Reviewerly/exHarmony huggingface dataset:
Homomorphic Encryption for Privacy-Friendly Augmented Democracy

Augmented democracy is a proposal to expand the ability of citizens to participate in the democratic decision process through a digital twin. Artificial intelligence would be used to diminish the load of citizens by recommending decisions based on expert knowledge and the citizens learned preferences. This paper explores the possibility to design citizen ’s avatars in a privacy preserving way. We formulate the problem as a Collaborative Filtering recommendation system and solve it with Matrix Factorisation. We use Homomorphic Encryption to build two privacy-preserving protocols and evaluate the practicality of our solutions with a toy example using the HEAAN encryption library.
Risk Prediction in the Life Insurance Industry Using Federated Learning Approach

In the business of Life Insurance, evaluating a customer’s application to assign a risk level is a task of utmost importance, as it helps in formulating policies and deciding the premium that the customer needs to pay. The advent of Machine Learning (ML) has made such tasks easier. Many insurance companies are adopting the same approach by using appropriate ML models. To make an effective model it is required that these companies collaborate and share their data, but the data stored by each company are very critical as it consist of customer’s private information and it is very risky to share them. To overcome such issues, Federated Learning (FL) is introduced which works in a distributed fashion without sharing the actual data. In this paper, we exploit FL to provide risk prediction in the Life Insurance Industry. The dataset from Kaggle Prudential Life Insurance Assessment is used for this study. To simulate different distributions of data among different clients we use the Dirichlet Process to partition the data. The different values of the concentration parameter is used to generate distributions that cover a spectrum of similarity along with the varying number of clients involved in the learning process. The results show the validity of the proposed approach.
Ultra-Short-term Photovoltaic Output Power Forecasting using Deep Learning Algorithms

Photovoltaic (PV) is becoming an attractive alternative in Norway in new zero-emission housing projects and in connection with hydropower reservoirs. However, fast-moving clouds result in abrupt changes in PV output power, which makes grid integration in such areas more challenging. One solution is to forecast the amount and variation of PV output power in advance. This paper, therefore, evaluates the performance of various DL (Deep Learning)-based forecasting models for a 20.15 kWp PV plant in Trondheim, Norway. The results show that a forecast model based on LSTM (Long Short-term Memory) network gives better performance in terms of RMSE (Root Mean Squared Error) for 15 minutes ahead forecast. This study can serve as the groundwork for future research into techniques and approaches that can result in a high-performing forecast model both in terms of accuracy and stability for the Norwegian climate.
Effects and Comparison of different Data pre-processing techniques and ML and deep learning models for sentiment analysis: SVM, KNN, PCA with SVM and CNN

In this paper, we have discussed different data pre-processing techniques and different machine learning and deep learning models which are used for sentiment analysis. The dataset used was &#x201C;Restaurant Reviews&#x201D; We have compared the results of different results of SVM, KNN, PCA with SVM and CNN models. Each of the different pre-processed datasets was passed to different machine learning and deep learning models and the results were compared to find the most useful data pre-processing technique for a particular model, so we can save resources (time and money) by concentrating our resources on that particular data pre-processing technique for that model.
Application of Blockchain in Healthcare

Blockchain appears to be the way of the future in today&#x2019;s technological age. Blockchain technology has recently proven itself worthy of praise by facilitating cryptocurrency financial transactions. Despite being widely researched, there have recently been growing worries about the privacy and security of patient data because of the use of a centralized system to handle patient data. The General Data Protection Regulation (GDPR) grants the subject the right to know where and how his or her personal data is stored, as well as who is privy to it and to what extent. Blockchain has been shown in numerous studies to be capable of supporting the safe and secure recording of patient data in the health-care system. This paper summarizes recent research into how blockchain technology can be successfully implemented in the healthcare field. In our assessment of such publications, we discovered that the majority of blockchain applications are limited when only briefly examined. The products are only used by a small number of people. Perhaps this is due to the fact that healthcare blockchain applications have more demanding authentication, interoperability, and record sharing requirements. However, the quality of the products is constantly improving.
Ranking Companies Based On VADER Sentiment Analysis

We live in the world where the advancement of technology has been taking place tremendously. On everything being digitalized people began to use electronic gadgets immensely. Most importantly communication has got advanced. Everyone are being able to express their views, opinion and comments readily through social media and other websites. These factors has increased the significance and more implementation of sentiment analysis. Sentiment analysis is a process where the polarities or emotions behind the given text is analyzed. Using sentiment analysis now we can easily classify the comment or views made by the people and give the quicker analysis of data. We came up with idea of using this concept this in ranking of the companies, which is a lot productive for the youth in decision making.
Variable Length Digit Recognition for Gujarati Language

In this paper, we describe a method to perform handwritten digit recognition for Gujarati - a regional Indian language. Our method can handle variable-length inputs, meaning that there are no limitations around the digit length for the input image. To our knowledge, this is the first attempt to do variable length digit classification for the Gujarati language numerals. We outline a two-step method to classify handwritten Gujarati numerals. The first step identifies connected components of the input image and predicts the numeric length of each connected component. The second step predicts the actual number that is contained within each connected component. The final result is a concatenation of individual predictions. Our Convolutional Neural Networks (CNN) architecture for this task has a low number of output classes (e.g. 30 classes for 3 digit classifier). Our method achieves 83.8&#x0025; test set accuracy for 1 to 4 digit Gujarati numerals. On the NIST19 dataset, our method achieves 96.1&#x0025; test set accuracy for 2 to 6 digit English numerals.
Skeleton-Based Hand Gesture Recognition by Using Multi-input Fusion Lightweight Network

Skeleton-based hand gesture recognition has achieved great success in recent years. However, most of the existing methods cannot extract spatiotemporal features well due to the skeleton noise. In real applications, some large models also suffer from a huge number of parameters and low execution speed. This paper presents a lightweight skeleton-based hand gesture recognition network by using multi-input fusion to address those issues. We convey two joint-oriented features: Center Joint Distances (CJD) feature and Center Joint Angles (CJA) feature as the static branch. Besides, the motion branch consists of Global Linear Velocities (GLV) feature and Local Angular Velocities (LAV) feature. Fusing static and motion branches, a robust input can be generated and fed into a lightweight CNN-based network to recognize hand gestures. Our method achieves 95.8% and 92.5% hand gesture recognition accuracy with only 2.24M parameters on the 14 gestures and 28 gestures of the SHREC’17 dataset. Experimental results show that the proposed method outperforms state-of-the-art (SOAT) methods.
Empirical Comparison of Block Relay Protocols

Block relay protocols play a key role in the performance and security of public blockchains. As a result, several such protocols have been deployed in the context of Bitcoin and its variants (e.g., legacy, compact block relay and Graphene) in an attempt to reduce bandwidth utilization. However, the relative performance of these protocols in realistic networking conditions (e.g., with nodes churning - joining and leaving the network) is still not known. This paper aims to fill this key knowledge gap using an experimental testbed of twelve full nodes connected to the Bitcoin Cash blockchain. With the aid of novel logging tools, we contrast the performance of these three protocols, in realistic scenarios, with respect to communication, delay, and block decoding success. Our main findings are that Graphene generally performs the best when nodes remain connected, boasting an average propagation delay of 190 ms (i.e., 29% lower than compact block and 80% lower than the legacy protocol). However, when nodes churn at a high rate, compact blocks may perform better. Through a careful temporal analysis, we identify some root causes of the protocol inefficiencies, together with potential mitigation. We have made our measurement framework and experimental logs publicly available to the broader research community.
Playing With Blocks: Toward Re-Usable Deep Learning Models for Side-Channel Profiled Attacks

This paper introduces a deep learning modular network for side-channel analysis. Our approach features a deep learning architecture with the capability to exchange parts (modules) with other neural networks. We aim to introduce reusable trained modules into side-channel analysis instead of building architectures from scratch for each evaluation, reducing the body of work. Our experiments demonstrate that our architecture feasibly assesses a side-channel evaluation, suggesting that learning transferability is possible using the architecture we propose in this paper.
On the Need and Opportunities for Digitalization of the Educational and Methodological Support of the Educational Process in the Context of Improving Its Quality Indicators

Purpose: to consider the possibility and expected effectiveness of introducing digitalization tools into the educational process using RPA technology to improve the quality indicators of educational and related processes. Methodology: to achieve this goal, we use the capabilities of system analysis, which allows us to combine and investigate the factors of the external and internal environment of the university that determine the quality indicators of the educational process, as well as the need and prospects for the introduction of digitalization. In turn, the synthesis of the data obtained and the conclusions made it possible to identify the main tool of digitalization and justify our choice. Moreover, the author’s argumentation was based on the results of qualitative research methods: focus groups and expert assessments, as well as methods of systematization and visualization of the data obtained. Findings: the analysis of the requirements of legislation in the field of education, the realities and trends in the development of information technology, as well as the actual practice of organization of business processes in an educational organization, allowed us to conclude that the introduction of digitalization in the educational process implies, on the one hand, a positive effect. Thus, scientific and pedagogical workers will no longer have to devote a significant part of their working and free time to the preparation of teaching materials, which will lead to an increase in the creative potential of teaching staff, motivation to work, the level of dissatisfaction, stress from paperwork and intra-university bureaucracy will decrease. The coordinating and controlling functions of the university management, readiness for all kinds of control and supervisory measures will also be optimized. On the other hand, the study revealed the possible risk levels of implementing RPA and the strength of their negative impact on business processes. Originality: the presented study is characterized by a high degree of originality, which is due to the nature of the study itself: the authors set a goal, the achievement of which involves solving specific pressing problems, rather than theoretical analysis and obtaining hypothetical conclusions. The research is based on specific processes, their subjects and objects in the organization under study, applied research methods that are situationally suitable for this particular problem. Conclusion: the presented analysis made it possible to argue for the need to introduce digital transformation technologies into the work of modern organizations, including the field of education since this will largely optimize routine processes at the university, improve the quality and efficiency of the formation of methodological and organizational components of the educational process.
Embedded Intelligence for Safety and Security Machine Vision Applications

Artificial intelligence (AI) has experienced a recent increase in use across a wide variety of domains, such as image processing for security applications. Deep learning, a subset of AI, is particularly useful for those image processing applications. Deep learning methods can achieve state-of-the-art results on computer vision for image classification, object detection, and face recognition applications. This allows to automate video surveillance reducing human intervention. At the same time, although deep learning is a very intensive task in terms of computing resources, hardware and software improvements have emerged, allowing embedded systems to implement sophisticated machine learning algorithms at the edge. Hardware manufacturers have developed powerful co-processors specifically designed to execute deep learning algorithms. But also, new lightweight open-source middleware for constrained resources devices such as EdgeX foundry have emerged to facilitate the collection and processing of data at sensor level, with communication capabilities to cloud enterprise applications. The aim of this work is to show and describe the development of Smart Camera Systems within S4AllCities H2020 project, following the edge approach.
Detecting Fake News in MANET Messaging Using an Ensemble Based Computational Social System

Mobile Adhoc Networks (MANETs) are utilised in a variety of mission-critical situations and as such, it is important to detect any fake news that exists in such networks. This research proposes an Ensemble Based Computational Social System for fake news detection in MANET messaging. As such this research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect fake news in MANET messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a satisfactory performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET messaging.
Forecasting Surface Velocity Fields Associated With Laboratory Seismic Cycles Using Deep Learning

Abstract It has been recently demonstrated that Machine Learning (ML) can predict laboratory earthquakes. Here we propose a prediction framework that allows forecasting future surface velocity fields from past ones for analog experiments of megathrust seismic cycles. Using data from two types of experiments, we explore the prediction performances of multiple Deep Learning (DL) and ML algorithms. In such a self‐supervised regression, no feature extraction is required and the entire seismic cycle is forecasted. The onset, magnitude, and propagation of analog earthquakes can thus be predicted at different prediction horizons. From all architectures tested in this study, convolutional recurrent neural networks (CNN‐LSTM and CONVLSTM) provide the best predictions although their performances depend on experiment characteristics and hyperparameters tuning. Analog earthquakes can be successfully anticipated up to a horizon of the order of their duration. This laboratory‐based study may open new avenues for transfer learning applications with data from natural subduction zones.
Gaussian noise parameter estimation based on multiple singular value decomposition and non‐linear fitting

Noise standard deviation (STD) is an important parameter in many digital image processing applications. This paper presents a Gaussian noise parameter estimation algorithm using multiple singular value decomposition (SVD) and non-linear fitting. The proposed algorithm adds known noise to the original noise image many times to generate a noise-corrupted image set and then performs SVD on each image. By analyzing the singular values of the noise-corrupted images, an overdetermined equation system with respect to the noise STD is established. The Gauss–Newton iteration method and backtracking Armijo line search are used to solve the equations, which improve the convergence speed and reduce computational cost. Compared with other methods, the mean error of the proposed algorithm on the TID2008 dataset is 0.028, which is several times lower than other methods. This shows that the performance of our estimator is significantly improved.
SL: A Reference Smartness Level Scale for Smart Artifacts

During the last two decades, many products, research projects and prototypes were announced with different characteristics and capabilities, all adopting the “smart” qualifier word. If a smartness property could not be defined simply as true or false, defining a suitable range was not an easy task. This issue led to the proposal of some classification models, frameworks and taxonomies for project classifications, but none of them provide a clear and pragmatic smartness scale able to classify smart artifacts and serve as a reference. This paper aims to propose a smartness scale to help research and non-research communities to better quantify and easily understand the features and autonomy of smart artifacts. The proposed smartness scale considers the main function of physical-device components in smart systems. The provided smartness scale is based on a uni-dimensional typology that defines 12 different smartness levels, created based on our definition of “smart artifact” and by following an evolutionary set of capabilities ranging from traceable-only and sensing-capable artifacts to autonomous, adaptable and self-driven artifacts. In order to show the feasibility of the proposed smartness scale, an analytic model was defined and applied to several research- and market-based artifacts tagged as smart in order to extract their smartness levels.
Feasibility of Network-layer Anonymity Protocols at Terabit Speeds using a Programmable Switch

The paper presents a Tbps-class anonymity router that supports both an anonymity protocol and IP by leveraging a programmable switch. The key design issue is to place both the compute-intensive header decryption function for anonymity protocol forwarding and the memory-intensive IP forwarding function on the processing pipes of a switch with satisfying its hardware requirements. A prototype router on a programmable switch achieves Tbps-scale forwarding.
Mitigating Noise in Ensemble Classification with Real-Valued Base Functions

In data-intensive applications, it is advantageous to perform some partial processing close to the data, and communicate to a central processor the partial results instead of the data itself. When the communication medium is noisy, one must mitigate the resulting degradation in computation quality. We study this problem for the setup of binary classification performed by an ensemble of functions communicating real-valued confidence levels. We propose a noise-mitigation solution that works by optimizing the aggregation coefficients at the central processor. Toward that, we formulate a post-training gradient algorithm that minimizes the error probability given the dataset and the noise parameters. We further derive lower and upper bounds on the optimized error probability, and show empirical results that demonstrate the enhanced performance achieved by our scheme on real data.
AutoVAE: Mismatched Variational Autoencoder with Irregular Posterior-Prior Pairing

The variational autoencoder (VAE) has been used in a myriad of applications, e.g., dimensionality reduction and generative modeling. VAE uses a specific model for stochastic sampling in latent space. The normal distribution is the most commonly used one because it allows a straightforward sampling, a reparameterization trick, and a differentiable expression of the Kullback–Leibler divergence. Although various other distributions such as Laplace were studied in literature, the effect of heterogeneous use of different distributions for posterior-prior pair is less known to date. In this paper, we investigate numerous possibilities of such a mismatched VAE, e.g., where the uniform distribution is used as a posterior belief at the encoder while the Cauchy distribution is used as a prior belief at the decoder. To design the mismatched VAE, the total number of potential combinations to explore grows rapidly with the number of latent nodes when allowing different distributions across latent nodes. We propose a novel framework called AutoVAE, which searches for better pairing set of posterior-prior beliefs in the context of automated machine learning for hyperparameter optimization. We demonstrate that the proposed irregular pairing offers a potential gain in the variational Rényi bound. In addition, we analyze a variety of likelihood beliefs and divergence order.
Identification Over Quantum Broadcast Channels

In the identification problem, as opposed to the information transmission task, the decoder only identifies whether a message of his choosing was sent or not. This relaxation allows for a double-exponential code size. An achievable identification region is derived for a quantum broadcast channel, and a full characterization for the class of classical-quantum broadcast channels. The results are demonstrated for a depolarizing broadcast channel. Furthermore, the identification capacity region of the single-mode pure-loss bosonic broadcast channel is obtained as a consequence. In contrast to the single-user case, the capacity region for identification can be significantly larger than for transmission.
Named entity recognition of power communication planning based on transformer

In order to improve the intelligence level of power communication network planning, a power communication planning named entity method based on Transformer is proposed to solve the problems of long text in power communication planning report and low information extraction efficiency. This paper takes the power communication planning text as the research object, uses the transformer model to pre-train the words in the planning text to obtain word vectors, and then uses the Bi-lstm-crf model to train with the pre-trained word vectors as input to screen the appropriate model. parameter to complete named entity recognition. Compared with the experimental simulation, the model named entity recognition accuracy using the transformer model pre-trained word vector is higher.