Reviewerly/exHarmony huggingface dataset:
What is a CSO Good For?

The CSO is not a security engineer, so let’s contrast the two jobs to create a picture of what we should and should not see.
An Analytic Model to Represent Relation between Finish Date of Job-Hunting and Time-Series Variation of Entry Tendencies

Currently, most university students in Japan use Internet portal sites for job-hunting activities. However, job-hunting activities are sometimes prolonged owing to a mismatch between a student and the company requirements. To solve this problem, it is important to find the students who may not be able to finish job-hunting early; this goal can be achieved by utilizing user behavior log data stored on an Internet portal site. This study proposes appropriate statistical model based on a latent class model. Specifically, we also apply clustering approach and takes account of timeseries variation. The proposed model enables us to analyze entry patterns from the viewpoint of time-series variation of job-hunting activities and to predict the finish date of job-hunting for each cluster. Through the simulation experiments, the effectiveness of the proposed method was clarified. We used actual data of students’ activities from an Internet portal site to demonstrate the effectiveness of the proposed method that considers the time series of the entry tendency of student users. By considering the time shift of students’ preferences, it became possible to extract students who tend to struggle in job-hunting activities. It is possible to specify students who should be supported by using the proposed model.
Approximate Trapdoors for Lattices and Smaller Hash-and-Sign Signatures.

We study a relaxed notion of lattice trapdoor called approximate trapdoor, which is defined to be able to invert Ajtai’s one-way function approximately instead of exactly. The primary motivation of our study is to improve the efficiency of the cryptosystems built from lattice trapdoors, including the hash-and-sign signatures.
Kriminālistiskās taktikas kā kriminālistikas sistēmas elementa būtība

KrimiNālistisKās taKtikas Kā KrimiNālistikas sistēmas ElEmENta būtība thE EssENcE of crimiNalistic tactics as thE ElEmENt of thE systEm of crimiNalistics summaryThe article is devoted to the problem of understanding criminalistic tactics including the optimal way of performing investigative activities provided for in the Republic of Latvia Law on Criminal Procedure and applied during the investigation of criminal offences.It is pointed out that the content and logical volume of the notion 'criminalistic tactics' is composed of some specific characteristics of criminalistic activities, and criminalistic tactics could be defined as a chapter of special part of criminalistics theory describing regularities in performing investigative activities.
Secure Montgomery Multiplication and Repeated Squares for Modular Exponentiation

The BMR16 circuit garbling scheme introduces gadgets that allow for ciphertext-free modular addition, while the multiplication of private inputs modulo a prime p can be done with 2(p - 1) ciphertexts as described in Malkin, Pastro, and Shelat's An algebraic approach to garbling. By using a residue number system (RNS), we can construct a circuit to handle the squaring and multiplication of inputs modulo a large N via the methods described in Hollman and Gorissen's multi-layer residue number system. We expand on the existing techniques for arithmetic modulo p to develop methods to handle arithmetic in a positional, base-p number system. We evaluate the ciphertext cost of both of these methods and compare their performance for squaring in various large moduli.
Noise Immunity Research of Multi-Frequency Information Transmission System with Code Division of Channels

In this paper, there is noise immunity research of one version of construction multi-frequency information transmission system with code division of user's channels. An engineering method for noise immunity estimate is developed. Error probability dependences on average signal-to-noise ratio are calculated for the cases of uniform multipath intensity profile. Researchable system comparison of noise immunity with single-frequency system of CDMA standard is made.
COMPARING DIZZINESS AND VERTIGO INVENTORY RESPONSES IN THAI AND THAI-CHINESE PEOPLE

It is acknowledged that ancestry may play a role in the likelihood of reporting motion sickness, based upon questionnaires in which symptoms are reported more frequently in individuals with Asian ancestry. This study compares motion sickness and related vertigo syndromes in Thai and Thai-Chinese populations. The Motion Sickness Questionnaire; Albany Panic and Phobia Questionnaire; Acrophobia Questionnaire; Body Symptoms Questionnaire and the Situational Characteristics Questionnaire were administered to 128 participants. Eighty-eight participants had a father, mother and all grandparents of Thai origin, while 44 participants had with at least one Chinese ancestor among parents or grandparents. All responses were similar between groups except regarding fear of heights, which is significantly higher in Thai participants without recent Chinese ancestors. Reported motion sickness sensitivity is similar between Thai and Chinese populations. The group differences for some fear of heights items may be linked to each group’ previous experience with heights. Results also suggest that although conquering a fear of heights might require specific visuo-vestibular adaptations, these adaptations alone may not be sufficient to lessen an individual’sfear of heights.
Automating Data Monitoring: Detecting Structural Breaks in Time Series Data Using Bayesian Minimum Description Length

In modern business modeling and analytics, data monitoring plays a critical role. Nowadays, sophisticated models often rely on hundreds or even thousands of input variables. Over time, structural changes such as abrupt level shifts or trend slope changes may occur among some of these variables, likely due to changes in economy or government policies. As a part of data monitoring, it is important to identify these changepoints, in terms of which variables exhibit such changes, and what time locations do the changepoints occur. Being alerted about the changepoints can help modelers decide if models need modification or rebuilds, while ignoring them may increase risks of model degrading. Simple process control rules often flag too many false alarms because regular seasonal fluctuations or steady upward or downward trends usually trigger alerts. To reduce potential false alarms, we create a novel statistical method based on the Bayesian Minimum Description Length (BMDL) framework to perform multiple change-point detection. Our method is capable of detecting all structural breaks occurred in the past, and automatically handling data with or without seasonality and/or autocorrelation. It is implemented with computation algorithms such as Markov chain Monte Carlo (MCMC), and can be applied to all variables in parallel. As an explainable anomaly detection tool, our changepoint detection method not only triggers alerts, but provides useful information about the structural breaks, such as the times of changepoints, and estimation of mean levels and linear slopes before and after the changepoints. This makes future business analysis and evaluation on the structural breaks easier.
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings.

Graph embeddings, a class of dimensionality reduction techniques designed for relational data, have proven useful in exploring and modeling network structure. Most dimensionality reduction methods allow out-of-sample extensions, by which an embedding can be applied to observations not present in the training set. Applied to graphs, the out-of-sample extension problem concerns how to compute the embedding of a vertex that is added to the graph after an embedding has already been computed. In this paper, we consider the out-of-sample extension problem for two graph embedding procedures: the adjacency spectral embedding and the Laplacian spectral embedding. In both cases, we prove that when the underlying graph is generated according to a latent space model called the random dot product graph, which includes the popular stochastic block model as a special case, an out-of-sample extension based on a least-squares objective obeys a central limit theorem about the true latent position of the out-of-sample vertex. In addition, we prove a concentration inequality for the out-of-sample extension of the adjacency spectral embedding based on a maximum-likelihood objective. Our results also yield a convenient framework in which to analyze trade-offs between estimation accuracy and computational expense, which we explore briefly.
<i>Digital Curation for Libraries and Archives</i>. By Stacy T. Kowalczyk. Santa Barbara, CA: Libraries Unlimited, 2018. Pp. xii+246. $60.00 (paper). ISBN 978-1-61069-631-9.

Previous articleNext article No AccessReviewsDigital Curation for Libraries and Archives. By Stacy T. Kowalczyk. Santa Barbara, CA: Libraries Unlimited, 2018. Pp. xii+246. $60.00 (paper). ISBN 978-1-61069-631-9.Brittney BergholmBrittney BergholmRobert H. Goddard Library, Clark University Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Library Quarterly Volume 89, Number 4October 2019 Article DOIhttps://doi.org/10.1086/704966 Views: 108Total views on this site For permission to reuse, please contact [email protected]PDF download Crossref reports no articles citing this article.
오디세이 학교 길잡이의 교육과정 개발 및 운영 경험 분석을 통한 교원양성과정에의 함의

This study analyzed the H Odyssey School teacher’s curriculum development and implementation experiences, with the underlying purpose being to explore the educational implications of the new competencies required for the pre-service teacher education. For this purpose, this study conducted a narrative inquiry by selecting one teacher of H Odyssey School as a research participant. The results were as follows: first, the teacher experienced a dilemma between the national curriculum and alternative education; second, the teacher tried to implement the competency centered curriculum; third, the teacher felt conflicted and deliberated about the curriculum as a process which they had not previously experienced in public education; fourth, the teacher planned the curriculum and expanded it to extend beyond the classroom to the local community; fifth, the teacher developed and carried out the curriculum to enable the students to have learning experiences as the subject of learning. As a result of this study, the characteristics and themes of the Odyssey school teacher as the curriculum developer and practitioner were derived. These findings will have applications with similar cases challenging alternative education in public education, and will suggest implications for the pre-service teachers' education.
Integrating Internet Directories by Estimating Category Correspondences

This paper focuses on two existing category hierarchies and proposes a method for integrating these hierarchies into one. Integration of hierarchies is proceeded based on semantically related categories which are extracted by using text categorization. We extract semantically related category pairs by estimating category correspondences. Some categories within hierarchies are merged based on the extracted category pairs. We assign the remaining categories to a newly constructed hierarchy. To evaluate the method, we applied the results of new hierarchy to text categorization task. The results showed that the method was effective for categorization.
Reflections on “Free Will: An Illusion or Opportunity?"

Review of: Volkov, D. B. 2018. Svoboda voli [Free Will]: illyuziya ili vozmozhnost’ ? [An Illusion or an Opportunity?] [in Russian]. Moskva [Moscow]: Kar’yera Press
Joint Image and Depth Estimation with Mask-Based Lensless Cameras

Mask-based lensless cameras replace the lens of a conventional camera with a custom mask. These cameras can potentially be very thin and even flexible. Recently, it has been demonstrated that such mask-based cameras can recover light intensity and depth information of a scene. Existing depth recovery algorithms either assume that the scene consists of a small number of depth planes or solve a sparse recovery problem over a large 3D volume. Both these approaches fail to recover the scenes with large depth variations. In this paper, we propose a new approach for depth estimation based on an alternating gradient descent algorithm that jointly estimates a continuous depth map and light distribution of the unknown scene from its lensless measurements. We present simulation results on image and depth reconstruction for a variety of 3D test scenes. A comparison between the proposed algorithm and other method shows that our algorithm is more robust for natural scenes with a large range of depths. We built a prototype lensless camera and present experimental results for reconstruction of intensity and depth maps of different real objects.
Corrective Feedback Used In Paragraph Development Writing Class At English Department Of Education Universitas Muhammadiyah Surakarta

The objectives of this study are to know: (1) the type of corrective feedback used by lecturer in paragraph development writing class of second language learning at English Department of Education Universitas Muhammadiyah Surakarta and (2) the students’ perspective about the lecturer’s feedback. This study used qualitative method and it was held at Muhammadiyah University of Surakarta. The researcher got the data by doing the interview and documentation. The techniques for collecting data were documentation and interview regarding teaching learning process using corrective feedback. The results point out that the lecturer of Paragraph Development Writing A, B class used three types of corrective feedback namely: written positive feedback, explicit correction and repetition. The students perceive that the feedback is very helpful and it encouraged them in the learning process.Keywords: Paragraph Development Writing, Types of Lecturer Corrective Feedback.
Quantum Coin Method for Numerical Integration

Monte Carlo integration approximates an integral of a black-box function by taking the average of many evaluations (i.e., samples) of the function (integrand). For $N$ queries of the integrand, Monte Carlo integration achieves the estimation error of $O(1/ \sqrt{N})$. Recently, Johnston introduced quantum supersampling (QSS) into rendering as a numerical integration method that can run on quantum computers. QSS breaks the fundamental limitation of the $O(1/ \sqrt{N})$ convergence rate of Monte Carlo integration and achieves the faster convergence rate. We introduce yet another quantum numerical integration algorithm, quantum coin (QCoin), and provide numerical experiments that are unprecedented in the fields of both quantum computing and rendering. We show that QCoin's convergence rate is equivalent to QSS's. We additionally show that QCoin is fundamentally more robust under the presence of noise in actual quantum computers due to its simpler quantum circuit and the use of fewer qubits. Considering various aspects of quantum computers, we discuss how QCoin can be a more practical alternative to QSS in quantum computers in the future.
There Are 10 Types of Vectors (and Polynomials): Efficient Zero-Knowledge Proofs of "One-Hotness" via Polynomials with One Zero.

We present a new 4-move special honest-verifier zero-knowledge proof of knowledge system for proving that a vector of Pedersen commitments opens to a so-called one-hot'' vector (i.e., to a vector from the standard orthonormal basis) from Zpn. The need for such proofs arises in the contexts of symmetric private information retrieval (SPIR), end-to-end verifiable voting (E2E), and privacy-preserving data aggregation and analytics, among others. The key insight underlying the new protocol is a simple observation regarding the paucity of roots of polynomials of bounded degree over a finite field. The new protocol is fast and yields succinct proofs: For vectors of length n, the prover evaluates Θ(Θlgn) group operations plus Θ(n) field operations and sends just Θ(Θlgn) group and field elements, while the verifier evaluates one n-base multiexponentiation plus Θ(llgn) additional group operations and sends just 2(λ+lgn) bits to obtain a soundness error less than 2-λ. (A 5-move variant of the protocol reduces prover upload to just λlgn bits for the same soundness error.) We have implemented both our new protocol and its closest competitors from the literature; in accordance with our analytic results, experiments confirm that the new protocols handily outperform existing protocols for all but the shortest of vectors (roughly, for vectors with more than 16-32 elements).
Creating Good User Experience in a Hand-Gesture-Based Augmented Reality Game

The dissemination of new innovative technology requires feasibility and simplicity. The problem with marker-based augmented reality is similar to glove-based hand gesture recognition: they both req ...
Recurrent Network and Multi-arm Bandit Methods for Multi-task Learning without Task Specification

This paper addresses the problem of multi-task learning (MTL) in settings where the task assignment is not known. We propose two mechanisms for the problem of inference of task’s parameter without task specification: parameter adaptation and parameter selection methods. In parameter adaptation, the model’s parameter is iteratively updated using a recurrent neural network (RNN) learner as the mechanism to adapt to different tasks. For the parameter selection model, a parameter matrix is learned beforehand with the task known apriori. During testing, a bandit algorithm is utilized to determine the appropriate parameter vector for the model on the fly. We explored two different scenarios in MTL without task specification, continuous learning and reset learning. In continuous learning, the model has to adjust its parameter continuously to a number of different task without knowing when task changes. Whereas in reset learning, the parameter is reset to an initial value to aid transition to different tasks. Results on three real benchmark datasets demonstrate the comparative performance of both models with respect to multiple RNN configurations, MTL algorithms and bandit selection policies.
A Corpus-based Analysis of Test Items in the Secondary School English Teacher Recruitment Examination: Focusing on English Education Questions of 2009-2019

The aims of this study are two-fold: to analyze the test items for English education of the secondary school English teacher recruitment examinations conducted between the years 2009 to 2019 and to explore the possibility of examining the test items based on keyword analysis. For this, a ‘test item’ corpus was compiled, comprising all the test items about English education from 2009 to 2019. From this corpus, keywords for each year were extracted using a corpus analysis program to identify the features of each year’s test items, and the keywords were classified into five sub-fields of English education and were compared with the sub-field classification from previous studies. The results revealed that keywords related to teaching 4 skills appeared most frequently in almost every examination even though keywords associated with each sub-domain were not evenly distributed. On the other hand, this keyword analysis was found to be a valid way of identifying the characteristics of each year’s test items in that the result from keyword-based item classification was similar to that from content-based item classification.
An analysis of the Syllabi on Classroom Management Related Courses in Elementary School Teacher Preparation Institutions

담임교사에게 필수적으로 요구되는 역량 중 하나는 학급경영 역량이다. 학급은 교육이 이루어지는 최전선이며, 학급이 성공적으로 경영될 때 그 교육의 성과가 더욱 효과적으로 나타나기때문이다. 특히 대부분의 시간을 학생들과 한 공간에서 함께하는 초등교사의 경우 학급경영 역량은 더욱 중요하다. 이러한 중요성에도 불구하고 현행 초등교원양성 프로그램에서 학급경영 역량을 얼마나 길러주고 있는지에 대한 연구는 거의 없다. 현행 교원양성 프로그램에서 학급경영과 관련되어 있는 과목은 2019년도 교원자격검정 실무편람에 명시되어 있듯 교직이론인 ‘교육행정 및 교육경영’과 교직소양인 ‘교직실무’이다. 이에 본 연구는 13개의 초등교원양성기관에서 가르치고 있는 두 과목의 강의계획서를 토대로 교과목명, 강의 개요 및 목표, 강의 내용을 비교분석하여 학급경영 역량이 교육대학교에서 얼마나 길러지고 있는지 탐색하였다. 이를 위해 교원자격 검정령 시행규칙, 교원자격검정 실무편람, 학급경영 관련이론 4가지, 그리고 학급경영 영역 9 가지를 준거로 삼았다. 연구결과 첫째, ‘교육행정 및 교육경영’과 ‘교직실무’의 교과목명은 1/3이 넘는 초등교원양성기관에서 법규에 명시된 명칭과 다르게 사용하고 있었다. 둘째, ‘교육행정 및 교육경영’의 강의내용은 학급경영 관련이론 위주로 운영되고 있었다. 셋째, ‘교직실무’의 강의내용은 ‘교육행정 및 교육경영’에 비해 학급경영의 이론보다 학급경영 영역에 더욱 초점이 맞추어져 있었으며, 대부분에서 행동경영, 외부관계의 학급경영 영역 관련 내용을 강의에 포함시키고 있었다. 끝으로 연구결과를 토대로 논의점 및 시사점을 제시하였다.The class is the front line of education, and when the class is successfully managed, the performance of the education is more effective. Especially, in the case of elementary school teachers in South Korea who spend most of their time in one space with students, the importance of classroom management competence intensifies. The purpose of this study is to analyze and explore whether the classroom management capacity is being developed in the current elementary school teacher training program in South Korea. For this purpose, the study analyzed syllabi of two subjects (i.e. ‘Education Administration and Education Management and ’Teaching Practice’) taught by all elementary school teacher training institutions in South Korea. The results show that names, contents of the lectures, quality of contents vary greatly by elementary teacher training universities. Based on the results of the study, the discussion points and implications are suggested.
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning

In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints. To solve the problem, we first convert it to a dual problem and then propose a general primal-dual algorithm to optimize the primal and dual variables iteratively. In each optimization iteration, we employ a proximal operator to search optimal solution in the primal space. We prove convergence of the proposed algorithm and show its non-asymptotic convergence rate. By utilizing the proposed primal-dual optimization technique, we propose a novel metric learning algorithm which learns an optimal feature transformation matrix in the Riemannian space of positive definite matrices. Preliminary experimental results on an optimal fund selection problem in fund of funds (FOF) management for quantitative investment showed its efficacy.
Test–retest reliability of goldmann perimetry in children with NF-1 associated optic pathway glioma

Neurofibromatosis type 1 (NF-1) associated optic pathway gliomas (OPGs) may cause profound loss of vision and visual field (VF). Many children undergo formal VF testing to monitor for signs of tumor progression. The purpose of this study is to determine the test–retest reliability and variability of testing using Goldmann perimetry in children with NF-1-associated OPGs in an effort to provide evidence-based guidelines for tumor progression.