Reviewerly/exHarmony huggingface dataset:
On the Segmentation of Astronomical Images via Level-Set Methods

Astronomical images are of crucial importance for astronomers since they contain a lot of information about celestial bodies that can not be directly accessible. Most of the information available for the analysis of these objects starts with sky explorations via telescopes and satellites. Unfortunately, the quality of astronomical images is usually very low with respect to other real images and this is due to technical and physical features related to their acquisition process. This increases the percentage of noise and makes more difficult to use directly standard segmentation methods on the original image. In this work we will describe how to process astronomical images in two steps: in the first step we improve the image quality by a rescaling of light intensity whereas in the second step we apply level-set methods to identify the objects. Several experiments will show the effectiveness of this procedure and the results obtained via various discretization techniques for level-set equations.
Design of Dual Priority Persistent CSMA Protocol Based on FPGA

In order to study the MAC layer protocol in the wireless sensor network under the actual channel condition, the field programmable gate array (FPGA) hardware circuit is used to provide a dual-priority implementation scheme for the persistence carrier sense multiple access (CSMA) protocol. According to the characteristics of each node in the network, analyzes the system throughput and energy consumption, and the FPGA is used to overcome the shortcomings of the existing implementation scheme and the article combines the actual application, changes the single channel mode, and realizes the dual channel data transmission. Finally, the simulation tests the correctness of the design.
Towards the Definition of the Value of Influence Factors in QoE-aware Management

It is a matter of fact that the more data about the Influence Factors (IF) is available, the more significant the impact of a proper Quality of Experience (QoE) oriented service management is. However, to understand how much of resources is worth investing to acquire and manage this data, it is important to know what is the real value of the data related to each IF. In this paper, we propose a preliminary approach to give an answer to this question.
Learning Convolutional Neural Networks with Deep Part Embeddings

We propose a novel concept of Deep Part Embeddings (DPEs), which can be used to learn new Convolutional Neural Networks (CNNs) for different classes. We define DPE as a neuron of a trained CNN along with its network of filter activations that is interpretable as a part of a class that the neuron contributes to. Given a new class $\mathcal{C}$, we explore the idea of combining different DPEs that intuitively constitute $\mathcal{C}$, from trained CNNs (not on $\mathcal{C}$), into a network that learns the class $\mathcal{C}$ with few training samples. An important application of our proposed framework is the ability to modify a CNN trained on n classes to learn a new class with limited training data without significantly affecting its performance on the n classes. We visually illustrate the different network architectures and extensively evaluate their performance against the baselines.
Principal polynomial features based broad learning system

Broad Learning System (BLS) offers an alternative way of machine learning in deep structure. BLS is established based on the idea of the random vector function-link neural network (RVFLNN) which eliminates the drawback of long training process and also provides the generalization capability in function approximation. In this paper, a principal polynomial features based broad learning system (PPFBLS) is proposed. In this method, the principal component analysis (PCA) is used for feature dimensionality reduction. The candidate features of degree d are constructed by the principal features of degree one and the principal features of degree d-1. The enhancement features of degree d is extracted by applying PCA on the candidate features of degree d. Ridge regression learning using the concatenated features of each degree are applied for pattern classification. Parameters in the feature extraction stage are optimized by PCA which is different with randomly initialization adopted by BLS and RVFLNN. Experimental results on the MNIST handwritten digits recognition data set and the NYU NORB object recognition data set demonstrate the effectiveness of the proposed method.
Conditions for improving the professional competence of primary school teachers in educational work

The teacher is a significant figure in the formation and development of the student's personality. In this regard, the task of preparing a primary school teacher of a new type is relevant. This article discusses such a topic of educational development in the Russian Federation as conditions for increasing the professional competence of an elementary school teacher. The urgency of the problem is caused by the strengthening of the requirements in all spheres of labor activity for professional training in the conditions of a market economy. The author dwells on the analysis of those qualities that are necessary for a modern primary school teacher to meet the needs of society in bringing up an adaptive, highly intelligent and free individual with basic competences and capable of subsequent adequate self-determination in the professional sphere. The teacher’s figure in the work is viewed as a tutor and facilitator.
Behavioural Biometrics Authentication Tested Using EyeWriter Technology

Biometric technology progressed over the past number of years from an esoteric technology to technology found in various devices such as smart phones, ATMs, pay points, physical access control mechanisms to name only a few. Behavioural biometrics is in many cases applied to strengthen authentication mechanisms such as passwords or voice recognition. In voice recognition the characteristics from the voice such as the pitch, dynamic range and the harmonics can be used for biometric authentication, but to strengthen the authentication decision, the way the person speaks (intonation) will also be applied to confirm authenticity. In this paper the research findings are presented where 25 candidates used the EyeWriter eye tracking technology to test if the behavioural movement of an individual's eye is unique enough to be used for secure biometric authentication. From the various tests performed during the experimental phase of this re-search, and scrutinized in this paper, the following was found: The behavioural aspect of a person's eye movement can indeed be used for behavioural biometric authentication, specifically if a one-to-one biometric authentication approach is followed. A one to many approach showed a lot of potential for future research, but considering the sample size tested, the results cannot easily be extrapolated to the population.
Multi-branch Aggregate Convolutional Neural Network for Image Classification

In terms of image classification, in order to obtain higher classification accuracy, different levels of feature information need to be extracted from the image. Convolutional neural networks are increasingly applied to image classification. However, the traditional convolutional neural network has insufficient feature information extraction, poor classification accuracy, and easy over-fitting. This paper proposes Multi-branch aggregation network framework based on deep convolutional neural network that can be applied to image classification. Based on the traditional convolutional nerve, the network width and depth network are increased without increasing the parameters to optimize and improve the network to further enhance the feature expression ability of the network, Enriched the diversity of feature sampling, increased image classification accuracy and prevented overfitting. The framework and traditional frameworks and other frameworks were compared and analyzed through a series of comparative experiments in two standard databases, CIFAR-10 and CIFAR-100, and the validity of the framework was demonstrated.
Data Analytics and Data monitoring Based on Database Recommendation - A Comparison

A comparison of various analysis algorithms based for recommendation systems used in the market and businesses has their usage without considering the fact that if used with correct algorithm can increase its efficiency. Comparison is obtained in this paper after studying and researching various obtained algorithms present currently. This paper presents an introduction to the concept of recommendation systems which are recently working on different domain and then comparison is made, that too employed for scalability, reliability, faster process and efficiency.
Person Tracking and Counting System Using Motion Vector Analysis for Crowd Steering

Video surveillance has been in use since a protracted time as an assistance to beat security and other problems. Historically, the video outputs area unit monitored by human operators and area unit sometimes saved to tapes for later use. Sensitive areas like shopping malls, banks, huddled public places want a strict police investigation and may require management of the flow of individuals mechanically. To do such automation, a wise video closed-circuit television is required for today’s world equipped with machine learning algorithms. In this project, a sensible visual closed-circuit television with person detection and following capabilities is bestowed. This can be used to regulate the flow of persons into the sensitive areas, which is often achieved by count the persons who are getting into and going through these areas, so knowing the overall capability a sensitive space is holing at any specific purpose of your time. Motion vector analysis is that the main construct that is used here to realize the following of the persons. This has a tendency to count the persons who are getting into and going out stationary cameras fixed points, the capability is obtained as distinction between the count of the persons entered and count of the persons who left the sensitive space. Any sensitive space would have a restricted house to accommodate. So it is necessary to prohibit the persons from getting into sensitive space, once the capability is reached to threshold price.
Human Perception Oriented Image Enhancement

We developed a DCT domain image enhancement method that not only improves human perceived quality, but also overcomes artifacts that inherently occur from image signal processing. Exploiting an human visual perception model, we developed a perceived contrast measure in the DCT domain. Based on the measure, we designed a filter to make images seem to be vivid as well as finely resolved without boosting up noise. And after investigating how blocking and ringing artifacts occur during processing image signals, we devised a scaling filter that avoids such artifacts. The objective and subjective evaluations verify that the proposed method consistently produces competitive performances.
Private-Preserving Analysis of Network Traffic

Network traffic analysis has important applications in many fields, such as the quality of service provisioning, network monitoring, network optimization, and so on. We can get regional network situation by analyze all parties’ data, however, the cooperation among multiple parties suffers an extra drag from privacy and regulation issues. In this work, we apply secure multi-party computation methods to analyze more than three parties’ traffic data without revealing the secrets themselves. We present a clustering method based on IP network prefix and apply it to the traffic. Through the results of clustering, we can get subnets with the most considerable traffic in the network, and make relevant network settings for these subnets.
Proactive Document Distribution with a Self-Enforcing Network

Nowadays, construction projects are planned and carried out by large, interdisciplinary and mostly spatially separated teams. Project members have different data sets, which are often exchanged via complex and error-prone e-mail traffic. This leads to cumbersome and often chaotic data storage. In the following, a method with SEN as core element is described that is intended to improve data exchange through pre-planning data synchronization. The aim of the project is to develop a cloud-based collaboration software for the construction industry that sustainably increases the quality and speed of construction processes and reduces errors caused by old or incorrect data.
The Design and Research of Human-computer Interactive interface on the Medical Infusion Pump

Medical infusion pump is a commonly used clinical nursing equipment, which is of great value in the field of medicine.On one side, this paper collected and analysed user requirements by conducting user research on nurses and patients; on the other hand, it analysed the software and hardware interface, combined with the present market used the medical infusion pump, proposed the design principle of human-computer interaction of medical infusion pump, and the improved design is carried out under this guidance finall for having a better usability and user experience.
Historical Research on the Promotion of Women's Physical Education in Prewar Japan: With a Focus on the Taisho Era*

The purpose of this study was to clarify how women's physical education was promoted in Japan, particularly around the Taisho era (1912-1926). Before commencing the main discussion, the circumstances leading up to the promotion of women's physical education around the turn of the twentieth century are reviewed, along with the status of women's physical education at that time. Then, the following three points are considered as specific topics for the present study: First, critical opinions on women's physical education are reviewed to examine the details underlying the problematic nature of its promotion, hitherto assumed. Second, measures for promoting women's physical education in view of these difficulties are examined through arguments for promoting measures put forward by leading figures in physical education. Third, the tendencies and problems surrounding proposed measures for promoting women's physical education in the Taisho era are clarified through an examination of critical opinions on these promotion measures.
Partitioning Itemset on Transactional Data of Configurable Items for Association Rules Mining

Association Rules Mining on transactional data is a very good way to help decision making on business strategy by finding what items people likely to buy together. In some cases, item is configurable, like PCs, cars, or gaming laptop, where people can choose what parts they want on their purchased item. This method can be used to determine what kind of configuration people most likely to choose and help seller on making business strategy. This kind of configurable item transactional data has one major difference from other transactional data, which is the fact that once one part in a category is selected, other parts in the same category will automatically be eliminated. This paper examines the uniqueness of these data and uses them to improve Association Rules Mining by proposing a method of partitioning itemset to dramatically reduce the number of scan needed to find frequent itemset, hence reduce execution time. From the experiment, the result shows that the proposed method can reduce the number of scan needed to generate frequent itemset while still delivering the same result, and when applied on FP-growth the proposed method generated the same rules as the standard FP-growth while still maintaining lower number of scan. The first section in your paper
Intelligent Analysis in Question Answering System Based on an Arabic Temporal Resource

In this article, we present the overall structure of our specific system for generating answers of temporal questions based on Arabic temporal resource, called Ar-TQAS (Arabic Temporal Question Answering System). This system deals with answering temporal questions involving several forms of inference to obtain a relevant answer. The Ar-TQAS system was built and the experiments showed good results with an accuracy of 72%.
Text Image Extraction and Summarization

with the huge amount of increase in data thesedays data processing has become very important. It filtersa large amount of data. The text mining tool finds relationbetween the words in text content and analyzes the resultsas well. Deriving quality information from text forms thecrux of data analysis or text mining. This paper focuses ona text mining application. Text information present inimages is recognized and is summarized according torequirement, i.e. number of lines that text needs tosummarize is dependent on user. Text mining thus is usedto save time of the user, increase the data efficiency. It isused to make computation on data that a human woulddefinitely fail to do, that is for analytics of large volumes ofdata. Hence text image extraction and summarization is anecessity in the current scenario. If the efficiency of theproposed model is optimized, this model of Text ImageExtraction and Summarization can be very beneficial. Itcan used as a ready to go , click image and get summaryapplication in a variety of situations. In this proposedmodel, even the number of lines the content has to bespecified, thus ensuring that the extent of summarization iscompletely user controlled.
METHODOLOGY FOR EFFICIENT PERFORMANCE OF MULTISCALE MODELING METHODS IN HETEROGENEOUS HARDWARE INFRASTRUCTURES

The paper presents a novel approach dedicated to analysis and optimization of multiscale methods execution on hetero-geneous hardware including processors, graphic cards and other accelerators. The methodology proposed in this paper is based on the investigation, which proved that efficiency of various numerical methods, used for computing purposes on different devices, often behaves unpredictably. This efficiency was analyzed in details and presented in this paper. On the basis of this assumptions two-step optimization procedure was proposed, i.e., before and during calculations, allowing to verify current efficiency and to propose new decomposition of computing domain. Both steps were described and verified in details by using loosely and fully coupled multiscale approaches. Performed validation with discussion of some unconsidered aspects is presented in the paper as well.
Large-Scale Models for Local-Area Networks

Objectives: United "splendid" information has incited various theoretical advances, including multicast applications and setting free dialect structure.Given the present status of client-server correspondence, futurists deplorably need the examination of red-dim trees.We perceive how 802.11b is associated with the perception of sensor frameworks.Methods/ Statistical Analysis: Our appraisal method addresses a value-fit research duty without anyone else.Our general appraisal approach hopes to exhibit three speculations: 1. that the region character split never again impacts optical drive space; 2. that ordinary barge in on rate stayed relentless across over dynamic periods of Macintosh SEs; ultimately; and 3. that ROM space carries on an exceptionally essential dimension contrastingly on our Personal Digital Assistants (PDAs).Findings: In this work, we affirmed that the acclaimed ambimorphic calculation for the enhancement of eXtensible Markup Language (XML) keeps running in Θ(n + n) time.Application: The traits of our structure, in association with those of more acclaimed applications, are deplorably continuously sensible.We mean to make Dentist available on the Web for bar list download.
A Simplicial Piecewise Linear Approach for Efficient Hardware Realization of Neural Networks : (Invited Presentation)

Neural Networks (NN) have been a matter of research because of their capability to solve complex problems where other topologies fail. However, current implementations of practical NNs require powerful computers for their deployment, like the ones used in data centers which typically require a high energy budget. This work proposes a simplicial piecewise linear algorithm as an alternative to implement NNs, which in addition can be implemented in low-power microelectronics. We illustrate the feasibility of the approach giving some examples where the simplicial algorithms replace conventional NNs with similar results.
A Design Flow Framework for Fully-Connected Neural Networks Rapid Prototyping

The current work deploys a framework for rapid prototyping of Fully-Connected Neural Networks (FCNs). The scope is to provide an automatic design flow that generates a template-based VHDL code considering the accuracy, the resource utilization and the design complexity. More precisely, the deployed tool incorporates hardware optimizations in the implementation of the multiplications, the activation function and the definition of the fixed-point types providing user-defined configurations thought a GUI. The FCNs of two applications (Alexnet and Lenet) were implemented to evaluate our approach. The results seem promising and prove the design flexibility of our framework generating optimized code that exceeds the 10K lines for each hardware instance within a few hours, while preserving low levels of latency that does not exceed 400 cycles for our applications.
Non-local Self-attention Structure for Function Approximation in Deep Reinforcement Learning

Reinforcement learning is a framework to make sequential decisions. The combination with deep neural networks further improves the ability of this framework. Convolutional nerual networks make it possible to make sequential decisions based on raw pixels information directly and make reinforcement learning achieve satisfying performances in series of tasks. However, convolutional neural networks still have own limitations in representing geometric patterns and long-term dependencies that occur consistently in state inputs. To tackle with the limitation, we propose the self-attention architecture to augment the original network. It provides a better balance between ability to model long-range dependencies and computational efficiency. Experiments on Atari games illustrate that self-attention structure is significantly effective for function approximation in deep reinforcement learning.
Task-Based Quantization for Massive MIMO Channel Estimation

Massive multiple-input multiple-output (MIMO) systems are the focus of increasing research attention. In such setups, there is an urgent need to utilize simple low-resolution quantizers, due to power and memory constraints. In this work we study massive MIMO channel estimation with quantized measurements, when the quantization system is designed to minimize the channel estimation error, as opposed to the quantization distortion. We first consider vector quantization, and characterize the minimal error achievable. Next, we focus on practical systems utilizing scalar uniform quantizers, and design the analog and digital processing as well as the quantization dynamic range to optimize the channel estimation accuracy. Our results demonstrate that the resulting massive MIMO system which utilizes low-resolution scalar quantizers can approach the minimal estimation error dictated by rate-distortion theory, achievable using vector quantizers.