Reviewerly/exHarmony huggingface dataset:
Virtualization-Based Security Techniques on Mobile Cloud Computing: Research Gaps and Challenges

The principle constraints of mobile devices are their limited resources such as processing capability, storage space and battery life. While cloud computing offers a vast computing resources services. A new idea emerged by including the cloud computing into mobile devices to augment the capacities of the mobile devices resources such as smartphones, tablet, and other personal digital assistant (PDA) which provides a robust technology called Mobile Cloud Computing (MCC). Although MCC have brought many advantages for the mobile users, it also stills suffer from security and privacy side of data while hosted on virtual machines (VM) on remote cloud’s servers. Currently, the eyes of the security expert’s community turned towards the virtualization-based security technique either on the Cloud or on the mobile devices. The new challenge is to develop secure methods in order to authenticate high sensitive digital content. This paper investigates the main challenges regarding the security and privacy issues in mobile cloud exactly focusing on the virtualization issue layer and give clear strengths and weaknesses of recent relevant virtualization security techniques existing in the literature. Hence, the paper provides perspectives for researchers in order to achieve as a future work.
A PARADIGM SHIFT IN DISTANCE EDUCATION IN RUSSIA TOWARDS OPEN, MASSIVE AND EXPERIENTIAL MODES OF TRAINING

A PARADIGM SHIFT IN DISTANCE EDUCATION IN RUSSIA TOWARDS OPEN, MASSIVE AND EXPERIENTIAL MODES OF TRAINING
Bot use Dimensions for Business Performance: use Perspectives and Acceptance

The aim of the present study is to analyze the use of the artificial intelligence Bot and its relation to business benefits, with emphasis on understanding acceptance and use factors based on clients' perspectives.In order to do so, a case study was carried out in a benchmark company of the private pension sector in Brazil.The Simplified Model of Dimensions of Information Technology Use for Business Benefit and the Unified Theory of Acceptance and Use of Technology (UTAUT2for consumers) were adopted as analysis method.Results pointed out that using Bot as business strategy brings benefits to quality, product and service flexibility, and to innovation, as well as that factors linked to social network use, experience and to the widespread of internet infrastructure in smartphones are responsible for low effort expectations, high performance expectations and for facilitating conditions favorable for Bot use.
Multi-Head Multi-Layer Attention to Deep Language Representations for Grammatical Error Detection

It is known that a deep neural network model pre-trained with large-scale data greatly improves the accuracy of various tasks, especially when there are resource constraints. However, the information needed to solve a given task can vary, and simply using the output of the final layer is not necessarily sufficient. Moreover, to our knowledge, exploiting large language representation models to detect grammatical errors has not yet been studied. In this work, we investigate the effect of utilizing information not only from the final layer but also from intermediate layers of a pre-trained language representation model to detect grammatical errors. We propose a multi-head multi-layer attention model that determines the appropriate layers in Bidirectional Encoder Representation from Transformers (BERT). The proposed method achieved the best scores on three datasets for grammatical error detection tasks, outperforming the current state-of-the-art method by 6.0 points on FCE, 8.2 points on CoNLL14, and 12.2 points on JFLEG in terms of F_0.5. We also demonstrate that by using multi-head multi-layer attention, our model can exploit a broader range of information for each token in a sentence than a model that uses only the final layer's information.
Study of Decoherence in Quantum Computers: A Circuit-Design Perspective

Decoherence of quantum states is a major hurdle towards scalable and reliable quantum computing. Lower decoherence (i.e., higher fidelity) can alleviate the error correction overhead and obviate the need for energy-intensive noise reduction techniques e.g., cryogenic cooling. In this paper, we performed a noise-induced decoherence analysis of single and multi-qubit quantum gates using physics-based simulations. The analysis indicates that (i) decoherence depends on the input state and the gate type. Larger number of $|1\rangle$ states worsen the decoherence; (ii) amplitude damping is more detrimental than phase damping; (iii) shorter depth implementation of a quantum function can achieve lower decoherence. Simulations indicate 20\% improvement in the fidelity of a quantum adder when realized using lower depth topology. The insights developed in this paper can be exploited by the circuit designer to choose the right gates and logic implementation to optimize the system-level fidelity.
A cloud computing separation model based on information flow

Abstract Separation is the primary consideration in cloud computing security. A series of security and safety problems would arise if a separation mechanism is not deployed appropriately, thus affecting the confidence of cloud end-users. In this paper, together with characteristics of cloud computing, the separation issue in cloud computing has been analyzed from the perspective of information flow. The process of information flow in cloud computing systems is formalized to propose corresponding separation rules. These rules have been verified in this paper and it is shown that the rules conform to non-interference security, thus ensuring the security and practicability of the proposed rules.
EDUCATIONAL SOFTWARE FOR STUDENTS FROM KINDERGARTENS AND LOWER PRIMARY SCHOOLS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 7603-7612ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1864Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
The application of Internet broadcast in College English Teaching

This paper expounds the characteristics of English broadcasting from the perspective of "Internet" and its application in college English teaching. Combined with its application in college English classroom, this paper puts forward a teaching mode of combining Internet English broadcasting with college English teaching in order to bring out the potential of the advantages of Internet English broadcasting in college English teaching and improve the effect of English teaching at colleges.
Reducing approximate Longest Common Subsequence to approximate Edit Distance

Given a pair of strings, the problems of computing their Longest Common Subsequence and Edit Distance have been extensively studied for decades. For exact algorithms, LCS and Edit Distance (with character insertions and deletions) are equivalent; the state of the art running time is (almost) quadratic and this is tight under plausible fine-grained complexity assumptions. But for approximation algorithms the picture is different: there is a long line of works with improved approximation factors for Edit Distance, but for LCS (with binary strings) only a trivial $1/2$-approximation was known. In this work we give a reduction from approximate LCS to approximate Edit Distance, yielding the first efficient $(1/2+\epsilon)$-approximation algorithm for LCS for some constant $\epsilon>0$.
INTRODUCTION TO THE METHOD OF PROBLEM BASED LEARNING FOR TEACHING FLUID MECHANICS

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 2577-2584ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0699Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Short Paper: Creating Adversarial Malware Examples using Code Insertion.

There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to adversarial examples allows malicious actors to evade classifiers. Adversarial examples are usually generated by adding small perturbations to the input that are unrecognizable to humans, but the same approach is not effective with malware. In general, these perturbations cause changes in the byte sequences that change the initial functionality or result in un-executable binaries. We present a generative model for executable adversarial malware examples using obfuscation that achieves a high misclassification rate, up to 100% and 98% in white-box and black-box settings respectively, and demonstrates transferability. We further evaluate the effectiveness of the proposed method by reporting insignificant change in the evasion rate of our adversarial examples against popular defense strategies.
Quality as Subjectively Measured Value

The paper identifies the problem of ensuring the reliability of measurement results of quality characteristics as subjective values and their correct application in logical and mathematical models of making decisions. The purpose of this study is to increase the reliability of expert evaluation of individual characteristics of the quality of processes, products, systems. The article describes basic methodological approaches to subjective measurements represented by the classical, operational and representational theories of measurement. The most acceptable for the purposes of ensuring the reliability of the expert evaluation of the single quality characteristics a representative theory, suggesting that the subjective value can be measured only in nominal or ordinal scales was determined. The contradiction is established: the possibility of measuring of single quality characteristics in the ordinal scale does not meet the needs of specialists in the field of quality, whouse subjective measurements to solve problems of analysis and decision-making, requiring the use of logical and mathematical models; in that way estimates should be expressed at least in the interval scale. The article substantiates the best solution of this problem by use of the rating scale which has properties of both ordinal and interval scales. Within the framework of the expert methods of quality measurements development two fundamental elements of the methodology of subjective measurements of subjective values from the standpoint of representative theory are formulated: 1) the rating scale as a modified scale of ranks, 2) the method of organization of the measurement process as a method of alternative assessment of expert preferences. Much attention is given to axiomatic of the rating scale having properties of both ordinal and interval scales. The algorithm of implementation of alternative assessment ' s of expert preferences method which is based on a special two-stage plan of alternative expert survey and statistical criterion of preferences ' stability was suggested. In conjunction, the methodologies of subjective measurements of subjective values ensure the correctness of the rating scale ' s formation and conversion of the values of quality characteristics in the form of ratings in the corresponding values expressed at least in the scale of intervals.
Automated Focal Loss for Image based Object Detection

Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter. Manually tuning this hyperparameter for each training task is highly time-consuming. With automated focal loss we introduce a new loss function which substitutes this hyperparameter by a parameter that is automatically adapted during the training progress and controls the amount of focusing on hard training examples. We show on the COCO benchmark that this leads to an up to 30% faster training convergence. We further introduced a focal regression loss which on the more challenging task of 3D vehicle detection outperforms other loss functions by up to 1.8 AOS and can be used as a value range independent metric for regression.
IMPROVEMENT OF THE LEARNING PROCESS THROUGH THE USE OF A MOBILE-LEARNING METHODOLOGY IN HIGHER EDUCATION

IMPROVEMENT OF THE LEARNING PROCESS THROUGH THE USE OF A MOBILE-LEARNING METHODOLOGY IN HIGHER EDUCATION
The Effectiveness of Nursing Process Implementation with Concept Mapping via Mobile Phone on Nursing Student Self-Efficacy in Critical Care Course

The Effectiveness of Nursing Process Implementation with Concept Mapping via Mobile Phone on Nursing Student Self-Efficacy in Critical Care Course
Applications of Quantum Annealing in Statistics

Quantum computation offers exciting new possibilities for statistics. This paper explores the use of the D-Wave machine, a specialized type of quantum computer, which performs quantum annealing. A general description of quantum annealing through the use of the D-Wave is given, along with technical issues to be encountered. Quantum annealing is used to perform maximum likelihood estimation, generate an experimental design, and perform matrix inversion. Though the results show that quantum computing is still at an early stage which is not yet superior to classical computation, there is promise for quantum computation in the future.
Perfect state transfer on weighted graphs of the Johnson scheme

We characterize quantum perfect state transfer on real-weighted graphs of the Johnson scheme $${\mathcal {J}}(n,k)$$ , which represent spin networks with non-nearest neighbor couplings. Given $${\mathcal {J}}(n,k)=\{A_{1},A_{2},\ldots ,A_{k}\}$$ and $$A(X) = w_0A_0 + \cdots + w_m A_m$$ , we show that X has perfect state transfer at time $$\tau $$ if and only if $$n=2k$$ , $$m\ge 2^{\lfloor {\log _2(k)} \rfloor }$$ , and there are integers $$c_{1},c_{2},\ldots ,c_{m}$$ such that We then characterize perfect state transfer on unweighted graphs of $${\mathcal {J}}(n,k)$$ . In particular, we obtain a simple construction that generates all graphs of $${\mathcal {J}}(n,k)$$ with perfect state transfer at time $$\pi /2$$ .
A novel elementary spatial expanding scheme form on SISR method with modifying Geman&amp;McClure function

Because of the feasible and impressive fallout, the classical Super-Resolution Reconstruction (SRR) is the contemporary algorithm for improving spatial information and reducing noise and SISR (Single-Image Super-Resolution) method, which is form on the classical SRR, is solely developed for improving spatial information. Disastrously, deficiency of the classical SISR method is conceptually computed from three specifications (b, h, k) and the simulating calculation of the optimized specifications for interpolating the better and higher spatial information images with highest PSNR is so burdersome. For figuring out this issue, the Geman&amp;Mcclure function is proposed to replace with the ordinary SISR function because this function is conceptually computed from only one specification (T), contrary to three specifications similar to classical SISR method hence this analytic article focuses to offer a novel elementary spatial expanding scheme form on SISR method with modifying Geman&amp;Mcclure function. Therefore, the fallout of a proposed spatial expanding scheme approximately matches to classical SISR method. From these reason, a novel elementary spatial expanding scheme is easily implemented for real works.
PERSONALITY TRAITS AS PREDICTORS OF INTERCULTURAL COMPETENCE IN RUSSIAN UNIVERSITY AND SCHOOL STUDENTS

PERSONALITY TRAITS AS PREDICTORS OF INTERCULTURAL COMPETENCE IN RUSSIAN UNIVERSITY AND SCHOOL STUDENTS
Bilingual-GAN: A Step Towards Parallel Text Generation

Latent space based GAN methods and attention based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respectively. Leveraging the two domains, we propose an adversarial latent space based model capable of generating parallel sentences in two languages concurrently and translating bidirectionally. The bilingual generation goal is achieved by sampling from the latent space that is shared between both languages. First two denoising autoencoders are trained, with shared encoders and back-translation to enforce a shared latent state between the two languages. The decoder is shared for the two translation directions. Next, a GAN is trained to generate synthetic "code" mimicking the languages' shared latent space. This code is then fed into the decoder to generate text in either language. We perform our experiments on Europarl and Multi30k datasets, on the English-French language pair, and document our performance using both supervised and unsupervised machine translation.
Optimization of the Subject Users Service in Mobile Internet Environment on the Library WeChat Public Service Platform

The article thoroughly analyzes the use of WeChat public service platform in libraries and puts forward strategic ways for the development of users service by optimizing the orientation on individualized, open and interactive, specialized and integrated users service on definite subjects (SUS), and analyzes its opti­mization strategies connected with relationship, vitality and the quality. According to the analysis results, the author concludes that the optimization of SUS in mobile Internet environment should be based on the optimized service model of mobile service terminal platform, so as to realize the deepening of the function of SUS and the diversification of service forms.
PEDAGOGICAL PREPARATION OF FUTURE TEACHERS OF PRACTICAL TEACHING

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 255-264ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0129Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Role Specific Lattice Rescoring for Speaker Role Recognition from Speech Recognition Outputs

The language patterns followed by different speakers who play specific roles in conversational interactions provide valuable cues for the task of Speaker Role Recognition (SRR). Given the speech signal, existing algorithms typically try to find such patterns in the output of an Automatic Speech Recognition (ASR) system. In this work we propose an alternative way of revealing role-specific linguistic characteristics, by making use of role-specific ASR outputs, which are built by suitably rescoring the lattice produced after a first pass of ASR decoding. That way, we avoid pruning the lattice too early, eliminating the potential risk of information loss.
Handwritten signature recognition with Gabor filters and neural network

Due to the necessary of verification systems and identification of persons from Handwritten signatures as a means of identification. This process has become an important requirement to ensure security and prevent fraud and impersonation. As biometric techniques progressed, it was necessary to use computer vision and image processing to increase the efficiency of this verification process. The aim of this work is to integrate the methods of image processing and intelligent techniques such as neural networks as well as statistical methods to build an efficient biometric system for the process of distinguishing signatures and identity verification. Multi-scale and multi-orientation analysis methods were used as a Gabor filter to distinguish the different signatures and their attribution to their true owners to upgrade this process when examining signature images and identifying owners. This work involves converting images to the frequency space using Gabor converter and then extracting the statistical and engineering characteristics of the images to form the matrix of the input to the back-propagation neural networks BPNN. A set of live images were collected as a database of people affiliated with the University of Mosul. The proposed work showed a 88.57% success rate compared with the results of similar work in the same field. The system was built using MATLAB 7.10.0 + (R2016).
Quantum-Assisted Clustering Algorithms for NISQ-Era Devices

In the NISQ-era of quantum computing, we should not expect to see quantum devices that provide an exponential improvement in runtime for practical problems, due to the lack of error correction and small number of qubits available. Nevertheless, these devices should be able to provide other performance improvements, particularly when combined with existing classical machines. In this article, we develop several hybrid quantum-classical clustering algorithms that can be employed as subroutines on small, NISQ-era devices. These new hybrid algorithms require a number of qubits that is at most logarithmic in the size of the data, provide performance improvement and/or runtime improvement over their classical counterparts, and do not require a black-box oracle. Consequently, we are able to provide a promising near-term application of NISQ-era devices.
A parallel clustering algorithm for logs data based on Hadoop platform

Log analysis is an important method to reflect the running status and user behavior of the network system, and is also an important way to ensure network security. In view of the fact that the storage or calculation of log data by a single host can not meet the requirements of large-scale data analysis, this paper proposes a clustering method of big data based on Map/Reduce distributed computing framework for Web logs. The experiments are taken on the Hadoop platform. The relations and rules that exist in the logs are examined and analyzed to obtain the potential information. This method can enable efficient storage, management, and mining analysis for the large-scale Web logs.
Learning Disentangled Representation in Latent Stochastic Models: A Case Study with Image Captioning

Multimodal tasks require learning joint representation across modalities. In this paper, we present an approach to employ latent stochastic models for a multimodal task image captioning. Encoder Decoder models with stochastic latent variables are often faced with optimization issues such as latent collapse preventing them from realizing their full potential of rich representation learning and disentanglement. We present an approach to train such models by incorporating joint continuous and discrete representation in the prior distribution. We evaluate the performance of proposed approach on a multitude of metrics against vanilla latent stochastic models. We also perform a qualitative assessment and observe that the proposed approach indeed has the potential to learn composite information and explain novel combinations not seen in the training data.
MATHEDU DIGITAL – PROJECT OVERVIEW AND REFLECTION ON SPECIAL ASPECTS OF DIGITIZATION IN MATHEMATICS TEACHER EDUCATION

MATHEDU DIGITAL – PROJECT OVERVIEW AND REFLECTION ON SPECIAL ASPECTS OF DIGITIZATION IN MATHEMATICS TEACHER EDUCATION