Reviewerly/exHarmony huggingface dataset:
The Economic Opportunity Cost for the Arab Spring Countries

This article examines the effect of political instability on countries caught in such a situation and also by their location. Using an interrupted time series model and an unrestricted error correction model, the indirect economic costs of political unrest are estimated for Tunisia, as an example of the Arab spring countries. Foreign direct investment fell by 17% in Arab countries. Indeed, the Arab Spring countries are the most affected countries in terms of investment as the fall of FDI for Tunisia by 21%. In the same direction, Egypt lost 92% of its FDI since it records $ 0.5 billion in 2011 against $ 6.4 billion in 2010 and also Libya 87% and 65% in Syria .This substantial loss is a warning sign that should be seriously taken into account by politicians and economists from the Middle East and the Arab Union, especially for countries whose resources are already limited.
Identity and Access Management for Libraries

Linked open data will change libraries in a dramatic way. It will redesign metadata, and display metadata on the web. In order to prepare for linked open data, libraries will gradually transition from authority control creating text strings to identity and access management using identifiers to select a single identity. The key step for moving into the linked open data for identity and access management for libraries is to correct thousands of incorrect names and subject access points in our online catalogs. This article describes a case study of the process of cleaning up unauthorized access points for personal and corporate names in the University of Illinois Library online catalog. The authors hope that this article will help readers of other libraries prepare for linked open data environment.
Virtual Machine Migration Strategy for Cloud Data Center Based on NSGAII

[Purpose] Cloud computing is currently a popular IT service mode. Improvement in resource utilization and reduction of energy consumption in the data center are essential for reducing operating costs and improving service quality. Therefore, this paper studies the virtual machine migration strategy with the purpose of optimizing the stability of physical nodes and the migration times of the virtual machine. [Method] First, this paper introduces the principle of NSGAII algorithm, the main flow and the steps of the algorithm. Secondly, it presents the virtual machine placement strategy based on the stability of physical nodes with the purpose of optimizing the migration times of the virtual machine. Finally, a mathematical model is presented. [Result] The method in this paper combines the advantages of GA-M and GA-S, which can improve the utilization rate of system resources and reduce the cost of virtual machine migration. [Conclusion] Experiments on CloudSim have proved the effectiveness of the algorithm, and compared with traditional algorithm, it can improve the efficiency of data center.
LibGuides: Children Literature Genres: Non-Fiction

This guide will help readers, educators, and parents identify common children literature genres so that the reading experience can be diversified by genre or to help readers figure out what genre they enjoy reading.
Complexity of Estimating Renyi Entropy of Markov Chains.

Estimating entropy of random processes is one of the fundamental problems of machine learning and property testing. It has numerous applications to anything from DNA testing and predictability of human behaviour to modeling neural activity and cryptography. We investigate the problem of Renyi entropy estimation for sources that form Markov chains.Kamath and Verd (ISIT’16) showed that good mixing properties are essential for that task. We prove that even with very good mixing time, estimation of entropy of order α > 1 requires Ω(K2−1/α) samples, where K is the size of the alphabet; particularly min-entropy requires Ω(K2) sample size and collision entropy requires Ω(K3/2) samples. Our results hold both in asymptotic and non-asymptotic regimes (under mild restrictions). The analysis is completed by the upper complexity bound of O(K2) for the standard plug-in estimator. This leads to an interesting open question how to improve upon a plugin estimator, which looks much more challenging than for IID sources (which tensorize nicely).We achieve the results by applying Le Cam’s method to two Markov chains which differ by an appropriately chosen sparse perturbation; the discrepancy between these chains is estimated with help of perturbation theory. Our techniques might be of independent interest.
US Copyright Office finds Yeezy Boost 350 creative, original and separable: registration granted

Abstract Letter from Karyn Temple, Register of Copyrights and Director, US Copyright Office, to Joseph Petersen, Kilpatrick Townsend &amp; Stockton LLP (8 May 2019) After refusing to register the Yeezy Boost 350 designs twice—initially as inseparable useful articles and later for want of originality and creativity—the US Copyright Office granted registration to the shoe designs.
Extracting and applying evaluation criteria for ontology quality assessment

Purpose The purpose of this paper is to formulate apposite criteria for ontology evaluation and test them through assessments of existing ontologies. Design/methodology/approach A literature review provided the basis from which to extract the categories relevant to an evaluation of internal ontology components. According to the ontology evaluation categories, a panel of experts provided the evaluation criteria for each category via Delphi survey. Reliability was gauged by applying the criteria to assessments of existing smartphone ontologies. Findings Existing research tends to approach ontology evaluation through comparison with well-engineered ontologies, implementation in target applications and appropriateness/interconnection appraisals in relation to raw data, but such methodologies fall short of shedding light on the internal workings of ontologies, such as structure, semantic representation and interoperability. This study adopts its evaluation categories from previous research while also collecting concrete evaluation criteria from an expert panel and verifying the reliability of the resulting 53 criteria. Originality/value This is the first published study to extract ontology evaluation criteria in terms of syntax, semantics and pragmatics. The results can be used as an evaluation index following ontology construction.
MODIFIKASI KRIPTOGRAFI KLASIK VIGENERE CIPHER MENGGUNAKAN ONE TIME PAD DENGAN ENKRIPSI BERLANJUT

The development of communication is increasingly rapid which does not escape from various threats such as eavesdropping or data theft, because that security aspect of data security is now very calculated. Various ways have been done to secure data that contains confidential information. One method used is to scramble the data into unclear content, so that when tapped it will be difficult to find out the actual information content. Encryption techniques by changing and randomizing the original form of data are called cryptography. Vigenere cipher is one type of classical cryptography algorithm that is popular and often used. This Vigenere cipher uses a substitution technique in encrypting the message where each plaintext character in the message will be encrypted into another character in the ciphertext based on the key used. One Pad algorithm is one algorithm that has perfection when encrypting and decrypting it. The basic concept of the One Time Pad algorithm is almost the same as the Vigenere algorithm, except that in this algorithm the key is generated randomly. In this paper, we will explain how to strengthen Vigenere cipher by modifying Vigenere ciphers. Modifications are carried out by applying One Time Pad encryption generated from the key and the next key generation technique using Vigenere encryption continues so that the key used for the coding will be different from the key used previously. With the use of this method, the connection between plaintext and ciphertext will become less and more difficult to solve cryptanalis. Keywords: Cryptography, Modification, Vigenere Cipher, One Time Pad, and Cyber Security .
Lampu Sein Helm Sepeda Berbasis Voice Recognition

Bicycles are not equipped with the turn signal. For driving safety, a bicycle helmet with a turn signal is designed with voice rrecognition. It is using the Arduino Nano as a controller to control the ON and OFF of turn signal lights with voice commands. This device uses a Voice Recognition sensor and microphone that placed on a bicycle helmet. When the voice command is mentioned in the microphone, the Voice Recognition sensor will detect the command specified, the sensor will automatically read and send a signal to Arduino, then the turn signal will light up as instructed, the Arduino on the helmet will send an indicator signal via the Bluetooth Module. The device is able to detect sound with a percentage of 80%. The tool can work with a distance of &lt;2 meters with noise &lt;71 db.
SCIM Protocol: Multi-Value Paging Extension

The System for Cross-Domain Identity Management (SCIM) specificationsdefine a profile of HTTP protocol and a schema that enable managingidentities in cross-domain scenarios. This specification extends SCIMprotocol resource retrieval and query functions to enable paging ofSCIM resources that contain large complex multi-valued attributes suchas SCIM Groups.
Transform your campus services with eForms and portal pages

Hyland Software accessible, section 508 compliant Jadu Content Portal eforms can help you transform your campus online services to your students, perspective students and staff.
Sudden Death: A New Way to Compare Recommendation Diversification

This paper describes problems with the current way we compare the diversity of different recommendation lists in offline experiments. We illustrate the problems with a case study. We propose the Sudden Death score as a new and better way of making these comparisons.
Watch It Twice: Video Captioning with a Refocused Video Encoder

With the rapid growth of video data and the increasing demands of various applications such as intelligent video search and assistance toward visually-impaired people, video captioning task has received a lot of attention recently in computer vision and natural language processing fields. The state-of-the-art video captioning methods focus more on encoding the temporal information, while lack of effective ways to remove irrelevant temporal information and also neglecting the spatial details. However, the current RNN encoding module in single time order can be influenced by the irrelevant temporal information, especially the irrelevant temporal information is at the beginning of the encoding. In addition, neglecting spatial information will lead to the relationship confusion of the words and detailed loss. Therefore, in this paper, we propose a novel recurrent video encoding method and a novel visual spatial feature for the video captioning task. The recurrent encoding module encodes the video twice with the predicted key frame to avoid the irrelevant temporal information often occurring at the beginning and the end of a video. The novel spatial features represent the spatial information in different regions of a video and enrich the details of a caption. Experiments on two benchmark datasets show superior performance of the proposed method.
Improving Writing Ability of Narrative Notes Using Constructivism Approaches Class V Students SDN 03 Batang Anai Kecamatan Batang Anai

This study aims to improve the ability to write narratives using the constructivism approach of the fifth grade students of SDN 03 Batang Anai. The type of research used is Classroom Action Research. The population in this study were fourth grade students of SDN 03 Batang Anai, Batang Anai District. The study was conducted during the second semester of the 2018/2019 academic year. This study focused on the average score of students' ability to write narrative essays in cycles I and II. The following data presents the results of the test: The ability to write students' narrative essays in cycle 1 is the percentage of classical average obtained from the narrative structure consisting of Theme, Flow, Characterization, Background, Viewpoint of the expository narrative with an average score of 56%. Looking at the circumstances that occur as well as the classical average, the average percentage of students' ability to write narrative essays in cycle 1 is only 56%, so this is far below the indicator of success set at 75%. In the second cycle the ability to write student narrative essays is the Theme, Flow, Characterization, Background, Viewpoint of the suggestion narrative with an average score of 78%.
KLASIFIKASI AUDIO MENGGUNAKAN WAVELET TRANSFORM DAN NEURAL NETWORK

Biodiversity that exists in nature shows the overall variation between living things both from the smallest levels, namely genes, species and eskosistem. One animal with a fairly high level of variation, namely birds chirping. Chirping has an identifier for each type both of the color of the feather, body shape, shape of the beak, food, how to find food and the most obvious is the difference in the chirping of birds. The problem faced is the number of species of birds chirping that are almost similar to each other so the introduction of birds with sound becomes quite difficult. This makes the introduction of birds with sound requires a special technique. The techniques used are transform wavelets and neural networks. At the end of the study, obtained Wavelet Package Decomposition extraction with training data used as many as 500 data. There are two preprocessing methods that are done by cutting and resampling (downsampling). The most optimal number of neurons to be used in hidden layers is 256 neurons with 500 epochs. The highest accuracy is 88.6% with momentum 0.2, learning rate 0.2 and wavelet daubechies2 while the lowest accuracy is 74.2% with momentum 0.8, learning rate 0.8 and wavelets haar. Keywords: Classification , Neural Network, Wavelet Transform, Haar, Daubechies2
Unsupervised Learning Framework of Interest Point Via Properties Optimization

This paper presents an entirely unsupervised interest point training framework by jointly learning detector and descriptor, which takes an image as input and outputs a probability and a description for every image point. The objective of the training framework is formulated as joint probability distribution of the properties of the extracted points. The essential properties are selected as sparsity, repeatability and discriminability which are formulated by the probabilities. To maximize the objective efficiently, latent variable is introduced to represent the probability of that a point satisfies the required properties. Therefore, original maximization can be optimized with Expectation Maximization algorithm (EM). Considering high computation cost of EM on large scale image set, we implement the optimization process with an efficient strategy as Mini-Batch approximation of EM (MBEM). In the experiments both detector and descriptor are instantiated with fully convolutional network which is named as Property Network (PN). The experiments demonstrate that PN outperforms state-of-the-art methods on a number of image matching benchmarks without need of retraining. PN also reveals that the proposed training framework has high flexibility to adapt to diverse types of scenes.
BASIC PRINCIPLES OF CREATING MOBILE LEARNING CONTENT FOR THE DEVELOPMENT OF FUTURE TEACHERS ENGLISH LANGUAGE COMPETENCE

The article is devoted to the actual issuethe implementation of mobile learning tools in the process of forming English language competence of future teachers and the creation of mobile learning content according to its main principles.The author analyzes modern views on mobile learning as a specific type of e-learning having its own advantages and disadvantages, studies the research works of foreign and Ukrainian scientists
The Pegasus World Cup III: Accelerate versus City of Light

The big showdown Between Accelerate, who had many grade I wins (5 in 2018) and City of Light, Something of a meaningful matchup as both were running their final races.
The Effect of User Experience Factors on Satisfaction and Continuous Use Intention of Delivery Apps

Recently, the mobile application has been competitively developed to increase the value of user experience. In food service industry, there are many different mobile delivery applications that connects the food service provider with customers which reflect the needs of customers. This study is to identify the delivery application factors that application users want and to verify the effects of those factors on satisfaction and continuous user intention.<BR> The result shows that the ease of use, reliability, and brand image has positive effect on satisfaction. Therefore, in order for customers to have continuous user intention, satisfaction should be preceded. This study could be used in various formats for the development and strategy establishment of the food delivery application market.
iCartoonFace: A Benchmark of Cartoon Person Recognition

Recent years have witnessed increasing attention in cartoon media, powered by the strong demands of industrial applications. As the first step to understand this media, cartoon face recognition is a crucial but less-explored task with few datasets proposed. In this work, we first present a new challenging benchmark dataset, consisting of 389,678 images of 5,013 cartoon characters annotated with identity, bounding box, pose, and other auxiliary attributes. The dataset, named iCartoonFace, is currently the largest-scale, high-quality, richannotated, and spanning multiple occurrences in the field of image recognition, including near-duplications, occlusions, and appearance changes. In addition, we provide two types of annotations for cartoon media, i.e., face recognition, and face detection, with the help of a semi-automatic labeling algorithm. To further investigate this challenging dataset, we propose a multi-task domain adaptation approach that jointly utilizes the human and cartoon domain knowledge with three discriminative regularizations. We hence perform a benchmark analysis of the proposed dataset and verify the superiority of the proposed approach in the cartoon face recognition task. We believe this public availability will attract more research attention in broad practical application scenarios.
To Dream Big, Think Small #IndieWeb

I am tired hearing about scale. In such a short time we have warped success to think it means millions overnight and billions in a
Visual-Relation Conscious Image Generation from Structured-Text

We propose an end-to-end network for image generation from given structured-text that consists of the visual-relation layout module and the pyramid of GANs, namely stacking-GANs. Our visual-relation layout module uses relations among entities in the structured-text in two ways: comprehensive usage and individual usage. We comprehensively use all available relations together to localize initial bounding-boxes of all the entities. We also use individual relation separately to predict from the initial bounding-boxes relation-units for all the relations in the input text. We then unify all the relation-units to produce the visual-relation layout, i.e., bounding-boxes for all the entities so that each of them uniquely corresponds to each entity while keeping its involved relations. Our visual-relation layout reflects the scene structure given in the input text. The stacking-GANs is the stack of three GANs conditioned on the visual-relation layout and the output of previous GAN, consistently capturing the scene structure. Our network realistically renders entities' details in high resolution while keeping the scene structure. Experimental results on two public datasets show outperformances of our method against state-of-the-art methods.
Learning Flexible Latent Representations via Encapsulated Variational Encoders

Learning flexible latent representation of observed data is an important precursor for most downstream AI applications. To this end, we propose a novel form of variational encoder, i.e., encapsulated variational encoders (EVE) to exert direct control over encoded latent representations along with its learning algorithm, i.e., the EVE compatible automatic variational differentiation inference algorithm. Armed with this property, our derived EVE is capable of learning converged and diverged latent representations. Using CIFAR-10 as an example, we show that the learning of converged latent representations brings a considerable improvement on the discriminative performance of the semi-supervised EVE. Using MNIST as a demonstration, the generative modelling performance of the EVE induced variational auto-encoder (EVAE) can be largely enhanced with the help of learned diverged latent representations.
Improvement of the Evaluation Efficiency in a Self-Evaluation Rubric System Using Coloring Method

工学教育(J.ofJSEE
Clinical Decision Support Systems: Effective Solution for Diagnosis, Treatment, and Management of Patients Affected by Poisoning

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On MILP-Based Automatic Search for Differential Trails Through Modular Additions with Application to Bel-T.

Using modular addition as a source of nonlinearity is frequently used in many symmetric-key structures such as ARX and Lai–Massey schemes. At FSE’16, Fu et al. proposed a Mixed Integer Linear Programming (MILP)-based method to handle the propagation of differential trails through modular additions assuming that the two inputs to the modular addition and the consecutive rounds are independent. However, this assumption does not necessarily hold. In this paper, we study the propagation of the XOR difference through the modular addition at the bit level and show the effect of the carry bit. Then, we propose a more accurate MILP model to describe the differential propagation through the modular addition taking into account the dependency between the consecutive modular additions. The proposed MILP model is utilized to launch a differential attack against Bel-T-256, which is a member of the Bel-T block cipher family that has been adopted recently as a national standard of the Republic of Belarus. In particular, we employ the concept of partial Differential Distribution Table to model the 8-bit S-Box of Bel-T using a MILP approach in order to automate finding a differential characteristic of the cipher. Then, we present a \(4\frac{1}{7}\)-round (out of 8) differential attack which utilizes a 3-round differential characteristic that holds with probability \(2^{-111}\). The data, time and memory complexities of the attack are \(2^{114}\) chosen plaintexts, \( 2^{237.14} \) \(4\frac{1}{7}\)-round encryptions, and \(2^{224}\) 128-bit blocks, respectively.
Corrections to “‘Security Concern’ as a Metric for Enterprise Business Processes” [Dec 19 4015-4026]

Presents corrections to the above titled paper.
Warren-Averbach line broadening analysis from a time of flight neutron diffractometer

The well known Warren-Averbach theory of diffraction line profile broadening is shown to be applicable to time of flight data obtained from a neutron spallation source. Without modification, the method is applied to two very different examples; a cold worked ferritic steel and a thermally stressed alumina-30% SiC composite. Values of root mean square strains averaged over a range of lengths for the ferritic steel were used to estimate dislocation densities; values were found to be in good agreement with geometrically necessary dislocation densities independently measured from similarly orientated grains measured from electron backscatter diffraction analysis. An analytical model for the ceramic is described to validate the estimate of root mean square strain.