Reviewerly/exHarmony huggingface dataset:
Preliminary studies identify male and female sound based on frequency, pitch and formant composition

Voice classification is one of the deeper research of speech recognition. Classification analysis of gender based sound with wav format using Praat software is a clever system that distinguish male and female voice through the system. This article aims to look at the similarities and differences in the pitch and formant frequencies using voice record for 35 samples. Subject for speech beginner musical scale pronunciation taken from college student of Sepuluh Nopember Institute of Technology aged 18 to 23 years old. From this study, the results from 100-150 Hz for men as well as 200-250 Hz for women. In addition to the pronunciation has a value of F1 ranges from 650 - 950 Hz, Formant 2 ranges from 1300-1500 Hz and formant 3 is in the range 2500-4000 Hz. 100% of the pronunciation "La" and "Fa" has a complete composition of the fundamental frequency and frequency bands for each scale of musical. Different with "Do" and "Sol" that have 15% of formant 1.
Preface to the special issue of Precambrian geological events and metallogeny, in honour of Academician Mingguo Zhai

In this novel investigation, we want to discuss the use of the direct analogy by converting only the classical formulas initially developed for the viscosity measurements into the electrical conductivity by adaptation of symbols. We have discussed the methods used to determine the critical concentration separating the various regimes of concentration. Also, we have extended by the direct adaptation of symbols some formulas to study the effect of the adsorption on the electrical conductivity in the vicinity of the glass cell of the conductimeter. In this work, we have used as counter-example, the set of the results obtained from our previous work (Saoudi et al., 2017) which have treated the electrical conductivity of the laccase from Trametes versicolor.
On the security of one-round meeting location determination protocol

With the advancement of smart-phones equipped with a geographical location determination system, most clients of these devices are eager to use location based services in a variety of applications for their daily activities. One of the popular location based services is the selection of a meeting location for a group of multiple users with different preferred meeting locations to gather at a certain location through a Meeting Location Determination Server (MLDS). In such services, in addition to minimizing the total cost for all group users, the location privacy of the group users must be preserved. Recently, Wang et al. proposed a practical meeting location determination protocol for a group of users called as Privacy-Preserving Fair Meeting Location Determination (PPFMLD) protocol. This protocol requires light computation and only one-round communication. Wang et al. claim that a group user and the MLDS separately cannot learn the group users' preferred meeting location by performing passive attacks. In this paper, we show that PPFMLD protocol cannot preserve the location privacy for the users against passive attacks performed by the group users or the MLDS which are curious and intend to learn information regarding the group users' preferred meeting locations.
A computational imaging approach for resolution enhancement in fiber bundle endomicroscopy

Confocal, multi-photon, and wide-field endomicroscopy often use coherent fiber-optic bundles to facilitate in vivo imaging. The narrow diameter and flexibility of these bundles allow excellent tissue access, but fabrication processes place a practical limit on fiber packing density, restricting the number of resolvable points in an image. Furthermore, the hexagonal packing of discrete fibers creates inter-fiber gaps that prevent some regions of the object from being imaged. We have combined compressed sensing (CS) principles with dispersive optics to simultaneously address these two fundamental limitations of the fiber bundle architecture. We previously reported a CS approach to improve the spatial resolution of bundle based imaging systems by recovering multiple resolvable points within each fiber (Dumas et al., Proc. SPIE 2018). This manuscript will discuss and integrate approaches for recovering object details that lie behind inter-fiber gaps with our CS-based method for resolving intra-fiber detail. First, we show that modifying our CS model to consider the whole field of view rather than a discrete point for each fiber can partially recover inter-fiber detail. Next, we outline how a dispersive component at the distal end of the bundle can be used to spectrally shift object detail such that information from all locations on the sample are transmitted through the bundle. We then implement image compounding techniques with our CS approach to produce a more continuous image. We demonstrate that our platform can produce images of biological samples with 65,536 resolved pixels using a fiber bundle with only 3,700 fiber cores.
Review of the book<i>Internet of Things, for Things, and by Things</i>

Abhik Chaudhuri’s book offers a comprehensive yet synthetic overview of the Internet-of-Things (IoT). Very easy to read, the book helps readers unfamiliar with the topic to make sense of wireless s...
Are You Talking to Me?

This study examines the extent to which instructions to self-explain vs. other-explain a text lead readers to produce different forms of explanations. Natural language processing was used to examine the content and characteristics of the explanations produced as a function of instruction condition. Undergraduate students (n = 146) typed either self-explanations or other-explanations while reading a science text. The linguistic properties of these explanations were calculated using three automated text analysis tools. Machine learning classifiers in combination with the features were used to predict instruction condition (i.e., self- or other-explanation). The best machine learning model performed at rates above chance (kappa = .247; accuracy = 63%). Follow-up analyses indicated that students in the self-explanation condition generated explanations that were more cohesive and that contained words that were more related to social order (e.g., ethics). Overall, the results suggest that natural language processing techniques can be used to detect subtle differences in students' processing of complex texts.
La partícula mmh Análisis descriptivo del uso de la partícula mmh en las conversaciones de estudiantes italianos de ELE

This paper shows the results obtained in the analysis of the mmh particle in spontaneous dyadic face-to-face interactions in Spanish of 18 Italian university students of three domains of Spanish as a foreign language (6 of level A1 / A2, 6 of level B1 and 6 of level C1) according to their functions and their location in the conversation.For our study we used the CIELE oral corpus (Pascual Escagedo 2015a).Following the theoretical and methodological assumptions of the Conversation Analysis, and the classifications of the support turns and discursive markers proposed by Cestero Mancera (2000b) and López and Borreguero (2010), respectively, we have found that mmh has played 5 functions: (1) signal by the listener to indicate that he is listening and following the interlocutor closely, (2) strategy to attenuate the turn taking without adding any other meaning, or adding that he is agreeing with the content of the previous message, or showing that he is understanding what is being said, (3) to communicate the speaker, as a collaborative answer and without taking the turn, his agreement with what is being said in the previous turn, (4) to request clarifications about what was said previously and, finally, ( 5) to answer questions.Regarding its location in the conversation, it has been more frequent the unique emission by the listener within narrative sequences.His initial position in the turn taking was also significant.Finally, we have added the results of the functions of the mmh particle to an earlier one (Pascual Escagedo 2015b), carried out with the same corpus, in order to better determine its role in the conversation.
eSense 2.0: Modeling Multi-agent Biomimetic Predation with Multi-layered Reinforcement Learning

Learning in multi-agent systems, especially with adversarial behavior being exhibited, is difficult and challenging. The learning within these complicated environments is often muddied by the multitudinous conflicting or poorly correlated data coming from the multiple agents and their diverse goals. This should not be compared against well-known flocking-type behaviors where each agent has the same policy; rather, in our scenario each agent may have their own policy, sets of behaviors, or overall group strategy. Most learning algorithms will observe the actions of the agents and inform their algorithm which seeks to form the models. When these actions are consistent a reasonable model can be formed; however, eSense was designed to work even when observing complicated and highly-interactive must-agent behavior. eSense provides a powerful yet simplistic reinforcement learning algorithm that employs model-based behavior across multiple learning layers. These independent layers split the learning objectives across multiple layers, avoiding the learning-confusion common in many multi-agent systems. We examine a multi-agent predator-prey biomimetic sensing environment that simulates such coordinated and adversarial behaviors across multiple goals. This work could also be applied to theater wide autonomous vehicle coordination, such as that of the hierarchical command and control of autonomous drones and ground vehicles.
On the Degree Sequence of 3-Uniform Hypergraph: A New Sufficient Condition

The study of the degree sequences of h-uniform hypergraphs, say h-sequences, was a longstanding open problem in the case of $$h>2$$ , until very recently where its decision version was proved to be NP-complete. Formally, the decision version of this problem is: Given $$\pi =(d_1,d_2,\ldots ,d_n)$$ a non increasing sequence of positive integers, is $$\pi $$ the degree sequence of a h-uniform simple hypergraph? Now, assuming $$P\ne NP$$ , we know that such an effective characterization cannot exist even for the case of 3-uniform hypergraphs. However, several necessary or sufficient conditions can be found in the literature; here, relying on a result of S. Behrens et al., we present a sufficient condition for the 3-graphicality of a degree sequence and a polynomial time algorithm that realizes one of the associated 3-uniform hypergraphs, if it exists. Both the results are obtained by borrowing some mathematical tools from discrete tomography, a quite recent research area involving discrete mathematics, discrete geometry and combinatorics.
Exploring Programming Semantic Analytics with Deep Learning Models

There are numerous studies have reported the effectiveness of example-based programming learning. However, less is explored recommending code examples with advanced Machine Learning-based models. In this work, we propose a new method to explore the semantic analytics between programming codes and the annotations. We hypothesize that these semantics analytics will capture mass amount of valuable information that can be used as features to build predictive models. We evaluated the proposed semantic analytics extraction method with multiple deep learning algorithms. Results showed that deep learning models outperformed other models and baseline in most cases. Further analysis indicated that in special cases, the proposed method outperformed deep learning models by restricting false-positive classifications.
Cyclic linear error-block codes

Linear error-block (LEB) codes were first introduced in 2006. They are a natural generalization of the traditional linear error correcting codes. In this paper, we define and we give an algebraic stady of cyclic LEB codes.
Improving scalar multiplication over elliptic curves

The elliptic curves scalar multiplication has been paid much attention in the recent years, and one of major interests in this field is to improve algorithm for elliptic curve cryptosystem. In this paper, we give detailed study of the efficiency issues in scalar multiplication on the elliptic curves. In particular, we present the cost of group operation in several coordinate system and bases representation. Moreover, we show that more optimization can be achieved when better combination of coordinate choise and bases representation are performed.
The Relationship Between Financial Development, Innovation and Economic Growth

Along with the globalization process, the relationship between the existence of an advanced financial system, financial development and economic growth has become one of the most debated issues. The financial system, development and development indicators, which play an important role in the overall success levels of the economy, are among the topics to be considered due to this importance. In this study, financial development, economic growth, and theoretical approaches are discussed. Moreover, the fact that the subject is empirically presenting evidence requires examination of this situation with studies in the literature. The presence of the findings obtained empirically, in particular Turkey's economy has made it necessary to include a large empirical literature. The generally accepted financial development indicators, which provide comparability in terms of countries, are examined in terms of financial markets and financial institutions in terms of depth, access, stability and efficiency during the period 2005-2015.
Analysis of Cutting-Edge Regression Algorithms Used for Data Analysis

Considering the growing applications of big data analytics in the various fields such as healthcare, finance, e-commerce, and web services, it is essential to continuously develop techniques useful for big data. Among various techniques used for big data analytics, regression analysis is very important. In this chapter, an attempt is made to take a detailed look into some of the main regression algorithms and their origin that are used for big data analytics. In this study, some of very famous works related to regression along with some latest research are analyzed. Regression is the process of deducing a predictive model for real-world information based on verified information that is already received. It is used for making predictions, optimizing solutions to complex problems, and understands trends in large and big data analytics. The goal of this study is to promote and facilitate a better understanding of regression algorithms that are in use in the real world for big data analytics.
Information Technologies for Learning Principles of Fault-Tolerant Systems

In this chapter, the authors present a methodology for developing a model-tracing cognitive tutor. The methodology is based on Bayesian probabilistic networks for generating pedagogical interventions. The presented probabilistic model increases fidelity assessment due to its ability of independently diagnosing the degree of mastery for every knowledge component involved in students' actions; fidelity assessment in education is the ability to represent students' cognitive states as close as possible for analysis and evaluation. The cognitive tutor was developed to promote a self-regulated learning approach with an open learner model. The open learner model let students change the learning flow by changing the assigned tasks. The authors explain in detail the structural construction and employed algorithms for developing a model-tracing cognitive tutor in the domain of fault-tolerant systems. Preliminary results and future work are also discussed to assess effectiveness of the proposed approach and its implication in actual educational programs.
Teacher and Librarian Partnerships in Literacy Education in the 21st Century Edited by JoronPihl, Kristin Skinstadvan derKooij and Tone CecilieCarlsten. Rotterdam, Sense Publishers, 2017, 152 pp. New Research – New Voices series, vol. 6. ISBN 978‐946‐3008‐98‐3 (hbk), ISBN 978‐946‐3008‐97‐6 (pbk), 978‐946‐3008‐99‐0 (eBook)

LiteracyVolume 53, Issue 3 p. 180-180 Book Review Teacher and Librarian Partnerships in Literacy Education in the 21st Century Edited by Joron Pihl, Kristin Skinstad Kooij and Tone Cecilie Carlsten. Rotterdam, Sense Publishers, 2017, 152 pp. New Research – New Voices series, vol. 6. ISBN 978-946-3008-98-3 (hbk), ISBN 978-946-3008-97-6 (pbk), 978-946-3008-99-0 (eBook) Joy Myers, Corresponding Author Joy Myers myersjk@jmu.edu James Madison UniversitySearch for more papers by this authorMelissa Adams-Budde, Melissa Adams-Budde West Chester UniversitySearch for more papers by this author Joy Myers, Corresponding Author Joy Myers myersjk@jmu.edu James Madison UniversitySearch for more papers by this authorMelissa Adams-Budde, Melissa Adams-Budde West Chester UniversitySearch for more papers by this author First published: 19 February 2019 https://doi.org/10.1111/lit.12185Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume53, Issue3September 2019Pages 180-180 RelatedInformation
Projected memory clustering

We present a new algorithm PMC (Projected Memory Clustering) for projected clustering of high dimensional data. It effectively discovers clusters described by affine subspaces parallel to the main axes of coordinate system. The number of clusters and dimensions of subspaces are selected automatically. Experiments performed on various types of data show that PMC detects clustering structures better than related projected clustering methods. Moreover, it is fast, which makes it suitable for practical use.
Fully semisupervised framework for visual domain adaptation

Unsupervised domain adaptation aims to utilize knowledge from a source domain to improve learning in a target domain in cases in which abundant labeled samples are available in the source domain, but no labels exist in the target domain. The two domains have the same feature space and label space but different distributions. Our study proposes a semisupervised framework in which labeled samples in the source domain and unlabeled target samples are fully utilized to learn a better classifier. The framework contains two stages: semisupervised feature learning and semisupervised classifier learning. In the first stage, a transformation is learned using labeled source samples and unlabeled target samples to map these data into a representation. In the second stage, a classifier is learned using all samples in the source and target domains of the representation. Furthermore, we propose a semisupervised feature learning approach (i.e., cross-domain discriminative analysis) to learn the transformation in the first stage by reducing the distribution discrepancies between domains and preserving discriminative information in the original data. In our experiments, image classification tasks were conducted using several well-known cross-domain datasets. The proposed method outperformed the state-of-the-art methods in most cases.
A Novel Approach on Prevention of a Mobile Exploration in Machine Using AI

This paper deals with a low cost solution to problem avoidance for a mobile machine using just a single Artifical Intelligennce. It allows the machine to navigate smoothly in an unknown environment, avoiding collisions, without having to stop in front of problems. The problem avoidance process is made up of three distinct stages - the mapping algorithm, the core problem avoidance algorithm, and the steering algorithm. The mapping algorithm takes the raw Artifical Intelligennce readings and processes them to create higher resolution maps from the wide-angle Artifical Intelligennce. The problem avoidance algorithm is based on the potential field theory which considers the machine to be a test charge that is repelled by all the problems around it, and which moves in the direction of the resultant of the forces acting on it. An algorithm which steers a mobile machine based on the differential drive system is also discussed.
IOT and Data Analytics Solution for Reducing Pollution, Accidents, and Its Impact on Environment

The goal of this chapter is to inspect and consider the answer for accidents and reactions to the accidents in the urban zones. Modules have been made to manage the colossal datasets and to bring interesting bits of knowledge into the outcomes. This is done by utilizing decision tree analysis.
Implementation Security Certification of a Quantum Key Distribution System through Device Characterization

It is indispensable for social deployment of quantum key distribution to certificate the security on the installed systems, or implementation security. In this talk, we will present several case studies on the implementation security certification.
Polygon Approximations of the Euclidean Circles on the Square Grid by Broadcasting Sequences

Euclidean circle approximation on the square grid is an important problem in digital geometry. Recently several schemes have been proposed for approximation of Euclidean circles based on Neighbourhood Sequences, which correspond to repeated application of the von Neumann and Moore neighbourhoods on a square grid. In this paper we study polygon approximations of the Euclidean circles on the square grid with Broadcasting Sequences which can be seen as a generalization of Neighbourhood Sequences. The polygons generated by Broadcasting Sequences are the Minkowski sums of digital disks defined by a given set of broadcasting radii. We propose a polynomial time algorithm that can generate Broadcasting Sequences which are providing flexible and accurate approximation of Euclidean circles.
Storytelling and Sensemaking through Data Visualization

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Research on the algorithm of electromagnetic leakage reduction and sequence of image migration feature retrieval

When the computer is working, it will transmit the electromagnetic leakage signal containing the video information and receive and process the electromagnetic leakage signal within a certain distance, which can reproduce the screen information and form the electromagnetic leakage restoration sequence image. Due to the noise in the receiving process and the fluctuation of the video line and field signal, the image of the electromagnetic leakage restoration sequence will be blurred and will drift between frames. The multi-frame cumulative averaging method can theoretically improve the signal-to-noise ratio of the reconstructed image, but the offset of the reconstructed image sequence caused by the electromagnetic leakage will bring adverse effects. Firstly, this paper analyzes the noise of electromagnetic leakage emission and restoration sequence images and the method of multi-frame cumulative averaging, as well as the cumulative averaging effect of multi-frame sequence images under the influence of image offset. Secondly, on the basis of theoretical analysis, an algorithm of image migration feature retrieval for electromagnetic leakage restoration sequence is proposed and validated, which achieves more accurate inter-frame matching, automatic offset calculation, and multi-frame sequence image accumulation and enhances the recognition of the electromagnetic leakage restoration image. Lastly, different algorithms are compared, and their effects are evaluated as well.