Reviewerly/exHarmony huggingface dataset:
Cryptocurrencies: technology, initiatives of banks and central banks, and regulatory challenges

Abstract This paper analyses the impacts of the innovation known as distributed ledger technology (DLT) on the monetary system and on financial activities. Private cryptocurrencies, such as Bitcoin, are permissionless means of payment, based on blockchain, a form of DLT. Evaluations suggested that these private cryptocurrencies could compete with the banks payment systems and even supplant state currency. The development of these technologies has the potential to modify profoundly monetary and financial practices, but there are no indications that they may threaten the centrality of state money and the banking system in the contemporary monetary order. Major international banks have developed cryptocurrencies for settlement systems and for interbank transactions, including the so-called stablecoins, issued by highly technological companies with on par conversion into state money. Some central banks are studying the launch of state cryptocurrencies that could coexist with their fiduciary state currency and even replace their paper currency. The use of this technology results in new challenges for regulation, including the fact that cryptocurrencies can be used for money laundering and by organized crime.
Matrix Stochastic Game with Q-learning for Multi-agent Systems

A model of matrix stochastic game for decision making in conditions of uncertainty is developed. A Q-learning method is proposed for solving a stochastic game with a priori unknown payoff matrices. The formulation of the game problem is performed, the Markov recurrent method and the algorithm for its solution are described. The results of computer simulation of stochastic game with Q-learning are obtained and analyzed. In this paper, the ranges of changes in the parameters of the game Q-method are experimentally established to ensure the convergence to one of the Nash equilibrium points. As the value of the current win discounting parameter increases, the variance of current winnings reduces, and the order of change for the learning step decreases, and the convergence rate of the game Q-method increases.
Getting to femtojoule optics – what physics and what technology?

Optical physics and well-chosen intimately-integrated devices allow dense, low-energy communications if we also use optical parallelism and timing precision to eliminate most receiver, time-multiplexing and timing circuits that otherwise dissipate most of the energy.
DESIGN OF BIOMETRIC PROTECTION AUTHENTIFICATION SYSTEM BASED ON K-AVERAGE METHOD

The method of biometric identification, designed to ensure the protection of confidential information, is considered. The method of classification of biometric prints by means of machine learning is offered. One of the variants of the solution of the problem of identification of biometric images on the basis of the k-means algorithm is given. Marked data samples were created for learning and testing processes. Biometric fingerprint data were used to establish identity. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. Experimental results indicate that the k-means method is a promising approach to the classification of fingerprints. The development of biometrics leads to the creation of security systems with a better degree of recognition and with fewer errors than the security system on traditional media. Machine learning was performed using a number of samples from a known biometric database, and verification / testing was performed with samples from the same database that were not included in the training data set. Biometric fingerprint data based on the freely available NIST Special Database 302 were used to establish identity, and the learning outcomes were shown. A new fingerprint scan that belongs to a particular person is compared to the data stored for that person. If the measurements match, the statement that the person has been identified is true. The machine learning system is built on a modular basis, by forming combinations of individual modules scikit-learn library in a python environment.
Aiding Intimate Violence Survivors in Lockdown: Lessons about Digital Security in the COVID-19 Pandemic

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An Extensive Study of Privacy Preserving Recommendation System Using Collaborative Filtering

Recommender system is the most information filtering-based system that deals with information overload by filtering vital information from large dynamically collected information according to the user’s choices, interest, or item’s behavior. The collaborative-based filtering recommender system is one of the best filtering approaches, which is very effective in a wide range of applications. The recommender system’s accuracy usually depends on the quality of the collected data, which cannot be collected from users without concerning their privacy requirements. Today’s recommender systems are obliged to collapse unless they provide a measure of privacy to users. However, privacy and accuracy are conflicting goals because preserving privacy requires a level of distortion in original data, which yields a decrease in recommender systems’ accuracy. Nowadays, providing privacy to sensitive information in a side recommender system is the main requirement with the best accuracy. This chapter summarizes all privacy-preserving methods for providing security and privacy to the user’s rating of a particular item.
RETRACTED ARTICLE: Multimedia digital art design based on double-buffer cooperation and distributed index

As people’s demand for application services continues to grow, traditional TCP and IP network architectures face many security and mobility challenges. In order to completely eliminate these problems, scientists have proposed a representative new Internet architecture, such as a content center network. Each node in the content center network has a data storage function. In a limited cache area, effective content is cached through collaboration between nodes so that users can retrieve the required data faster. Double-cache collaboration means that peers on the network can not only store and retrieve data from their own cache, but also store and retrieve data from the caches of other peers. Although this affects the cache hit rate, it can improve cache utilization and improve network performance. Taking multimedia works as an artistic whole, it analyzes the movement form and work characteristics of multimedia digital artworks, movement form and style, and movement form and plot relationship of works on the existing basis, and explores the entire movement design of multimedia works. Based on distributed index technology and R tree batch loading technology, a distributed index based on spatio-temporal data flow is proposed. The index uses a two-layer distributed index structure composed of an external B+ tree and an internal R tree to achieve effective storage of a large amount of spatio-temporal data, and provide low-latency, high-concurrency indexing services for the external world.
Local Feature Normalization

In deep learning, Batch Normalization (BN) is a widely used fundamental technique in Convolutional Neural Networks (CNNs) to improve the training speed and generalization capability of CNNs due to its effectiveness and simplicity. However, BN only focuses on global features and normalizes the whole feature map while ignoring the importance of the local feature. In this paper, we proposed the local feature normalization layer (LFN) to solve this problem by enhancing the features’ local area competition. These CNNs with LFN can leverage local regions with rich feature information. After normalized by LFN, if a feature of the local region is more expressive, its value will be bigger, and conversely, the value will be smaller. We also discussed how to use LFN better in CNNs in detail and solve some algorithm conflict problems. The LFN layer should be used after the ReLU in the first few layers. And LFN should not be used in front of the Max-pooling layer. Experimental results show that various CNN+LFN achieved better accuracy on the image classification tasks CIFAR dataset and ImageNet dataset than the same neural network with other popular normalization methods.
Challenges in hospital-acquired coronavirus disease 2019 (COVID-19) surveillance and attribution of infection source

Abstract We performed surveillance for hospital-acquired COVID-19 (HA-COVID-19) and compared time-based, electronic definitions to real-time adjudication of the most likely source of acquisition. Without real-time adjudication, nearly 50% of HA-COVID-19 cases identified using electronic definitions were misclassified. Both electronic and traditional contact tracing methods likely underestimated the incidence of HA-COVID-19.
Current Directions of Legal Ensuring National Security

The article reveals the problematic issues for ensuring financial security in the context of globalization in global financial systems. The modern stage of development of state financial systems is characterized by difficulties in ensuring national financial security. Despite the urgency of this problem, the international community is focusing on ensuring economic security during the formation of universal and regional legal instruments, leaving out of sight issues of legal and practice-oriented guidance in financial security. The article examines the main directions of implementation of state policy aimed at improving the effectiveness of legal and national and international means to ensure financial security in the context of the globalization of growing threats to economic security. The focus is on the modernization of legal instruments to regulate public relations related to financial security. Particular attention was paid to the correlation of international legal bases, national legislation, and regional laws. It is concluded that the development of state financial policy that guarantees the security of the budgetary system is fundamental.
A Similarity Score Model for Aspect Category Detection

Aspect-based Sentiment Analysis (ABSA) aims to extract significant aspects of an item or product from reviews and predict the sentiment of each aspect.Previous similarity methods tend to extract aspect categories at the word level by combining Language Models (LM) in their models.A drawback for the LM model is its dependence on a large amount of labelled data for a specific domain to function well.This work proposes a mechanism to address labelled data dependency by a one-step approach experimenting to decide the best combinatory architectures of recurrent-based LM and the best semantic similarity measures for fostering a new aspect category detection model.The proposed model addresses drawbacks of previous aspect category detection models in an implicit manner.The datasets of this study, S1 and S2, are from standard SemEval online competition.The proposed model outperforms the previous baseline models in terms of the F1-score of aspect category detection.This study finds more relevant aspect categories by creating a more stable and robust model.The F1 score of our best model for aspect category detection is 79.03% in the restaurant domain for the S1 dataset.In dataset S2, the F1score is 72.65% in the laptop domain and 75.11% in the restaurant domain.
Co-Designing Hardware and Models for Efficient On-Device ML Inference

Deep learning inference at the edge continues to deliver state of the art results on real-world applications involving images, video, speech, and human activity. The workhorse behind these advances—increasingly complex neural network models—continue to grow in size and computational requirements. These advances place significant demand on the energy-constrained hardware platforms responsible for executing such models and are driving trends like low-precision number formats, network pruning, and complexity-reducing network transforms. This talk will discuss emerging research aimed at co-designing neural networks and hardware to enable even larger, more complex models to operate on highly-constrained hardware platforms.
Technology, Nonlinearities and the Determinants of Inequality: New Panel Evidence

Relying on data for a panel of 90 advanced and emerging economies spanning the 1970-2015 period and System-GMM dynamic panel estimation, we extend the standard ‘Kuznets-curve’ empirical framework to investigate how globalisation, financial sector development and technology influence changes in the distribution of income. We take account of potential nonlinearities for all the factors involved and distinguish two technological categories: General-Purpose Technology (GPT) and Investment-Specific Technology (IST). Our empirical findings highlight technological progress as playing a key role in determining inequality dynamics. In particular, we find strong evidence supporting the ‘Schumpeterian view’ of the interplay between changes in income inequality and IST. This is characterised by a U-shaped pattern indicating that, beyond a certain threshold, IST worsens inequalities – in line with technology turning from labour-complementary to labour-replacing. The results are robust to the inclusion of additional socioeconomic and institutional quality variables in the models. In light of the growing automation process we are witnessing in recent years, these findings have significant implications for policy strategies aimed at reducing income inequality.
Solving Interdisciplinary Problems with Inverse-designed Photonics Integrated Circuits

We present latest advances in using the photonics integrated devices and circuits for solving interdisciplinary problems. Especially, we focus on the efficient inverse-design optimization methodology, and ways to promote the circuits in multiple application scenes.
Geospatial Detection of Hidden Lithologies along Taiping to Ipoh Stretch of the Highway Using Medium Resolution Satellite Imagery in Malaysia

A highway that is an essential link to major cities is prone to different geological hazards. The first step to tackling this problem is identifying the type of lithologies underneath the region’s bedrock. Detecting lithologies in a tropical rainforest area such as Malaysia may be a difficult task and logistically intricate due to inaccessibility to some areas. To overcome this challenge, band rationing was performed on the surface reflectance band of Landsat 8 OLI in the GIS environment regarding Malaysian locales. Four different band ratios were generated along with composite bands. The band ratios used for this study are 6/5 7/6 4/7, 5/4 6/5 7/6, 6/7 4/2 5/4, and 6/5 6/7 4/2. The band ratio 4/2 is used to map iron oxides due to their absorption capability in the blue region and high reflectance capability in the red region. The band ratio 6/7 is used for the lithological mapping of clay minerals and kaolinite due to the high and low reflectance in bands 6 and 7, respectively. While band ratio 5/4 is used for Alteration minerals, and ratio 6/5 is used for ferrous minerals. The generated outcome shows the lithological combination as granites, limestones, phyllites, and metasandstones. The metasediments and limestones region are also well discriminated within small areas of the study area. The best band ratio of 5/4 6/5 7/6 illustrates the most distinct spectral reflectance and absorption capability bringing out the weathered medium-grained granites. A new up-to-date lithologic map of the Taiping to Ipoh area is proposed based on the interpretation of image results and field verification work. Results show that the proposed methods have great potential for lithological mapping of tropical regions. Thus, this technique is recommended for mapping different types of granitic rocks, which is the most abundant rock underlying the study area along the highway.
Consideration on the construction of school physical educationunder the background of sports power

This paper expounds and reflects on the construction of school physical education under the background of sports power through the method of literature and logical analysis.Through the study, it is found that the cultural construction and organizational form of school physical education should be further improved under the influence of the development of school education in the new era.With the development of economy and the improvement of people's living standard, it is particularly important to strengthen the construction of school physical education and cultivate high-quality talents.The author thinks that Wang Qingguo proposed the optional class model of P.E., which can not only stimulate students' interest in P.E., but also facilitate the implementation of P.E.teaching process and the construction of school P.E.
Compact Finite-State Super Transducers for Grapheme-to-Phoneme Conversion in Highly Inflected Languages

Finite-state transducers are suitable for compact representation of pronunciation dictionaries, which are an important component of speech synthesis systems. In this paper, we first revise and analyse several properties of finite state transducers regarding their size minimization, which can be achieved by their determinization and minimisation. In scope of a novel experiment, we demonstrate that for highly inflected languages, their minimum size starts to decrease when the number of words in the presented pronunciation dictionary reaches a certain threshold. This phenomenon motivated us to introduce a new type of finite-state transducers, called finite-state super transducers, which allow the representation of pronunciation dictionaries with a smaller number of states and transitions using the existing determinization and minimization algorithms. A finite-state super transducer can accept and convert words that are not in the original represented pronunciation dictionary. The resulting phonetic transcriptions of these words may be incorrect, but we demonstrate on new data that the error rates are comparable to the performance of the state-of-the-art grapheme-to-phoneme conversion methods.
Monolithic Integration of III-V/Si Hybrid MOS Optical Phase Shifter and InGaAs Membrane Photodetector

We present proof-of-concept monolithic integration of III-V/Si hybrid MOS optical phase shifter and InGaAs membrane photodetector for Si programmable photonic integrated circuits. We demonstrated responsivity of &gt; 1.0 A/W at O-band owing to avalanche amplification.
Detecting and Fixing Nonidiomatic Snippets in Python Source Code with Deep Learning

We present an algorithm to find nonidiomatic snippets in Python source code and replace them with cleaner and more performant alternatives. The algorithm is divided into three subtasks: (i) the snippets are localized, then for each snippet (ii) the type of the nonidiomatic pattern, and (iii) the key variables are determined. The subtasks of localizing patterns and extracting variables are solved as Sequence Tagging tasks with LSTM networks. Determining the nonidiomatic pattern type is a classification problem, which we solve by training a feedforward neural network. We evaluate the process on a dataset containing more than 13 000 programs coded by students.
How to Get Information in Online Environments? A Comparison of the Use of Net Generation Tools

It is anticipated that there will be differences in the use of the Internet and mobile technologies in accessing information amongst the new generations (digital natives) depending on the development level of the country they live in. Globalization further reinforces these differences. In this study, these differences are investigated for three countries having different levels of development: the United Kingdom, Malta, and Turkey. A total of 443 participants are chosen from university universities studying in different departments in 2017-2018 spring term. Variance analysis (ANOVA) has been used in identifying the differences between the study groups’ access to information of the three countries. Findings show differences between the three countries in the use of social networks such as Facebook, Instagram, Twitter and Snapchat. In addition, there are differences in the use of mobile technology devices Notebook, Tablet and Smartphones in accessing information. Finally, differences were identified in the use of information sources in accessing information. These differences are in the use of search engines, wiki, social networks, blogs, and digital libraries. No differences were found in terms of digital newspapers. The findings are discussed comparatively with reference to the scholarly work done in the field and recommendations are given.
A Compression-Compilation Framework for On-mobile Real-time BERT Applications

Transformer-based deep learning models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. In this paper, we propose a compression-compilation co-design framework that can guarantee the identified model meets both resource and real-time specifications of mobile devices. Our framework applies a compiler-aware neural architecture optimization method (CANAO), which can generate the optimal compressed model that balances both accuracy and latency. We are able to achieve up to 7.8x speedup compared with TensorFlow-Lite with only minor accuracy loss. We present two types of BERT applications on mobile devices: Question Answering (QA) and Text Generation. Both can be executed in real-time with latency as low as 45ms. Videos for demonstrating the framework can be found on https://www.youtube.com/watch?v=_WIRvK_2PZI
A resource-effective QKD field-trial in Padua with the iPOGNAC encoder

We describe a novel low-error and calibration-free polarization encoder for Quantum Key Distribution, called iPOGNAC. The device, together with the Qubit4Sync synchronization, has been used to realize a resource-effective QKD field-trial in Padua, Italy.
Simplifying Measurement-Device-Independent Quantum Key Distribution with Directly Modulated Laser Sources

We demonstrate a new MDI-QKD system design employing gain-switched, injection-locked lasers to directly generate GHz-clock-rate encoded states, greatly simplifying MDI-QKD and improving state-of-the-art performance by an order of magnitude.
The use of e-learning in motivating students to excel towards learning outcomes

Researchers focus on the efforts of teachers to improve learning outcomes. Researchers examined the use of e-learning in motivating student achievement on learning outcomes. The influence of student activity on learning outcomes. Student achievement motivation on learning outcomes. The reason for the low quality of student learning makes the author interested in studying this problem. This paper contributes to using e-learning methods, student activeness, and student achievement motivation and can improve learning outcomes. Researchers find a digital-based learning process, the use of e-learning teaching methods in students' achievement motivation towards learning outcomes. The authors see student learning outcomes after using e-learning. Respondents of the study were 35 students who took Christian religious education subjects. The results of the study revealed that independent use of the material received very high scores. Student activeness is carried out correctly and effectively, the highest perceived to be at a mean value of 4.35. Students who take advantage of e-learning have high learning outcomes. The use of e-learning methods, student activeness, and student achievement motivation is implemented correctly and effectively. It was believed to be able to improve the learning outcomes of Christian Religious Education.