Reviewerly/exHarmony huggingface dataset:
Classification of Normal Versus Malignant Cells in B-ALL Microscopic Images Based on a Tiled Convolution Neural Network Approach

In this paper we present a method based on the existing convolution neural network architecture of AlexNet for the purpose of classifying microscopic images of B-ALL white blood cancer cells. This classification problem is especially challenging due to lack of conspicuous morphological differences between normal and malignant cell nuclei. Therefore, we designed a machine learning pipeline that focused on the texture of the staining images. Briefly, our approach divides the cell image into several overlapping tiles and trains a modified version of AlexNet on the tiles. Only those tiles are retained which are fully contained within the cell image. Several such networks were trained in an ensemble fashion using different training–validation data splits. For a given test image, the tiles are generated and ran through all the trained networks. The outputs of all networks along with the nucleus area are then fed into a simple decision tree, which generates the final prediction. The proposed method was developed in the context of the ISBI 2019 C-NMC challenge. The final testing results demonstrated a classification-weighted F1 score of 0.8307 using 2586 test images. The results demonstrate the possibility of making relatively accurate predictions using only local texture features.
Heterogeneous knowledge for sustainable planning: Notes from ontology-based experimentations

This paper proposes a study approach to the city as composition of layers, each layer presenting some characteristic aspect of the city based on some type of ontological classification. Traditional analysis and functional views of cities offer only a partial, and sometimes misleading, understanding of the city because such methods tend to modularize the city without modelling its complexity. By taking an ontological perspective we aim to develop integrated ontological models. Moreover, we give a look at the subjective knowledge of city inhabitants. This kind of knowledge is not always valorised in traditional scientific approaches. Yet it is essential for understanding whether a change can be embraced and sustainably supported by the agent community, the living component of the city system. The present work aims to explore a different modularization approach where the city is seen as an integrated system that should not be subdivided in different ways depending on the type of problem one aims to solve. Indeed, one can meaningfully divide the city into parts only taking into account the nature of these entities and of the connections they form. Without analysing the structural and dynamic organization of cities, their processes and composing objects, any decomposition of the city would likely miss critical interconnections and dependences. The final perspective is to build a general ontology-based framework aimed at creating a new generation of knowledge-based models and tools for city modelling and simulation, more oriented to sustainable planning.
Deeply Encoding Stable Patterns From Contaminated Data for Scenery Image Recognition

Effectively recognizing different sceneries with complex backgrounds and varied lighting conditions plays an important role in modern AI systems. Competitive performance has recently been achieved by the deep scene categorization models. However, these models implicitly hypothesize that the image-level labels are 100% correct, which is too restrictive. Practically, the image-level labels for massive-scale scenery sets are usually calculated by external predictors such as ImageNet-CN. These labels can easily become contaminated because no predictors are completely accurate. This article proposes a new deep architecture that calculates scene categories by hierarchically deriving stable templates, which are discovered using a generative model. Specifically, we first construct a semantic space by incorporating image-level labels using subspace embedding. Afterward, it is noticeable that in the semantic space, the superpixel distributions from identically labeled images remain unchanged, regardless of the image-level label noises. On the basis of this observation, a probabilistic generative model learns the stable templates for each scene category. To deeply represent each scenery category, a novel aggregation network is developed to statistically concatenate the CNN features learned from scene annotations predicted by HSA. Finally, the learned deep representations are integrated into an image kernel, which is subsequently incorporated into a multiclass SVM for distinguishing scene categories. Thorough experiments have shown the performance of our method. As a byproduct, an empirical study of 33 SIFT-flow categories shows that the learned stable templates remain almost unchanged under a nearly 36% image label contamination rate.
Decentralized multisensor estimation of motion parameters of an object moving along a complex trajectory

Abstract The paper addresses the problem of multisensor estimation of the motion parameters of an object moving along a complex trajectory which consists of parts of the uniform linear and circular motion subject to noisy measurements. To solve the problem, we describe the object motion as a set of linear stochastic models responsible for different parts of the trajectory and use a decentralized multisensor algorithm for estimating the object state vector based on the information form of the Kalman filter. The results of numerical experiments confirm the applicability of the proposed approach.
Assessing the Quality of Visualization Metaphor of Fuzzy Cognitive Maps on the Basis of Formalized Cognitive Clarity Criteria

The paper presents continuation of research in the field of constructing a visualization metaphor of cognitive models based on fuzzy cognitive maps. The focus is on the spatial metaphor as the basis for representation metaphor formation. A method is proposed for quality assessment of a spatial metaphor of a fuzzy cognitive map based on formalized cognitive clarity criteria defined in the previous part of the study. To this end, methods have been developed to formalize several nontrivial criteria of cognitive clarity. An example is given that confirms correctness of the proposed method for assessing the quality of a visualization metaphor.
Visual Saliency Based on Two-Dimensional Fractional Fourier Transform

Visual saliency is very helpful for image detection and image processing. This paper proposes a novel visual saliency model. First, the proposed model can extract a saliency map with high precision and compound the linear combination of saliency map. Second, based on two-dimensional fractional Fourier transform, the proposed model generates a robust saliency map from the input image with Gaussian or salt-and-pepper noise. In order to reveal the noise influence from the given image, we provide a concept called the noise sensitivity scale (NSS). Third, using the image database from MSRA10K, we analyze the precision-recall and ROC curve and experimentally demonstrate that the proposed model can evaluate human fixation to some extent.
A Hierarchical Student’s t-Distributions Based Unsupervised SAR Image Segmentation Method

We introduce a finite mixture mode using hierarchical Student’s distributions, called hierarchical Student’s t-mixture model (HSMM), for SAR images segmentation. The main advantages of the proposed method are as follows: first, in HSMM, the clustering problem is reformulated as a set of sub-clustering problems each of which can be solved by the traditional SMM algorithm. Second, a novel image content-adaptive mean template is introduced into HSMM to increase its robustness. Third, an expectation maximization algorithm is utilized for HSMM parameters estimation. Finally, experiments show that the HSMM is effective and robust.
Visualization of Food Bank/Agency Distribution

Food banking generates large amount of structured and unstructured data typically stored and reported in spreadsheet format. The growing scale of data storage limit the effectiveness of traditional spreadsheet data exploration methods used by decision makers. Spreadsheets are generally convenient and easy to use for processing data, but less so for big data stores due to limitations such as capacity, computing performance, interactivity, and plotting capability. Visual analytics was proposed as an approach to improve information seeing using data provided by the Food Bank of Central and Eastern North Carolina. SAS Visual Analytics was used to generate interactive dashboards that included a geo map based on spreadsheet data for agency food distribution. Feedback from the Food Bank’s management indicate substantial interest for visual analytics as a decision aid. A formal study is planned to evaluate decision-making performance with the dashboard in comparison to existing food bank methods.
A Detailed Study and Analysis on Different Machine Learning Solutions Available in the Field of Face Tracking

Face tracking in today's age is a lesser explored field with a lot of potential to improve security in the society. The aim of this paper is to study and compare different machine learning techniques used for tracking faces in a video. We have encompassed various algorithms in our study, namely, Kalman filtering, Fuzzy geometric set model, Principal Component Analysis, etc.
MODELS OF RANDOM SYSTEMS WITH CONTROLLED SYMMETRY IN STREAM ENCRYPTION

Information safety plays an important role in modern technologies. A stream encryption is one of the common means for information safety support. The sequences with pseudo-random characteristics are often required in the algorithms of stream enciphering. Random systems are recently used as a source of pseudo-random numbers with desired statistical properties. A small length of a sequence period is one of the known problems of the generators of pseudo-random numbers based on chaos realized with a small length of a digit grid. The disturbance of a path or a parameter of random system nonlinearity is one of possible solutions of a short period problem. In this paper there is considered a new approach to the increase of a period length through the change of a symmetry factor in the random model of a memristive circuit. The approach offered is based on switching two values of the symmetry factor in accordance with the output of a shear register with the linear feedback. To confirm the effectiveness of the disturbance circuit described the lengths of a period for the disturbed and original model of the memristive system are estimated. The properties of the output sequences caused by a generator on the basis of the model with controlled symmetry are confirmed by the results of the correlation analysis and NIST statistical testing. The results obtained can be used in cryptographic applications and also at the designing of safe communication systems.
A Comparative Study of Two Dimensional Legendre &amp; Chebyshev Wavelet with an Extended case

Abstract In this paper, we present an extended case of Two Dimensional Legendre &amp; Chebyshev wavelet to solve a system of PDE’s and a Comparative Study of these two wavelets. In this article, we construct an operational matrix from two Dimensional Chebyshev and Legendre wavelets which is then used to convert PDE into some algebraic equations and hence we solve by the method of collocation. A new operational matrix was derived and it is utilized to convert PDE with BVP to a set of algebraic equations. Some examples are given to evaluate the effectiveness of the newly found approximation technique and compared with the existing result
Situation Awareness in Modeling Industrial-Natural Complexes

The concept of situation awareness (SA) is adapted and concretized for the previously developed situational conceptual model of an industrial natural complex. The features of the presented approach consist in quantitative assessing of the three main levels of achieving SA and taking into account the possibility of transferring the modelling object from the normal operation mode to an abnormal or emergency situation. Analysis of SA is performed in a discretized state space of this object with an expertly synthesized metric. The research allows to objectify measuring SA level for decision makers involved in managing complex’s components.
A Large Scale Study for Identification of Sarcasm in Textual Data

With the increase in the penetration of the Internet and the widespread acceptability of social networking sites, more people are coming forward to express their views and opinion about various topics. This has given a huge boost to the textual and multimedia content generated by these websites, giving opportunities to researchers and analysts to nd and generate patterns from this data. The problem of identification of sarcasm in textual data is quite challenging due to lack of annotation, intonation and facial expression. Big companies are spending millions in finding out, whether people were praising or mocking about their product, they can get the idea about the market trends and needs. Law enforcement agencies may also get benefit from this as they would be able to distinguish legitimate threats from exaggerations on the online social networks. A data-driven approach based on the neural networks and the concepts of deep learning has been evaluated using a blend of deep convolutional networks (CNN) and long short term memory (LSTM). The technique has been applied to the Self-Annotated Reddit Corpus (SARC) ( http://nlp.cs.princeton.edu/SARC/ ), a large corpus for sarcasm research. The technique for domain specific and general data is also probed, as given in the dataset so as to check the accuracy of the proposed method. It has been observed that blending of the models further improves the accuracy of simple CNN model, and yields a more computationally efficient model of accuracy compared to standalone models. Our method has achieved an overall average precision of 73%.
Design and Implementation of Sport Virtual Museum Management System Based on ASP.NET

Abstract At present, more common sports virtual museums are written in the language, but there are several problems in the following need to manually write code, which makes the efficiency of the creation of a museum is relatively low. Unable to connect to the database, the user can not use the unified management and use of the information. Due to the response of the event in the roaming process is the use of sensor mechanism, the function is relatively simple, so it can not meet the needs of users in the process of roaming. The sports virtual museum system mentioned in this article can solve the above three problems well. The system provides a convenient and efficient relics database management, database management, the establishment of virtual Sports Museum two-dimensional graphs of, sports virtual museum 3D browsing and other functions, the system is divided into three module user and relics database management module, physical virtual museum design module, Physical Virtual Museum browsing module. For user and relics database management module of cultural information and user information in the database management operations, the user can the cultural relics of the relevant information and model file and texture file stored in the database on the server according to the demand.
Online Evaluations of Features and Ranking Models for Question Retrieval

We report our work on the NTCIR-14 OpenLiveQ-2 task. From the given data set for question retrieval on a community QA service, we extracted several BM25F-like features and translation-based features in addition to basic features such as TF, TFIDF, and BM25 and then constructed multiple ranking models with the feature sets. In the first stage of online evaluation, our linear models with the BM25F-like and translation-based features obtained the highest amount of credit among 61 methods including other teams’ methods and a snapshot of the current ranking in service. In the second stage, our neural ranking models with basic features consistently obtained a major amount of credit among 30 methods in a statistically significant high number of page views. These online evaluation results demonstrate that neural ranking is one of the most promising approaches to improve the service.
A Lightweight Region Proposal Network for Task Specific Applications

Quickly and cheaply finding areas of interest within an image can save computationally intensive image processing in a vision pipeline. Existing region proposal networks are either too general (finding all objects in an image) or too complex (providing fine-tuned bounding boxes for classification). We propose a straightforward region proposal network that simply scores parts of the image based on whether or not they contain an object of interest. This calculation can be carried out quickly and for many applications including autonomous driving only a small fraction of of the image area may contain objects of interest. We trained our network on an autonomous robot soccer dataset with similar characteristics to the popular KITTI autonomous driving dataset and achieved a recall greater than 95% while eliminating on average over 80% of the image area from further processing.
Online Information Augmented SiamRPN

Recently, many Siamese network based object tracking methods have been proposed and have shown good performances. These method give two images to two identical artificial neural networks as the inputs and find the target area based on the similarity measured by the Siamese network. However, the measure used in the Siamese network is based on the offline training, and therefore, easily fail to adapt to online changes. In this paper, we propose to apply a distance measure which considers the relative position between the objects and the histogram information as additional online information. This additional information prevents the tracking to fail when hard negative cases appear in the scene.
A New Approach to Reducing the Distortion of the Digital Image Natural Model in the DCT Domain When Embedding Information According to the QIM Method

One of the areas of digital image processing is the steganographic embedding of additional information into them. Digital steganography methods are used to ensure the information confidentiality, as well as to track the distribution of digital content on the Internet. Main indicators of the steganographic embedding effectiveness are invisibility to the human eye, characterized by the PSNR metric, and embedding capacity. However, even with full visual stealth of embedding, its presence may produce a distortion of the digital image natural model in the frequency domain. The article presents a new approach to reducing the distortion of the digital image natural model in the field of discrete cosine transform (DCT) when embedding information using the classical QIM method. The results of the experiments show that the proposed approach allows reducing the distortion of the histograms of the distribution of DCT coefficients, and thereby eliminating the unmasking signs of embedding.
A Level Set Method for Natural Image Segmentation by Texture and High Order Edge-Detector

Active contour model has been a widely used methodology in image segmentation. However, due to the texture complexity of natural images, it unavoidably faces many difficulties. In this paper, we propose a novel method to accurately segment natural images by texture and high order edge-detector. Firstly, we calculate local covariance matrix which is estimated from image gradient information within the local window, and use the eigenvalues of matrix to describe local texture feature of the image. Then, in order to suppress the effect of perplexing background, we introduce a high order edge-detector which can eliminate the background as much as possible while it can save the object boundary. Finally, the intensity term, texture term and edge-detector term are incorporated into the level set method to segment natural images. The proposed method has been tested on many natural images, and experimental results show the segmentation performance of the proposed method is better than prior similar state-of-the-art methods.
Iot Secured Disjunctive XOR Two Factor Mutual Authentication for Users

Aims: The paper has introduced an algorithmic modification of M.L.Das's previous work done of " IoT Mutual Authentication". Background: IoT has proven that if there exists a thing on the earth then it is bound to be connected to the internet to tell its feature on its own. IoT plays a remarkable role in all aspects of our daily lives it covers entertainment, sports, healthcare, education, security, automobiles, industrial as well as home appliances and many more real time applications. To ease our everyday activities, it reinforcing the way people interact with their surroundings. This holistic view brings some major concerns in terms of security and privacy. Objective: The objective of the work is to increase security and protect the algorithm from various attackers so to use with real time application. Methods: In this we have used XoR (Exclusive OR) operation and because of this we can protect the algorithm from DoS attacks, bypass attack, intruder attack and allow user to change password. Conclusion: The proposed work has secured all the connected IoT devices to work inde-pendently and secured because every device has to verify at each step in the IoT system before initiating its operation.
Augmented Reality System for Multi-robot Experimentation in Warehouse Logistics

The application of tools as augmented reality has been developing innovative solutions for the industrial scenario. In this context, this work presents an industrial plant of a warehouse, where augmented reality is used to represent virtual loads to be transported by multiple small mobile robots. The results promote an application developed in ROS, with virtual and real objects sharing the same environment, producing an excellent scenario to development and experimentation to new approaches for automation in warehouses or smart factories.
Organizational and pedagogical basis for building professional and pedagogical outlook of HEI students

The article is devoted to the issues of organizational and pedagogical basis for building professional and pedagogical outlook of HEI students. The purpose of the research is to study the distinct manifestation of the students’ professional and pedagogical outlook. To facilitate the research, questionnaire methods, summative and formative assessment were used. The degree of distinct manifestation of the professional and pedagogical outlook was determined by the method ‘The degree of the manifestation of professional and pedagogical outlook’. Based on the structure of pedagogical activity (according to N.V. Kuzmina), the main components of professional teachers’ outlook are compiled of gnostic, communicative, organizational, design, constructive, innovative and informational components. The formative assessment revealed low degree of distinct manifestation of the students’ professional and pedagogical outlook. The obtained results provided the basis for doing the groundwork to prerequisite organizational and pedagogical conditions in order to form future educators’ pedagogical. Organizational and pedagogical groundwork programmes imply high use of active and interactive forms of lecturing, interactive seminars, case methods, interactive lectures, etc.