Reviewerly/exHarmony huggingface dataset:
Developing a Cloud Computing Framework for University Libraries

Our understanding of the library context on security challenges on storing research output on the cloud is inadequate and incomplete. Existing research has mostly focused on profit-oriented organizations. To address the limitation within the university environment, the paper unravels the data/information security concerns of cloud storage services within the university libraries. On the score of changes occurring in the libraries, this paper serves to inform users and library managers of the traditional approaches that have not guaranteed the security of research output. The paper is built upon the work of Shaw and the cloud storage security framework, which links aspects of cloud security and helps explain reasons for university libraries moving research output into cloud infrastructure, and how the cloud service is more secured. Specifically, this paper examined the existing storage carriers/media for storing research output and the associated risks with cloud storage services for university libraries. The paper partly fills this gap by a case study examination of two (2) African countries’ (Ghana and Uganda) reports on research output and cloud storage security in university libraries. The paper argues that in storing university research output on the cloud, libraries consider the security of content, the resilience of librarians, determining access levels and enterprise cloud storage platforms. The interview instrument is used to collect qualitative data from librarians and the thematic content analysis is used to analyze the research data. Significantly, results show that copyright law infringement, unauthorized data accessibility, policy issues, insecurity of content, cost and no interoperable cloud standards were major risks associated with cloud storage services. It is expected that university libraries pay more attention to the security/confidentiality of content, the resilience of librarians, determining access levels and enterprise cloud storage platforms to enhance cloud security of research output. The paper contributes to the field of knowledge by developing a framework that supports an approach to understand security in cloud storage. It also enables actors in the library profession to understand the makeup and measures of security issues in cloud storage. By presenting empirical evidence, it is clear that university libraries have migrated research output into cloud infrastructure as an alternative for continued storage, maintenance and access of information.
The Implementation of K-Means Algorithm for Cluster Majoring to New Students in SMKN 2 of South Tangerang

The diversity of majors in vocational schools of SMKN 2 South Tangerang makes some students confuse of their choices. Determination of majors is important because it will affect the academic activities of students. The purpose of the right majoring is so that students can learn optimally, and be able to equip themselves with competency skills according to their talents, interests and abilities when entering the workforce. This study applies the Clustering Method with the K-means Algorithm, to help students determine their majors, also helps the school in clustering majors. Determination of these majors is based on 320 student data with attributes of the National Examination during Junior High School (Mathematics, English, Indonesian, and Science), Registration Pathways, and Gender. Calculations that occur as many as 7 iterations with the K-Means Clustering Method, with the final centroid: C1 = 1.55; 4.08; 3.65; 4.06; 3.36; 1.93. C1 represents the Industrial Electronics Engineering Major, with the results of 49 students. C2 = 1; 3.69; 3.26; 2.55; 3.09; 1.96. C2 represents Light Vehicle Engineering majors, with 95 students. C3 = 1,98; 4,09; 3,60; 2,79; 3,083; 1,94. C3 = 1.98; 4.09; 3.60; 2.79; 3,083; 1.94. C3 represents the Accounting major, with 96 students. C4 = 1.18; 3.90; 3.39; 2.18; 2.02; 1.93. C4 represents the Multimedia major, with a total of 44 students And C5 = 1,055; 3.72; 2; 2,389; 2.86; 1.97. C5 represents the Motorcycle Business Engineering major with 36 students.
Link Budget Analysis and Architecture of a Proposed VSAT Based E-Learning System for ARCSSTE-E

Abstract E-Learning is aderivative of information technology and has been generally recognized as a modern tool for learning and teaching other than the conventional method of learning (which involves physical contact between the student and the instructor). The case study for the proposed system is the African Regional Centre for Space Science and Technology Education in English (ARCSSTE-E) in Nigeria. Thepurposeof the centre is to develop quality indigenous space scientists in English speaking African countries for the development of various application of space science and technology for sustainable national and regional development. Therefore, to accomplish this, the centre plans to develop the skills and knowledge of candidates in four significantareasof space science and technology such as; Remote Sensing &amp; GIS, Satellite Meteorology and Global Climate, Satellite Communication and Basic Space and Atmospheric Sciences. This paper presents the VSAT architecture, link margin calculations and link budget analysis done to implement the proposed VSAT based E-learning system for the Centre. This is a conceptual frame work. The proposed system is to enhance the centre with tremendous potential to reach out to people within Anglophone countries that cannot make it down to ARCSSTE-E due to lack of sponsorship, constraint in time, distance,Financial or other challenges.
Towards a Programmable World

Microcontrollers execute much of the logic that makes the appliances and infrastructure around us work. Recent years have seen a significant increase in the microcontroller capabilities, with modern designs including multiple 32-bit processor cores and integrated wireless communications. At the same time, microcontrollers still typically execute fixed code burned into their firmware, leaving much of their capacity unused. In this paper we propose a Lua-based framework for microcontrollers, capable of dynamically receiving code for execution from nearby devices via wireless networks. We show that this framework allows multiple nearby microcontroller devices to be dynamically orchestrated to compose complex services. This serves as a step towards making the physical world around us dynamically programmable, enabling the creation of new local and pervasive applications that are deeply integrated into the physical world.
An Empirical Evaluation of AMR Parsing for Legal Documents

Many approaches have been proposed to tackle the problem of Abstract Meaning Representation (AMR) parsing, help solving various natural language processing issues recently. In our paper, we provide an overview of different methods in AMR parsing and their performances when analyzing legal documents. We conduct experiments of different AMR parsers on our annotated dataset extracted from the English version of Japanese Civil Code. Our results show the limitations as well as open a room for improvements of current parsing techniques when applying in this non-trivial domain.
FusionNet: Incorporating Shape and Texture for Abnormality Detection in 3D Abdominal CT Scans

Automatic abnormality detection in abdominal CT scans can help doctors improve the accuracy and efficiency in diagnosis. In this paper we aim at detecting pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic cancer. Taking the fact that the existence of tumor can affect both the shape and the texture of pancreas, we design a system to extract the shape and texture feature at the same time for detecting PDAC. In this paper we propose a two-stage method for this 3D classification task. First, we segment the pancreas into a binary mask. Second, a FusionNet is proposed to take both the binary mask and CT image as input and perform a binary classification. The optimal architecture of the FusionNet is obtained by searching a pre-defined functional space. We show that the classification results using either shape or texture information are complementary, and by fusing them with the optimized architecture, the performance improves by a large margin. Our method achieves a specificity of 97% and a sensitivity of 92% on 200 normal scans and 136 scans with PDAC.
Implementation of High Speed and Low Area Extended Euclidean Inversion over Ternary Fields

Hardware implementation of the extended Euclidean algorithm (EEA) over ternary field introduces many challenges, include degree evaluations during and after each iteration of the algorithm. This paper presents a novel realization of the traditional EEA over ternary fields in a concurrent manner, resolving the issues stated above by using a former systolic architectural approach. Polynomial division and multiplication in GF(3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ) are performed concurrently. Accordingly, an EEA-based ternary inverter is built. Then, the complexity of the proposed inverter is analyzed in comparison with efficient designs in the literature, concluding that our design has the lowest area-time complexity.
Design of an Electronic Voting System Using a Blockchain Network

Design of a scalable electronic voting system, which, based on a generic model designed for this application called voting cell, guarantees the integrity of the information through the use of a private network Blockchain. For the validation of the system, the implementation of a cell was carried out, for which fifty voters and three voting options were enabled. The stored data was intentionally modified to corroborate the error correction method used by the block chain networks and thus ensure the integrity of the voting system results.
Image sorting via a reduction in travelling salesman problem

The authors define and approximately solve the problem of unsupervised image sorting that is considered as a kind of content-based image clustering. The content-based image sorting is the creation of a route that passes through all the images once, in such an order that the next one from the previous image has similar content. In the end, an image ordering (e.g. slideshow) is automatically produced, so that the images with similar content should be close to each other. This problem resembles the problem known in the literature as ‘travelling salesman problem’ (TSP). In this work, the authors have proposed two classes of methods (the nearest-neighbour and genetic methods) that have also been applied on the TSP problem. Their benefits on computational efficiency and accuracy are discussed over six datasets that have been created from the GHIM-10K dataset. The experimental results demonstrate that the proposed methods efficiently solve the image sorting problem, producing image sequences that almost agree with human intuition.
Wu–Mendel Approach for Linguistic Summarization: Practical Considerations and Solutions

The Wu-Mendel approach for linguistic summarization (LS) using IF-THEN rules is analyzed in this paper from a practical-implementation point of view. Several practical considerations on the Wu-Mendel approach are presented in this paper, and three practical solutions are proposed in order to enhance the efficiency of the approach, namely to reduce the processing time and memory usage in LS. The proposed solutions have been verified by carrying out the corresponding experimental analysis. The scripts created and implemented during the experimental verification process are made publicly available and provided with the tutorial.
Poster

We propose a QoE-based analysis approach for real-time adaptive 360-degree video streaming measurements, focusing on the correlation between objective video metrics and subjective end-user scores.
Research on Prediction Method of Power System Transient Disturbed Trajectory Based on Self-Remembering Model

Aiming at the transient stability disturbed trajectory prediction, a real-time prediction method of transient stability disturbed trajectory based on selfremembering gray Verhulst model is proposed. At the same time, considering the typical dynamic characteristics of the transient stability disturbed trajectory and its strong nonlinearity and stochastic volatility, a gray Verhulst model with self-remembering is constructed. Based on this mathematical prediction model, a real-time prediction scheme based on adaptive model parameters is proposed. The scheme adopts equal-dimensional innovation and rolling prediction method, which can make full use of WAMS real-time measurement data and continuously update the sample data to improve prediction performance. Model parameter adaptability. Finally, the rapidity and effectiveness of the transient instability identification based on the predicted trajectory are simulated and verified in the New England 10-machine 39-bus system. The results show that the method can effectively improve the rapidity of transient instability identification, which can be used for subsequent emergency. Control to get more time.
Fast distributed MAP inference for large-scale graphical models

In every domain of life and society, real-world data gains pull for both a more informed decision making and citizenship. Social and human phenomena carry intricate and unknown dependencies unreachable by traditional machine learning approaches, like regression or classification. How to extract value from large amounts of complex and noisy data? Assuming we know the generative model of our data, inference itself is a combinatorial problem. In this work we put forward a distributed, approximate inference method that attains better accuracy than the centralized LP relaxation of the inference problem, even when the solution of the LP is improved by a local nonconvex method.
Improvement of Human Resources Performance Through Online Presence Applications Based on Android Using UML - Iconix process

This study aims to build and design an online presence system based on Android using the Unified Modeling Language (UML) Iconix Process.This modeling is designed using: domain models, use case diagrams, robustness diagrams, sequence diagrams, and graphical user interface (GUI) storyboards.To implement this modeling, software requirements specification (SRS) is needed, so that the modeling design is in accordance with user needs.this is the modeling of the performance of human resources in universities using UML software -Iconix Process, which produces software requirements specifications.With this SRS, it can reduce the time and cost of building an online android presence application.
Target Tracking Algorithm for Specific Person Based on Multi-loss of Siamese Network

Deep learning is increasingly used in target tracking, due to the CNN's powerful feature learning capabilities. Speed and accuracy are important for specific target tracking. Target tracking based on the Siamese network is potential in these two aspects. The logistic loss function is employed to train Siamese network. The logistic loss function is distinguishable between different targert. However, due to human deformation and occlusion, human characteristics will change significantly, and the distance of intra-class target is larger than inter-class. The Center loss function is effective to reduce the distance of intra-class, so that we use multi-loss function including logistic loss function and center loss function, which make the inter-class dispersion and intra-class compactnessas much as possible. We evaluated our trackers in the OTB benchmark, and the experimental results show that the improved algorithm performs better than many excellent trackers in terms of correct rate while maintaining real-time speed.
Multi-label classification of technical articles based on deep neural network

This paper uses word-embedding and deep neural networks to build a multi-label classification model based on technical articles. In this paper, we use deep learning algorithms to train word vectors based on numerous technical articles, and then with the abstracts and corresponding CNKI labels of these articles as input of network is trained for compare and research the prediction results of different networks, finally determining the threshold by statistical distribution for label screening. Through parameter tuning, model fusion and data augmentation, the accuracy of multi-tag prediction network reaches 92.05%. Multi-label classification based on deep neural network has advantages in simple preprocessing, high accuracy and computational efficiency.
Digital Image Watermarking based onSine Hyperbolic Transformation

In this paper, Sine Hyperbolic transformation based image watermarking scheme has been proposed. Multiple watermark images are considered for embedding process. Watermarked image is more vulnerable to security attacks such as noise interference, geometrical attacks and LSB neutralization attack. In the proposed algorithm, watermarks are embedded into host at various positions. The recovered watermarks are compared among themselves with respect to each pixels to get resultant watermark. Due to this, any modifications in pixel values of watermark image will be retrieved from other similar watermarks after de-watermarking process and hence provides better results against security attacks.From the security analysis, better results are obtained for the proposed algorithm compared to other existing techniques. The algorithm utilizes less execution time compared to other existing methods.
Improved Framework of Gaussian Filter for Nonlinear System with Correlated Noises

This paper investigates the stochastic uncertain system with correlated noise. The stochastic uncertainties are caused by the multiplicative noises existing in the state function. Moreover, the multiplicative noises and the measurement noises are synchronously cross-correlated. A novel framework of Gaussian filter (GF) is proposed for this kind of stochastic uncertain systems. As the probability of multiplicative noises conditioned on measurement noises contains more correlation information than its original probability, the conditional Gaussian distributions is utilized to improve the GF accuracy. What is more, the spherical-radial cubature rule is exploited to calculate the Gaussian quadrature. The cubature Kalman filter with multiplicative correlated noises (CKF-MCN) is designed. The numeral example shows the accuracy and effectiveness of the CKF-MCN.
Recent Trends in the Use of Graph Neural Network Models for Natural Language Processing

Graphs are powerful data structures that allow us to represent varying relationships within data. In the past, due to the difficulties related to the time complexities of processing graph models, graphs rarely involved machine learning tasks. In recent years, especially with the new advances in deep learning techniques, increasing number of graph models related to the feature engineering and machine learning are proposed. Recently, there has been an increase in approaches that automatically learn to encode graph structure into low dimensional embedding. These approaches are accompanied by models for machine learning tasks, and they fall into two categories. The first one focuses on feature engineering techniques on graphs. The second group of models assembles graph structure to learn a graph neighborhood in the machine learning model. In this chapter, the authors focus on the advances in applications of graphs on NLP using the recent deep learning models.
Video Productions at Saint Joseph Hospital: A Teaching Hospital in Denver, CO

The authors evaluated a new video production service, through our Library’s Multi-Media Service’s Department, compiling 4 years of data. Assessment included the impact of internal video productions on educational/training programs (general Medical Education and Nursing Education), on other hospital departments, and on reduced cost for outsourced video productions, through our limited/low budget program. Data was collected through automated statistical-gathering tools utilizing Vimeo and HealthStream, and via internal and manual record keeping. We detail our positive outcomes, challenges, and path-forward in this article.
Results of Investigations and Recommendations Regarding How to Reduce Excessive Weight in People

In this paper we describe some important aspects regarding the problem of excessive weight in people and attempts to reduce this. Very often people suffer from this situation and do not use effective methods; for example, they may take inappropriate medical tablets/drugs, turn to a witch-doctor or fortune-teller, try to eat only vegetables and fruit, and so forth. Unfortunately, these approaches do not work well in all cases. Due to age and health condition, not everybody can maintain regular exercise. This motivates the approach described in this paper, where our methods for three persons help both to maintain normal weight over many years and to reduce it incrementally to a comfortable level. Calculations were made using results in MathCad for the investigations and recommendations regarding how to reduce excessive weight in people.
Formalization of General Topology in Coq — A Formal Proof of Tychonoff's Theorem

The order, algebra and topology of the Bourbaki group are the foundation of modern mathematics. On the basis of the proof assistant Coq, the formal system of these three major parental structures can be constructed completely. On the basis of the formalization of axiomatic set theory, this paper present the formal framework of general topology. This paper completes the formalization of some basic definitions of general topology, including base, subbase, topology, and compact space. In addition, the formal proof of the Alexander subbase theorem is proposed on the basis of Tukey's lemma. Finally we complete the formal proof of Tychonoff's theorem, which is famous in general topology. All formal processes have been verified in Coq, which demonstrates the reliability, readability and rigor.
Research on complexity evolution of marketing evaluation data based on fractional calculus

Abstract Firstly, the paper introduces the definition of Fourier transform of fractional calculus equation, and then clarifies the discretization algorithm of fractional calculus Fourier. Secondly, the Fourier transform is applied to the processing of marketing data signals, including market signal detection and parameter estimation, signal de-drying and so on. The paper finds that the discretization algorithm of fractional calculus Fourier has strong theoretical and practical significance in the detection and complexity evolution of marketing evaluation data.
Experimental Device-Independent Certification of a SIC-POVM

Symmetric, informationally complete, positive operator-valued measurements (SIC-POVM) are fundamental in quantum information and foundations of quantum theory[1]. For example, SIC-POVMs are crucial for optimal quantum state tomography, quantum key distribution with optimal trade-off between security and key rate, device-independent certification of optimal randomness from one bit of entanglement, and information-based interpretations and operational reconstructions of quantum theory. Experimentally, photonic experiments of qubit and qutrit tomography, and quantum key distribution[2] have aimed for SIC-POVMs. However, these experiments rely on specific models for parts of the experimental setups and do not allow us to test whether the target measurements are truly SIC-POVMs in a "device independent" (DI) way that is, without relying on any modelling of the devices.