Reviewerly/exHarmony huggingface dataset:
Distance learning as a method of teaching piano class

This article explores the specificity of teaching piano class in the university in the conditions of distance learning. The author suggests to consider distance learning as an active method of carrying out cognitive activity, i.e. a method of educating future piano performers, as well as from the perspective of modern organizational innovation. The article gradually covers certain historical prerequisites for the existence of distance learning method in music education in the middle of the XX century, as well as the conditions for its successful implementation. The author describes the formats of distance learning, Internet platforms that allow online and offline interaction of the participants of educational process. Based on the opinions of reputable educators-performers, as well leaning on the own experience of teaching classes remotely, the author determines the practical significance and feasibility of using the distance form of learning as a method of teaching future musicians. The conclusion is made that the all-round modernization, implementation and development of distance learning technology in the sphere of traditional education does not take into account the specificity of music and art universities. Despite all the flaws and imperfections of distance innovation, the author finds positive aspects, which would allow the pedagogues to implement distance learning to a certain extent for achieving certain educational objectives.
The design of an improved intelligent guide stick for the blind

Abstract There are about 17 million blind people in China, which means that one out of every 80 people is blind. However, due to the lack of facilities and policies to help the blind, many blind people can’t enter some normal places, which brings lots of inconveniences to the blind. In order to make blind people travel more conveniently and safely, this paper designs an improved intelligent guide stick to provide security guide for the blind’s daily travel and their precise location for their guardians. The location data and the images of surroundings were sent to the guardian’s smart phone to help them find the blind especially in a multistory building. In this paper, 10 blind people walked with the help of the improved guide stick in a 3-story building that was divided into 12 different areas. The guardians received text messages and real-time images sent by the guide stick to determine where the blinds were. Finally, 300 groups of data were obtained, among which 282 groups were got when the blind people were successfully located and found, with the successful finding rate of 94%. The experimental results show that with the help of the improved guide stick, the blind can be located more precisely and effectively.
Dish-ID: A neural-based method for ingredient extraction and further recipe suggestion

The paper presents a method for meal recognition, ingredient extraction and recipe suggestion in the Russian language. The proposed algorithm consists of several consecutive stages. On the first stage the model extracts a list of ingredients from a photo of the dish, based on which recipes on the second stage are selected. Two ingredient extraction architectures were tested for the first stage and three recipe matching methods for recipe suggestion are proposed. In addition, the algorithm was incorporated into the telegram-bot which provides friendly user experience. Source code is at https://github.com/Alenushldish_id_sirius.
Exploring Driving-Aware Salient Object Detection via Knowledge Transfer

Recently, general salient object detection (SOD) has made great progress with the rapid development of deep neural networks. However, task-aware SOD has hardly been studied due to the lack of task-specific datasets. In this paper, we construct a driving task-oriented dataset where pixel-level masks of salient objects have been annotated. Comparing with general SOD datasets, we find that the cross-domain knowledge difference and task-specific scene gap are two main challenges to focus the salient objects when driving. Inspired by these findings, we proposed a baseline model for the driving task-aware SOD via a knowledge transfer convolutional neural network. In this network, we construct an attention-based knowledge transfer module to make up the knowledge difference. In addition, an efficient boundary-aware feature decoding module is introduced to perform fine feature decoding for objects in the complex task-specific scenes. The whole network integrates the knowledge transfer and feature decoding modules in a progressive manner. Experiments show that the proposed dataset is very challenging, and the proposed method outperforms 12 state-of-the-art methods on the dataset, which facilitates the development of task-aware SOD.
New Variants of SDLS Algorithm for LABS Problem Dedicated to GPGPU Architectures

Low autocorrelation binary sequence (LABS) remains an open hard optimisation problem that has many applications. One of the promising directions for solving the problem is designing advanced solvers based on local search heuristics. The paper proposes two new heuristics developed from the steepest-descent local search algorithm (SDLS), implemented on the GPGPU architectures. The introduced algorithms utilise the parallel nature of the GPU and provide an effective method of solving the LABS problem. As a means for comparison, the efficiency between SDSL and the new algorithms is presented, showing that exploring the wider neighbourhood improves the results.
Parallelized C Implementation of a Merkle Tree

Merkle trees are primarily known for being an attribute found in blockchain technology. They are used for encrypting data by hashing values multiple times to avoid incidents such as hash collisions, or the successful guessing of hash values. Merkle trees are not only a useful feature found on the blockchain but in the field of Cyber Security in general. This paper outlines the process of implementing a Merkle tree as a data structure in C++ and then parallelizing it using OpenMP. The final result is a Merkle tree password storing program with reduced running-time and the ability to operate on multiple processors. The validity of this program is tested by creating a Merkle tree of the correct passwords, storing the value of the root node, and then building a second tree where a single incorrect password is stored within that tree. The two trees are passed through an audit function that compares the root nodes of the two trees. If they are different, then the tree in question has been tampered with.
The Evolution of Competition and Cooperation for Cloud Ecosystem: A Niche Theory Perspective

The cloud ecosystem becomes a new trend, which makes the competition of cloud providers has gradually evolved into the competition among cloud ecosystems. The purpose of this paper is to analyze the competition and cooperation relationship among participants in ecosystem and its impact on the sustainable development, gives growth strategies for cloud providers in fierce environment. The study analyses the structure and the main relations in cloud ecosystem. Through discussing the calculation methods of niche breadth and overlap degree, it establishes a dynamic evolution model of competition and cooperation among the main enterprises. According to an empirical analysis of cloud providers in Ali Cloud ecosystem, it shows the evolution law of the competition and cooperation.
Copycat process in the early stages of einselection

We identify and describe unique early time behavior of a quantum system initially in a superposition, interacting with its environment. This behavior---the copycat process---occurs after the system begins to decohere, but before complete einselection. To illustrate this behavior analytic solutions for the system density matrix, its eigenvalues, and eigenstates a short time after system-environment interactions begin are provided. Features of the solutions and their connection to observables are discussed, including predictions for the continued evolution of the eigenstates towards einselection, time dependence of spin expectation values, and an estimate of the system's decoherence time. In particular we explore which aspects of the early stages of decoherence exhibit quadratic evolution to leading order, and which aspects exhibit more rapid linear behavior. Many features of our early time perturbative solutions are agnostic of the spectrum of the environment. We also extend our work beyond short time perturbation theory to compare with numerical work from a companion paper.
Kerala Floods: Twitter Analysis Using Deep Learning Techniques

As innovation and Web developed, online networking has gotten significant and unavoidable in our lives. In any event, during emergency or regular cataclysms, individuals utilize social media communication more than any other methods for correspondence. Thus, these online social media platforms contain colossal measure of data identified with such occasions or occurrences. We can utilize this online information to get a great deal of data which can be additionally used to comprehend the occasions through different viewpoints. In this paper, we have extracted Kerala floods related tweets from Twitter and grouped them into different classifications utilizing natural language processing models like bidirectional encoder representations from transformers (BERT), XLNet, Ernie 2.0. It was seen that Ernie 2.0 gave better result as compared with the other natural language processing models utilized for classifying the extracted tweets. These automatic text classification models designed using the above deep learning techniques can be useful in giving data while planning the arrangement and preventive measures for catastrophic events like floods. Investigation of these tweets can likewise give discernment in taking care of such catastrophes in a superior and proficient manner.
VLSI Hardware Architecture for Gaussian Process

Gaussian process (GP) is a popular machine learning technique that is widely used in many application domains, especially in robotics. However, GP is very computation intensive and time consuming during the inference phase, thereby bringing severe challenges for its large-scale deployment in real-time applications. In this paper, we propose two efficient hardware architecture for GP accelerator. One architecture targets for general GP inference, and the other architecture is specifically optimized for the scenario when the data point is gradually observed. Evaluation results show that the proposed hardware accelerator provides significant hardware performance improvement than the general-purpose computing platform.
Effective Utilization of Whitening for Person Identification

The growth of importance of Person Identification(PI) is so drastic these days, since it helps support the growth of many other fields like surveillance, robotics etc. Computer Vision Enthusiasts are working on making this robust to various factors such as noise, scale, pose, intensity, illumination, translation, expression and many more. Whitening is a pre-processing technique which makes an image robust to noise, intensity and illumination. There are many feature detectors such as FLANN matching algorithms, which use relatively lesser resources, but are not robust to one or more of the above mentioned problems. However, in general whitening is usually used with machine learning algorithms, which are already robust to these problems. This paper hence compares results obtained using SVD whitening and ZCA whitening to without pre-processing on four distinct pipelines. The first two pipelines use SIFT extracted keypoints and SURF extracted keypoints matched by KD-Tree FLANN matcher and classified using confidence probability matching. These pipelines as we know are not robust to the problems solved by whitening. They are implemented on ORL, grimace and faces 95 database. The third and fourth pipeline includes machine learning algorithms to classify the features extracted by Spatial Pyramid Matching and Earth Moverâ€™s Distance on Dense SIFT keypoints. They were implemented on ORL, faces 95 and FEI database. Results show that SVD whitening and ZCA whitening increases the accuracy more when FLANN matcher is used than when machine learning algorithms are used to classify the images. In both cases, we encounter a scenario where the accuracy is slightly lesser than the accuracy obtained without whitening. However, we find that the decrease in accuracy is more when machine learning algorithms are used than when FLANN matchers are used.
A Deep Image Coding Scheme With Generative Network to Learn From Correlated Images

This paper provides a method to build a deep learning image coding system based on inverse problem, choosing a suitable measurement operator to reduce the amount of information transmitted at the sender, and reconstructing the original image by tackling the inverse problem at the receiver. Unlike most compressed sensing (CS) methods, the proposed coding scheme does not rely on sparsity but uses the structural priors of the generative adversarial networks (GAN) to solve the inverse problem. The proposed model trains the GAN to learn a mapping from the latent space to the sample space formed by correlated images on the cloud. Then the measurements are used to localize the optimal latent variable in the representation space which corresponding to the original image in the sample space. The proposed method encodes and transmits the measurements instead of the original image, which greatly reduces the cost of transmission while ensuring the quality of the reconstructed the image at high compression ratios. To the best of our knowledge, this is the first time to introduce the GAN-based inverse problem in the field of the deep image coding area. The experimental results show that the visual quality of the images generated by the proposed scheme is better than the traditional encoding scheme JPEG2000. Especially in the case of extremely high compression ratios, the proposed scheme can still maintain good performance.
Tracking of Dynamical Processes with Model Switching Using Temporal Convolutional Networks

This paper considers the problem of tracking and predicting dynamical processes with model switching. The classical approach to this problem has been to use an interacting multiple model (IMM) which uses multiple Kalman filters and an auxiliary system to estimate the posterior probability of each model given the observations. More recently, data-driven approaches such as recurrent neural networks (RNNs) have been used for tracking and prediction in a variety of settings. An advantage of data-driven approaches like the RNN is that they can be trained to provide good performance even when the underlying dynamic models are unknown. This paper studies the use of temporal convolutional networks (TCNs) in this setting since TCNs are also data-driven but have certain structural advantages over RNNs. Numerical simulations demonstrate that a TCN matches or exceeds the performance of an IMM and other classical tracking methods in two specific settings with model switching: (i) a Gilbert-Elliott burst noise communication channel that switches between two different modes, each modeled as a linear system, and (ii) a maneuvering target tracking scenario where the target switches between a linear constant velocity mode and a nonlinear coordinated turn mode. In particular, the results show that the TCN tends to identify a mode switch as fast or faster than an IMM and that, in some cases, the TCN can perform almost as well as an omniscient Kalman filter with perfect knowledge of the current mode of the dynamical system.
Multi-View Clustering Through Self-Weighted High-Order Similarity Fusion

Recently, multi-view clustering methods based on high-order sample affinities to ease learning complex structures attract much attention. However, most of the methods used pre-defined similarity, which is easy to be corrupted by noises and yield suboptimal performance. To tackle with this issue, this paper proposes a novel multi-view clustering method, named by WHSF, which seeks to learn a self-weighted high-order similarity. The high-order similarity is formulated to flexibly capture the intrinsic structure of data, characterized by fusing the interactions across views. A high-order regularization based on the defined similarity is incorporated into the model and assigned with weight parameters, enabling the model to focus on mutual information among views. Extensive experiments on four real-world datasets show that the proposed WHSF outperforms benchmark multi-view methods and can reveal a reliable structure concealed across multiple views.
Enhanced Distant Supervised Open Information Extraction

In this paper, we proposed a new Open IE task which is extracting triplets from sentences with a given head entity. In order to make use of triplets in knowledge base for Open IE, we intensify the distant supervision assume that the two entities and the predict word should be in the same sentence when we construct the distant supervision dataset. We propose a novel language independent sequence labeling relation extraction method, and treating the head entity and the sentence as input, expecting tagging the relation word and the tail entity simultaneously. We construct distant supervised dataset with a Chinese knowledge base. Experiments show that our method achieves a higher F-score over state-of-the-art Chinese Open IE method in a heterogeneous real word labeled dataset and achieves 84%, 86% top 100 manual evaluation precision in two different real world non homologous datasets. Besides, experiment results show the ability of our proposed method to discover new relation words which didn't appear in training dataset.
TAP: A Two-Level Trust and Personality-Aware Recommender System

Recommender systems (RSs) have been adopted in a variety set of web services to provide a list of items which a user may interact with in near future. Collaborative filtering (CF) is one of the most widely used mechanism in RSs that focuses on preferences of neighbours of similar users. Therefore, it is a critical challenge for CF models to discover a set of appropriate neighbors for a particular user. Most of the current approaches exploit users' ratings information to find similar users by comparing their rating patterns. However, this may be a simple idea and over-tested by the current studies, which may fail under data sparsity problem. Recommender system as an intelligent system needs to help users with their decision making process, and facilitate them with personalized suggestions. In real world, people are willing to share similar interest with those who have the same personality type; and then among all similar personality users pope may only take advice and recommendation from the trustworthy ones. Therefore, in this paper we propose a two-level model, TAP, which analyzes users' behaviours to first detect their personality types, and then incorporate trust information to provide more customized recommendations. We mathematically model our approach based on the matrix factorization to consider personality and trust information simultaneously. Experimental results on a real-world dataset demonstrate the effectiveness of our model.
MGARL: Multiple Graph Adversarial Regularized Learning

Graph Convolutional Networks (GCNs) have been commonly studied for graph learning tasks, such as semi-supervised learning, clustering etc. However, many existing GCNs are generally conducted on single graph data and thus can not be applied directly to multi-graph data that consists of various types of edges between nodes. To address this issue, in this paper, we propose a novel multiple Graph Adversarial Regularized Learning (mGARL) framework for multi-graph data representation and learning. mGARL aims to learn an optimal structure invariant/consistent representation for multiple graphs by employing a novel Encoder-Decoder architecture with adversarial learning regularization. It can incorporate the structural information of multiple graphs simultaneously for the node’s representation. We apply the proposed mGARL on the multi-view semi-supervised learning tasks. Experimental results on several datasets demonstrate the effectiveness and benefits of the proposed mGARL model.
Platforms and Tools Within the HyperLedger Framework

During this last decade, the blockchain (BC) paradigm is being required in several use cases and scenarios in particular for security, privacy, and trust provisioning. Accordingly, the research community and developers developed several emulation tools and frameworks for BC-based systems performance analysis. Making an adequate decision regarding the choice of the most suitable tool that can be used to develop and validate the performances of a specific BC-based system or application still requires more investigation. In this context, this chapter describes and highlights the most features and characteristics of the BC platforms and tools within the Hyperledger framework. The goal is to illustrate the advantages and the limitations of several BC tools and development environments within Hyperledger. In addition, this chapter provides an insight into BC 3.0 as the new generation of BC that meets the requirements of the smart application.
Simple Anti-fraud Document Authentication Concept for Public Services

In the communication with the public administration paper documents or simple digital documents are the predominant medium. The resulting core problems of filing, authenticity checking and reusability of documents are addressed by the following paper. An example is the examination of tax return documents, which generally is a rather long process [], we focus in this paper to the example of the challenge for German tax offices with an typical tax return process. A number of approaches, such as DigiLocker in India [], seeks to digitally store official government-issued document. However, unfortunately paper-based systems are still used in many cases. We propose a blockchain-based approach for checking documents only once and to save the result of the check in a forgery-proof and audit-proof manner. The decentralized approach of blockchain technology is preferred hence it also eliminates physical barriers as far as possible. Furthermore, the concept should be simple, easy and flexible to implement in order to reach a number of public institutions.
Research on the Innovation of Blended English Teaching Mode Based on Superstar Platform in Higher Vocational Colleges

In recent years, with the country's increasing attention to higher vocational education, many higher vocational colleges have made a certain degree of rectification in the teaching mode. English, as a key subject, was first rectified. With the continuous development of computer technology, English teaching in higher vocational colleges has been upgraded step by step. Nowadays, the mode of obtaining teaching resources through network has gradually entered people's vision. Under the Superstar platform mode, teachers and students interact with each other in real time through the teaching courses and teaching resources in cyberspace, so as to promote the reform of teaching mode. In this paper, the traditional English teaching is reformed by means of experimental comparison, and a questionnaire survey is conducted among students and teachers. The experimental results show that both students and teachers are satisfied with the English teaching mode of Superstar platform. Under the practice of the blended teaching mode, the students in higher vocational colleges have made great progress in English listening, speaking, reading and writing. At the same time, the teaching under this mode has achieved the goal of modern talent cultivation to a certain extent.