Reviewerly/exHarmony huggingface dataset:
An Internet Resource Extracting Relation Model based on Distant Supervision and Deep Learning

Relational extraction of large-scale text data is one of the key issues in natural language processing. Facing large-scale Internet data resources, this paper proposes a relationship extraction model based on distant supervision and deep learning. The model build entity-to-package based on distant supervision to solve the high computational complexity and time cost of Internet large-scale data. Model's input embeddings based on BERT and entity position, which can improve the text feature representation ability. And the model utilized attention mechanism to optimize the entity-to-package tag attention parameters to alleviate the wrong label problems arising from distant supervision learning. In experiments, the performance of the proposed model exceeds that of other baseline models. The results demonstrate the positive contribution of our model in Internet large-scale relation extraction.
Machine Learning in Healthcare: Breast Cancer and Diabetes Cases

This paper provides insights into a workflow of different applications of machine learning coupled with image analysis in the healthcare sector which we have undertaken. As case studies, we use personalized breast cancer screenings and diabetes research (i.e., Beta-cell mass quantification in mice and diabetic retinopathy analysis). Our tools play a pivotal role in evidence-based process for personalized medicine and/or in monitoring the progression of diabetes as a chronic disease to help for better understanding of its development and the way to combat it. Although this multidisciplinary collaboration provides only succinct description of these research nodes, relevant references are furnished for further details.
An Online Supervised Learning Framework for Crime Data

Crime analysis is a law enforcement function that involves systematic analysis for identifying and analyzing patterns and trends in crime and disorder. Crime analysis also plays a role in devising solutions to crime problems, and formulating crime prevention strategies. A lot of previous work has been done on analytic of crime demographics and geographic prior to this project. The unfortunate, frequent nature of crime, and the fact that it often follows a geographic and demographic pattern which requires constant updation makes this data set an excellent choice for online learning based models for analytics.
An Image Authentication Scheme Based on Chaotic Maps

Watermark is a method to protect the intellectual property rights of digital media. The production of multimedia content has increased in recent years, and the protection of copyright has put forward high requirements. Different watermarking methods are proposed to satisfy the trade-off between imperceptibility while maintaining appropriate embedded data capacity. In general, the transform domain watermarking, such as wavelet-based methods; yields better results than spatial algorithms. Therefore, this paper introduces a new measure. A block and content based localization and restoration image authentication scheme is proposed. In this scheme, the watermark of each block is an encrypted form of its content features embedded in another block selected by the ergodic matrix of chaotic sequences. Experiments show that this scheme can detect and locate any tampering of 8X8 pixels and above, and can restore 40% of the damaged to understandable images.
Research on Security of Access Control for Cloud Computing Environment

Cloud computing is considered as one of the most dominant paradigms in the Information Technology industry these days. It offers new cost effective services. With the rapid development of cloud computing, cloud security issues are becoming increasingly prominent and urgently need to be dealt with. Access control, as an important measure for traditional information and system protection, can effectively prevent illegal users from accessing protected objects, limit users’ permission, and protect information resources from illegal use and access. This paper mainly discusses the improvement of the existing access control model in the cloud computing environment, the cloud data security protection technology, the introduction of risk perception and trust mechanism to implement access control to dynamically detect user identity and authorization technology for the vulnerabilities in the cloud system. We also point out some future research directions in cloud computing.
On the Connection Between Deutsch-Jozsa Algorithm and Bent Functions

Abstract It is presently shown that the Deutsch-Jozsa algorithm is connected to the concept of bent function. Particularly, it is noticeable that the quantum circuit used to denote the well-known quantum algorithm is by itself the quantum computer that performs the Walsh transform of a Boolean function. Consequently, the output from the Deutsch-Jozsa algorithm when the hidden function is bent corresponds to a flat spectrum of quantum states.
LiB

The growing number of undergraduated thesis produced at the end of undergraduated courses in Brazil are sometimes available only in physical libraries or have limited digital access, making it difficult for researchers and the general population to access this information. This paper presents the development of a system that uses data analysis and information retrieval techniques capable of providing full-text search, as well as the recommendation of works based on their similarity using the Latent Dirichlet Allocation (LDA) technique. The goal of this solution is to offer higher education institutions in Brazil a platform capable of providing digital access to the documents they produced, enabling the dissemination of scientific production. The tool was validated using data from a real educational institution, and the search and recommendation mechanisms proved to be efficient for their purpose.
Introduction of Society for the Study of Robotics in Tokyo Institute of Technology and its Relation to Engineering Education

The university’s club activities are motivated by pure curiosity and are not frequently discussed in the context of engineering education. However, individual interest can be the most vital driving force to grow up as a young engineer. This report introduces Society for the Study of Robotics at Tokyo Institute of Technology and discusses its relation to engineering education.
Unsupervised Learning for MRI Brain Tumor Segmentation with Spatially Variant Finite Mixture Model in Reversible Jump MCMC Algorithm

Abstract MRI brain tumor segmentation is an important topic in medical image processing. Manual segmentation is risky and time-consuming when the MRI is in low quality. The automatic segmentation can be a solution to manage this problem. This paper proposed an improved modeling approach for unsupervised learning trough Spatially Variant Finite Mixture Model (SVFMM). The main contribution is the automation of the SVFMM algorithm to find the optimum number of clusters. This is achieved by employing the birth-death random process in Bayesian Reversible Jump MCMC. Validation of the proposed model is done by calculating the Correct Classification Ration (CCR) in comparison to the original SVFMM and Gaussian Mixture Model (GMM). The proposed model provides similar performance in image segmentation compared to the original SVFMM but is better than GMM. However, SVFMM-RJMCMC is faster and more efficient in finding the optimum number of clusters.
중국인 학습자의 온라인 한국어 수업 만족도 및 학습효율에 관한 연구: D 대학교 한국어 전공 중국인 대학생을 대상으로

With the rapid development of information technology, people learn with a diverse array of learning methods ranging from the traditional face-to-face learning style to online learning style. The main purpose of this study is to understand Chinese college students’ satisfaction and learning efficiency of online Korean language education. Chinese students majoring in the Korean language of different levels at D university are taken as the subjects for questionnaires and interviews. The questionnaire data was analyzed with SPSS 25.0. The study finds that there are significant differences in the satisfaction of online learning among different levels of students, but there is no significant difference in the learning efficiency among them.
A keypoint-based object detection method with attention mechanism and feature fusion

Recently, there is a new object detection framework that does not require anchor boxes, which refers to the realization of object detection tasks by detecting key points. CenterNet identifies an object with single keypoint, namely the center point of its bounding box. It finds other attributes at the same time through key point estimation, such as the size and the orientation of the object. In this work, a global attention module is introduced to the backbone called Hourglass to enhance feature extraction with the global context information. A multilevel fusion method is also added to the Hourglass to integrate the feature maps of different levels, and further improve the detection capability. Combining the two methods, the new network achieves 46.1% AP with multi-scale testing on MS COCO.
A service-based recommendation system to assist decision making in a small and medium company

Collaboration and resource sharing have attracted a great interest among companies. Our contribution relates to this context, providing a decision-making support for companies. We propose a platform to facilitate resource sharing between collaborative companies, through a recommendation system. This system provides similar services that respond to a company's needs. The approach presented in this paper includes the choice of a representation model for the services as well as the selection of an appropriate measure of similarity. This latter is mainly based on a comparative study of three measures of similarity using several performance evaluation measures. In order to evaluate our approach, the user feedback is integrated with the decision-making process.
An Algorithm for Target Detection Based On Aggregation Multi-Scale Feature

Abstract In order to alleviate the multi-scale problem caused by the scale change between object instances, pyramids are widely used in target detection. Although these target detectors with characteristic pyramid structure have achieved good results, they have some limitations because they simply construct characteristic pyramids according to the skeleton multi-scale pyramid structure originally used for target classification tasks. In this study, based on M2Det, a multi-scale feature with richer multilevel information is proposed to construct a more effective feature pyramid to detect targets of different scales.First, the basic features with multi-level features will be fused and extracted from the backbone.Then, the basic features are input into the M module, and the features generated by each P module are used as the features of the detection object in the form of dense connection. Finally, the attention mechanism is introduced into the L module to assemble the features with the same scale and construct a feature pyramid for target detection. On the COCO dataset, MLPNet implements the results of AP37.8.
A Novel Dormand-Prince Based Hybrid Chaotic True Random Number Generator on FPGA

This study presents a novel Dormand-Prince-based hybrid chaotic True Random Number Generator Design (TRNG) that can be used for secure communication and cryptographic applications on Field Programmable Gate Array (FPGA). In this design, a chaotic oscillator unit has been implemented with an FPGA-based Sprott-Jafari chaotic oscillator model suitable with IQ-Math fixed point number and IEEE 754-1985 floating point number standards. Random numbers have been produced with the quantization of the results generated by the chaotic oscillator. XOR has been performed with FPGA-based ring oscillator structure on the post-processing unit so as to enhance the randomness. The differential equation of the chaotic system used in the TRNG design was modelled using Dormand-Prince numerical algorithm method. The design on FPGA has been realized in two separate number formats including 32-bit (16I-16Q) IQ-Math fixed point number standard and 32-bit IEEE 754-1985 floating point number standard. The realized designs have been coded in VHDL, a hardware description language, and the Xilinx ISE 14.7 program has been used for the system design. Two separate TRNG designs have been synthesized and tested for the Virtex-6 (XC6VLX240T-1FF1156) FPGA chip. The maximum operating frequency of the TRNG with 32-bit IQ-Math fixed point number standard is 344.585 MHz and the throughput is approximately 344 Mbit/s. The maximum operating frequency of the TRNG with 32-bit IEEE 754-1985 floating point number standard is 316.756 MHz and the throughput is 316 Mbit/s. 1 Mbit sequence has been generated by both designed TRNG systems. Randomness analysis of the generated numbers has been performed in accordance with the NIST 800-22 tests and the generated numbers have successfully passed all of the tests.
Smoothly Constrained Extended Kalman Filter

For nonlinear state estimate, the EKF is an effective technique the base point error should be noted. The paper proposes a smoothly constrained extended Kalman filter for the nonlinear Gaussian mode. The statistical noise of the system is modeled as a constrained mathematical model, the objective function is established based on the maximum posterior probability estimate criterion, the feasible area is approximated by numerical methods, wherein the feasible set is selected and propagated. The calculation of the high-order Hessian matrix and the irreversible ill-conditioned matrix that may occur during the state update process can be avoided, and the calculate stability is enhanced. Simulation experiments verify the effectiveness of the proposed algorithm.
Construction of strongly optimal binary linear code with sets representation

A binary linear code of length n over Fq is a subspace of Fqn. A code has three parameters that attached to it, namely length, dimension, and minimum distance. A code with length n, dimension k and minimum distance d is often called [n, k, d]- code. Usually, when two parameters are given, then we want to find a code that has the best value for the last parameter. Based on Gilbert-Varshamov bound, if a [n, k, d]-code exists and can not be expanded, we call it a strongly optimal code. In this paper, we created a theorem based on Gilbert-Varshamov bound for the sets representation.
A Pointer Meter Recognition Method Based on Image Processing

Aiming at the problems of large error in manual reading of pointer meter and limited application environment, this paper proposes a pointer-based instrument recognition method based on image processing. Firstly, the acquired instrument image is preprocessed, and the pointer area is extracted by using image processing methods such as edge detection and contour tracking; Secondly, the LSD algorithm is used to extract the straight line segments of the graduated scale, and the center and radius of the dial are determined by fitting the circle at the midpoints of these straight line segments; Thirdly, the central tick mark is used to extract the main tick marks in the tick loop band, and the accuracy of the main tick mark extraction is improved by checking the relationship of the angle difference between adjacent tick marks; Then, the improved Hough transform method is used to detect the pointer line, the KNN algorithm performs character recognition, and the adjacent characters satisfying the preset distance and angle relationship are combined and stored to complete the matching of the main tick marks and values; Finally, a distance method is used to automatically read the pointer meter. The experimental results show that the method can get the meter reading better under different illumination and background conditions, and the error is smaller than the manual reading. The algorithm is stable and reliable.
3D Euclidean Distance Transform with Multiple Backgrounds and Its Application in Hepatic CT Image Processing

A distance transform is a representation of an image that assigns each point of the image with the distance to the nearest background. Due to its rotational invariance, Euclidean distance transform (EDT) is very popular in many applications. In this paper, the procedure of 3D-EDT computation with multiple backgrounds is illustrated by using a 5*5*5 image as an example. In hepatic CT image processing, the information of vascular territory of a liver is important for surgical planning. We use 3D-EDT to determine the vascular territory with vessel branches as multiple backgrounds and the result shows the effectiveness of 3D-EDT.
Capsule Networks based on Cross Product

In Capsule Networks(CapsNets), the dynamic routing mechanism is used to generate the digital capsules, the lengths of which represent the probabilities that the entities exist. However, the dynamic routing has high computational complexity, which makes CapsNets not efficent than other modern deep networks. Inspired by the geometry meaning of cross product, we calculate the cross product of the feature and weight capsules, whose length is used as a measure of the probability of the input sample belonging to different classes. It is shown in the simulation that the resulting capsules tend to be more sparse with the increase of the dimension of the feature capsule, besides, some subcapsules have greater effects on the classification. The represented model improves the performace of the ResNet backbones by 23% on CIFAR-10 dataset and that of tht CNN by 17% on MNIST dataset.
A Systematic Review of Context-Aware Technologies Applied to Buildings Comfort

Context-aware technologies can support automation and control of systems promoting inhabitants’ comfort while safeguarding energy efficiency. In this study, a systematic review of the literature was performed to analyze how context-aware systems with impact in the comfort of buildings’ inhabitants are being developed and how their translation is being addressed. Seventeen studies were included in this systematic review. As a main conclusion, the included articles report comfort solutions based on context-aware technologies still far from being consolidated, mainly because there is a lack of robust evaluation trials.
Context-aware Model Selection for On-Device Object Detection

A deep neural network (DNN) has become a key technique in many intelligent application domains. Since the DNN model has dozens of layers and millions of levels of parameters, the machine has to execute computation-intensive workloads. Therefore, it is difficult to perform DNN inference in resource-constrained devices such as mobile devices. In this paper, we propose a context-aware model selection for on-device DNN inference. The proposed model selection chooses a DNN model corresponding to a spatiotemporal domain based on the context information of the device. Since a context-aware model detects related objects by spatiotemporal domain, it has a low dimension of parameters. In resource-constrained environments, the proposed context-aware model enables high-accuracy inference at low latency. To evaluate the performance of the proposed model selection, we conduct comparison experiments with the existing object detection model. Through experiments, we confirm that the context-aware model performs better than the existing trained models when on-device object detection is performed. Finally, we discuss the limits of the proposed model selection.
Building a research university in the Arab region

research-article Share on Building a research university in the Arab region: the case of KAUST Author: E. N. (Mootaz) Elnozahy King Abdullah University of Science and Technology, Thuwal, Saudi Arabia King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaView Profile Authors Info & Claims Communications of the ACMVolume 64Issue 4April 2021 pp 46–49https://doi.org/10.1145/3447717Online:22 March 2021Publication History 0citation1,701DownloadsMetricsTotal Citations0Total Downloads1,701Last 12 Months271Last 6 weeks23 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
A combat evaluation method based on index rules

Although the existing combat evaluation technology can support the definition and evaluation of the index system by users to a certain extent, it cannot clearly explain the characteristics of the index. The hierarchical relationship and the inclusion relationship among the index items, and the setting and use of weight values are not dear. It is so difficult for users to understand and use that this paper proposes a combat evaluation method based on index rules. Through the establishment of data standards for the internal structure of the evaluation index system, different evaluation needs have been met. We also provide a graphical index system construction environment, so that users can dearly set the relationship between indicators and indicator preference, and improve the accuracy and effectiveness of the definition of the evaluation index system. The example of aircraft flight performance evaluation index system deeply proved this method.
Structure Factors of Gold Mineralization Formation on the Example of Kumak Gold Deposit (Eastern-Urals Uplift)

Ore-controlling faults and magmatism manifestations are considered in the paper, which reflect the features of the conditions for the formation of gold mineralization in rift structures. It is noted that the mesh of mineralized cracks of the Kumak deposit was formed as a result of many tectonic impulses manifested at different orientation of the deformation plane. With the divergent tectonics structures and formation of the diagonal shifts-spreads on the cleavage background favorable conditions for the penetration of hydrothermal solutions and deposition of gold mineralization in area of Kumak deposit were created.
Emergence of monogamy under static and dynamic scenarios

Characterizing multipartite quantum correlations beyond two parties is of utmost importance for building cutting edge quantum technologies, although the comprehensive picture is still missing. Here we investigate quantum correlations (QCs) present in a multipartite system by exploring connections between monogamy score (MS), localizable quantum correlations (LQC), and genuine multipartite entanglement (GME) content of the state. We find that the frequency distribution of GME for Dicke states with higher excitations resembles that of random states. We show that there is a critical value of GME beyond which all states become monogamous and it is investigated by considering different powers of MS which provide various layers of monogamy relations. Interestingly, such a relation between LQC and MS as well as GME does not hold. States having a very low GME (low monogamy score, both positive and negative) can localize a high amount of QCs in two parties. We also provide an upper bound to the sum of bipartite QC measures including LQC for random states and establish a gap between the actual upper bound and the algebraic maximum.