Reviewerly/exHarmony huggingface dataset:
Ground control and cloud booths

This chapter is concerned with the idea of long-distance, real-time music collaboration in contemporary music production contexts. In 2007, Carot et al. noted that there are &quot;several technical and cultural barriers that need to be overcome in order to bring networked music performance in the mainstream&quot;. In the thirteen years since, the networked audio landscape has developed significantly. It contributes to both those technical and cultural conversations, addressing two specific areas. Firstly, through discussion of a practical case study, the chapter investigates new format for long-distance, real-time work – namely Audinate's Dante Audio Over IP with its new multi-zone capabilities. Secondly, it considers and discusses concepts, terminology and nomenclature in relation to the experience of music production in a long-distance, real-time context. Through this discussion, the chapter proposes a terminology that supports the experience, represents the technical process and makes use of existing terms and concepts already part of wider culture to bridge academic research with mainstream usage.
The stability of neural networks under condition of adversarial attacks to biomedical image classification

Recently, the majority of research and development teams working in the field deep learning are concentrated on the improvement of the classification accuracy and related measures of the quality of image classification whereas the problem of adversarial attacks to deep neural networks attracts much less attention. This article is dedicated to an experimental study of the influence of various factors on the stability of convolutional neural networks under the condition of adversarial attacks to biomedical image classification. On a very extensive dataset consisted of more than 1.45 million of radiological as well as histological images we assess the efficiency of attacks performed using the projected gradient descent (PGD), DeepFool and Carlini – Wagner (CW) methods. We analyze the results of both white and black box attacks to the commonly used neural architectures such as InceptionV3, Densenet121, ResNet50, MobileNet and Xception. The basic conclusion of this study is that in the field of biomedical image classification the problem of adversarial attack stays sharp because the methods of attacks being tested are successfully attacking the above-mentioned networks so that depending on the specific task their original classification accuracy falls down from 83–97 % down to the accuracy score of 15 %. Also, it was found that under similar conditions the PGD method is less successful in adversarial attacks comparing to the DeepFool and CW methods. When the original images and adversarial examples are compared using the L2-norm, the DeepFool and CW methods generate the adversarial examples of similar maliciousness. In addition, in three out of four of black-box attacks, the PGD method has demonstrated lower attacking efficiency.
Trajectory Correction Strategy based on Relative Deviation between Projectile and Target

For a higher accuracy of projectiles, a novel trajectory correction fuze is proposed. In this design, sensors and actuators are pre-designed and optimized. Based on the projectile flight model, the working principle of trajectory correction is explained, and the trajectory correction strategy is proposed. In the trajectory correction strategy, the detection deviation and the response of the correction actuator are analyzed. The effect of the strategy is verified by the example of closed-loop simulation. The results show that the accuracy of this method is obviously improved, which is suitable for the terminal correction of mortars.
Formulating a theory about interoperability among heterogeneous software systems, based on the Semat kernel

Doctor en Ingenieria-Sistemas e Informatica Linea de investigacion en ingenieria de software
Ve’rdd. Narrowing the Gap between Paper Dictionaries, Low-Resource NLP and Community Involvement

We present an open-source online dictionary editing system, Ve rdd, that offers a chance to reevaluate and edit grassroots dictionaries that have been exposed to multiple amateur editors.The idea is to incorporate community activities into a state-of-the-art finite-state language description of a seriously endangered minority language, Skolt Sami.Problems involve getting the community to take part in things above the pencil-and-paper level.At times, it seems that the native speakers and the dictionary oriented are lacking technical understanding to utilize the infrastructures which might make their work more meaningful in the future, i.e. multiple reuse of all of their input.Therefore, our system integrates with the existing tools and infrastructures for Uralic language masking the technical complexities behind a user-friendly UI.
Correction to: Fair Outlier Detection

The original version of this chapter was revised. The first author’s name was corrected to Deepak P.
Securing Cloud Computing Environment with the help of Fully Homomorphic Encryption

Abstract Since its inception, cloud computing has faced a number of challenges, like data migration, adoption of new policies etc. but the most challenging and important among them has been the security and risk issues related to it. Cloud computing provides a user, the provision to use various computing resources over the internet on a leased basis. This new model of computing has gained its popularity among various organizations and individuals due to its cost effectiveness and versatile features. But as it is common to any other computing infrastructure, cloud computing environment is also prone to security threats, may be up to a larger extent than traditional computing, which is hindering its growth. Using fully homomorphic schemes, as proposed in this paper, it would be possible for a user to store their data in the cloud in an encrypted form and perform some specific computations on the encrypted data, without the cloud server’s knowledge about the data and computations at all. Hence it will help the cloud service providers to develop a secured cloud infrastructure and build trust among the users. Keywords: cloud computing, cloud security, fully homomorphic encryption
THE INFLUENCE OF PODCASTS IN LEARNING ENGLISH VOCABULARY OF TWELVE GRADE STUDENTS OF SMA NEGERI 2 BURU

The Presence of Podcasts is such an interesting technology to facilitate language learner development. The online or offline accessibility through a smartphone or laptop offer convenience, learning anytime and everywhere. The Audios and videos enable students for their learning accelerations .This study aimed to study the influence of podcasts as technology for Students’ English Vocabulary mastery, and to investigate the students’ responses towards podcasts. This research employed quantitative research with a quasi-experimental design. The Participants were 60 Students of SMA Negeri 2 Buru grade XII IIS, Namlea, Buru , Maluku Province. Data related to student’s vocabulary mastery were collected by implementing pre-test, post-test, and distributing questionnaires to find out students' responses towards the use of Podcast Technology. Pre-test and post-test were conducted for .The 60 participants were then classified into 30 Students of Experimental Group. Moreover, 30 Students of Control Group .The data collected were then analyzed using IBM SPSS. The mean score resulted from the experimental group were 52.83 in the pretest and reached 63.83 in the Post Test, while the mean score resulted from Control Group were 50.5 in the Pre-Test and 55.33 in Post Test. The results indicated that the vocabulary mastery of the students in both groups was developed, however the experimental group, which implemented podcast, showed the higher improvement than non-podcast. The difference score between both groups was 8.5. The students also showed the positive responses toward the utility of podcast in teaching and learning processes.
A Study of Oral English Teaching Mode Based on Electronic Files in a Cloud Environment

With the development and popularization of cloud computing, the fixed cost of the online system is almost reduced to zero. In the past, because of the high cost, electronic file technology, which can not be widely used, has entered people’s vision. This paper first summarizes the main contents of cloud computing and electronic learning files, analyzes the current situation of oral English teaching, and then puts forward the oral English teaching mode based on electronic learning files in the cloud computing environment, and conducts an empirical study. The results are of great significance to promote the research of Cloud Computing Assisted English education and serve the oral English teaching. At the same time, it proves that the oral English teaching mode based on electronic learning files is of great practical significance in the information-based teaching reform of colleges and universities in China.
Modal set theory *

Modal set theory draws in particular upon contemporary modal logic, the logic of necessity and possibility. One simple and obvious motivation for modal set theory is the fact that, from a realist perspective that takes the existence of sets seriously, sets have philosophically interesting modal properties. This chapter focuses on the technical development of modal set theory and its intuitive motivations without any close critical attention paid to surrounding philosophical questions. It devotes to the development of modal set theory with regard to a rather more directed inquiry into both the nature and structure of sets that is motivated in particular by the attractive prospect of a satisfying explanation of Russell’s Paradox. The years 1897–1903 saw the emergence of a string of related paradoxes concerning the notions of number, set, class, property, proposition, and truth. Metaphysical modality would be unsuitable for our present purposes because pure sets are taken to exist of metaphysical necessity if at all.
BIUST & AAU 2020 virtual open week celebrations : open science global North-South partnerships, what exists for African researchers

This is a collaborative work of the following sponsors:Botswana International University of science and Technology Association of African UniversitiesDigital Science, Symplectic elementTraining Centre in Communication
Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.
Leveraging Tacit Information Embedded in CNN Layers for Visual Tracking

Different layers in CNNs provide not only different levels of abstraction for describing the objects in the input but also encode various implicit information about them. The activation patterns of different features contain valuable information about the stream of incoming images: spatial relations, temporal patterns, and co-occurrence of spatial and spatiotemporal (ST) features. The studies in visual tracking literature, so far, utilized only one of the CNN layers, a pre-fixed combination of them, or an ensemble of trackers built upon individual layers. In this study, we employ an adaptive combination of several CNN layers in a single DCF tracker to address variations of the target appearances and propose the use of style statistics on both spatial and temporal properties of the target, directly extracted from CNN layers for visual tracking. Experiments demonstrate that using the additional implicit data of CNNs significantly improves the performance of the tracker. Results demonstrate the effectiveness of using style similarity and activation consistency regularization in improving its localization and scale accuracy.
Effects of an Extensive e-Book Reading Programme on Middle School EFL Students

Extensive reading (ER) in EFL is an approach to language learning that involves reading as much as possible for pleasure or information. ER improves reading speed and vocabulary acquisition and helps develop positive attitudes towards reading in general. The widespread use of digital devices such as mobile phones and tablets in and out of school has considerably widened the reading possibilities for EFL learners. ER through mobile devices is an emerging area of study and this chapter aims to contribute to the research by investigating its effects on vocabulary learning and student attitudes towards using mobile devices for their reading. The study records the results of an e-book ER programme at lower secondary school level in the Milan metropolitan area in Italy. The participants were 72 11-year-old students from two state schools. The students read 16 e-books at CEFR A1 and A2 levels over the course of eight months alongside the standard EFL school programme. Following a mixed method approach, the teachers, who were also researchers, asked students to complete a test on vocabulary items selected from the e-books both pre- and post-programme. Results from the analysis of vocabulary tests and questionnaires show positive effects of e-reading on incidental vocabulary learning and increased motivation towards EFL reading on mobile devices.
Interaction between project tasks and risk management tasks in software development

In software development processes, there is a clear challenge to the application of danger organization in projecting organization, especially the integration of danger organization with projecting organization in understanding the requirements and discussions during the developing procedure. Though, the benefit of applying the interaction of risk management tools with project management tasks is very good in reducing losses, especially when reviewing risks and management practices in identifying risks for each project assignment.Here, scheme and risk organization functions in theory are discussing, interrelationships, and practices in software development. Where we focus on the assumptions that we assume that the risks of developing software can be in one of the project tasks (main task or sub-task). And we show that there is an interactive relationship within the project tasks and the danger organization tasks. As a result, we obtain that using an interactive approach between project management tasks and project tasks in light of the excitement and catalytic goal of software development operations reduces expected losses.
Variation in Universal Dependencies annotation: A token-based typological case study on adpossessive constructions

In this paper we present a method for identifying and analyzing adnominal possessive constructions in 66 Universal Dependencies treebanks. We classify adpossessive constructions in terms of their morphological type (locus of marking) and present a workflow for detecting and analyzing them typologically. Based on a preliminary evaluation, the algorithm works fairly reliably in adpossessive constructions that are morphologically marked. However, it performs rather poorly in adpossessive constructions that are not marked morphologically, so-called zero-marked constructions, because of difficulties in identifying these constructions with the current annotation. We also discuss different types of variation in annotation in different treebanks for the same language and for treebanks of closely related languages. The research focuses on one well-circumscribed and universal construction in the hope of generating more interest in using UD for cross-linguistic comparison and for contributing towards developing yet more consistent annotation of constructions in the UD annotation scheme.
Feature Extraction and Classification of Blood Cell on Medical Hyperspectral Imagery for Multi-Graph Convolution Neural Network

Cell Kind, in particular that of White Blood Cells (WBCS), Performs a totally critical characteristic inside the problem of prognosis and manipulate of essential sicknesses. Compared to standards of optical microscopic imaging ie., Hyperspectral imagery, blended with each spatial and spectral facts, affords more wealthy information for recognizing cells. This Proposed Research work emphasizes a completely unique blood-mobile classification framework which mixes a modulated Gabor wavelet and deep Convolutional Neural Network(CNN) kernels named as Multi-Graph Convolution Neural Network  (MGCNN) is proposed primarily based on clinical Hyperspectral imaging. For each convolution layer, multi-scale and orientation Gabor operators are taken dot product with preliminary CNN kernels.  The importance of the work is to transform the convolution kernels into the frequency vicinity to research competencies. By combining characteristics of Gabor wavelets, the capabilities determined through modulated kernels at one in all a type of frequencies and orientations are extra consultant and discriminative. Experimental consequences display that the proposed version can achieve better class usual overall performance than traditional CNNs and broadly-used resource is as  Support Vector Machine (SVM) techniques, particularly in education small-pattern-length situations. There are currently numerous classification methods for the CNN using Hyperspectral Imaging can easily build up an end-to-end model, and there is no need to design complicated hand-crafted features and its performance is competitive to some traditional methods, such as Support Vector Machine.
Interoperability Maturity Models for Consideration When Using School Management Systems in South Africa: A Scoping Review

Presented in: Proceedings of the International Research Conference, IRC 2020, ix, Prague, 3-4 September 2020
Open-Vocabulary Models for Source Code (Extended Abstract)

Statistical language modeling techniques have successfully been applied to large source code corpora, yielding a variety of new software development tools, such as tools for code suggestion, improving readability, and API migration. A major issue with these techniques is that code introduces new vocabulary at a far higher rate than natural language, as new identifier names proliferate. Both large vocabularies and out-of-vocabulary issues severely affect Neural Language Models (NLMs) of source code, degrading their performance and rendering them unable to scale.In this paper, we address this issue by: 1) studying how various modelling choices impact the resulting vocabulary on a large-scale corpus of 13,362 projects; 2) presenting an open vocabulary source code NLM that can scale to such a corpus, 100 times larger than in previous work, and outperforms the state of the art. To our knowledge, this is the largest NLM for code that has been reported.
Mobile learning: el uso de Whatsapp en el aprendizaje del inglés

La universalizacion en el uso de los dispositivos moviles ha posibilitado su aplicacion en diferentes circunstancias, en particular en tiempos de aislamiento social donde los metodos tradicionales de la ensenanza se han visto en desuso. El objetivo de este articulo ha sido demostrar la incidencia del uso de WhatsApp en el proceso de ensenanza-aprendizaje del uso real del ingles, para potenciar el desarrollo de la competencia comunicativa, interactiva e intercultural, desde el contexto sociocultural en que aprenden los estudiantes del 5to curso de la carrera Educacion de Lenguas Extranjeras de la Universidad de Pinar del Rio “Hermanos Saiz Montes de Oca”. Cuba. Se obtienen resultados en el desarrollo de habilidades metacomunicativas y metalinguisticas. Igualmente, facilita la transmision cultural entre los estudiantes al mismo tiempo que los ayuda a sobreponerse a sus miedos referidos al uso de la lengua en la comunicacion social, logra la motivacion en los estudiantes en el aprendizaje de lenguas desde la psicologia del exito y constituye una alternativa en la ensenanza de lengua en la modalidad no presencial a distancia. Sin embargo, la participacion de algunos estudiantes se mantiene baja, capitulo pendiente para futuras investigaciones.Palabras clave: Mobile learning, WhatsApp, ingles real, didactica de lenguas extranjeras. ABSTRACTThe universalization in the use of mobile devices has enabled its application in different circumstances, particularly in times of social isolation where traditional teaching methods have been deprecated. This article aims at the demonstration of the incidence of the use of WhatsApp in the teaching-learning process of the real English use; to enhance the development of communicative, interactive and intercultural competence from the learning sociocultural context of 5th year students from BA Education Major Foreign Languages in the University of Pinar del Rio “Hermanos Saiz Montes de Oca”, Cuba. Results were obtained in the development of metacommunicative and metalinguistic skills. Moreover, it facilitates cultural transmission between students while helping them to overcome their fears toward the use of language in social communication. It achieves motivation in students in language learning from the psychology of success and constitutes an alternative in language teaching in the distance modality. However, the participation of some students remain low, a pending chapter for future research.Keywords: Mobile learning; WhatsApp; real English; foreign language didactic.
Kohonen Network Modeling for Lemma Recognition in Bangka Dialect of Malay

There are numbers of lemmas in the Bangka Dialect of Malay (BDoM) Dictionary. However, tracking the lemmas that have been used before is an impractical work because there are thousands of lemmas that must be traced one by one. This research intends to minimize the search burden of many lemma data using the Kohonen network. By using the Kohonen network, it is sufficient to do training on the sample data provided which is far less than the actual data. The result of this training is then used to obtain the number of lemmas that are successfully identified by the Kohonen network. The result can also be used to propose a lemma so that the status of acceptance of the proposed lemma can be determined. The overall result that is variable is obtained because of the optimization used. From the test result, it is also found that there is no linear relationship between the level of learning rate and the number of names that were successfully identified. Further research that can be done are the inclusion of non-BDoM lemma training data, changing Kohonen network parameters, or using other recognition methods.