Reviewerly/exHarmony huggingface dataset:
Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring

This paper introduces a novel, transfer-learning-based approach to include physics into data-driven normal behavior monitoring models which are used for detecting turbine anomalies. For this purpose, a normal behavior model is pretrained on a large simulation database and is recalibrated on the available SCADA data via transfer learning. For two methods, a feed-forward artificial neural network (ANN) and an autoencoder, it is investigated under which conditions it can be helpful to include simulations into SCADA-based monitoring systems. The results show that when only one month of SCADA data is available, both the prediction accuracy as well as the prediction robustness of an ANN are significantly improved by adding physics constraints from a pretrained model. As the autoencoder reconstructs the power from itself, it is already able to accurately model the normal behavior power. Therefore, including simulations into the model does not improve its prediction performance and robustness significantly. The validation of the physics-informed ANN on one month of raw SCADA data shows that it is able to successfully detect a recorded blade angle anomaly with an improved precision due to fewer false positives compared to its purely SCADA data-based counterpart.
MIRecipe: A Recipe Dataset for Stage-Aware Recognition of Changes in Appearance of Ingredients

In this paper, we introduce a new recipe dataset MIRecipe (Multimedia-Instructional Recipe). It has both text and image data for every cooking step, while the conventional recipe datasets only contain final dish images, and/or images only for some of the steps. It consists of 26,725 recipes, which include 239,973 steps in total. The recognition of ingredients in images associated with cooking steps poses a new challenge: Since ingredients are processed during cooking, the appearance of the same ingredient is very different in the beginning and finishing stages of the cooking. The general object recognition methods, which assume the constant appearance of objects, do not perform well for such objects. To solve the problem, we propose two stage-aware techniques: stage-wise model learning, which trains a separate model for each stage, and stage-aware curriculum learning, which starts with the training data from the beginning stage and proceeds to the later stages. Our experiment with our dataset shows that our method achieves higher accuracy than the model trained using all the data without considering the stages. Our dataset is available at our GitHub repository.
A Real-Time Oral Cavity Gesture Based Words Synthesizer Using Sensors

The present system experimentally demonstrates a synthesis of syllables and words from tongue manoeuvers in multiple languages, captured by four oral sensors only. For an experimental demonstration of the system used in the oral cavity, a prototype tooth model was used. Based on the principle developed in a previous publication by the author(s), the proposed system has been implemented using the oral cavity (tongue, teeth, and lips) features alone, without the glottis and the larynx. The positions of the sensors in the proposed system were optimized based on articulatory (oral cavity) gestures estimated by simulating the mechanism of human speech. The system has been tested for all English alphabets and several words with sensor-based input along with an experimental demonstration of the developed algorithm, with limit switches, potentiometer, and flex sensors emulating the tongue in an artificial oral cavity. The system produces the sounds of vowels, consonants, and words in English, along with the pronunciation of meanings of their translations in four major Indian languages, all from oral cavity mapping. The experimental setup also caters to gender mapping of voice. The sound produced from the hardware has been validated by a perceptual test to verify the gender and word of the speech sample by listeners, with ∼ 98% and ∼ 95% accuracy, respectively. Such a model may be useful to interpret speech for those who are speech-disabled because of accidents, neuron disorder, spinal cord injury, or larynx disorder.
Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.
International Research Progress and Evolution Trend of Interpersonal Trust—Prospects under COVID-19 Pandemic

Retrospecting articles on interpersonal trust is of great importance for understanding its current status and future development in the context of the COVID-19 pandemic, especially, with the widespread use of Big Data and Blockchain. In total, 1532 articles related to interpersonal trust were collected as research database to draw keyword co-occurrence mapping and timeline mapping by VOSviewer and CiteSpace. On this basis, the research content and evolution trend of interpersonal trust were systematically analyzed. The results show that: (1) Data cleaning by code was first integrated with Knowledge Mapping and then used to review the research of interpersonal trust; (2) Developed countries have contributed the most to the research of interpersonal trust; (3) Social capital, knowledge sharing, job and organizational performance, Chinese Guanxi are the research hotspots of interpersonal trust; (4) The research hotspots on interpersonal trust evolve from the level of individual psychology and behavior to the level of social stability and development and then to the level of organization operation and management; (5) At present, the research on interpersonal trust is in the outbreak period; fMRI technology and Big Data and Blockchain technology gradually become vital research tools of interpersonal trust, which provides significant prospects for the following research of interpersonal trust under the COVID-19 pandemic.
Strengthening Experiential Learning: Innovative Virtual Engagement Strategies of Community Education Organizations

This study focused on the impact of COVID-19 on K-12 access to community education organizations such as museums, theaters, and art studios. Participants from five community education organizations were interviewed to explore and understand their experiences of developing and promoting virtual resources. While each organization responded differently, three approaches for adaptation and innovation were critical: existing virtual presence, collaboration, and responding to e-learning fatigue. Organizations found that the leveraging of technology in the short term may enhance K-12 access to their resources in the future.
A Distributed Ledger Based Platform for Automated Energy Performance Assessment

This paper presents a decentralized district energy management platform leveraging permissioned Blockchain, Smart Contracts, IoT, and Cloud Computing. This platform facilitates digitizing assets and data in a decentralized energy ecosystem in a trusted manner without a central point of authority, enabling the implementation of distributed applications to create new value-added services. As part of the distributed application design, this paper investigates Blockchain integration and trust aspects. Different Blockchain configurations are deployed and analyzed under real-world operating conditions, using a Blockchain network based on the Hyperledger Fabric framework. Our analysis shows that the platform can handle up to 150 transactions per second with a 0.5 second latency under the baseline configurations provided. The paper further investigates strategies to improve scalability to meet future application requirements.
Self-Adaptive MATLAB-Based Gauss–Hermite Quadrature Filter for State Estimation in Nonlinear Continuous-Discrete Stochastic Systems

This research aims at presenting our novel Continuous-Discrete Gauss-Hermite Quadrature Filter (CD-GHQF) intended for treating continuous-discrete stochastic systems, whose process model is simulated by a nonlinear Itô -type Stochastic Differential Equation (SDE) but whose measurement model has a discrete-time and nonlinear fashion. We exploit the so-called "continuous-discrete" approach, which is rooted in solving the Moment Differential Equations (MDEs) arisen in the time-update step of this filter. In particular, we implement the variable-stepsize MATLAB ODE solver ode45 for an automatic integration of the mentioned MDEs in every sampling period of the state estimation run. This MATLAB code establishes the self-adaptation facility of the filter under consideration. The performance of our novel state estimation algorithm is assessed within a challenging radar tracking scenario of flight control.
A Research on the Development and Application of Blockchain Technology in Industrial Internet of Things

Blockchain as an emerging information technology has developed rapidly in recent years. Its technical features complement each other with the basic characteristics of Industrial Internet of Things (IIoT). Applying blockchain to IIoT can not only fully express the application value of blockchain, but also provide improvements in the IIoT field greatly. In this paper, we present a research of blockchain technology in the aspects of basic concept, technical architecture and development. Then, this paper discusses about IIoT for Industry 4.0. Moreover, we analyze the fit degree of blockchain with IIoT, and put forward the integrated system architecture. Finally, the future directions and challenges in the development of IIoT integrated with blockchain are prospected, so as to provide reference for theoretical research and application in this filed.
3D Holoscopic Image Compression Based on Gaussian Mixture Model

We introduce a Gaussian Mixture Model (GMM) framework for 3D holoscopic image compression in this paper. The elemental-images of the 3D holoscopic image are predicted using GMM and the parameters of GMM are estimated using the common Expectation-Maximization (EM) algorithm. GMM Model Optimization (GMO) is used in this framework to select the optimal number of distributions and avoid local optimum of EM at the same time. A three-dimensional distribution-rotation based decomposition is proposed to change covariance parameters to meaningful features and improve the coding efficiency. The features and the remaining parameters of the GMM are encoded using fixed-length bits. A feature-based dictionary is proposed in this framework to match the similar Gaussian distributions utilizing the similar GMM features. And the offsets of the matched distributions are recorded as motion vectors to replace the similar areas in the elemental-images of the 3D holoscopic image. The residual between the original image and the prediction is encoded using Screen Content Coding Extension of High Efficiency Video Coding (HEVC-SCC). Experimental results show that our method performs better than HEVC-SCC, two coding methods based on pseudo-sequences and a state-of-the-art content-based compression method with Gaussian process regression.
An Efficient Batch Verification Scheme for SM2 Signatures

SM2 cryptographic algorithm is an elliptic curve public key algorithm released by China’s State Cryptography Administration, which includes digital signature algorithms, key exchange protocols, public key encryption algorithms, etc. As SM2 is widely used in identity authentication and authorization services in scenarios such as e-government, e-commerce and national economy, we found that there is less research on batch verification scheme for SM2 signatures to improve the performance of multi-verification tasks. In this paper, we provide an efficient batch verification scheme for SM2 signatures which is based on the public key verification and calculation of original algorithm. Different from single verification at a time, it can improve the calculation speed by verifying multiple signatures together. Aiming at the possibility of future use in industrial applications, we then provide a highly available implementation of the algorithm. Also, we discuss the verification performance for different quantity of signers and signatures.
Survey of Virtual Machine Deployment Algorithms in Cloud Data Center

Unreasonable utilization of cloud data center resources has caused a series of problems such as low utilization of data center resources. Optimizing the method of virtual machine deployment is one of the effective ways to solve these problems. Effectively reducing the energy consumption and economic costs of cloud data centers and ensuring high-quality service quality have become the main goals of virtual machine deployment optimization. This article analyzes the deployment of virtual machines in cloud data centers, discusses various methods of virtual machine deployment, summarizes and categorizes virtual machine deployment strategies, that is, from the perspectives of virtual machine deployment optimization goals and virtual machine deployment algorithm types. Classification, where optimization goals include single-objective optimization (such as cost, energy consumption, performance, and SLA default rate) and multi-objective optimization (two-objective optimization, three-objective optimization, and more than three-objective), and the types of virtual machine deployment algorithms include determinism Algorithms, heuristic algorithms, meta-heuristic algorithms, etc. Finally, the future research directions of cloud data center virtual machine deployment optimization and key issues to be solved are given.
Conclusion: Conceptualizing and Innovating Education and Work with Networked Learning

The aim of this concluding chapter is to reflect on the book’s central theme of conceptualizing and innovating education and work with networked learning and on the individual chapters’ contributions to developing this theme. In the first section, we present an overview of the book’s chapters which are sorted into three main parts: Part 1: Professional Learning; Part 2: Learning Networks’ Development and Use of Digital Resources; and Part 3: Innovating Networked Learning. We also highlight that a first chapter provides some background to the three parts of the book by tracing the conceptualization of networked learning’s basic terms in past conference papers. In the second section of the Conclusion, we identify a set of issues emerging out of the chapters—and indeed of the International Conference on Networked Learning 2020 from which the chapters stem. In our view, these issues constitute focus areas for future research: design for collaboration in networked learning, complexity of online networked learning in diverse physically located environments, the nature of learning and cognition, and politics and ethics in networked learning.
Dissecting Membership Inference Risk in Machine Learning

Membership inference attacks (MIA) have been identified as a distinct threat to privacy when sensitive personal data are used to train the machine learning (ML) models. This work is aimed at deepening our understanding with respect to the existing black-box MIAs while introducing a new label only MIA model. The proposed MIA model can successfully exploit the well generalized models challenging the conventional wisdom that states generalized models are immune to membership inference. Through systematic experimentation, we show that the proposed MIA model can outperform the existing attack models while being more resilient towards manipulations to the membership inference results caused by the selection of membership validation data.
CNN-based Passenger Detector for Public Transport Vehicles

Autonomous vehicle technology is becoming relevant not only for researchers but for industries as well. Many studies focus on autonomous cars as primary importance. The growing amount of research enables to improve the economy, safety and the environment. In this paper we want to give attention to the public transport system, more precisely to the bus and train systems. Our study is about the computer vision system and its integration into these vehicles with the aim to recognize passengers waiting at the bus stops or train stations. In particular, the network proposed follows the keypoint estimation approach, which uses a feature extraction backbone in order to generate a low resolution heatmap and, after detecting the peaks on it, regresses to the bounding box sizes and positions. The experimental results achieved an F-Score <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\beta=2)$</tex> value of 86% using a ResNet50 backbone.
Multi-input-output Fusion Attention Module for Deblurring Networks

In recent years, coarse-to-fine networks have shown good performance in image deblurring. The image details recovered better by inputting multi-scale images, while the computation will be high. To design a efficient deblurring network, we use a new coarse-to-fine strategy for image deblurring. We use UNet as the backbone network, feeding one image of different scales at each layer of the network to obtain sharp images with better details. In addition, we have designed information supplement blocks that can effectively supplement the information of different scale images. Finally, to be able to obtain better information about the image, we introduce an attention mechanism. It is experimentally shown that our network performs well on different metrics.
Faster Private Rating Update via Integer-Based Homomorphic Encryption

In encryption-based privacy-preserving recommender systems (PPRS), the user sends encrypted ratings to the server. An encrypted rating vector can have thousands of ciphertexts, causing a communication overhead. In some encryption-based PPRS proposed in the literature, if a user wants to rate a single item, he/she is required to send the entire rating vector to hide which item was rated. A user’s rating value and the item that is being rated both should remain private. This can be seen as a variant of the classical PIR-write problem. The goal is that each time a user wants to modify any data block, the communication should be minimal from the user.In encryption-based PPRS, the ratings are required to be encrypted using homomorphic schemes so that the server can generate recommendations. Arjan proposed a private rating update protocol for the recommender system applications, whereas Lipmaa and Zhang gave a protocol for a more general database scenario. We propose a hybrid approach that combines the advantages of each protocol, yielding a more efficient protocol. Our approach has constant user-side computation, and it reduces the communication and computation overhead at the server-side compared to previous approaches.
An Introduction to Machine Learning in a Nutshell

Deep Learning, Machine Learning and Artificial intelligence are buzzwords that has piqued the curiosity of many researchers. One of the most major and notable advancements in the field of computer science has been the ability to the computer systems that they can act, think and make decisions like human being. Over time, various algorithms have been developed to make such machines and computer systems that can copy human brains, and multiple programming languages has been utilized to implement such algorithms. To give the unparalleled Data and calculation availability power, interested in leveraging data-driven machine learning technologies to solve challenges that traditional engineering solutions hindered by algorithm or modeling flaws has revived. That study seeks to investigate the fundamental concepts of machine learning and to attempt to implement a few of its applications in Python.
Predicting Alert Source Device using Machine Learning Algorithms

In a large distributed virtualized environment, predicting the alerting source from its text seems to be daunting task. This paper explores the option of using machine learning algorithm to solve this problem. Unfortunately, our training dataset is highly imbalanced. Where 96% of alerting data is reported by 24% of alerting sources. This is the expected dataset in any live distributed virtualized environment, where new version of device will have relatively less alert compared to older devices. Any classification effort with such imbalanced dataset present different set of challenges compared to binary classification. This type of skewed data distribution makes conventional machine learning less effective, especially while predicting the minority device type alerts. Our challenge is to build a robust model which can cope with this imbalanced dataset and achieves relative high level of prediction accuracy. This research work stared with traditional regression and classification algorithms using bag of words model. Then word2vec and doc2vec models are used to represent the words in vector formats, which preserve the sematic meaning of the sentence. With this alerting text with similar message will have same vector form representation. This vectorized alerting text is used with Logistic Regression for model building. This yields better accuracy, but the model is relatively complex and demand more computational resources. Finally, simple neural network is used for this multi-class text classification problem domain by using keras and tensorflow libraries. A simple two layered neural network yielded 99 % accuracy, even though our training dataset was not balanced. This paper goes through the qualitative evaluation of the different machine learning algorithms and their respective result. Finally, two layered deep learning algorithms is selected as final solution, since it takes relatively less resource and time with better accuracy values.
Keyword-based Topic Modeling and Keyword Selection

Certain type of documents such as tweets are collected by specifying a set of keywords. As topics of interest change with time it is beneficial to adjust keywords dynamically. The challenge is that these need to be specified ahead of knowing the forthcoming documents and the underlying topics. The future topics should mimic past topics of interest yet there should be some novelty in them. We develop a keyword-based topic model that dynamically selects a subset of keywords to be used to collect future documents. The generative process first selects keywords and then the underlying documents based on the specified keywords. The model is trained by using a variational lower bound and stochastic gradient optimization. The inference consists of finding a subset of keywords where given a subset the model predicts the underlying topic-word matrix for the unknown forthcoming documents. We compare the keyword topic model against a benchmark model using viral predictions of tweets combined with a topic model. The keyword-based topic model outperforms this sophisticated baseline model by 67%.