Reviewerly/exHarmony huggingface dataset:
Assessment of the Quality of Subject Training of Bachelors and Masters in the Changing of the Socio-Economic Conditions

The problem of the improvement of the subject training of university students in the context of the transition of the Russian higher school to a multi-level structure (bachelor’s degree program, master’s degree program, and postgraduate training program) is important because of the new socio-cultural conditions that determine the need for rapid adaptation of graduates, as the development of the industry is faster than the full cycle of training for it. The authors identifi ed the following modern trends in the development of society and education: high rate of world variability, informatization of living environment, and globalization processes. These trends in the development of society in the context of educational practice determine the requirements for the subject training of students. The problem of assessing the quality of subject training is solved by the authors on the example of mathematical training of bachelors and masters in the discipline of economics. Taking into account the competence-based format of modern education, a scientifi c approach to the assessment of learning outcomes at each stage of mathematical training (subject, interdisciplinary, and professional levels) from the perspective of an open education system has been proposed. The competencies cluster of the mathematical component of professional training, considering the requirements of employers has been developed. Monitoring of the creative pragmatic skills of students which provide building of adaptive capacities has been conducted. The results of the pedagogical measurements show the positive dynamics of the quality of subject training in the changing socio-economic conditions.
Ensuring Readability of Electronic Records Based on Virtualization Technology in Cloud Storage

With the rapid development of E-commerce and E-government, there are so many electronic records have been produced. The increasing number of electronic records brings about storage difficulties, the traditional electronic records center is difficult to cope with the current fast growth requirements of electronic records storage and management. Therefore, it is imperative to use cloud storage technology to build electronic record centers. However, electronic records also have weaknesses in the cloud storage environment, and one of them is that once electronic record owners or managers lose physical control of them, the electronic records are more likely to be tampered with and destroyed. So, the paper builds a reliable electronic records preservation system based on coding theory. It can effectively guarantee the reliability of record storage when the electronic record is damaged, and the original electronic record can be restored by redundant coding, thus ensuring the reliable storage of electronic records.
Multi-Task Learning for

Hikaru Omori, Mamoru Komachi. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Deep Visual Sharing With Colorblind

Visual sharing between color vision deficiency (CVD) and normal-vision audiences is challenging due to the need of simultaneous satisfaction of multiple binocular visual requirements, in order to offer a color-distinguishable and binocularly fusible visual experience to CVD audiences, without hurting the visual experience of the normal-vision audiences. Existing methods enable the feasibility of visual sharing but are not quite suitable for practical usage due to their instable and time-consuming optimization nature. In this paper, we propose the first deep-learning based solution for solving this visual sharing problem. Our method outperforms the existing solution in terms of all evaluation metrics. To achieve this, we propose to formulate this binocular image generation problem as a generation problem of a difference image , which can effectively enforce the binocular constraints. We also propose to retain only high-quality training data and enrich the variety of training data via intentionally synthesizing various confusing color combinations. With these, we train up a high-quality neural network model. Through multiple quantitative measurements and user study, we demonstrate this learning-based approach can significantly improve the quality of generated results with fast performance.
Unsupervised anomaly detection via generative adversarial networks

Unsupervised anomaly detection is a fundamental problem in various research areas and application domains, namely the discrimination of abnormal samples from normal samples where training data are only composed of one class (normal) while testing data contains both among which the majority are normal samples. However, previous works can not effectively fit the distribution of high dimensional data and suffers from low AUC scores which measures the classification performance of imbalanced data. To solve these problems, we propose an unsupervised anomaly detection model based on GAN, i.e., UAD-GAN. Specifically, we adopt transfer learning to extract visual features with pre-trained Inception-v3 model and use the discriminator to detect anomalies. UAD-GAN can fit the data distribution and detect anomalies efficiently. Extensive experiments show that UAD-GAN achieves state-of-the-art performance compared to other approaches.
SpeechLens: A Visual Analytics Approach for Exploring Speech Strategies with Textural and Acoustic Features

Public speaking is an effective way to move, persuade, and inspire. While many guidelines have been presented to teach public speaking skills, they are often based on anecdotal evidence and not customizable. Exploring high-quality speeches such as TED Talks could provide insights to eliminate limitations in existing guidelines. This study aims to explore and identify narration strategies by conducting visual analysis into the textural and acoustic information in public speeches. We present SpeechLens, an interactive visual analytics system to explore large-scale speech dataset with multiple level-of-details. SpeechLens features a novel focus+context design to enable intuitive and smooth analysis. Case studies indicate the effectiveness and usefulness of our approach.
Supporting Internet-Based Location for Location-Based Access Control in Enterprise Cloud Storage Solution

The emergence of the Internet allows the enterprise to implement telework policy in order for the employee to work and access company file anytime, anywhere. But it raises the challenge for the enterprise to enforce physical access control on enterprise’s files to the employee outside the enterprise network. One of the solutions for the enterprise to enforce physical access control of its files is implementing location-based access control (LBAC) model to allow the employee to access a file at the pre-determined location. We propose an extension of the LBAC model to include Internet-based location as an input for location constraint used for the access control decision and authorization where Internet-connected device is mapped into a physical location using the IP geolocation method. Our approach could be used as an alternative user’s location determination and location verification methods, especially for mobile devices with minimum self-geolocation capability. We implement our proposal into proof-of-concept enterprise cloud storage solution called CloudRAID for Business (CfB) by combining Internet-based geolocation service, delay-based measurement technique, and open source information. Our evaluation’s result shows that our implementation is able to grant user access control of the system once the user’s location is in the pre-determined location.
From Models to Software Through Automatic Transformations: An AADL to ROS End-to-End Toolchain

Modelling complex systems is a common practice and de facto standard across most application domains in engineering. Although it would seem unreasonable - and quite impractical - to build a structure as complex as a bridge without a reference blueprint detailing how to arrange all of its building blocks, in Software Development, and, particularly in the context of Robotics, examples adhering to rigorous modelling routines are still relatively rare to find. Yet, models help understanding complex problems while pinpointing their potential solutions, through abstraction. Further, models aid communication, i.e., the unambiguous exchange of reasoning processes across the involved agents. The complexity of Robotic Software Systems suggests that a widespread application of modelling techniques, from the very initial implementation stages, would (i) ease the definition, engineering and debugging of the related sub-features significantly, and (ii) guide collaborative efforts towards a common standard. To this aim, we presented a toolchain conceived for parsing an input AADL model into a compilable code suite. Keeping the model building and the linkage of the robot application with the ROS environment in the developer's hands, this framework delegates all the remaining tasks to an automated code generator, producing a fully-functioning ROS packages (i.e., already configured and ready for compiling) as output. We first presented the discussed framework, highlighted its related advantages - when compared to the only other similar approach found in the literature -, and used it as an exemplary use case, to prompt broader discussions on the benefits of model-based software development in Robotics.
RT distance and weight distributions of Type 1 constacyclic codes of length 4ps over Fpm[u]

For any odd prime $p$ such that $p^m \equiv 1 \pmod{4}$, the class of $\Lambda$-constacyclic codes of length $4p^s$ over the finite commutative chain ring ${\cal R}_a=\frac{\mathbb F_{p^m}[u]}{\left\langle u^a \right\rangle}=\mathbb F_{p^m} + u \mathbb F_{p^m}+ \dots + u^{a-1}\mathbb F_{p^m}$, for all units $\Lambda$ of $\mathcal R_a$ that have the form $\Lambda=\Lambda_0+u\Lambda_1+\dots+u^{a-1}\Lambda_{a-1}$, where $\Lambda_0, \Lambda_1, \dots, \Lambda_{a-1} \in \mathbb F_{p^m}$, $\Lambda_0 \,{\not=}\, 0, \, \Lambda_1 \,{\not=}\, 0$, is investigated. If the unit $\Lambda$ is a square, each $\Lambda$-constacyclic code of length $4p^s$ is expressed as a direct sum of a $-\lambda$-constacyclic code and a $\lambda$-constacyclic code of length $2p^s$. In the main case that the unit $\Lambda$ is not a square, we show that any nonzero polynomial of degree $
Fully Contextualized Biomedical NER

Recently, neural network architectures have outperformed traditional methods in biomedical named entity recognition. Borrowed from innovations in general text NER, these models fail to address two important problems of polysemy and usage of acronyms across biomedical text. We hypothesize that using a fully-contextualized model that uses contextualized representations along with context dependent transition scores in CRF can alleviate this issue and help further boost the tagger’s performance. Our experiments with this architecture have shown to improve state-of-the-art F1 score on 3 widely used biomedical corpora for NER. We also perform analysis to understand the specific cases where our contextualized model is superior to a strong baseline.
Graph Element Networks: adaptive, structured computation and memory

We explore the use of graph neural networks (GNNs) to model spatial processes in which there is no a priori graphical structure. Similar to finite element analysis, we assign nodes of a GNN to spatial locations and use a computational process defined on the graph to model the relationship between an initial function defined over a space and a resulting function in the same space. We use GNNs as a computational substrate, and show that the locations of the nodes in space as well as their connectivity can be optimized to focus on the most complex parts of the space. Moreover, this representational strategy allows the learned input-output relationship to generalize over the size of the underlying space and run the same model at different levels of precision, trading computation for accuracy. We demonstrate this method on a traditional PDE problem, a physical prediction problem from robotics, and learning to predict scene images from novel viewpoints.
Extraction of Speech Pitch and Formant Frequencies using Discrete Wavelet Transform

Pitch and Formant frequencies are important features in speech processing applications. The period of the vocal cord's output for vowels is known as the pitch or the fundamental frequency, and formant frequencies are essentially resonance frequencies of the vocal tract. These features vary among different persons and even words, but they are within a certain frequency range. In practice, just the first three formants are enough for the most of speech processing. Feature extraction and classification are the main components of each speech recognition system. In this article, two wavelet based approaches are proposed to extract the mentioned features with help of the filter bank idea. By comparing the results of the presented feature extraction methods on several speech signals, it was found out that the wavelet transform has a good accuracy compared to the cepstrum method and it has no sensitivity to noise. In addition, several fuzzy based classification techniques for speech processing are reviewed.
Spatial Audio Coding without Recourse to Background Signal Compression

The MPEG-H 3D audio standard applies singular value decomposition (SVD) to the input higher order ambisonics data, then encodes each predominant (foreground) sound component independently using a standard core audio codec. The residual (background) signal is encoded in the ambisonic domain. This paper is motivated by the observations: i) separate coding of SVD components ignores spatial inter channel masking effects; ii) compression in both SVD and ambisonic domains is difficult to perceptually optimize; iii) Only few predominant components are encoded due to the prohibitive side information cost of specifying SVD basis vectors. The proposed coding architecture overcomes the first two concerns by performing all compression in the SVD domain with a masking threshold that is calculated jointly for all encoded components, thereby accounting for cross-component masking. The third shortcoming is circumvented by a novel method for extending a given set of SVD basis vectors at no side information cost, by computing (at both encoder and decoder) basis vectors to span the null space of the transmitted basis vectors. Experimental results provide evidence for substantial objective and subjective gains.
Evaluation of Feature Selection for Anomaly Detection in Automotive E/E Architectures

As we move towards higher levels of automation in autonomous driving, we see an increase in functionality that either assists or takes over in both normal and emergency scenarios. These new functionalities can be switched off by the user for personalisation. We aim to recognise mistimed and/or unintended deactivation of vehicle functions, in particular, driver assistance functions (ADAS), at run-time. This will be done in addition to already applied methods at design time. Upon recognition of the occurrence, we propose to inform the user and the original equipment manufacturer (OEM) in order to improve both the future and the current system behaviour and to support development processes. Based on eight customer datasets, we evaluated our approach on a total of 17 state-of-the-art ADAS functions per participant, yielding to a total of 136 runs. We observed that during 24 among them, the user de-activated the functions at least once for more than a few seconds. For 13 of these 24 runs, we were able to detect and flag possible non-nominal behaviour over the full trace.
Arabic Discourse Analysis: A Naïve Algorithm for Defective Pronunciation Correction

This manuscript summarizes a novel study to detect and correct voice anomalies contained in Arabic discourses. These objectives are attained by following some fundamental steps. The first consists in classifying the Arabic produced healthy or pathological vocal signals. Second, the identification of problematic phonemes takes place. The proposition of algorithm allowing the correction of defective pronunciations presents the aim of the latter task. We are satisfied with the obtained results. In deed, the elaborated algorithm has attained a correction performance of 90% based on 52 Arabic voice sequences covering male and female, healthy and pathological speeches and clustering several areas. Consequently, researchers of topics related to speech processing can benefit from our proposition in the conception and development of their systems.
The Impact of Stalling on the Perceptual Quality of HTTP-based Omnidirectional Video Streaming

Recent years have witnessed increasing advances in immersive media technologies. Omnidirectional videos, which record a 360-degree spherical visual scene, have gained popularity at an accelerated speed. However, streaming this visual media is extremely bandwidth demanding when compared to traditional 2D videos, thus challenging current video streaming strategies and network conditions. Understanding how human visual system perceives the omnidirectional video becomes an urgent research topic. In this work, we focus on investigating the impact of stalling on the perceptual quality of omnidirectional video streaming. A systematic experiment with various phases was conducted to obtain the quality perception results towards omnidirectional video stalling. Experimental results showed that human visual system views the omnidirectional video in a different manner if compared with traditional 2D videos. This difference further triggers an alleviation in the annoyance level of stalling. A questionnaire focused on revealing the underlying reason regarding this difference was further designed. Findings of this work provide insights into the visual quality perception of stalling events during omnidirectional video playback. Conclusions of this paper can be used as guidelines for the design of video streaming protocols as well as network dimensioning.
Banklaves: Concept for a Trustworthy Decentralized Payment Service for Bitcoin

We explore challenges of and present a concept for a decentralized payment service which is based on trusted execution environments. The system guarantees that users can always cash out their funds without depending on the cooperation of other network members, hence minimizing the trust required in other network members. We present an overview of the system, motivate key components for a secure architecture and provide a communication protocol. We prove that the payment service users can cash out their funds at any time without any dependence on other network members.
Fuzziy Digital Filter for Robotic Manipulator Operation

In this article it is described the operation principle of a robotic manipulator ARMino device and the connection diagram of its electrical components. The device includes: An Arduino Mega control board, four servos, four potentiometers, a prototyping board, a computer. Turning the shafts on the potentiometer adjusts the position of the servo spindles. When the voltage on the potentiometer’s pin changes, the voltage at the analog inputs of the microcontroller changes. Then, in the microcontroller, the voltage is scaled to the value of the servo rotation angle. After that, the joints of the robotic manipulator are rotated. During the operation of the ARMino robotiс arm, a contact bounce problem appeared, significantly reducing the accuracy of positioning and the smooth movement of the ARMino joints. To solve this problem, a digital filter was developed. This article describes the digital filter working algorithm, which consists of four steps. One of the steps consists on finding the digital filter coefficients, which regulate the signal voltage level transmitted to the servo motors, and its transition process time which forms the signal edge. The main problem developing a digital filter is that the standard procedure of finding the digital filter coefficients, the coefficients are given by a recommended range of values, which complicates choosing from this range, a single value and transmitting it to the servos. To solve this problem, a fuzzy digital filter was developed, the algorithm of which consists of six steps. The first step determines the input variables degree of truth. The second step is to calculate the degrees of truth of the fuzzy rules preconditions. The third step is to calculate the degrees of truth of the fuzzy rules conclusions by using the process of finding the maximum values. The fourth step is the defuzzification stage in which a precise value of the fuzzy digital filter coefficient is calculated. The fifth step is the output voltage transmitted to the servos. In the sixth step, the output voltage in the microcontroller is converted to the angle value and the servo is given the command to rotate. This article presents numerical simulation of the fuzzy digital filter algorithm, using as an example the servo responsible of the ARMino base rotation. Experimental studies on the functioning of the fuzzy digital filter have been carried out, confirming the expediency of its use. The graphics of the transition process of the robotic manipulator base movement without and with the use of a digital filter are given.
Robust Graph Signal Sampling

This paper considers the graph signal sampling problem when some of the selected samples are lost or unavailable due to sensor failures or adversarial erasures. We formulate a robust graph signal sampling problem where only a subset of selected samples are received, and the goal is to maximize the worst-case performance. We propose a novel greedy robust sample selection algorithm and study its performance guarantees. Our numerical results demonstrate the performance improvement of the proposed algorithm over the existing schemes.
Free Form Evolution for Angry Birds Level Generation

This paper presents an original approach for building structures that are stable under gravity for the physics-based puzzle game Angry Birds, with the ultimate objective of creating levels with the minimum number of constraints. This approach consists of a search-based procedural level generation method that uses evolutionary algorithms. In order to evaluate the stability of the levels, they are executed in an adaptation of an open source version of the game called Science Birds. In the same way, an open source evolutionary computation framework has been implemented to fit the requirements of the problem. The main challenge has been to design a fitness function that, first, avoids if possible the actual execution of the simulator, which is time consuming, and, then, to take into account the different ways in which a structure is not structurally sound and consider them in different ways to provide a smooth landscape that eventually achieves that soundness. Different representations and operators have been considered and studied. In order to test the method four experiments have been carried out, obtaining a variety of stable structures, which is the first path for the generation of levels that are aesthetically pleasing as well as playable.