Reviewerly/exHarmony huggingface dataset:
Distributed Scores and Audio on Mobile Devices in the Music for a Multidisciplinary Performance

In an attempt to uncover the strengths and limitations of web technologies for sound and music notation applications, driven by aesthetic goals and prompted by the lack of logistic means, the author has developed a system for animated scores and sound diffusion using browser-enabled mobile devices, controlled by a host computer running Max and a web server. Ease of deployment was seen as a desirable feature in comparison to native application computer-based systems – such as Comprovisador, a system which has lent many features to the one proposed herein. Weaknesses were identified motivating the design of mitigation and adaptation strategies at the technical and the compositional levels, respectively. The creation of music for a multidisciplinary performance entitled GarB’urlesco has served as a case study to assess the effectiveness of those strategies. The present text is an extended version of a paper presented at CMMR 2019, in Marseille.
An Emerging Solution for Detection of Phishing Attacks

In this era of computer age, as more and more people use internet to carry out their day to day work so as hackers performs various security attacks on web browsers and servers to steal user’s vital data. Now Electronic mail (E-mail) is used by everyone including organizations, agency and becoming official communication for the society as a whole in day to day basis. Even though a lot of modern techniques, tools and prevention methods are being developed to secure the users vital information but still they are prone to security attacks by the fraudsters. Phishing is one such attack and its detection with high accuracy is one of the prominent research issues in the area of cyber security. Phisher fraudulently acquire confidential information like user-id, passwords, visa card and master card details through various social engineering methods. Mostly blacklist based methodology is used for detection of phishing attacks but this method has a limitation that it cannot be used for detection of white listed phishing. This chapter aims to use machine learning algorithms to classify between phishing E-mails and genuine E-mails and helps the user in detecting attacks. The architectural model proposed in this chapter is to identify phishing and use J48 decision tree classifier to classify the fake E-mail from real E-mail. The algorithm presented here goes through several stages to identify phishing attack and helps the user in a great way to protect their vital information.
The fractal of insulator leakage current with the tunable Q-factor wavelet transform

In the harsh climate and the surrounding environment under the influence, high-voltage insulators are likely to be seriously polluted, threatening the safety of the power system and the normal working life of residents. In this paper, by extracting relevant characteristics to realize insulator flashover monitoring, the tunable Q-factor wavelet (TQWT) and fractal dimension are studied, so as to obtain the theoretical knowledge of tunable Q-factor wavelet fractal dimension. The conclusion is obtained through experiments: Compared with the box dimension, tunable Q-factor wavelet can describe the leakage current changing rule more accurately in the insulator pollution flasticity process, and it is also more suitable as an effective feature quantity for pattern recognition of insulator flashover development.
A Novel Softmax Regression Enhancement for Handwritten Digits Recognition using Tensor Flow Library

Background and Aim: Handwritten Digit Recognition has a wide variety of applications in postal mail order, phone records search, automatic car number plate recognition, and in the medical sector that observed how Machine Learning makes the daily tasks simpler and more efficient. This paper aims to improve the classification accuracy of existing handwritten digit systems, thus improve their efficiency. Methodology: The proposed system consists of an enhanced decision function by adding a “Bias Probability” Function. The function adds negative weights to the output classes (0-9) that have a high positive bias and add a positive weight to the output classes that have a high negative bias to neutralize the effect of this high negative bias. Therefore, the Bayesian Classifier function has been enhanced thereby improving the accuracy of classification, which will further improve the performance of the multiclass probability categorization. Result: An increase of 5.6% was observed in the overall accuracy of handwritten digit classification using the Modified National Institute of Standards and Technology (MNIST) Dataset. Conclusion: From the results, it is clear that the proposed system, enhances the main decision function to further improve the accuracy with no significant increase in the processing time.
WITHDRAWN: Hybrid algorithm for performance improvement in Cloud load balancing

Cloud computing has cemented itself as a vital cog in technical world. Cloud computing is beneficial to all industries from manufacturing to healthcare. Cloud computing revolves around virtualization. All through the latest couple of years, conveyed figuring has emerged as quite possibly the most remarkable principles in the IT business and has pulled in noteworthy thought from both insightful world and industry. A bunch of cloud-based virtual machines measure all client demands. Burden adjusting is a method of spreading loads across different bodies, for example, CPUs, drives, workers or different PCs. The critical point of burden adjusting is to accomplish a vastly improved utilization of energy. This article has a half breed load offsetting calculation with the best round, push and equivalents proliferating qualities of the cooperative effort.
Identification of Misleading Location Information in Compiler Diagnoses

The location of compilation errors are usually reported by compilers to facilitate quick fixing of such compiler errors. However, sometimes such location information could be incorrect or misleading, which significantly reduces the chance of quick fixing. To this end, in this paper, we propose an automated approach, called iMiLi, to identify misleading location information in compiler diagnoses. We generate potentially illegal programs (called mutants) by mutating legal programs, and compile such mutants. If the compiler generates error diagnoses on a mutant, we extract the location information from the resulting diagnoses. The location information is suspicious if it does not point to the source code where the associated mutation is conducted. Then we propose heuristics for each kind of mutation operators to exclude such suspicious but correct location information. We evaluate the proposed approach on a state-of-the-practice compiler (i.e., Eclipse Compiler for Java, known as ECJ). iMiLi successfully identifies seven categories of incorrect/misleading location information in diagnoses of ECJ.
Communicative Component of Future Teachers’ Readiness for Organizing Online Communication

The research objectives are as follows: to examine the communicative component of future teachers’ readiness for organizing online communication and to identify pedagogical conditions of organizing students’ activity to acquire this competence. The article analyses the structure of the communicative component of future teachers’ readiness for organizing online communication, describes the methodology to develop this type of readiness, considers the possibility of organizing academic disciplines into blocks taking into account their potential for developing students’ communicative competence and network communicative competence. The author identifies pedagogical conditions for organizing students’ activity that involves educational material preparation, projection of communicative situations, choice of educational technologies, evaluating the pedagogical potential of electronic resources, planning on the stages of communicative activity in electronic informational-educational environment. Scientific originality of the paper lies in the fact that, contrary to previous studies focused on describing the system of future teachers’ training under the conditions of contact interaction of educational process participants, the article identifies the structure of the communicative component of future teachers’ readiness for organizing online communication, reveals pedagogical conditions that should be taken into account when organizing students’ activity to acquire communicative competences. The conducted research allows concluding that the communicative component of future teachers’ readiness for organizing online communication is based on the network communicative competence formed in the process of planning and accompanying students’ activity, taking into account pedagogical conditions.
Potential of the higher mathematics course in digital education

Rapid changes taking place at present in the society determine the vector of education development, form requirements to an educated person. The connectivism theory, disclosing the features of education environment under conditions of education digitization, allows to outline the learning strategy in individual subjects. The connectivism education is a complex process including plunging into information environment, maximum use of accumulated knowledge, skills to determine timeliness of the acquired knowledge and its relevance under modern conditions, various trends of communication among participants in learning, ability to detect links among various areas of knowledge, theories, concepts. Objective of the research is to substantiate opportunities of application of connectivism education environment during studying mathematics in higher school, to disclose potential of the higher mathematics course and to confirm experimentally the efficiency of the proposed learning strategy. Methods used in the research were as follows: analysis, statistic methods, generalization, systematization, the method of rising from the abstract to the concrete upon consideration of functionality of digital products, system approach, experimental verification of research results. Results and novelty of the research implied the following: substantiation and development of principles of connectivism learning theory, development of the strategy for construction and implementation of educational programs of higher mathematics course, experimental verification of the dependence between the level of mastering the higher mathematics course and the level of use of digital technologies. The use of the connectivism learning theory allowed to outline the approaches to expansion of content of the higher mathematics course. The main features of the presented course are plunging of students into information environment by means of assignments aimed at maximum use of programs for symbolic mathematics. Studying of certain sections of the higher mathematics course, in particular, analytic geometry and linear algebra, is exemplified based on the developed learning strategy.
IoT Management Solution: A Case Study of Fill Level Monitoring for Gravel Silos

Abstract In order to make ubiquitous system, technologist uses different type of equipment in perception layer, for the environment sensing and sensory data processing. This equipment’s are sensors, data processing and sending devices. Sensors sense the environment and create the data accordingly. Sensor generated data need to be processed with some processing device and can be send over the network. This research paper introduced the basic application based on IoT. The application includes Ultrasonic distance sensor and NodeMCU for obtaining the fill level of the gravel silos. We are publishing our data over the web using Zerynth studio portal and showing the analysis graph of application near real time using the Internet of Things (IoT) concept. Paper also describes server configuration on the NodeMCU and real-time response to web browser over the WLAN.
Concepts of methodology for digitalization of additional education of children and youth

The preconditions for the research are based on sustained trends of the influence of development rates of digital economy, information space in Belarus on dynamics of digital transformation of additional education of children and youth (AECY). Analysis of AECY digital transformation revealed specific properties of didactic singularity of information education environment, network clustering of resources, dynamics of media environment contour, e-technologization of didactics. The established preconditions of stability of AECY digitization as a constant of transfer to regulations predetermined the aim of this work: development, substantiation of methodological concepts of AECY digitization. The research methods included theoretical and empirical analysis, expert appraisals, simulation, forecasting. The results are comprised of development of a set of organizational and network, environmental, instrumental, technological, institutional concepts of scientific methodology of AECY digitization. The obtained results were verified in the project titled “Implementation of Republican methodological cluster as a resource of development of additional education of children and youth” (2017-2020) on the premises of National Center of Artistic Activity of Children and Youth (Minsk) in accordance with the Program of innovations of the Ministry of Education, Belarus. The novelty of the results is in introduction of the following categories into scientific usage of pedagogical science: didactic singularity of information and education environment, structure of media environment contour. The novelty of the results is reflected in authentic intelligent product of scientific and practical significance in the context of Belarus National Strategy of Sustainable Social and Economic Development–2030.
Implementation of a visual raise hand function in PowerPoint

Objective: Replacing face-to-face lessons by remote teaching due to COVID-19 led to a markedly reduced interaction between students and lecturers. In our opinion, one of the main reasons for this is the raise hand function of the respective web conference systems, which (independent of the system used) results in an unobtrusive that can easily be missed by the lecturer. Given the necessary focus on one's own presentation, questions can therefore only be perceived with a considerable time delay and can only be integrated into the lessons to a limited extent. Thus, the idea arose to display question requests of the auditorium by a clear visual in PowerPoint® itself. Methodology: With Visual Basic for Applications (VBA), Microsoft PowerPoint® holds an integrated programming language that extends its functionality. Accordingly, VBA was used to program a routine running in the background of the presentation, which periodically retrieves the contents of a web-based signal file in a cycle of a few seconds. The content of this file, in turn, can be modified by the students by calling up an URL (i.e. from any Internet-capable device) - this results in a (customizable) visual in PowerPoint® that is temporarily visible and does not further interfere with the presentation. Conclusion: With the concept presented here, a raise hand function was realized in PowerPoint®, which manifests itself as a clear visual independent of the web conferencing system used. This enables the lecturers to respond instantly to questions from the audience during live transmission of lectures.
Parameters selection and optimization in profiled side channel analysis based on convolutional neural network

Profiled side channel analysis is considered as the most powerful side channel analysis by obtaining a copy of the attacking targe. With the population of deep learning technology in side channel analysis, Benadjila. R et al. proposed a profiled side attacks strategy based on convolutional neural network for AES with first-order mask countermeasure. However, this paper only implements training analysis while keeping the number of convolutional layers in the convolution module consistent and without trying to use common convolution kernel sizes. This paper conducts parameter selection experiments on the number of convolutional layers under the convolution module in the convolutional neural network and the size of the commonly used convolution kernel, and proposes a new optimized network model.As a result, this model can find out the correct key within 54 traces, which is better than the 180 traces record of Benadjila. R et al.
Using Cucumber Plants Leaves A Cover To Hiding Encryption Text

Abstract The article presented a way to increase the efficiency of hiding information by relying on the leaves of the cucumber plant and using it as a cover to hide the information after applying a set of digital image processing techniques to this image. As for the text to be hidden, the text is initially encrypted based on an innovative cryptographic algorithm, three criteria were used to measure The efficiency of the algorithm is psnr, mse, rmse.
Context, Structure and Syntax-aware RST Discourse Parsing

This paper describes the architecture of a shift-reduce parsing framework that (1) leverages the representational powers of BERT and Hierarchical Attention Networks (HANs) and (2) jointly learns syntactic features to perform discourse parsing. Through this model, one can obtain context, structure and syntax-aware text representations that help carry out accurate parsing. Through experiments performed on corpora in five different languages, it is empirically shown that this model's performance rivals that of state-of-the-art parsers. Further, via qualitative analysis of predictions obtained, it is shown that HANs help extract important words and segments from large texts that are relevant for relation classification.
Special Arcs in Projective Plane Over Galois Field of Order Sixteen

Abstract The main objective of this paper is to classify the special sets in the projective planes PG(2, t ) with the value of t = 16, and we found the arcs of the third degree ( k , 3) arcs in P G (2, t ), and the parameters of cods n, k, d were calculated and whether they are complete or incomplete, and the corresponding codes were formed.
AGENCY IN COMPOSITIONAL WORKFLOW: HOW YOU WRITE AFFECTS WHAT YOU WRITE

Abstract The organising of tools, whether real or virtual, is an essential part of composition that is often overlooked. This article aims to generate a discussion about how a composer's working environment can permeate the compositional process and contribute to or inform their ideations. Central to this understanding, in terms of agency, has been to consider workflow as a subset of Actor–Network Theory. Here, a musical composition is viewed as a multiplicity of relationships between workflow and ideations, and suggests an expanded practice of musical creation that explores the various agencies that shape a composition. Nevertheless, this article is not intended as a comprehensive account, but only, as Bruno Latour would put it, ‘to add in a messy way to a messy account of a messy world’.
On The Deployment of Tweak-in-Plaintext Protection Against Differential Fault Analysis

In an article from HOST 2018, which appears in extended form in the Cryptology ePrint Archive, Baksi, Bhasin, Breier, Khairallah, and Peyrin proposed the tweak-in-plaintext method to protect block ciphers against a differential fault analysis (DFA). We argue that this method lacks existential motivation as neither of its two envisioned use cases, i.e., the electronic codebook (ECB) and the cipher block chaining (CBC) modes of operation, is competitive. Furthermore, in a variant of the method where nonces are generated using a linear-feedback shift register (LFSR), several security problems have not been anticipated for. Finally, we analyze the security level against a brute-force DFA more rigorously than in the original work.
Tracking of High Maneuvering Targets in Low Data Rate Measurement

Under the condition of low data-rate measurement, it tends to mistrack or lose the target of high maneuvering targets by using the traditional single model tracking algorithm. This paper proposes a tracking method using Interacting Multiple Model (IMM) algorithm combined with Probabilistic Data Association (PDA) algorithm to solve the problem. Several measurement filtering thresholds are tuned to improve the accuracy of track association. Experiments show that this method achieves good tracking results.
A Prototype System for Recommending Images Supplementing Web News for NIE in Elementary Schools in Japan

Newspaper in Education (NIE) is an educational approach that uses newspapers as teaching materials. It is being implemented at educational institutions, especially elementary schools. However, contents of newspaper articles are difficult for elementary school students, and there are not many articles with images such as figures and photographs supplementing their contents. Therefore, teachers' loads have been increasing, because they have to prepare teaching materials supplementing the contents of news articles. The purpose of our study is to construct a system to recommend images that supplement the contents of Web news articles for elementary school teachers. This paper proposes a system consisting of four modules: query generation, image classification, rearrangement of the order of images, and display of images. Finally, we evaluated the validity of the supplementary images obtained using the prototype system.
Movies as an Aid to Teach Principles of Cybersecurity and Cybercrime in Higher Education

A pilot effort to raise students’awareness on the human factors of cybersecurityand cybercrime is presented. One day, instead oflectures, a commercial film was projected. Thestudents were able to identify most of the cyberattacksrelated to the technical backgroundgiven in the course, while they missed some of thefrauds committed by the actors, related to thepsycho-social and legal dimensions, which werenot covered in the syllabus. The students hadthe opportunity to acquire some insight aboutthe latter while enjoying the movie. The wholeexperiment was evaluated positively. Thus, it becameclear to the students that cybersecurity hasalso a psycho-social dimension which should notbe underestimated.
Cross-Modal Sound-Image Retrieval Based on Deep Collaborative Hashing

In recent years, with the development of deep learning, cross modal sound image retrieval has made some progress. However, there are still some bottlenecks in the existing cross-modal audio and image retrieval methods: 1. How to establish an effective correlation between voice and image to improve the retrieval accuracy; 2. How to reduce the storage of large-scale cross-modal data and accelerate the retrieval speed. In order to solve the above problems, this paper proposes a new deep collaborative hashing cross modal audio image retrieval method (ssch), which can generate hash codes with low storage capacity and fast retrieval. In particular, ssch can use the similarity of deep features to establish the semantic relationship between speech and image, and ssch method can fuse image feature vector and audio feature vector to learn hash code cooperatively. In addition, for hash code learning, our method tries to maintain the semantic similarity of binary code and reduce the information loss generated by binary code. Experimental results show that ssch algorithm has better retrieval performance than other advanced cross modal retrieval methods.
Classification of Computer Graphic Images Using Deep Learning

Computer graphics is extensively used in the applications such as video gaming, cinemas, advertisements, training, 3D modeling and many more. To classify images used in these fields, pre-trained convolutional neural network models such as VGG16 and VGG19 are adopted through transfer learning and also performance of these models are evaluated using stochastic gradient descent (SGD) with momentum and adaptive moment (AdaM) optimizers. Experimental results show that VGG19 outperforms VGG16 by obtaining an average classification accuracy of 97%.