Reviewerly/exHarmony huggingface dataset:
Optical Matrix Manipulation Based on Frequency Comb Modulation and Dispersed Time Delay

We propose a novel optical matrix manipulation based on frequency comb modulation and dispersed time delay. The sequence autocorrelation is demonstrated at 1.18×10−11 MAC/s and convolution of binary images is performed at 5.12×10−11 MAC/s.
Grammatical Difficulties of Automatic Translation of Scientific and Technical Literature

The article is devoted to the analysis of grammatical difficulties encountered in the process of automatic translation. The paper discusses the advantages and disadvantages of the SDL Trados automatic translation service. The types of grammatical errors when translating scientific and technical texts in SDL Trados are classified, the ways of overcoming them are outlined. Key words: scientific and technical literature, automatic translation, grammatical difficulties.
Teacher Use of iPads in the Classroom of a South African Public School

This chapter argues that when implementing technologies such as iPads in developing country educational contexts, there are different factors to consider than when implementing in the developed world. It is important to consider these to reap benefits that improve the inclusivity of education for all. The chapter examines teacher use of iPads in the classroom of a township school in South Africa and the benefits and challenges experienced by teachers in using the devices. Qualitative data were collected through interviews with teachers. The findings of the study indicated that overall the effect of iPad use by the teachers was positive. The use of iPads resulted in the teachers having access to quality multi-media and educational apps to teach their subjects which led to learners' increased class involvement and independent learning. The study revealed that effective use of iPads requires teachers that are adequately trained to use the iPad in relation to subject specific content, a reliable wireless connection, technical support, and mitigation of learners' distractions.
Single-mode squeezed vacuum state orthogonalization via photon-addition operation

We construct some orthogonal states for single-mode squeezed vacuum state (SVS) and study their nonclassicality by virtue of the quadrature squeezing and the Wigner function. The orthogonalizer is based on the photon-addition operation, i.e., the repeated (m times) application of the photon creation operator. The orthogonal state and the photon-addition single-mode SVS are same for odd m but different for even m. The squeezing effect of the orthogonal states will exhibit when the origin squeezing parameter is greater than a certain value. Moreover, the negativity of the Wigner function shows their nonclassicality.
Problems in Educational Production

Previous articleNext article No AccessReview EssayProblems in Educational Production The Testing Charade: Pretending to Make Schools Better. By Daniel Koretz. Chicago: University of Chicago Press, 2017. Common Core: National Standards and the Threat to Democracy. By Nicholas Tampio. Baltimore: Johns Hopkins University Press, 2018. The Case against Education: Why the Education System Is a Waste of Time and Money. By Bryan Caplan. Princeton, NJ: Princeton University Press, 2018.Scott F. AbernathyScott F. AbernathyUniversity of Minnesota Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Politics Volume 81, Number 2April 2019 Sponsored by the Southern Political Science Association Article DOIhttps://doi.org/10.1086/702227 Views: 154Total views on this site HistoryPublished online February 25, 2019 © 2019 by the Southern Political Science Association. All rights reserved.PDF download Crossref reports no articles citing this article.
Information Augmentation, Reduction and Compression for Interpreting Multi-layered Neural Networks

The present paper aims to propose a new type of learning method for interpreting relations between inputs and outputs in multi-layered neural networks. The method is composed of information augmentation, reduction and compression component. In the information augmentation component, information in inputs is forced to increase for the subsequent learning to choose appropriate information among many options. In the information reduction component, information is reduced by selectively choosing strong and active connection weights. Finally, in the information compression component, information contained in multi-layered neural networks is compressed by multiplying all connection weights in all layers for summarizing the main characteristics of connection weights. The method was applied to the improvement of an EC (electric commerce) web site for better profitability. The method could clarify relations between inputs and outputs and its interpretation was more natural than that by the conventional logistic regression analysis. The results suggest that multi-layered neural networks can be used to improve generalization and in addition to interpret final results, which is more important in many applications fields.
Modeling Mentor-Mentee Dialogues in Film

With a view to inform the design of a mentor-like synthetic agent that is to engage in a coherent and consistent in character conversation with human subjects, we conducted a data-driven analysis of verbal communication between fictional mentor and mentee characters in films. While in our earlier work the focus was on the conversation strategies of mentor characters, here we present the extended model, wherein conversation activity of both mentor and mentee characters is accounted for. To examine and to formalize local communication actions and extended goals that the two characters achieve jointly, categories of intents, projects and relationship phases were introduced. The resulting annotated corpus of mentor and mentee characters' utterances was analyzed qualitatively and quantitatively. In furtherance of the automated in-character dialogue generation task, a range of the state-of-the-art approaches to automated utterances classification was evaluated.
Network Embedding with Deep Metric Learning

Network embedding has attracted an increasing amount of attention in recent years due to its wide-ranging applications in graph mining tasks such as vertex classification, community detection, and network visualization.Network embedding is an important method to learn low-dimensional representations of vertices in networks, aiming to capture and preserve the network structure.Almost all the existing network embedding methods adopt the so-called Skip-gram model in Word2vec.However, as a bag-of-words model, the skip-gram model mainly utilized the local structure information.The lack of information metrics for vertices in global network leads to the mix of vertices with different labels in the new embedding space.To solve this problem, in this paper we propose a Network Representation Learning method with Deep Metric Learning, namely DML-NRL.By setting the initialized anchor vertices and adding the similarity measure in the training progress, the distance information between different labels of vertices in the network is integrated into the vertex representation, which improves the accuracy of network embedding algorithm effectively.We compare our method with baselines by applying them to the tasks of multi-label classification and data visualization of vertices.The experimental results show that our method outperforms the baselines in all three datasets, and the method has proved to be effective and robust.
Analysis and Comparison of Uncertain Means Clustering Algorithm

Clustering analysis is an important method of multivariate statistical analysis. It has important applications in pattern recognition, artificial intelligence, automatic control and other fields. An iterative algorithm called uncertain means clustering is defined by analyzing the contribution of the features to the sample and calculating the degree of membership based on the weight. In this paper, we use the uncertain means clustering algorithm to cluster IRIS data to test the clustering accuracy, convergence speed and robustness of the algorithm. At the same time, compared with the traditional clustering algorithm, which K-Means clustering algorithm and KNN clustering algorithm, the experimental results show that the uncertain means clustering algorithm has good performance in the accuracy and convergence speed of the sample data sets, and is an effective unsupervised clustering algorithm.
Verified Newton–Raphson Iteration for Multiplicative Inverses Modulo Powers of Any Base

We identify two faults in a published algorithm for fast computation of multiplicative inverses modulo prime powers. We patch the algorithm and present machine-assisted proofs of correctness of the repair. Our formal proofs also reveal that being prime is an unnecessary demand for the power base, thus attributing a wider scope of applications to the repaired algorithm.
Telecom compatible quantum key distribution - learning from classical coherent communication

Quantum key distribution (QKD) enables secure cryptography that is safe against future attacks by quantum computers. We show recent advances in continuous variable QKD and highlight similarities and differences to classical coherent communication.
Review of the possibility of knowledge in educational sciences

There are many trends that determine all the possibilities that exist in the education sciences. These are oriented to the formation of the human being. The current development is demarcated by the use of active methodologies through science, research, technology and innovation. where the Universities advance in the professional formation of the students through the virtualization of the education. In the present work we intend to review the current state of the research work at national and international level in relation to the different ways of covering the teaching - learning processes. For that, an exhaustive review of the bibliographical sources was made through the use of several specialized databases such as Ebsco, ISI Web of science, Latindex Open Access, Scielo and Scopus. The articles classified as most relevant by these databases were selected taking into account their citation index. As a result you can see a variety of possibilities that still use the traditional forms of education and citizenship training based on traditional master classes where the teacher is the protagonist of academic activity and you can also see new methodologies that apply the new possibilities of education based on virtualization and independent training based on a more globalized and participatory education where the student is the protagonist of their learning.
Contextual Binding: A Deductive Apparatus in Artificial Neural Networks

The Artificial neural networks are key mechanisms in artificial intelligence, especially in machine learning. A close examination of this mechanism reveals some philosophical challenges in the current approach. With all the emphasis on the techniques used in the mining of the great volume of datasets collected and available, inductive reasoning is well employed to find out characteristics and identify patterns. However, deductive reasoning is not sufficiently utilized. In order to bring the current approach to the right track, this paper proposes a novel approach in which a deductive apparatus is made use of. This deductive apparatus is built on contextual binding, which sets a priority and provides guidance for the processing of various types of datasets collected and available, thus making the processing efficient and effective. The benefits and implementation of this new approach are also discussed.
Extraction and Annotation of News Topics From TV Streams for Web Video Sharing

Today, with increased internet access, users are often interested in new content-based multimedia applications of high added value such as interactive TV, video on demand (VoD), and catch-up TV services such as YouTube or Dailymotion frameworks. Despite the easy and rapid access to media information of these services, they present the risk of the wide propagation of fake news. As a solution, the authors propose that the input for these services must be from a trustworthy traditional media, precisely TV program content. So, the automatic process of TV program identification and their internal segmentation facilitate the availability of these programs. In this chapter, the major originality of the authors' approach is the use of contextual and operational characteristics of TV production rules as prior knowledge that captures the structure for recurrent TV news program content. The authors validate their approach by experiments conducted using the TRECVID dataset that demonstrate its robustness.
Convex and Concave Vertices on a Simple Closed Curve in the Triangular Grid

We propose a classification of convex and concave vertices on a simple closed curve in the triangular grid into two types, based on the angle turn the curve makes at the vertex. We prove a combinatorial property for the number of convex and concave vertices of different types.
Design and Analysis of a Stair Climbing Wheel Chair

This paper aims to bring forward a means to climb stairs easily for older and disabled people. A stair climbing wheel chair machine, a semi-automated vehicle, which has the capability to climb the stairs easily. a wheel chair is designed and analyzed with planetary gear system attachment for the wheels. The structure and assembly of planetary wheel basic parameters required have been calculated for climbing steps without slipping. The stress analysis on wheel chair has been performed for its various operating conditions. When the wheelchair climbs stairs, there is danger of falling down the stairs, hence in order to protect the user to avoid this kind of situation to happen a ratchet mechanism locking system is proposed. Most wheelchairs are oblique during the process of climbing up and down stairs and the user will feel uncomfortable as it can easily turnover, which poses a big safety risk. In order to overcome this problem, a seat backrest adjusting device is proposed for this wheelchair, so before the wheelchair climbs up and down stairs, this device will adjust an angle for the seat and backrest to make sure the seat of the wheelchair keeps level with the ground all the time. The wheel chair assembly is modeled and its components are analyzed by considering three different materials under various likely possible loading conditions on wheel chair with the help of finite element analysis software and the results are presented.
What Have We Talked About?

The SIGCSE-Members listserv has been archiving posts by the Computer Science Education community for the past 22 years. This paper characterizes the post collection, in order to better understand the nature of the community from a quantitative perspective. We apply a number of email mining techniques, including a topical analysis through N-grams. Threads, posters, and posts are characterized in terms of duration and temporally. We also demonstrate how emails from the listserv can be successfully classified using machine learning algorithms, and report on an unsuccessful attempt to predict thread popularity. All of the scripts we used to collect, process, and analyze the data are freely available in the hopes that other researchers will replicate, refine, and extend our results.
Personal Data Privacy and Protection in the Meeting, Incentive, Convention, and Exhibition (MICE) Industry

With the new developments in information technologies, personal and business data have become easily accessible through different channels. The huge amounts of personal data across global networks and databases have provided crucial benefits in a scientific manner and many business opportunities, also in the meeting, incentive, convention, and exhibition (MICE) industry. In this chapter, the authors focus on the analysis of MICE industry with regards to the new regulation (GDPR) of personal data protection of all EU citizens and how the industry professionals can adapt their way of business in light of this new regulation. The authors conducted an online interview with five different meetings industry professionals to have more insight about the data produced with its content and new regulations applied to the industry. The importance of personal data privacy and protection is discussed, and the most suitable anonymization techniques for personal data privacy are proposed.
Industry Revolution 4.0 and Education System in Malaysia

Industry Revolution 4.0 (IR 4) is highly rated and greatly establishing itself. The future of our world looks so promising. The revolution is happening, and the impact is already seen in many sectors. Education is one of the sectors that needs to be in line with the growth of the technology to produce a world class work force. In Malaysia, the government had earlier made many changes in education through its National Higher Education Action Plan 2007-2010 in transforming teaching and learning process focusing on technology. Innovative and entrepreneurial skills must be possessed by new graduates and they must deal with complexity with cognitive flexibility. This is because they need to accept robots as their working colleagues apart from humans. The need for better communication and collaborative skills will be far more important than ever. Graduates must acquire self-learning skills to remain relevant in the era of rapid changes.
Semantics for Big Data Sets

Information technology, computer science, etc. have been developed more and more in many countries in the world. Their subfields have already had many very crucial contributions to everyone life: production, politics, advertisement, etc. Especially, big data semantics, scientific and knowledge discovery, and intelligence are the subareas that are gaining more interest. Therefore, the authors display semantics for massive data sets fully in this chapter. This is very significant for commercial applications, studies, researchers, etc. in the world.
Optimal designs of nulling resistor compensation and a robust bias‐based op‐amp circuit using an evolutionary approach

This article suggests an evolutionary optimisation method namely symbiotic organisms search (SOS) algorithm-based area optimised designs of a nulling resistor compensation circuit and a robust bias-based complementary metal oxide semiconductor (CMOS) analogue operational amplifier (op-amp) circuit. SPICE simulator is used to validate the results obtained by using the optimisation technique SOS. The results obtained by SPICE simulation confirm that all the design specifications are accurately satisfied for both the circuits considered here. The results also prove that the SOS algorithm is superior to the formerly reported methods in terms of the gain, power dissipation, area etc., for both the individual circuits.
Applications of Image Compression on Agricultural Image Data Analysis

With the advent of the cutting-edge technologies in information repository and communication, data storage is rapidly increased, and it is required to reduce the size of the data. Especially in the case of agricultural image data like types of plants, crops, seeds; kinds of diseases and their remedial pesticides; and the agricultural satellite; images require a huge volume of memory space to store. To avoid this problem, it is required to reduce the size of the data and redundancy of the data. To overcome this problem, this chapter proposes a compression method, based on an adaptive Gaussian Markov random field model for agricultural image data compression, where the images are assumed to be a Gaussian Markov random field. The parameters of the model are estimated, based on the Bayesian approach. The authors use arithmetic coding to store seed values and parameters of the model as it augments the compression ratio. They also have studied the use of the M-H algorithm, which updates the parameters and through which the image contents such as untexturedness are captured.
Alternative online resources as an approach in the physics education

As the online resources start to take a significant part in every aspect of our lives students from the Plovdiv University „Paisii Hilendarski” have developed a strategy for implementing video materials in the education process in the field of physics. There are many different advantages of this method from which both students and tutors can benefit in comparison with the standalone conventional educational approaches. The basic idea of the project we are working on is to develop a sustainable and scalable system of online resources which will help the better understanding of basic principles and subjects in the physics education. Even more important is the fact that they will be created by students for students. Many scientists and researchers can confirm the benefits of using such resources – it provides better time efficiency, the language used is more comprehensive for students. However the accuracy is constantly monitored by experienced specialists in the matter being explained. In this article, we share our experience with the preparation of the online didactic video materials and evaluation of their effectiveness. We believe that the topic of the article is up to date and the methodology described in it is applicable to other subjects. This article is dedicated to physics teachers and researchers in the physics education.
Analysis of decoy-state measurement-device-independent quantum key distribution system

The performance of measurement-device-independent quantum key distribution (MDI-QKD) with different numbers of decoy-state are compared. The statistical fluctuation due to the finite length of data is considered based on the standard statistical analysis. The simulation results show that two-decoy-state method is a nearly optimal estimation in the asymptotic case. In the condition of considering statistical fluctuations, the finite length of raw key will slightly decrease the secret key rate. In all simulation cases, the key rate is maximized by optimizing the intensities of the signals. Our numerical simulation may provide valuable theoretical reference for the practical MDI-QKD experiments.
The Complexity of Minimal Inference Problem for Conservative Constraint Languages

We study the complexity of the inference problem for propositional circumscription (the minimal inference problem) over arbitrary finite domains. The problem is of fundamental importance in nonmonotonic logics and commonsense reasoning. The complexity of the problem for the two-element domain has been completely classified. In this article, we classify the complexity of the problem over all conservative languages. We consider a version of the problem parameterized by a set of relations (a constraint language), from which we are allowed to build a knowledge base, and where a linear order used to compare models is a part of an input. We show that in this setting the problem is either Π P 2 -complete, coNP-complete, or in P. The classification is based on a coNP-hardness proof for a new class of languages, an analysis of languages that do not express any member of the class, and a new general polynomial-time algorithm solving the minimal inference problem for a large class of languages.
Methods of organizing the research activities of high school students by means of mathematics

The article considers the modern method of organizing the research activities of high school students by means of mathematics. The essence of the research activities of high school students is determined and the main features of educational research are presented. The author provides a detailed description of the teacher’s action system, which organizes the research activities of high school students in mathematics lessons. The research work in the modern school is analyzed.