the survival of humanity and set it on the path of sustainable development.Owing to that fact, the problem of information security is attracting increasing interest; it is assuming primary importance among the rest of security types.This problem is investigated with the following methods.First, we reveal the nature of information security, its forms, and content within the framework of the modern information society.Second, we define the possible ways information security affects the development of society.
ArcGIS&amp;lt;sup&amp;gt;TM&amp;lt;/sup&amp;gt; and Principal Component Analysis of Probe Data to Micro-Map Minerals in Round Top Rare Earth Deposit

Rare earth elements (REEs), especially heavy rare earth elements (HREEs), are in demand for their current and emerging applications in advanced technologies.Here we perform computer-driven micro-mapping at the millimeter scale of the minerals that comprise Round Top Mountain, in west Texas, USA.This large rhyolite deposit is enriched in HREEs and such other critical elements as Li, Be, and U. Electron probe microanalysis of 2 × 2 mm areas of thin sections of the rhyolite produced individual maps of 16 elements.These were superimposed to generate a 16-element composition at each pixel.Principal components analysis of elements at each pixel identified the specific mineral at that site.The pixels were then relabeled as the appropriate minerals, thereby producing a single mineral map.The overall mineral composition of the 7 studied samples compared favorably with prior analyses of the Round Top deposit available in the literature.Likewise the range of porosity in the maps was consistent with that of previous direct measurements by water saturation.This new statistical and GIS-based technique provides a robust and unbiased approach to electron microprobe mapping.The study further showed that the high-value yttrofluorite grains exhibited little tendency to cluster with other late-stage trace minerals and that the samples extended the previously documented overall homogeneity of the deposit at field scale to this microscopic scale.
Robust Denoising Technique for Ultrasound Images Based on Weighted Nuclear Norm Minimization

Image denoising is an efficacious pre-processing requisite procedure of ultrasound image investigation. In this study two denoising techniques adopted and evaluated to compare their performance. The widespread use of ultrasound images facilitates the diagnosis of various diseases. They pose several challenges and hence efficient pre-processing pipelines are essential to extract useful diagnostic information from the images. Much light is thrown on the Common carotid artery (CCA) images in this study. Two approaches are endorsed for image denoising involving and converting to grayscale for effective diagnosis. Weighted nuclear norm minimization (WNNM) approach is found to be more impressive and better. This also bolstered the validation methods computed in the work. It pretends that the study is useful in extracting diagnostic information. The experimental results impart authenticity to the proposed technique in the adequate analysis of ultrasound images. The principle objective of this work is to aid and accentuate the succeeding processing stages such as segmentation and object recognition to facilitate accurate and exact diagnosis.
Neuron Selecting: Defending Against Adversarial Examples in Deep Neural Networks

Recent studies have demonstrated that deep neural networks are vulnerable to adversarial examples, i.e. inputs crafted by applying small but intentionally perturbations to legitimate examples to mislead the models. Adversarial examples pose a serious threat to the application of deep neural networks in safety-critical scenarios. Inspired by human visual system, we propose a novel method called Neuron-Selecting to defend against adversarial examples. The main idea of the Neuron-Selecting is to select the vital few neurons that contribute to the final right predictions and filter out the trivial many neurons that are activated by perturbations. Experiments on MNIST and CIFAR-10 models show that the Neuron-Selecting can effectively defend against the state-of-the-art attacks, especially ones that have small perturbations but high attack success rate. We believe our work provides a new perspective to defend against adversarial examples.
Leveraging prior concept learning improves ability to generalize from few examples in computational models of human object recognition

Abstract Humans quickly learn new visual concepts from sparse data, sometimes just a single example. Decades of prior work have established the hierarchical organization of the ventral visual stream as key to this ability. Computational work has shown that networks which hierarchically pool afferents across scales and positions can achieve human-like object recognition performance and predict human neural activity. Prior computational work has also reused previously acquired features to efficiently learn novel recognition tasks. These approaches, however, require magnitudes of order more examples than human learners and only reuse intermediate features at the object level or below. None has attempted to reuse extremely high-level visual features capturing entire visual concepts. We used a benchmark deep learning model of object recognition to show that leveraging prior learning at the concept level leads to vastly improved abilities to learn from few examples. These results suggest computational techniques for learning even more efficiently as well as neuroscientific experiments to better understand how the brain learns from sparse data. Most importantly, however, the model architecture provides a biologically plausible way to learn new visual concepts from a small number of examples, and makes several novel predictions regarding the neural bases of concept representations in the brain. Author summary We are motivated by the observation that people regularly learn new visual concepts from as little as one or two examples, far better than, e.g., current machine vision architectures. To understand the human visual system’s superior visual concept learning abilities, we used an approach inspired by computational models of object recognition which: 1) use deep neural networks to achieve human-like performance and predict human brain activity; and 2) reuse previous learning to efficiently master new visual concepts. These models, however, require many times more examples than human learners and, critically, reuse only low-level and intermediate information. None has attempted to reuse extremely high-level visual features (i.e., entire visual concepts). We used a neural network model of object recognition to show that reusing concept-level features leads to vastly improved abilities to learn from few examples. Our findings suggest techniques for future software models that could learn even more efficiently, as well as neuroscience experiments to better understand how people learn so quickly. Most importantly, however, our model provides a biologically plausible way to learn new visual concepts from a small number of examples.
Performance Analysis of Off-Line Signature Verification

To reduce fraud in financial transactions, signature verification is important for security purposes. In this paper, an attempt has been made to analysis the performance of off-line handwritten signature verification using image-based features. Photocopies and scanned documents are considered as the best possible evidence in the situations when the original documents are either lost or damaged. Although the photocopies are the filtered images of original information and do not reproduce details as in the original documents. In this paper, combinations of four features, i.e., Average object area, mean, Euler number and area of signature image is used to verify the signature. Publically available database BHsig260 is used. In this database, two types of signature are available, i.e.,Bengali and Hindi. Proposed work shows that accuracy of Hindi off-line signature verification is 78.5% with sample size of 15 and accuracy of Bengali off-line signature verification is 69.1 with sample size of 20.
Maximize Resource Utilization Using ACO in Cloud Computing Environment for Load Balancing

Load balancing over the cloud environment for computing is not a new problem. However, balancing the loads in proper and efficient way to maximize resource utilization is an issue or a problem. This paper focuses that how to balance the loads and use the resources in maximum utilization using CloudSim tool. The average number of cloudlets and the total cost are the key parameters those are used to interpret and analyze the certain results. While loading the balance, these parameters are distinguished cost and failing ratio of the obtained results. The results are used to take care of enhancing the proper resource utilization using ACO algorithm. An ACO is a better approach to provide the higher great ability in terms of usage of virtual machine, bandwidth, number of clouds, memory, etc. The work can be carried out by the improving designing new modified ACO and minimum execution time.
Improving cancer diseases classification using a hybrid filter and wrapper feature subset selection

In the classifi cation of cancer data sets, we note that they contain a number of additional features that infl uence the classifi cation accuracy.There are many evolutionary algorithms that are used to defi ne the feature and reduce dimensional patterns such as the gray wolf algorithm (GWO) after converting it from a continuous space to a discrete space.In this paper, a method of feature selection was proposed through two consecutive stages in the fi rst stage, the fuzzy mutual information (FMI) technique is used to determine the most important feature selection of diseases dataset through a fuzzy model that was built based on the data size.In the second stage, the binary gray wolf optimization (BGWO) algorithm is used to determine a specifi c number of features affecting the process of classifi cation, which came from the fi rst stage.The proposed algorithm, FMI_BGWO, describes effi ciency and effectiveness by obtaining a higher classifi cation accuracy and a small number of selected genes compared to other competitor algorithms.
COMPLEX-IT: A Case-Based Modelling and Scenario Simulation Platform for Social Inquiry

COMPLEX-IT is a case-based, mixed-methods platform for social inquiry into complex data/systems, designed to increase non-expert access to the tools of computational social science (i.e., cluster analysis, artificial intelligence, data visualization, data forecasting, and scenario simulation). In particular, COMPLEX-IT aids social inquiry though a heavy emphasis on learning about the complex data/system under study, which it does by (a) identifying and forecasting major and minor clusters/trends; (b) visualizing their complex causality; and (c) simulating scenarios for potential interventions. COMPLEX-IT is accessible through the web or can be run locally and is powered by R and the Shiny web framework.
A Survey on Privacy Preserving Approaches on Health Care Big Data in Cloud

To reform and enhance healthcare services, the use of information technology is increasing in an unprecedented manner. This technological advancement results in generating substantial medical data from various Healthcare related things. This Medical Big data has a maximum prospective to upgrade patients’ treatment and follow up. We anticipate to flare-up some preventable infections, and we can significantly reduce the Healthcare delivery cost, by using the Medical Big data. One specific pattern observed in the medical services industry is serial shifting data to the cloud. By shifting data to the cloud, the accessibility of the patient’s medical history in real-time is possible and it can reduce costs. Though we enjoy multiple advantages of cloud computing for Medical Big data, we face specific security issues of our data stored in the cloud. In this paper, a brief analysis on various approaches used to protect the healthcare related information stored in the cloud and further they are secured using cryptographic techniques.
Some Results of Experimentation with the Anthropomorphic Robotic System in Remote Control Master-Slave Mode.

At present, the requirements for the characteristics of the “cosmonaut-anthropomorphic robotic system-environment” ergatic system, the procedure for evaluating the performance of a cosmonaut-operator when using anthropomorphic robots, proposals for the functionality of this ergatic system and the ways of its application in order to increase the operational quality of existing and future manned space complexes can be determined and substantiated only as a result of carrying out experimental studies using an anthropomorphic robotic system.
A Multi-Feature Fusion Based Vehicle Logo Recognition Approach for Traffic Checkpoint

Abstract In this paper, we propose a vehicle logo recognition algorithm based on multifeature fusion using a hierarchical classification approach, which can be applied at traffic checkpoints. First, a typical database of vehicle logos is set up based on surveillance images recorded at traffic checkpoints. Next, three features, HOG, Curvature histograms, and GIST, are extracted and three corresponding first level classifiers are trained using the support vector machine (SVM) algorithm. The probability that a certain test sample belongs to a certain kind can be obtained by predicting the sample with each level-1 classifier. All these probabilities are then concatenated and used as features for training a second-level SVM classifier. The resultant new classifier is used for classifying the vehicle logos of the test set. The experimental results show that the proposed approach to hierarchically integrate multiple features provides excellent accuracy for the vehicle logo recognition task.
Speaker Embedding Extraction with Multi-feature Integration Structure

Recently x-vector has achieved a promising performance of speaker verification task and becomes one of the mainstream systems. In this paper, we analyzed the feature engineering based on the x-vector structure, and proposed a multi-feature integration method to further improve the feature representation of speaker characteristic. The proposed multi-feature integration method could be implemented in two ways, with the symmetric branches and the asymmetric branches, respectively, to incorporate different types of acoustic features in one neural network. While each branch processed one type of acoustic features on the frame level, the outputs of the two branches for each frame were spliced together as a super vector before being input into the statistics pooling layer. The experiments were executed on the VoxCeleb1 data set, and the results showed that the proposed multi-feature integration method obtained a 22.8% relative improvement over the baseline in EER value.
Vertical Lane Line Detection Technology Based on Hough Transform

Abstract Lane detection technology is widely used in the field of automatic driving. In this paper, Hough transform is used to detect the vertical straight lanes of interest. Firstly, the edge features of the image are extracted by image preprocessing. Then, the region of interest in the image is delineated. Then, the region contour image is transformed by Hough transform to find the straight line segment in the image. Finally, the straight line end is filtered and simulated by least square method. Combine the linear equation and mark the lane line.
Research on the path optimization of unmanned rolling impaction for high embankment of airport

Quality control of embankment compaction has an important influence on the durability and stability of the high embankment airport. Now, the unmanned compaction quality control mainly depends on the path tracking effect of the unmanned roller. The path tracking error of the roller increased obviously in the field test. The error from the perspective of the discontinuity of the planning path curvature was analyzed by a simulation model built based on the pure pursuit algorithm. Then the arc and elliptical arc were selected to simplify the planning path, and the results show that the optimized arc path made the tracking error reduced effectively. In order to further reduce the error, the inflection point of the straight line and the arc was smoothed by the method of rounding and cubic B-spline curve fitting, respectively. The simulation results show that the rounding process and the B-spline curve fitting have significant effects on improving the path tracking of the vehicle.
Manipuri Handwritten Character Recognition by Convolutional Neural Network

Handwritten character recognition is an essential field in pattern recognition. Its popularity is increasing with the potential to thrive in various applications such as banking, postal automation, form filling, etc. However, developing such a system is a challenging task with the diverse writing style of the same character, and present of visually similar characteristics. In this paper, a recognition system is proposed using a deep neural network. The performance of the network is investigated on a self-collected handwritten dataset of Manipuri script contributed by 90 different people of varying age and education. A total of 4900 sample images is considered for the experiment and recorded a recognition rate of 98.86%.
Color image encryption method based on computer generated hologram and &lt;i&gt;θ&lt;/i&gt; modulation

In this paper, a new method of encrypting a color image based on &lt;i&gt;θ&lt;/i&gt; modulation is proposed by using the tricolor principle and computer-generated hologram (CGH) technology. The encryption process includes the &lt;i&gt;θ&lt;/i&gt;-modulated three primary color components and the coding of computer-generated hologram, which is implemented in a Fresnel diffraction and spatial filtering system. Firstly, the color image modulated by the first random phase key is divided into three encryption channels by red laser, green laser, blue laser, and tricolor filters. Each channel is introduced by a transmissive amplitude-type sinusoidal grating with different directions, which is used to separate the three primary color components in the spatial spectrum plane. Secondly, the modulation results of tricolor components are superimposed together to form a compound image, and the phase truncation of the superposition result is performed to achieve the asymmetric encryption. Finally, the amplitude of the compound image is modulated by the second random phase key and is encoded into a binary real-value gray-color CGH by Roman-type coding method. Therefore, the gray-color information of the original image is completely hidden in the encrypted CGH, which is more general and deceptive in the storage and transmission process. Decryption is an inverse process of the encryption. Firstly, the encrypted CGH is placed on the input plane of the spatial filtering and Fresnel diffraction system. Secondly, the demodulation of CGH phase key and the spatial filtering based on optical filter are performed. Finally, the color plaintext image is obtained by using the correct Fresnel diffraction. The simulation results show the validity and feasibility of the proposed method. In addition, the anti-noise attack and anti-shearing attack performance of this color image encryption method are investigated. Compared with results from the three presented methods reported in the literature, our investigated results demonstrate that this method has good robustness to noise attack and shearing attack, and has obvious advantages when the attack noise density is larger. Due to the characteristics of high security and anti-noise, we believe that this color image encryption method promises to have important applications in the information transmission and multi-user authentication.
The eddy current diagnostics method for the plastically deformed area sizes evaluation in non-magnetic metals

The aim of this research is to reveal and determine the sizes of plastically deformed areas in metallic non-magnetic materials with the eddy current method.The manuscript contains computational studies to assess the feasibility of using the eddy current method to determine the size of the plastically deformed areas in the metal.The authors developed a two-dimensional mathematical model of the interaction of the electromagnetic fi eld with the control object.The model included poly-harmonic fi eld excitation in a locally deformed plate, and the deformation was modeled in the form of plastically deformed areas under the indents obtained by the ball indentation.The developed model helped to establish the correlation dependencies and linked the informative parameters of the eddy current method with the size of the metal's plastically deformed zone.The authors obtained the calibration curve for copper, the values of which allowed to determine the factual sizes of the plastically deformed area.