Reviewerly/exHarmony huggingface dataset:
Deep learning of sub-regional breast parenchyma in mammograms for localized breast cancer risk prediction

Breast cancer risk prediction refers to the task of predicting whether a healthy patient is likely to develop breast cancer in the future. Breast density and parenchymal texture features are well-known imaging-based breast cancer risk markers that can be qualitatively/visually assessed by radiologists or even quantitatively measured by computerized software. Recently, deep learning has emerged as a promising strategy to solve tasks in a variety of classification and prediction scenarios, including breast imaging. Building on this premise, we propose a deep learning-based modeling method for breast cancer risk prediction in a case-control setting purely using prior normal screening mammogram images. In addition, considering the fact that clinical statistics shows that the upper outer quadrant is the most common site of origin for breast cancer, we designed a simple experiment on 226 patients (a total of 1,632 images) to explore the concept of localized breast cancer risk prediction. We built two deep learning models with the same settings but fed one with the top halves of the mammogram images (corresponding to the outer portion of a breast) and the other with the bottom halves (corresponding to the inner portion of a breast). Our preliminary results showed that the top halves have a higher prediction performance (AUC=0.89) than the bottom halves (AUC=0.69) in predicting the case/control outcome. This indicates a relation between localized imaging features extracted from a sub-region of the full mammogram images and the underlying risk of developing breast cancer in this specific sub-region.
Research on Dynamic Virtual Machine Scheduling Strategy Based on Improved Genetic Algorithm

In the actual application, when the virtual machine is created and the virtual machine is dynamically migrated, only the memory utilization of the host is considered, and other resource indicators, such as CPU utilization, are not considered, so the resource utilization may be unbalanced. This leads to unreasonable allocation of cloud computing resources in virtual machine scheduling. In order to solve the above problems, this paper proposes a dynamic virtual machine scheduling strategy based on improved genetic algorithm, which fully considers the CPU utilization and memory utilization of the host, and obtains the minimum dynamic migration overhead virtual machine scheduling scheme under the condition that the above resource utilization is relatively balanced. Finally, the algorithm was verified by CloudSim simulation experiment. Experimental data prove that the proposed algorithm has balanced resource utilization and effectively solves the load imbalance problem in virtual machine scheduling.
Cashless campus - An interoperable electronic fare management standard for college campuses

The cards that can be used for a combination of services are known as smart cards. Smart cards are being used widely in various sectors mainly in health and educational sectors. Some of the banks have started issuing these smart cards in combination with other agencies. This paper proposes an interoperable smart card system that provides students with a campus identification card along with a debit card cum Automated Teller Machine card which can be used by the students as their student identity card, library card, cards for fee payment or bill payment cards at various food courts or shopping malls This paper also discuss about the satisfaction rate among the students and fields of improvement.
Molecule ground state energy estimation via continuous-time quantum walks

The application of continuous quantum walks of electron on a graph composed of coupled quantum dots to molecule energy estimation is considered. The graph considered corresponds to the molecule Hamiltonian matrix. The presence of electron in each quantum dots is monitored by nearby quantum point contacts. The system is set to a state with an overlap with the ground state of the system. Then, system evolves, and finally, the ground state energy is assessed. The influence of noise produced by the point contacts on the estimation performance is also investigated.
UnfairDuelMerge: Merging with Even Fewer Moves

Abstract The merging problem consists of forming an ordered sequence with n elements from two ordered sequences with n1 and n2 elements (where n=n1+n2). One recent development in in-place merging is a minimum storage algorithm (DuelMerge) that requires half the number of moves in average, when compared to other partition-based algorithms. The reduction is achieved by limiting the exchange to blocks with equal sizes, and using a particularly suited exchange method. The algorithm proposed in this paper (UnfairDuelMerge) reduces the number of moves even further, by limiting the exchange to blocks where the left side is one element larger than the right side, and using the floating hole technique to perform the exchange. As its predecessor, the algorithm is stable, in-place and the merge is asymptotically performed with a O(nlognn) runtime. A formal description of the method is provided, as well as an analysis of stability, memory cost and time complexity. Experimental results show that the proposed algorithm outperforms other partition-based solutions when applied directly to merge ordered sequences and when used within MergeSort.
Information Extraction and Visualization of Unstructured Textual Data

There is a large amount of textual data on the web that has to be analyzed manually by humans in order to use it in meaningful ways. Techniques that can analyze the data, convert it to meaningful information, connect it with other sources of information, and allow querying would be extremely useful. There are different kinds of textual information available with each kid catering to a different kind of audience. This paper presents an information extraction approach that is a modified traversal algorithm on dependency parse output of text to extract all subject predicate object pairs from text while ensuring that no information is missed out. The output format is designed specifically to fit on a node-edge-node model and form the building blocks of a network that makes understanding of the text and querying of information from corpus quick and intuitive.
Research and Design of Cloud Storage Platform for Field Observation Data in Alpine Area

With the rapid increase of field observation data volume in alpine regions, there are many problems exist in the data storage for geoscience researchers, such as lack of sufficient hardware storage devices, high maintenance costs, and incomplete storage environment. Nowadays, Cloud Storage technology which based on the open source Cloud Computing platform can effectively solve these problems. Therefore, this paper constructs and designs the field observation data Cloud storage platform in the alpine region based on the Apache Hadoop Cloud platform to realize the functions of creating, uploading and browsing of field observation data files in the Cloud Storage, so as to meet the needs of researchers to store observation data, share information and backup and so on. The system also can enable the efficient management of server resources, and provide large-scale data processing capabilities.
Flowcharting Templates

The Smithsonian has begun to post online descriptions of objects in its collections. Figures and accounts are now available for roughly 2500 objects from the math and computer collections— perhaps a quarter of the total. Our focus has been on small objects that are easily examined. Hence I recently cataloged the two dozen flowcharting templates in the collections. This brief account is intended to encourage readers to add their own reminiscences to the online records, creating a much richer resource.
Optimal Classifier Parameter Status Selection Based on Bayes Boundary-ness for Multi-ProtoType and Multi-Layer Perceptron Classifiers

Recently, we proposed a new method to select an optimal classifier parameter status (value) using our new criterion that is referred to as uncertainty measure and directly evaluates the Bayes boundary-ness of estimated boundaries. The utility of the method was shown in a task of selecting an optimal Gaussian kernel width, which closely approximates the linear Bayes boundary in the feature space produced by Support Vector Machine classifier. In this paper, we apply the method to two types of classifiers whose class boundaries are basically nonlinear: Multi-ProtoType (MPT) classifier and Multi-Layer Perceptron (MLP) classifier. From experiments using a synthetic dataset and four real-life datasets, we show that our method can provide an optimal (in size and value) classifier parameter status, which basically corresponds to the nonlinear Bayes boundary in given feature spaces, for MPT and MLP classifiers.
Non-profiled Mask Recovery: The Impact of Independent Component Analysis

As one of the most prevalent SCA countermeasures, masking schemes are designed to defeat a broad range of side channel attacks. An attack vector that is suitable for low-order masking schemes is to try and directly determine the mask(s) (for each trace) by utilising the fact that often an attacker has access to several leakage points of the respectively used mask(s). Good examples for implementations of low-order masking schemes include the table re-computation schemes as well as the masking scheme in DPAContest V4.2. We propose a novel approach based on Independent Component Analysis (ICA) to efficiently utilise the information from several leakage points to reconstruct the respective masks (for each trace) and show it is a competitive attack vector in practice.
Temperature Prediction of Solar Array Vacuum Heat Test Based on Deep Belief Network

The vacuum heat test is an indispensable test item in the development of spacecraft. In the vacuum heat test, the correct interpretation and prediction of temperature data is very difficult and very important. This paper proposes a thermal test temperature prediction method based on deep belief network. The method includes the construction of deep belief network, layer-by-layer pre-training of model parameters, and adopts the Adam algorithm to replace the traditional steepest gradient descent method to complete the supervised fine tuning process, so that it converges to the method described in this paper can effectively completoptimal solution more quickly. Finally, the case analysis is carried out with real test data. The results show that the e the thermal test temperature prediction.
A method of projective transformations graph adjustment for planar object panorama stitching

This paper proposes an improvement for an existing and widely spread approach of panorama stitching for images of planar objects. The proposed method is based on projective transformations graph adjustment. Evaluation is presented on a heterogeneous dataset which contains images of Earth’s and Mars’s surfaces, images taken using a microscope, as well as handwritten and printed text documents. Quality enhancement of panorama stitching method is illustrated on this dataset and shows more than twofold reduction in the accumulated computation error of projective transformations.
Sandra Deshors, <i>Multidimensional Perspectives on Interlanguage. Exploring</i> may <i>and</i> can <i>across Learner Corpora</i>

Abstract This article reviews Multidimensional Perspectives on Interlanguage. Exploring may and can across Learner Corpora 9782875584755
Hyperspectral Images Segmentation Using Active Contour Model for Underwater Mineral Detection

In this paper, we design a novel underwater hyperspectral imaging technique for deep-sea mining detection. The spectral sensitivity peaks are in the region of the visible spectrum, 580, 650, 720, 800 nm. In addition, to the underwater objects recognition, because of the physical properties of the medium, the captured images are distorted seriously by scattering, absorption and noise effect. Scattering is caused by large suspended particles, such as in turbid water, which contains abundant particles, algae, and dissolved organic compounds. In order to resolve these problems of recognizing mineral accurately, fast and effectively, an identifying and classifying algorithm is proposed in this paper. We take the following steps: firstly, through image preprocessing, hyperspectral images are gained by using denoising, smoothness, image erosion. After that, we segment the cells by the method of the modified active contour method. These methods are designed for real-time execution on limited-memory platforms, and are suitable for detecting underwater objects in practice. The Initial results are presented and experiments demonstrate the effectiveness of the proposed imaging system.
Structure of a lesson on the development of the students’ creative personality in the CFCT-TRIZ pedagogical system

The article describes the theory and practice of introducing the system of creative education CFCT-TRIZ into the Voronezh State Technical University.The system was developed by Professor, Doctor of pedagogical sciences Zinovkina M.M. We give an example of teaching the substantive part of the course for the students of the construction specialty. The article will be of interest to the specialists working in the field of creative pedagogy.
The Finance Aspects of Building a Small Business in Kurdistan Region-Iraq: A Comparison with Sweden

The study aimed to identify the finance aspects that are associated with starting and running small business enterprise in Kurdistan, in comparison with Swedish small business enterprises. &#x0D; In caring out the study, a qualitative research method was used. The most important method data was sourced by primary data using interviews, utilizing a questionnaire. The researchers also used purposive sampling technique to select some of the top ranking personnel in Sherko Engineering Group, Jamal Company, Tanyayi Café, Cafe Clarintte, Café Master and Marianne Bageri. The analytical strategy used was in relying on theoretical propositions. &#x0D; On the basis of the analysis of data collected during the interview, the study revealed that small business owners in developing countries such Kurdistan face many problems when compared with developed countries like Sweden. The finance problems facing Kurdish small business owners are ranked as follows: lack of finance in starting and developing business, non use of accounting and finance Standards, having few sources of finance and private banks, inability in determining money need and start up costs, lake of a factor for distinguishing between personal and business money, poor infrastructures, inappropriate legislation, lack of managerial skills in managing money; further problems are corruption and inflation. Small business owners in Sweden are face with the problems of high increase of work absentee due to cases of sicknesses by personnel and the effect of the weather on business. Also they face very few the problems that Kurdish small business has related finance aspects when establishing and managing the business, they have support of government completely.
Enhanced Medical Image De-noising Using Auto Encoders and MLP

Preserving the original characteristics of an image which is transmitted across a channel having different kinds of noise (i.e., either, uniform, linear or Gaussian noise) is a crucial task, hence it has become a state of art for the researchers in retrieving the original characteristics of the image by using different denoising and image retrieving techniques. In earlier, many techniques have been proposed such as patch wise denoising (e.g., Sliding Window), block matching (e.g., BM3D), shallow and wide deep learning algorithms which achieved a promising accuracy, yet failing in preserving the prominent characteristics of an image which is a crucial task in Bio-Medical Instrumentation systems. So, we proposed few algorithms which could preserve the smallest possibilities of denoising the medical images and achieved a maximum accuracy of 99.98% for SDAE (In Tensorflow Background), 99.97% for SDAE (In Theano Background) and 99.99% for Multi-Layer Perception (MLP) technique and later compared these with the accuracies of the existing methods.
Skill acquisition and transfer through common subgoals

The main challenge of using robots in social environments such as houses is coping with the frequent changes in tasks. Since it is infeasible to come up with an implementation for all possible cases of all tasks, robots should find solutions for new problems by themselves. So, learning is one of the major abilities for a robot to deal with changing tasks. However, it is generally time-consuming to find even a near-optimal solution for complex tasks through learning. On the other hand, learning in humans is a never-ending process and much faster, thanks to transferring prior knowledge. In this work, we build a knowledge base (called as skill library) from the subsets of the tasks discovered during the learning process. Since most of the tasks encountered have common subsets, the skill library enables us to transfer previous experiences while learning the strategy of a new task. The robot progressively accumulates skills to reduce the difficulty of learning the forthcoming tasks. We choose navigation in different unknown environments as the test bed. The results show a significant improvement especially on the performance of the robot in the initial episodes, a substantial reduction in the cost of the overall learning process, and in the convergence time to the (near-)optimal policy.
Polar Topographic Derivatives for 3D Face Recognition: Application to Internet of Things Security

We propose Polar Topographic Derivatives (PTD) to fuse the shape and texture information of a facial surface for 3D face recognition. Polar Average Absolute Deviations (PAADs) of the Gabor topography maps are extracted as features. High-order polar derivative patterns are obtained by encoding texture variations in a polar neighborhood. By using the and Bosphorus 3D face database, our method shows that it is robust to expression and pose variations comparing to existing state-of-the-art benchmark approaches.
Load-aware virtual machine placement and dynamic migration mechanism for OpenStack

Cloud computing technology in the epoch of large data, which subvert the traditional data computing storage model completely, it is a new type of computing service model. And an increasing number of individuals become the subscribers of the cloud computing, the size of the data center is also growi ng, the problem of the low utilization of resource and high energy consumption are serious. Then how can we use resources efficiently, reduce energy consumption and improve service quality for the large data center. It is an important method to improve the performance of the entire cloud computing server cluster by the improvement of the cloud computing platform. This paper has studied in-depth research on mainstream open source cloud computing platforms such as OpenStackand improved the resource scheduling mechanism which existed. The works are composed of two parts: the virtual machine initial placement mechanism based on resource performance perceptionand the dynamic migration mechanism of virtual machine based on load perception.
PECULIARITIES OF FOREIGN STUDENTS ADAPTATION IN RUSSIAN UNIVERSITIES (ON EXAMPLE OF KUBSTU): THE SECOND STAGE OF THE RESEARCH

The article is devoted to the analysis of foreign student’s adaptation to educational environment of a Russian university. Characteristics of the main stages of educational migrant’s adaptation to the new social environment are given; characteristics of types of adaptation behavior are given. Results of the previous stage of the study of the adaptation of students of the preparatory faculty of KubSTU are summarized, and results of the second stage of research are presented. Analysis of data obtained at the second stage of questionnaire allowed us to draw conclusions about the degree of satisfaction of foreign students with the quality of education, the nature of relations with teachers and student community, the main difficulties of adapting to life in Russia, and everyday, economic and cultural aspects of adaptation. It is concluded that learning in Russian and organization of life are the main difficulties for those who adapt at the current stage. The study of sociocultural, communicative and economic adaptation showed that foreign students do not experience serious problems: there is a clear positive change in the level of adaptability compared to the previous stage. As a result of the study, it was determined that students could not yet overcome all the problems related mainly to educational and household adaptation, but on the whole, the adaptation process is carried out successfully due to the favorable psychological climate, maintenance of ethnocultural identity, positive experience of social interaction and optimistic assessment of their capabilities in difficulties overcoming.
Robust multi-model adaptation regression with local feature space representation

Learning visual models of object categories generally requires a large number of training examples. We show in this paper that it is possible to uncover much discriminative information about a visual category from a small number of examples with just fewlabeled data. The key insight is that, rather than learning from scratch with the original feature representation, we learn an optimal target model with the local feature space representations by leveraging some prior models pre-trained on other related datasets, no matter how different they might be. This target model can be obtained by learning a robust regression scheme with a multi-source adaptation regularization term, which is formally formulated as a Robust Multi-model Adaptation Regression (RMAR) framework with local feature space representations of the target data. This framework is composed of three core components: (1) local consistency Laplacian regularization by constructing a self-adaptive affinity graph based on local feature space representation (LFSR), which aims to augment the discriminative space of the target domain; (2) robust regression learning scheme with feature selection based on l2,1-norm minimization by explicitly considering the regression noise/outlier as well as the regression residual; and (3) scatter constrained multi-model adaptation regularization for generalizing the established robust regression framework to exploit the prior models, under the assumption that multiple auxiliary discriminative models can help the semi-supervised learning from few labeled samples. Lastly, we further propose an effective extension to RMAR, i.e., ensemble LFSR-graph Laplacians regularization framework. The optimization algorithms for RMAR and its extension can be efficiently solved by the alternating iterative strategy, and the iteration convergence can be theoretically guaranteed. Experiments over several real datasets show the promising performance of our methods compared with several representative state-of-the-art works.
imBBO: An Improved Biogeography-Based Optimization Algorithm

Biogeography based Optimization (BBO) is a new evolutionary optimization algorithm based on the science of biogeography for global optimization. However, its direct-copying-based migration and random mutation operators make it easily possess local exploitation ability. To enhance the performance of BBO, we propose an improved BBO algorithm called imBBO. A hybrid migration operation is designed to further improve the population diversity and enhance the algorithm exploration ability. Empirical results demonstrate that our imBBO effectively gains the high optimization performance by comparing with the original BBO and three BBO variants for 23 out of 30 CEC’2017 benchmarks. Moreover, our imBBO presents a faster convergence speed.