Reviewerly/exHarmony huggingface dataset:
Detection of Health-Preserving Behavior Among VK.com Users Based on the Analysis of Graphic, Text and Numerical Data

Health preservation is one of the urgent priorities for any group of people. There is a lot of research currently underway on diagnosing and monitoring health using data from social media. In this paper, the problem of the automatic classification of users of the Russian-language social network VK.com in terms of whether they lead a healthy lifestyle is considered. To solve this problem, various types of information was collected from user profiles: text, numerical and graphic data. The users then took a lifestyle and health survey. The results of this survey were used in order to split the users into groups according to the degree of adherence to a healthy lifestyle. The survey results were used to train various binary classifiers. The best results (about 0.76 F1-score) in our experiment were shown by a model that was trained on combined features (images from users public “walls”, as well as N-gram features compiled from text from the users public “walls”). These results were achieved using the following machine learning models “multilayer perceptron”, “naive Bayesian classifier” and “k nearest neighbours”.
Infrared Pedestrian Tracking With Graph Memory Features

Challenging due to the absence of color information and low resolution, infrared pedestrian tracking is still of great importance in video surveillance for its ability to distinguish and track persons in night vision. Recent researches show that the CF (Correlation Filters) based methods have a satisfying performance on the infrared pedestrian tracking task when combined with deep features. However, most of these algorithms only rely on the general convolutional features and online model updating to trace object variation, resulting in the lost track of pedestrians with large deformation and infrequent poses. In this paper, we propose a long-term inter-frame graph memory feature as a stronger descriptor for infrared pedestrians. We show that this cross-frame memory graph can enrich deep features with time-variant pedestrian appearance information, boosting the robustness and accuracy of the baseline CF tracker. Extensive experiments and analyses are carried out on the widely-used PTB-TIR dataset, demonstrating the effectiveness and universality of the proposed graph memory features for infrared pedestrians.
Designing a System Architecture for the Management of the Recovered Parts from End-of-Life Aircraft Using Fuzzy Decision Making and Blockchain

The recovery of high-value parts from end-of-life (EoL) aircraftAircraft as a business model is a main source of revenue. Following airworthiness regulations, performing parts inspections and certifications and tracing documentation are critical for the safety and efficiency of parts trading. In this chapter, an architecture is proposed based on fuzzy intelligent decision making andBlockchain blockchain-enabled network for real-time data accessibility of multi-actors involved in the ecosystem of EoL aircraft treatment and the effective management of the recovered parts after disassembly. This architecture has three layers: the management layer contains fuzzy inference system models for revenue, supply and demand management as well as data mining module, blockchainBlockchain layer for tracing parts, certifications, and following airworthiness regulations supported by a connection layer. The application perspective for EoL aircraftAircraft is also discussed.
Sustainability Means Inclusivity: Engaging Citizens in Early-Stage Smart City Development

The challenge of how cities can be designed and developed in an inclusive and sustainable direction is monumental. Smart city technologies currently offer the most promising solution for long-term sustainability, but the impact of such solutions will be significantly reduced without long-term, widespread adoption by citizens. In this article, we present the rationale behind an ongoing interdisciplinary research project titled “Sustainability means inclusivity (SuMIn)” that aims to address exactly this problem by using a par-ticipatory design approach. Focusing on a smart city test site in Sweden where sensors are currently being deployed to collect data on, for example, noise, par-ticles, vehicle numbers, and types, the goal is to bring local residents and government representatives into dialogue with technical developers by adopting a “meet-in-the-middle” approach.
Analysis of Change of Market Value of Bitcoin Using Econometric Approach

Blockchain not only represents the latest innovation in information and communications technology (ICT) but also lays the foundation stone for the development of numerous economic models which are characterized by decentralization, privacy, security and transparency. One of the pre-eminent products of the transparent digital database of transactions that is claimed to be inviolable is the cryptocurrency. The cryptocurrency is often christened as Bitcoin since it is the most convenient and easy to use out of all and has consequently gained enormous traction in contemporary economic and business models, radically transforming payment mechanisms for the better. Numerous studies in the past have been undertaken that aim at examining the underlying reason behind Bitcoin’s value over the years. The study involves usage of econometric models to analyze how various variables at the macro- and micro-level affect the Bitcoin price trend in the long and short term. The objective of this research analysis is not only to aid investors in making prudent investment decisions in Bitcoin by closely monitoring the influential exogenous and endogenous variables but also to structurally understand the working relationship and economic consequences of these variables in the cryptocurrency space. After checking for stationarity of the variables under study, ARDL model was used followed by the Bounds F test and UECM model (where required). It was found that the variables under study explained 99.32% variation in BTC market value in the long run, some of which had a positive relationship while some had negative. Some were also found to be not significantly influencing its market value.
Off Line Handwritten Signature Verification Based on Feature Fusion

At present most of the research on offline handwritten signature is based on a single language and the problems of the sparse signature image, weak feature representation ability and low verification rate have not been well solved. In this paper, the off-line handwritten signature images of two different languages including Chinese and Kazakh are used as experimental data. the experimental results show that even a small amount of training data. The accuracy rate of this paper in multi-lingual off-line handwritten signature verification can still reach 96.74% compared with related work the verification effect of this method is higher.
Research on Resource Scheduling Algorithm for The Cloud

With the rapid development of cloud computing, how to allocate the reasonable resources to the task according to the different needs of users has become the core of cloud task scheduling. Particle Swarm Optimization (PSO) algorithm is simple and convergent to the optimal solution, but it easily falls into the local optimal solution at the optimization process. In this paper, the probabilistic sampling of the simulated annealing algorithm (SA) is added to the PSO algorithm to improve the shortcomings of the PSO-SA algorithm. This purpose is that it not only avoids failing into the local optimal solution, but also ensures the diversity of the population. By introducing the trust relation into the task scheduling problem of the cloud, it has been combined with the PSO-SA algorithm. Finally, the effectiveness of the algorithm is verified by using of CloudSim. The experimental results show that the PSO-SA algorithm can meet the requirements of the reliability of cloud service quality.
Developing Open Test Tasks

An internationally established method for identifying the characteristic professional work tasks is the method of expert specialist workshops (→ 3.3.2).
A Streaming Approach for Association Rule Analysis of Spanish Politics on Twitter

The technological era in which we live has supposed an exponential rise in the quantity of data daily-generated in the Internet. Social networks and particularly Twitter has been one of the most disruptive factors in this era, allowing people to share easily opinions and ideas. Data generated in this social network is an example of streams, which are outlined by the challenges that arise from their particular features: continue, unlimited, high-speed arrivals, demand of fast reaction and with changes over time (known as concept drifts). The dynamism that characterizes this type of problem requires from a streaming analysis in order to perform an adequate treatment. In this situation, data stream mining appears as an emergent field of data science with specialized machine learning techniques according to the nature of streams. One of the most prominent tasks in this field is association stream mining, which focuses on the problem of dynamical extraction of interesting association rules from data features in a situation where it is not possible to assume an priori data structure and there is an evolution of these data features over the time. This paper aims to carry out a proof of concept focused on politics by studying a real collection of tweets related to the 2019 Spanish Investiture process. Thereby, Fuzzy-CSar-AFP algorithm has been applied in order to carry out an online analysis of association rules among a collection of terms of interest from our Twitter database.
Automatic generation of a 3D sign language avatar on AR glasses given 2D videos of human signers.

In this paper we present a prototypical implementation of a pipeline that allows the automatic generation of a German Sign Language avatar from 2D video material. The presentation is accompanied by the source code. We record human pose movements during signing with computer vision models. The joint coordinates of hands and arms are imported as landmarks to control the skeleton of our avatar. From the anatomically independent landmarks, we create another skeleton based on the avatar’s skeletal bone architecture to calculate the bone rotation data. This data is then used to control our human 3D avatar. The avatar is displayed on AR glasses and can be placed virtually in the room, in a way that it can be perceived simultaneously to the verbal speaker. In further work it is aimed to be enhanced with speech recognition and machine translation methods for serving as a sign language interpreter. The prototype has been shown to people of the deaf and hard-of-hearing community for assessing its comprehensibility. Problems emerged with the transferred hand rotations, hand gestures were hard to recognize on the avatar due to deformations like twisted finger meshes.
Comparison Of Feature selection Methods For Supervised Learning

The technology related to the transmission of wireless networks are enhanced and with that many devices are connected to the internet to work consistently among them. Then the utilization of networks multi-folded and unknown persons known as intruders begin to access data without any intimation to the user. To control them the IDS are designed, where the unknown access is detected and prevented from further access. In the pre-processing stage of IDS,   the various irrelevant features are identified and eliminated by the process of feature selection of large data stored. The mutual information between features and class is evaluated in the feature selection process which is followed in all the previous proposed papers. But there is weak interaction between features and class which is a common drawback. To overcome this problem various methodologies based on mutual information like JMIM and NJMIM is proposed by Bennaser et.al with a classification accuracy of 90.77% with 8 features using Parkinson's dataset and NJMIM with 83% noted from the graph with sonar dataset which outperformed CMIM, DISR, mRMR, JMI, IG. In Redundant Penalty feature mutual information (RPFMI), the relationship between features and class is given much importance with a classification accuracy of 99.77% using the KDDCUP dataset and in MMIFS the classification accuracy is 94% with the same dataset. So, this study paves the way to develop more techniques in feature selection.
WearMask in COVID-19: Identification of Wearing Facemask Based on Using CNN Model and Pre-trained CNN Models

The rapid worldwide spread of Coronavirus Disease 2019 (COVID-19) has led to global outbreak. To prevent the further spreading of this virus thus ensuring people's health safety, wearing face mask is mandatory in all public places strictly imposed by the respective governments of the countries all over the world. To assess the situation whether people are wearing mask, automatic identification of face mask in public places is a timely solution and that's where our 'WearMask in COVID-19' project primarily stands for. As part of our project, to identify whether people wear mask or not, this article analyzes and compares the effectiveness of using CNN architecture built from scratch with pre-trained ResNet50 and MobileNet-v2 models. These models are trained on a combined face mask image dataset having mostly low resolution simulated masked face images and some high resolution real masked face images. The models work through three steps: image pre-processing, image feature extraction and classification using the models above, and model evaluation. Experimental results show that an in-house CNN structure that we have developed from scratch performs better than the ResNet50 model and is comparable to the performance of the MobileNet-v2 model.
The Magic Mode of Everyday Objects: Bridging the Digital and Physical Environments of Play with Monnom

We present an online digitally-enhanced environment Monnom (https://www.playmonnom.com) for children. Monnom offers body-object interaction and its system only requires a webcam. Its algorithm recognizes physical objects within view and drives the interaction with a digital canvas. By moving around and adding objects to their physical space, children can fictionalize its meaning and create their own place(s) within it with feedback from the digital environment.
The Necessity of Changes in the Higher Education in Bulgaria

This work is an attempt for a brief analysis of the new "Strategy for the development of higher education in the Republic of Bulgaria for 2021 – 2030" and is searching for an answer to the new challenges facing the education system. Searching and finding the right solutions in this direction does not only require specific skills and competencies, but also sufficient experience, information and knowledge in this direction. This challenge cannot and should not be the action of just one person, but of a group of scientists, researchers and politicians with a sufficiently high level of expertise that would contribute to the development of an objective and accurate action plan.
Differentially-Private Sublinear-Time Clustering

Clustering is an essential primitive in unsupervised machine learning. We bring forth the problem of sublinear-time differentially-private clustering as a natural and well-motivated direction of research. We combine the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -median sublinear-time results of Mishra et al. (SODA, 2001) and of Czumaj and Sohler (Rand. Struct. and Algorithms, 2007) with recent results on private clustering of Balcan et al. (ICML 2017), Gupta et al. (SODA, 2010) and Ghazi et al. (NeurIPS, 2020) to obtain sublinear-time private <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -median algorithms via subsampling. We also investigate the privacy benefits of subsampling for group privacy.
Emotion Analysis Base on Capsule Network

Fine-grained sentiment analysis is a research hotspot in the field of natural language processing. Among them, short text sentiment analysis because of the less content. It is difficult to obtain better results using only a single feature extraction network. The article proposes a new hybrid model that uses BERT as the word vector layer for word vector mapping and uses bidirectional gated recurrent network and self-attention capsule network in turn for feature extraction, which solves the problem of the single capsule network's weak ability to extract time series information. At the same time, we propose a self-attention routing algorithm, which can be calculated in parallel to improve model speed. The experimental results on the Emobank sentiment regression dataset show that the mixed structure achieves the best results in the three dimensions of emotion valence, arousal and dominance, and the model is faster than the EM dynamic routing algorithm training.
Motor Fault Diagnosis System Based on Single Chip Microcomputer and Artificial Intelligence

An artificial intelligence motor fault diagnosis system based on STM32 single-chip microcomputer and deep learning is designed. After algorithm optimization and a small number of sample learning, the real-time recognition rate of fault diagnosis can reach 96.07%. First, the hardware circuit is designed, the ADXL335 acceleration sensor is used to collect the motor running vibration signal, and the Kalman filter is used to improve the sampling accuracy. Then the Kalman filtered signal is output to the host computer through the serial port, and then converted into the deep learning model for training and recognition using FFT. Finally, experiments are carried out on a self-made motor fault diagnosis simulation experiment platform, and the results show that the system has a better recognition effect.
Phase retrieval of nanosecond laser pulses based on temporal shearing interferomentry

Temporal shearing interferometry is proposed to measure the temporal phase distribution of nanosecond laser pulses. In the proposed scheme, the pulse to be measured is divided into two pulses with a delay of hundreds of picoseconds in between, arbitrary one of the two pulses is added to by an appropriate amount of frequency shift, then is combined with the remaining pulse, thereby obtaining the temporal shearing interferogram that is recorded by a normal photodiode. The temporal phase distribution is calculated by an adaptive algorithm based on Fourier transform, and further the precise spectra of the measured pulse can also be calculated according to the Fourier relation between time domain and spectral domain. Based on the systematic analysis of the principle of the technology, the proposed technology is verified by numerical simulation. And the influence of the variable parameters including noise, relative delay, relative intensity on the measured error are systematically analyzed in the simulation. And the results show that the proposed nanosecond temporal phase diagnostic technique has a good performance when the signal noise ratio of the interferogram is above 15 dB, the relative delay of the pulses is between 0.5% and 28% and the relative intensity is above 0.1%. The proposed method is verified experimentally in a nanosecond laser system with central wavelength of 640 nm and pulse width of 20 ns. And the calculated spectra obtained from the temporal shearing interferogram match well with the spectra measured by a scanning Fabry-Perot interferometer. This proposed technique does not use any nonlinear optical effects, thus it can be applied to the diagnostic of nanosecond laser pulse centered at any wavelength. Hence, it provides a simple experimental setup for implementing the higher-accuracy diagnostic of the temporal phase distribution of nanosecond laser pulses.
The transaction cost analysis and future development of the “blockchain + audit” industry—— based on the analytic hierarchy model and the perspective of industry life cycle

The rise of “blockchain +” has a great impact on all fields and even the whole social and economic situation, and is no exception to the audit work. Blockchain has the characteristics of non-tampering, timestamp, grid consensus and so on, which is in line with the requirements of the audit work to pursue efficiency and effect. Under such a development background, the “blockchain+audit” industry arises from this. The integration of blockchain into audit engagements, in turn, changes the proportion of audit transaction costs. Based on this background, the Analytic Hierarchy Process (AHP) is used to analyze the transaction cost of audit. In view of the problems found in the current transaction cost analysis of “blockchain+audit”, combined with the research on the future development trend based on the theory of technological innovation and industry life cycle, this paper puts forward some suggestions to effectively control the audit cost and improve the audit efficiency. The aim is to provide a practical reference basis for the better application of blockchain technology in auditing.
Revolution: A Spatial-specific Convolution for Image Super-Resolution

Recently, deep convolution neural networks achieve remarked performance in single image super-resolution(SISR) due to their strong feature representation ability. However, most existing SR methods mainly use standard convolution in each layer, neglecting to explore the various feature of spatial domain. Standard convolution focuses on spatial invariance by using the same convolution kernel in space. It deprives kernels of the ability to adapt the different spatial positions, hence hindering the diverse of feature. Later, a new convolution operator, Involution, was proposed to implement spatial-specific convolution. But it calculated the convolution kernel parameters of surrounding pixels only by utilizing the characteristics of the central pixel, which was unreasonable and limited its performance. To address these issues, in this paper, we propose a novel convolution named Revolution, which not only incorporates spatial-specific to capture different spatial information, but also considers the relevance of the pixels to determine convolution kernel parameters to further learn more realistic feature representations. On the other hand, the Revolution we proposed makes the number of parameters smaller. Experiments demonstrate that our method obtains obvious improvement in terms of indicators and visual effects.