Reviewerly/exHarmony huggingface dataset:
A New Binarization Method For Degraded Printed Document Images

In this work, we propose a new method to binarize the degraded printed images. This binarization use the texture information calculated from the edge image to enhance the binarization results for degraded documents. As pre-prossing stage, the wavelet transform followed by the wiener filter were used. Then, texture information has been extracted from the edge image. After, the extracted information will be combined with the texture features computed from the pre-processed image, to estimate the binarization threshold using Niblack’s method. Experiments are conducted on a set of document image with different types of degradation and DIBCO Datasets, the results were compared with three binarisation methods which are: Sauvola, Niblack and Rani’s methods. The results affirm that our binarization technique provides good results in existence of different deterioration as compared with other methods.
Performance Analysis of Channel Estimation for Massive MIMO Communication Using DL-Based Fully Connected Neural Network (DL-FCNN) Architecture

The latest research for applying deep learning in wireless communications gives several opportunities to reduce complex signal processing. The channel estimation is important to study the nature of the varying channel and to calculate channel state information (CSI) value which is utilized at the receiver to nullify the interference which occurs during multipath transmission. In the current article, considering the massive Multiple Input Multiple Output (MIMO) channel model, a DL approach is developed with a fully connected neural network (NN) architecture which is used to estimate the channel with minimum error. The proposed DL architecture uses an openly available channel dataset. Further, using generated pilot symbols of lengths 2 and 4, the performance of DL-based Fully connected NN (DL-FCNN) is analyzed to estimate the channel in uplink massive MIMO communication. The obtained results demonstrate that the channel estimation performance was calculated in terms of normalized mean square error((NMSE) for different values of SNR added at receiver base station (BS) to the signals over the range of BS antennas. Also, the channel estimation error over a large number of BS antennas for massive MIMO scenarios is observed, and it is observed that the NMSE reduces with a greater number of antennas. Hence, it can be inferred that the DL models will be the future for most physical layer signal processing techniques such as channel estimation, modulation detection, etc. within massive MIMO networks.
Evaluation of Interaction with Musculo-skeletal Avatar in VR Space

The presentation of the results of musculoskeletal analysis in sports and other physical activities is effective for improving skills. By presenting the information in VR, the experience is closer to the real space, and the feedback is more efficient than using a monitor. In this case, it is effective if the difference of interaction between the VR space and the real space is small. In this study, we reproduce the real space in the VR space by simultaneously capturing environmental information and human motions, and verify the spatial interaction by conducting an experiment to reproduce the passing motion of people.
Bipolar felicitous fuzzy graphs

In this paper, bipolar felicitous fuzzy graphs (BFFGs), regular bipolar felicitous fuzzy graphs (RBFFGs), irregular bipolar felicitous fuzzy graphs (IBFFGs), neighborly irregular bipolar felicitous fuzzy graphs (NIBFFGS) are introduced. We have proved honeycomb networks and some other networks to be bipolar felicitous fuzzy graphs (BFFGs). Later, some properties of bipolar felicitous fuzzy graphs (BFFGs), regular bipolar felicitous fuzzy graphs (RBFFGs) are discussed.
Informatization of Teaching Management of Business English Major in College Under the Background of Big Data

In order to promote the development of higher education of our country level, so education teaching information management has become the effective way of college education development, and it is the inevitable requirement of development in the information age society. The purpose of this paper is to provide better suggestions for college teaching management informatization by studying the teaching management informatization of college business English majors under the background of big data. This paper studies to big data education pattern as the background, English teachers, students as the research object, with the importance of business English in the MOOC environment as a starting point, to find the relationship between business English teaching, and the MOOC, based on the consistency of the main research object on the development of the realizing large data environment put forward schools, English teachers, and students full development strategy.
Graph Attentive Leaping Connection Network for Chinese Short Text Semantic Classification

Chinese short text semantic classification is a ubiquitous task that widely occurs in natural language processing. The existing methods are generally utilized in English, leading to multiple limitations: Unable to capture word-level abundant semantic when the input tokens are character sequence. It would be more vulnerable to data sparsity and the presence of out-of-vocabulary (abbr., OOV) words if utilizing word-based models, and thus more prone to overfitting. The very few approaches that consider both granularities are still limited. To tackle these problems, we propose a novel Graph Neural Network. Our model adopts the word lattice graph to keep multi-granularity information and utilizes the pre-trained model to obtain powerful semantics. Additionally, the attention mechanism and the layer leaping connection enable better structure-aware representation. Experimental results on three Chinese datasets demonstrate that our model achieves state-of-the-art performance in short text classification models.
My Brief Life as a Pheromone Salesgirl

Other| November 01 2021 My Brief Life as a Pheromone Salesgirl Robin Gow Robin Gow Search for other works by this author on: This Site Google the minnesota review (2021) 2021 (97): 20–23. https://doi.org/10.1215/00265667-9335660 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Robin Gow; My Brief Life as a Pheromone Salesgirl. the minnesota review 1 November 2021; 2021 (97): 20–23. doi: https://doi.org/10.1215/00265667-9335660 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll Journalsthe minnesota review Search Advanced Search The text of this article is only available as a PDF. Copyright © 2021 Robin Gow2021 Article PDF first page preview Close Modal You do not currently have access to this content.
Case Study 1: Red Hat Enterprise Linux (RHEL) Atomic Host

Red Hat® Enterprise Linux (RHEL) Atomic Host is a secure, lightweight, and minimal-footprint operating system optimized to run Linux containers. A member of the Red Hat Enterprise Linux (RHEL) family, its Atomic Host system couples the flexible, modular capabilities of Linux containers with the reliability and security of RHEL in a reduced footprint, to decrease the attack surface and provide only the packages needed to power hardware and run containers. Red Hat’s vision for containerized application delivery on an open hybrid cloud infrastructure is comprehensive, including portability across bare metal systems, virtual machines, and private/public clouds. By choosing Red Hat Enterprise Linux Atomic Host, customers can take advantage of the fast pace of innovation from open-source community projects like the Docker project and Project Atomic while maintaining a stable platform for production deployment. Customers can concentrate on customizing and developing containerized applications while Red Hat maintains the underlying Linux platform on which these depend. Atomic Host was chosen to act as the pilot study for introducing the concept of Innovation Scorecard to measure the success of deployed modus operandi improvements within this agile software development project.
Robust Detection and Tracking Algorithm for Pedestrian Targets with Posture Change

In the intelligent monitoring system, pedestrian detection and tracking is the basis of behavioral analysis, pedestrian retrieval and other intelligent analysis technologies. In order to solve the problems caused by posture change and occlusion, this paper proposes a robust pedestrian target detection and tracking algorithm, which is based on YOLOv3 and DeepSORT. In order to reduce the impact of pedestrian posture change, we introduce a person re-identification feature that resists posture change on the tracking algorithm DeepSORT. For pedestrian occlusion, inspired by Spindle Net, we integrate seven local appearance features extracted from different regions of the human body together to form robust global appearance features, making the tracking more accurate. Experimental results prove that the algorithm proposed in this paper shows robust pedestrian detection and tracking performance on both subjective visual effects and objective metrics, which can meet the needs of actual applications.
On the Use of Ontological Approach to the Advanced Driver-assistance System Design

This paper presents the possibility to use an ontological approach to represent and arrange knowledge when designing the advanced driver-assistance system (ADAS). It explains the main ontology concepts, analyzes the methods of their use in order to solve the mentioned task, and considers certain current approaches to ontology creation within the implementation of separate ADAS subsystems. The paper provides case studies of using ontologies to solve the task of assisting the driver on the road and establishing inference rules using the reasoner. Case studies focus on detecting dangerous situations during vehicle movement. The paper attempts to add new facts to the ontology not included in it at the design stage and explores the possibility of applying the case-based reasoning technique in similar systems. The research presents methods of case-based reasoning using ontology and a relevant example of vehicle diagnosis. It also outlines advanced research directions of using the ontological approach to private task-solving algorithms in ADAS systems. All case studies in this paper were implemented using protégé ontology editor.
Reduction of Training Data from Large Datasets Using Encoder and Decoder Algorithm Without Much Compromise of Accuracy

The objective of this paper is to minimize the number of samples required for training algorithms involving support vectors while maximizing knowledge of the target class. A method is proposed which uses autoencoder in conjunction with farthest boundary point extraction for selecting most promising frontier points from the original sample. Farthest frontier points are chosen using a geometrical approach for estimating extreme points of a class and autoencoder for learning a compressed representation of the data. For experimentation, we have used datasets of MNIST, Iris, credit card fraud detection, Indian Pines, Human Activity Recognition Database.
GRADUATED THESIS AS A CHALLENGE FOR MASTER'S STUDENTS: THE CASE OF R. GLIER KYIV MUNICIPAL ACADEMY OF MUSIC

This study examines the practice of writing graduated theses by students who have participated in music competitions. The research method was a focus group. The reasons for this practice are described. It is proved the student cannot comply with the prescribed procedure for writing graduated these. It is concluded that it is time to legitimize this practice. The basis for legitimizing one recognizes its existence, its acceptance by both students and leaders, the positive effect of its use. Measures to legitimize this practice are proposed.
Optimization of Vehicle Trajectory Planning for Autonomous High-Speed Overtaking

There are some problems when the traditional artificial potential field method is applied to vehicle trajectory planning: The rectangular obstacle potential field cannot meet the requirements for obstacle avoidance behavior as early as possible and the planned trajectory cannot consider vehicle dynamics constraints. At the same time, because of the switching of the lane centerline in the overtaking situation, the obstacle avoidance controller produces a fluctuating and unsmooth trajectory. In view of the above problems, an obstacle avoidance trajectory planning controller for high-speed structured environment is proposed. The controller constructs overtaking feasible potential field area (OFPFA) and combined with MPC. In addition, an optimization strategy for overtaking lane-changing trajectory is designed to optimize trajectory when the controller fast switching of the centerline of lane in the overtaking process. The designed controller is verified in MATLAB simulation environment and is turn out feasible.
Rethinking the Effectiveness of Selective Attention in Neural Networks

The introduction of the attention mechanism is considered as an important innovation in the recent development of neural networks. Specifically, in convolutional neural networks (CNN), selective attention is utilized in a channel-wise manner to recalibrate the output feature map based on different inputs dynamically. However, extra attentions and multi-branch calculations introduce additional computational cost and reduce model parallel efficiency. Therefore, we rethink the effectiveness of selective attention in the network and find that the bypass branch computation is redundant and unnecessary. Meanwhile, we establish an equivalent relationship between Squeeze-and-Excitation Networks (SENet) and Selective Kernel Networks (SKNet), which are two representative network architectures with the feature attention mechanism. In this paper, we develop a new network architecture variant called Elastic-SKNet by reducing the calculation in the bypass branch of SKNet. Furthermore, we utilize the differentiable Neural Architecture Search (NAS) method to quickly search for the reduction ratio in each layer to further improve model performance. In the extensive experiments on ImageNet and MS-COCO datasets, we empirically show that the proposed Elastic-SKNet outperforms existing state-of-the-art network architectures in image classification and object detection tasks with lower model complexity, which demonstrates its further application prospects in other fields.
Active Machine Learning in Regression Problems

In the scale of the big data, it is hard to track exact features from which machine learning algorithm has made associations. Moreover, it has no understanding of underlying processes, so it can be overly sensitive to changes in the training data. But as the data amount and complexity of machine learning algorithms are continuously growing, it is crucial to find mechanisms to ascertain their reliability and to reduce the amount of necessary data labeling to train the algorithm. Active machine learning may be one of the solutions. Its main objective is to achieve higher algorithm accuracy with less labeled data for training. But the mechanics by which the active learning algorithm selects the next data sample for labeling might be utilized to gain an understanding of associations created by the machine learning algorithm. This paper describes the development of the active machine learning method for regression problems. It results in a demonstration of the system's prototype which is used to train meta models for computer experiments.
Shape and Pole Estimation for Small-Bodies on Approach

The available mission data on approach to any small body will encompass a degree of error in both the body shape and orientation estimates as well as the navigation solution. This work aims to demonstrate the capability of shape-from-silhouette modeling to develop a surface and pole estimate during the approach phase of a small-body mission and compare rough shape model results to the current state-of-the-art in small body mapping. Monte Carlo analysis is implemented to investigate full state-space robustness to error in the original spin pole estimates, taken from simulated lightcurve observations on the approach trajectory. The final results presented show a robust shape estimation procedure and characterize the full consequences of pole error in the resulting model up to navigation standards.
A New Fractional-Order HNN System and its Application in Image Encryption*

In this work, we propose a new fractional-order chaotic system based on the model of 4-neuronsbased Hop eld Neural Network (HNN). By using Adomain decomposition method, the proposed fractional-order chaotic system is solved. With the orders changing, the proposed fractionalorder system shows rich dynamical characteristics. Then, based on the pseudo-random numbers (PRNs) generated by the proposed system, a new construction method of multiple hash index chain is designed. And a new image encryption algorithm is designed according to the multiple hash index chain. The safety test results show that the design encryption algorithm has higher security performance. Finally, the 4-neurons-based HNN fractional-order system is implemented by Multisim circuit simulation. The experimental results show the feasibility of the theoretical analysis.
INTER-IoT Architecture for Platform Interoperability

The number and diversity of IoT platforms is huge, causing an unsustainable ecosystem fragmentation. INTER-IoT proposes a solution to bridge heterogeneous IoT platforms, promoting interoperability at different abstraction layers. Modeling is a key engineering mechanism to abstract existing platforms requirements and functionalities, to capture common, reusable properties, so to realize general approaches that are capable of addressing different degrees of interoperability. Based on the significant results of the IoT-A EU funded project, INTER-IoT has therefore defined a Reference Model for IoT Platforms Interoperability, a Reference Architecture, and a complete interoperability system.
New Words Detection Research for Programming Domain

At present, there is no open domain vocabulary in the programming domain, which leads to many errors in Chinese word segmentation in programming domain. In order to promote the technical progress of word segmentation and other related tasks, the new words detection in the programming domain has important research significance. In this paper, the current phrase quality estimate method (ClassPhrase) for high-quality phrase extraction task is introduced into new words detection task. According to the existing domain vocabulary, we use the distant supervision method to generate labels, so that the label quality of the generated model training is higher, so as to train a better classification model to better complete the new words detection task. At the same time, the problem-solving reports are made public for the open data set lacking of text specification in the programming domain. The experimental results show that the method is effective in the programming domain. The F1_Score (F1) is 71.78 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , which is better than the methods based on statistics and word embedding.
Model Finding for Exploration

We survey recent results in model finding, focusing on the notion of a model finding assistant to help users, even users not trained in logic, understand their software artifacts. The technical results discussed have all been previously published; the presentation here highlights two themes: (i) geometric logic and homomorphism orders as natural foundations for model finding, and (ii) an implemetation dichotomy between direct model finding and model finding with the aid of SAT- and SMT-solvers. We give generic high-level algorithms for the central problems of programming against such solvers; lower-level details are determined based on the category of homomorphisms being used.
Conducting Effective Online Interviews in an Academic Library

In this column, we share in the brief literature review video interviewing trends that work in the corporate world to make the interview process in an academic library as smooth and consistent as possible. Furthermore, we share our best practices for online interviewing in academic libraries that we have developed from reading the literature and our own experiences. The best practices that we developed may or may not work for your academic library’s hiring team, but hopefully what we suggest can be modified to work for your team, culture, and resources.
Research of Identity Privacy-preserving Scheme Based on CP-ABE

Data security and access control is one of the most challenging ongoing research work in cloud computing, because of users outsourcing their sensitive data to cloud providers. According to cloud computing privacy protection, an identity privacy-preserving scheme based on CP-ABE is proposed. It reduced the performance overhead, overcome untrustful three sides. Its frame is based on trusted third party. By analyzing its security, it indicates that the scheme can not only insure the access control of different users, but also can protect user's identity privacy.
Unsupervised Person Re-identification via Diversity and Salience Clustering

Obtaining large-scale annotations for person re-identification tasks is very difficult and hard to deploy in real scenarios. Existing unsupervised person re-identification methods focus on one-shot learning and domain adaptation, but they still rely on the labels of the source domain or a small number of sample labels. To solve this problem, we formulate a very simple but effective unsupervised person re-identification method: Diversity and Salience Clustering (DSC). The method generates a stable clustering feature space for unsupervised re-identification by considering the diversity and salience of pedestrian samples. As a pure unsupervised person re-identification model, our approach does not use any camera annotations, pedestrian labels and source domain data. Extensive experiments on two image datasets demonstrate that our proposed method outperforms the state-of-the-art unsupervised person re-identification methods.
Unbiased No-Switching Continuous-Variable Quantum Key Distribution

We report a practical no-switching continuous-variable quantum key distribution protocol with unbiased quadrature detection, which shows the potential to compensate for the asymmetry imperfections of practical heterodyne detector.
Dictionary Learning-based Reference Picture Resampling in VVC

Versatile Video Coding (VVC) introduces the con-cept of Reference Picture Resampling (RPR), which allows for a resolution change of the video during decoding, without introducing an additional Intra Random Access Point (IRAP) into the bitstream. When the resolution is increased, an upsampling operation of the reference picture is required in order to apply motion compensated prediction. Conceptually, the upsampling by linear interpolation filters fails to recover frequencies which were lost during downsampling. Yet, the quality of the upsampled reference picture is crucial to the pre-diction performance. In recent years, machine learning based Super-Resolution (SR) has shown to outperform conventional interpolation filters by far in regard to super-resolving a previ-ously downsampled image. In particular, Dictionary Learning-based Super-Resolution (DLSR) was shown to improve the inter-layer prediction in SHVC [1]. Thus, this paper introduces DLSR to the prediction process in RPR. Further, the approach is experimentally evaluated by an implementation based on the VTM-9.3 reference software. The simulation results show a reduction of the instantaneous bitrate of 0.98% on average at the same objective quality in terms of PSNR. Moreover, the peak bitrate reduction is measured to 4.74% for the “Johnny” sequence of the JVET test set.