Reviewerly/exHarmony huggingface dataset:
Education of Video Classification Based by Neural Networks

In this paper an artificial neural network for video classification were presented. Artificial neural networks were essential part of the video classification. As they are more and more used for applications doing of video classification, it is desirable to introduce them in the educational process. After introduction to the topic, basic theory about architecture neural networks for video classification continues. Firstly, the 3D Convolution Neural network (3DCNN) using UCF50-action recognition database was applied. Next the Convolutional Long Short-Term Memory (ConvLSTM) on the same dataset was used. Finally, these neural networks using confusion matrix were compared. The all experimental results using UCF50-Datatset were performed. The achieved experimental results demonstrate the effectiveness of neural networks (3D CNN and ConvLSTM) in educational process.
Computational Analysis of Machine Learning Algorithms to Predict Heart Disease

Heart disease is one of the most common problems and also a disease whose rate of increase has been higher in recent years. The complex task associated is exploitation of hidden patterns for effective and accurate prediction. There is plenty of data generated every year from various health institutes. This research focuses on development of supervised machine learning models for prediction of target heart disease. We have used publicly available heart disease datasets from University of California, Irvine (UCI) data repository for Cleveland, Switzerland, Hungarian and Long Beach. Various forms of preprocessing steps such as handling the missing values and null values, removal of duplicate entries is employed on these datasets in order to use it for developing effective models. The correlation between the features set and the target variable is studied. The prediction models are developed using effective machine learning techniques like Logistic Regression, Decision Trees, Naive Bayes, K-nearest neighbors, ensembles AdaBoost and XGBoost. The predictive performance of the developed models is compared with the help of stable accuracy measures like accuracy, precision, recall, F1-seore, Cohen's kappa and Area Under the Curve score. K- nearest neighbors was the best model for the Cleveland dataset with 86.81% accuracy. AdaBoost algorithm gave us the highest accuracy of 98% for Switzerland dataset. Bernoulli Naive Bayes predicted the heart diseases for Hungarian dataset with 84.26% accuracy. XGBoost gave better accuracy of 82.20% for VA Long Beach dataset. The results of the study advocate the applicability of machine learning techniques to predict heart disease.
Building an Automated and Self-Aware Anomaly Detection System

Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. It can be very challenging to proactively monitor a large number of diverse and constantly changing time series for anomalies, so there are often gaps in monitoring coverage, disabled or ignored monitors due to false positive alarms, and teams resorting to manual inspection of charts to catch problems. Traditionally, variations in the data generation processes and patterns have required strong modeling expertise to create models that accurately flag anomalies. In this paper, we describe an anomaly detection system that overcomes this common challenge by keeping track of its own performance and making changes as necessary to each model without requiring manual intervention. We demonstrate that this novel approach outperforms available alternatives on benchmark datasets in many scenarios.
A Pre-trained Clinical Language Model for Acute Kidney Injury

Pre-trained contextual language models such as BERT have dramatically improved performances for many NLP tasks recently. However, few have explored BERT on specific medical domain tasks such as early prediction for Acute Kidney Injury (AKI). Since much of the clinical information is contained in clinical notes that are largely unstructured text, in this paper, we present an AKI domain-specific pre-trained language model based on BERT (AKI-BERT) that could be used to mine the clinical notes for AKI early prediction. Our experiments on MIMIC-III dataset demonstrate that AKI-BERT can yield performance improvements for AKI early prediction.
A Frequency Estimation Algorithm under Local Differential Privacy

With the rapid development of 5G, the Internet of Things (IoT) and edge computing technologies dramatically improve smart industries' efficiency, such as healthcare, smart agriculture, and smart city. IoT is a data-driven system in which many smart devices generate and collect a massive amount of user privacy data, which may be used to improve users' efficiency. However, these data tend to leak personal privacy when people send it to the Internet. Differential privacy (DP) provides a method for measuring privacy protection and a more flexible privacy protection algorithm. In this paper, we study an estimation problem and propose a new frequency estimation algorithm named MFEA that redesigns the publish process. The algorithm maps a finite data set to an integer range through a hash function, then initializes the data vector according to the mapped value and adds noise through the randomized response. The frequency of all interference data is estimated with maximum likelihood. Compared with the current traditional frequency estimation, our approach achieves better algorithm complexity and error control while satisfying differential privacy protection (LDP).
ConvNet Based Single Image Deraining Methods: A Comparative Analysis

Deraining essentially is removing the visual effect of rain from the captured video or image. It is broadly classified into two fields video deraining and image deraining. Rainy images are a complex blend of base image layer and rain layer. Captured images having drops of rain suffer from uneven brightness where regions occupied by rain are overexposed compared to other regions. After the advent of deep learning, there has been a renewed interest in developing more effective approaches. Many deep CNN-based approaches have been devised to effectively derain images. We have summed up these approaches while focusing on the ConvNet based ones to establish part by part comparison of each phase of the rain removal process: image preparation, model design, basic blocks, training. In this paper, we have analyzed the performance of the considered approaches on both qualitative and quantitative grounds.
Veri Merkezi Topolojilerindeki Anahtarlama Aygıtlarında Enerji Tasarrufuna Yönelik Aritmetik Çalışma

Data Centre facilities are rapidly increasing as parallel computing schemes are ever growing. Those kinds of facilities are designed according to different topologies, but many of them are underutilized, leading to energy misuse in many of the switches providing network interconnection for the end hosts. In this paper, some common Data Centre topologies are studied, such as Fat Tree, Leaf and Spine, N-Hypercube and Folded N-Hypercube, in order to find out their performances in terms of energy saving purposes for the same number of hosts hanging on each design, hence obtaining a coherent comparison among them all. It is to be said that each topology has its own benefits and drawbacks, meaning that each one may be the best option depending on the initial conditions of an implementation. However, in the conditions established for steady latency and jitter, it seems that Fat Tree topologies get better results as the size of the Data Centre grows up, whereas Leaf and Spine does it otherwise.
Developing a visually impaired older people Virtual Reality (VR) simulator to apply VR in the aged living design workflow

In the context of the rapid development of the computer visualisation technology, some advanced visual applications based on Virtual Reality (VR) has been widely used in military, medical, design and various of other professional fields. In Architecture, Engineering, and Construction (AEC) industries, VR as an innovative visualisation tool can create realistic Virtual Environment (VE) to architects or engineers, which help them detect problems in projects intuitively. Therefore, VR is a desirable tool to simplify the workflow in the AEC industry. However, applications of VR in the architecture design industry is still at presenting design result stage and has not been applied in the design process, due to the limitations of technical conditions of VR. Although the mainstream VR technology in AEC can provide a virtual environment that almost close to the real environment, VR should focus more to simulate the human actions or real conditions rather than focus on the quality of the rendering. In architecture design for older people, architects can intuitively understand the conditions of the elderly via using VR technology. Here we show a visually impaired old people VR simulator, and the efficiency and effectiveness of the simulator will be evaluated with real projects. We found that some image processing software or 3D game engines can accurately simulate the visual impairment of the elderly in a VR environment. However, in the architecture design industry, it is difficult for designers to use 3D game engines to complete simulation in the design process, because of the complex simulation process. Therefore, this study will develop a simulator that is simple to use to help architects simulate visual impaired older people in their projects, which can help them design a more reasonable product. This study will base on architecture design consulting company Marchese partners (MP) serving for the elderly. The simulator will be developed and tested in their VR studio, and their projects and design workflow will be used as case studies to evaluate the efficiency and effectiveness of the simulator.
Human-inspired models for tactile computing

The development of Tactile Internet with Human-in-the-Loop (TaHiL) applications faces many challenges for a successful interplay between Cyber-Physical System (CPS) and humans. Technical constraints on the communication latency, computation time, energy, and failure rates have to be met to ensure a seamless and safe integration. Furthermore, the systems developed should be able to predict human actions to enable timely reactions, support humans in their decisions, and to explain the system's behavior to the human appropriately. Currently, there is a lack of models and algorithms that evaluate human goal-directed behavior while fulfilling the constraints required in Tactile Internet (TI) applications. In this chapter, we sketch first ideas towards automated human-style reasoning using stochastic operational models that mimic human decision-making. For this, we consider recent insights from cognitive neuroscience and formal methods, bridging the gap between the human brain and computation. Besides being useful to predict human behavior, we expect our models also to be eligible for efficient decision-making by machines. The latter is motivated by the fact that the human brain is undoubtedly efficient when operating on hard constraints—our operational models for human reasoning could hence be the basis for novel algorithms that meet the requirements on TI applications. We present prerequisites, first steps, and concepts, as well as challenges towards an integrated automated human-style reasoning.
Blockchain and smart sukuk: new determinant of development of the sukuk market

Sukuk are the most popular Islamic products and have experienced the most development and development since their launch. However, this market suffers from large numbers of obstacles and challenges that the Islamic financial community is committed to containing and solving them. The non-standardization of practices, the high costs of the issuing operation, and in particular the lack of diversity of investors and issuers of sukuk, which currently is limited to governments and large companies, are all among the main development challenges. of the sukuk market. Thus, and by their specificities which make it possible to stall this large number of obstacles, the "smart Sukuk" have become the future of emissions of sukuk in the Islamic financial industry. Their structure using blockchain and smart contract technology improves transparency and eliminates fraud and speculation in sukuk transactions. This new way of issuing sukuk has also been designed to avoid the participation of intermediaries and save their additional costs, which is currently one of the main obstacles to the development of the current sukuk market. In our article, we are committed to studying the central question: Blockchain and smart sukuk, are they a new determinant of development of the sukuk market? Thus, we will present the smart sukuk, their application methods, their current uses and we will focus on their specificities which allow to overcome the current obstacles of the development of the sukuk market.
Auctions in Renewable Energy and the Grid Parity Using Solar Photovoltaic Technology in Peru

Abstract This paper, of the network parity situation of photovoltaic solar energy with conventional electrical energy sources, such as hydroelectric and thermoelectric for Peru is analyzed. For this purpose, a review of how the electrical tariff is set for the residential end-user of conventional energy sources has been made. Furthermore, it is shown what it consists about, the cost leveled of energy for the case of photovoltaic solar energy and how this has been used for the calculation of the electric rate of this type of source. A review of the auctions of photovoltaic solar plants that are installed in: Moquegua, Tacna and Arequipa is carried out. The solar photovoltaic tariff has been obtained by auctions, between the year 2010 to 2016, these have decreased significantly until reaching network parity. The costs of both energy sources are compared.
Sounding feet

The project emphSounding Feet explores the creative possibilities of interactively controlling sound synthesis through pressure sensitive shoe inlays that can monitor minute body movements. The project is motivated by the authors' own experience of working with interactive technologies in the context of dance. This experience has led to the desire to more closely relate the sensing capabilities of an interactive system to a dancer's own body awareness which prominently involve aspects of inner perception. The outcome of this project demonstrates that such an approach can help to establish interactive musical scenarios for dance that are not only more intuitive to work with for dancers but that also offer new possibilities for composers to tap into aspects of the dancers' expressivity that are normally hidden for an audience.
Another Tool for Structural Operational Semantics Visualization of Simple Imperative Language

Teaching formal methods, especially semantics of programming languages is an important aspect of theoretical informatics. The learning process often includes a lot of mathematics and learning different notations, which appears to be very difficult for students. In this paper, we present a new interactive tool for visualization of the structural operational semantics of a simple imperative program. We demonstrate our approach on the example of a simple program, where we visualize the inference process of small steps semantic method.
Distributed Functional Signature with Function Privacy and Its Application

We introduce a novel notion of distributed functional signature. In such a signature scheme, the signing key for function <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>f</mi> </math> will be split into <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> shares <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <msubsup> <mrow> <mtext>sk</mtext> </mrow> <mi>f</mi> <mi>i</mi> </msubsup> </math> and distributed to different parties. Given a message <math xmlns="http://www.w3.org/1998/Math/MathML" id="M4"> <mi>m</mi> </math> and a share <math xmlns="http://www.w3.org/1998/Math/MathML" id="M5"> <msubsup> <mrow> <mtext>sk</mtext> </mrow> <mi>f</mi> <mi>i</mi> </msubsup> </math> , one can compute locally and obtain a pair signature <math xmlns="http://www.w3.org/1998/Math/MathML" id="M6"> <mfenced open="(" close=")" separators="|"> <mrow> <msub> <mrow> <mi>f</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mi>m</mi> </mrow> </mfenced> <mo>,</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </mrow> </mfenced> </math> . When given all of the signature pairs, everyone can recover the actual value <math xmlns="http://www.w3.org/1998/Math/MathML" id="M7"> <mi>f</mi> <mfenced open="(" close=")" separators="|"> <mrow> <mi>m</mi> </mrow> </mfenced> </math> and corresponding signature <math xmlns="http://www.w3.org/1998/Math/MathML" id="M8"> <mi>σ</mi> </math> . When the number signature pairs are not enough, nobody can recover the signature <math xmlns="http://www.w3.org/1998/Math/MathML" id="M9"> <mfenced open="(" close=")" separators="|"> <mrow> <mi>f</mi> <mrow> <mfenced open="(" close=")" separators="|"> <mrow> <mi>m</mi> </mrow> </mfenced> <mo>,</mo> <mi>σ</mi> </mrow> </mrow> </mfenced> </math> . We formalize the notion of function privacy in this new model which is not possible for the standard functional signature and give a construction from standard functional signature and function secret sharing based on one-way function and learning with error assumption. We then consider the problem of hosting services in multiple untrusted clouds, in which the verifiability and program privacy are considered. The verifiability requires that the returned results from the cloud can be checked. The program privacy requires that the evaluation procedure does not reveal the program for the untrusted cloud. We give a verifiable distributed secure cloud service scheme from distributed functional signature and prove the securities which include untrusted cloud security (program privacy and verifiability) and untrusted client security.
Evaluation of graphic data corruptions impact on artificial intelligence applications

Abstract The article is devoted to the problem of the reliability of applications based on artificial intelligence. The authors made an attempt to evaluate the impact of the graphic data distortion at the input of a convolutional neural network on the result of image classification. The experiment is based on the fault injection method. A series of independent tests were carried out for such distortions as Gaussian noise, salt and pepper, speckle and Poisson noise, as well as median blur, motion blur, scene brightness changing, rotation, rain, and snow. The results showed that Gaussian noise was the least critical distortion; environmental conditions (rain, snow, brightness) and image rotation up to 20 degrees are less critical than focus losing and motion blur, while the most critical distortion is speckle noise. It was verified that preprocessing the input data of the neural network improves the accuracy of image recognition.
ATA: Attentional Non-Linear Activation Function Approximation for VLSI-Based Neural Networks

In this letter, we present an attentional non-linear activation function approximation method called ATA for VLSI-based neural networks. Unlike other approximation methods that pursue the low hardware resources with a high recognition accuracy loss, the ATA utilizes the pixel attention to focus on the important features to keep the recognition accuracy and reduce resource cost. Specifically, attention applied in the activation function is realized by the approximated activation functions with different fitting errors for VLSI-based neural networks. The important features are highlighted by the piecewise linear function and improved look-up table with low fitting error, while the trivial features are ignored with the large fitting error. Experimental results demonstrate that the ATA outperforms other state-of-the-art approximation methods in recognition accuracy, power and area.
Volleyball track acquisition and intelligent analysis technology

Volleyball has been developing rapidly since 1996. It has been widely used both in competitive events and among ordinary people. However, with the continuous improvement of the sports level, the traditional manual training methods cannot meet the existing technical requirements. It is a mainstream method to analyze the track of volleyball by the computer in volleyball training. However, there are still some technical problems such as low precision and incomplete analysis. Therefore, this paper puts forward the research of volleyball track acquisition and intelligent analysis technology. In this paper, the shortcomings of the existing technology are systematically analyzed, and on this basis, the optimization and improvement scheme is proposed. The core technology of this project is to improve the original image preprocessing technology and strengthen the system’s feature extraction ability. Finally, combined with the CAMSHAFT moving object tracking algorithm, the technical scheme of this paper is formed. Through a series of technical improvements, the system effectively improves the ability to extract and analyzing the track of volleyball. In order to further verify the practical effectiveness of this scheme, a number of comparative experiments including algorithm accuracy comparison experiment, trajectory recognition detection, and algorithm signal-to-noise ratio verification are carried out. The object of comparison is the current mainstream common filtering algorithm. Through the analysis of experimental data, this method is more accurate than the common filtering algorithm in the extraction of volleyball trajectory, which effectively improves the comprehensive performance and robustness of the traditional method.
Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering

We propose both the first embarrassingly parallel consensus variational inference algorithm and a new consensus Monte Carlo algorithm for efficient implementation of Bayesian nonparametric mixture models. The proposed algorithms are based on a group clustering approach, and they substantially accelerate inference and reduce memory costs compared with standard Markov chain Monte Carlo and variational inference algorithms for clustering. We demonstrate that our proposed algorithms are significantly faster than competing methods while maintaining the same clustering accuracy. Due to their simplicity and embarrassingly parallel nature, our proposed algorithms are straightforward to implement and widely applicable beyond the models and applications considered in this paper.