Reviewerly/exHarmony huggingface dataset:
Implementation of Simple Verifiability Metric to Measure the Degree of Verifiability of E-Voting Protocol

Verifiability is one of the parameters in e-voting that can increase confidence in voting technology with several parties ensuring that voters do not change their votes. Voting has become an important part of the democratization system, both to make choices regarding policies, to elect representatives to sit in the representative assembly, and to elect leaders. the more voters and the wider the distribution, the more complex the social life, and the need to manage the voting process efficiently and determine the results more quickly, electronic-based voting (e-Voting) is becoming a more promising option. The level of confidence in voting depends on the capabilities of the system. E-voting must have parameters that can be used as guidelines, which include the following: Accuracy, Invulnerability, Privacy and Verifiability. The implementation of the simple verifiability metric to measure the degree of verifiability in the e-voting protocol, the researchers can calculate the degree of verifiability in the e-voting protocol and the researchers have been able to assess the proposed e-voting protocol with the standard of the best degree of verifiability is 1, where the value of 1 is is absolutely verified protocol.
Distance-based online pairs generation method for metric networks training

In this work, we consider the pairs generation algorithm based on the distances between elements in metric space. The right generation of training data is an actual issue, and its solution leads to better neural network learning. Understanding the properties of the source data, we can select pairs for training in such a way that the network will pay more attention to elements that are close in the metric space and have different classes. However, the problem arises when these properties are difficult to extract from the data and a more universal pairs generation method is needed. Our method generates pairs using the results of the network from previous iterations, in parallel with the training process itself. Thus, we do not need to evaluate the properties of elements ourselves, and we can use absolutely any data as learning objects. We demonstrate this approach using the example of Korean character recognition, and also compare it with other commonly used pair generation methods.
Encrypted Traffic Classification Using Graph Convolutional Networks

Traffic classification plays a vital role in the field of network management and network security. Because of the continuous evolution of new applications and services and the widespread use of encrypted communication technologies, it has become a difficult task. In this paper, we study the classification of encrypted traffic, where the purpose is to firstly distinguish between Virtual Private Networks (VPN) and regular encrypted traffic, and then classify the traffic into different traffic categories, such as file, email, etc. The available information in encrypted traffic classification is composed of two parts: the complex traffic-level features and the diverse network-side behaviors. To fully utilize these two parts of information, we propose an approach, called Encrypted Traffic Classification using Graph Convolutional Networks (ETC-GCN), which incorporates traffic-level characteristics with convolutional neural networks (CNN) and network-wide behavior with graph convolutional networks (GCN) in the communication network. We compare the proposed approach with existing start-of-the-art methods on four experiment scenarios, and the results demonstrate that ETC-GCN can improve the classification performance by considering the information of neighbor endpoints that communicated, and the internal features of the traffic together.
A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News.

We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018) coreference system on Dutch datasets of two domains: literary novels and news/Wikipedia text. The results provide insight into the relative strengths of data-driven and knowledge-driven systems, as well as the influence of domain, document length, and annotation schemes. The neural system performs best on news/Wikipedia text, while the rule-based system performs best on literature. The neural system shows weaknesses with limited training data and long documents, while the rule-based system is affected by annotation differences. The code and models used in this paper are available at https://github.com/andreasvc/crac2020
Improving the Head Pose Variation Problem in Face Recognition for Mobile Robots

Face recognition is a technology with great potential in the field of robotics, due to its prominent role in human-robot interaction (HRI). This interaction is a keystone for the successful deployment of robots in areas requiring a customized assistance like education and healthcare, or assisting humans in everyday tasks. These unconstrained environments present additional difficulties for face recognition, extreme head pose variability being one of the most challenging. In this paper, we address this issue and make a fourfold contribution. First, it has been designed a tool for gathering an uniform distribution of head pose images from a person, which has been used to collect a new dataset of faces, both presented in this work. Then, the dataset has served as a testbed for analyzing the detrimental effects this problem has on a number of state-of-the-art methods, showing their decreased effectiveness outside a limited range of poses. Finally, we propose an optimization method to mitigate said negative effects by considering key pose samples in the recognition system’s set of known faces. The conducted experiments demonstrate that this optimized set of poses significantly improves the performance of a state-of-the-art, cutting-edge system based on Multitask Cascaded Convolutional Neural Networks (MTCNNs) and ArcFace.
Pengaruh Kondisi Sosial Ekonomi terhadap Motivasi Belajar Siswa

Artikel ini bertujuan menguji pengaruh kondisi sosial ekonomi terhadap motivasi belajar siswa di SMAN 1 Abuki. Penelitian dilakukan dengan menggunakan pendekatan kuantitatif melalui teknik korelasional. Hasil penelitian menunjukkan bahwa pengaruh kondisi ekonomi orang tua terhadap motivasi belajar siswa adalah positif dan signifikan. Kesimpulan ini mengandung makna bahwa kondisi ekonomi orang tua yang baik memiliki arah positif dan nyata dalam meningkatkan prestasi belajar siswa. Kata Kunci: Kondisi Sosial Ekonoomi, Motivasi Belajar AbstractThis article aims to examine the effect of socio-economic conditions on student motivation at SMAN 1 Abuki. The research was conducted using a quantitative approach through correlational techniques. The results showed that the effect of the economic conditions of parents on student motivation is positive and significant. This conclusion implies that the economic conditions of good parents have a positive and real direction in increasing student achievement. Keywords: Socio-Economic Conditions, Learning Motivation
How to Regulate Bitcoin? Decentralized Regulation for a Decentralized Cryptocurrency

Bitcoin is a distributed system. The greatest dilemma it poses to the current legal and regulatory systems is that it is hardly possible to regulate a distributed network in a centralized fashion as decentralized permissionless blockchain-based cryptocurrencies are antithetical to the existing centralized structure of monetary and financial regulation. By shifting the policy debate from whether to regulate bitcoin and other decentralized cryptocurrencies to how to regulate them, this paper proposes a more nuanced policy recommendation for regulatory intervention in the cryptocurrency ecosystem. This policy approach relies on a decentralized regulatory architecture that is built upon the existing regulatory infrastructure and makes use of the existing as well as the emerging middlemen in the industry. It argues that instead of regulating the technology or the cryptocurrencies at the code or protocol layer, which might not be desirable, even if feasible, the regulation should target the applications and use-cases of cryptocurrencies. Such a regulatory strategy can best be implemented through directing the edicts and interdictions of regulation towards the middlemen, and can be enforced by the existing financial market participants and traditional gatekeepers such as banks, payment service providers and exchanges, as well as new emerging participants, such as large and centralized node operators and miners that are likely to replicate the functions of the traditional gatekeepers.
Task-Agnostic and Adaptive-Size BERT Compression

While pre-trained language models such as BERT and RoBERTa have achieved impressive results on various natural language processing tasks, they have huge numbers of parameters and suffer from huge computational and memory costs, which make them difficult for real-world deployment. Hence, model compression should be performed in order to reduce the computation and memory cost of pre-trained models. In this work, we aim to compress BERT and address the following two challenging practical issues: (1) The compression algorithm should be able to output multiple compressed models with different sizes and latencies, so as to support devices with different kinds of memory and latency limitations; (2) the algorithm should be downstream task agnostic, so that the compressed models are generally applicable for different downstream tasks. We leverage techniques in neural architecture search (NAS) and propose NAS-BERT, an efficient method for BERT compression. NAS-BERT trains a big supernet on a carefully designed search space containing various architectures and outputs multiple compressed models with adaptive sizes and latency. Furthermore, the training of NAS-BERT is conducted on standard self-supervised pre-training tasks (e.g., masked language model) and does not depend on specific downstream tasks. Thus, the models it produces can be used across various downstream tasks. The technical challenge of NAS-BERT is that training a big supernet on the pre-training task is extremely costly. We employ several techniques including block-wise search, search space pruning, and performance approximation to improve search efficiency and accuracy. Extensive experiments on GLUE benchmark datasets demonstrate that NAS-BERT can find lightweight models with better accuracy than previous approaches, and can be directly applied to different downstream tasks with adaptive model sizes for different requirements of memory or latency.
INFORMATION AND COMMUNICATION TECHNOLOGIES. INFLUENCE ON KNOWLEDGE PROCESSES FOR SCHOOLCHILDREN

Modern society is at a new informational stage of development. This led to the modernization of communication tools and the emergence of new types of cognitive activity of man, in particular, «clip thinking. Since «clip thinking” is formed in childhood, an assessment of the impact of Internet communication and the use of «gadgets» on the formation of cognitive processes in children, as one of the components of human potential, can help in solving this problem. The purpose of the manuscript was to assess the impact of the use of ICTs and electronic devices on the formation of memory, attention, thinking, imagination, digital skills in school children. It was determined that the constant use of «smart»technology and Internet resources by children has an adverse effect on the formation of memorization, thinking and attention skills, as well as academic performance and school motivation. Scientific novelty lies in the ability to assess the level of development of cognitive processes in schoolchildren under the influence of ICT using the sub-index of intellectual development developed by the scientific group, the index of the human potential of the child population. The obtained data can be used in the formation of family policy programs, the development of educational organizations, professional development of teachers, psychologists, social educators, parents.
Designing for Ambiguity: Visual Analytics in Avalanche Forecasting

Ambiguity, an information state where multiple interpretations are plausible, is a common challenge in visual analytics (VA) systems. We discuss lessons learned from a case study designing VA tools for Canadian avalanche forecasters. Avalanche forecasting is a complex and collaborative risk-based decision-making and analysis domain, demanding experience and knowledge-based interpretation of human reported and uncertain data. Differences in reporting practices, organizational contexts, and the particularities of individual reports result in a variety of potential interpretations that have to be negotiated as part of the forecaster’s sensemaking processes. We describe our preliminary research using glyphs to support sensemaking under ambiguity. Ambiguity is not unique to public avalanche forecasting. There are many other domains where the way data are measured and reported vary in ways not accounted explicitly in the data and require analysts to negotiate multiple potential meanings. We argue that ambiguity is under-served by visualization research and would benefit from more explicit VA support.
Who Supports Using Cryptocurrencies and Why Public Education About Blockchain Technology Matters?

In the decade following the introduction of blockchain distributed ledger technology and cryptocurrencies, adoption of the technology lags far behind its potential. Past research identifies knowledge-based trust and understanding as critical to the adoption of technological innovations, particularly in regard to individual willingness to use online financial instruments. Despite negative perceptions of technology identified as key barriers to individual adoption, to the best of our knowledge, little systematic research examines individual attitudes towards the use of blockchain technology or cryptocurrency. We utilize a survey experiment to examine how common discussion contexts surrounding cryptocurrencies influence openness to adoption in comparison to the US dollar. We find that an increased openness to cryptocurrency adoption is associated with messages reflecting (1) the independence of cryptocurrency from political or central bank management and (2) when information describing the security features of blockchain are included. This is consistent with prior research that the socio-technical complexity of a system requires process description in responses. A second important finding suggests that individuals most open to cryptocurrencies as a substitute for the dollar are those benefitting the least from the existing financial system.
Genuine entanglement, distillability and quantum information masking under noise

Genuineness and distillability of entanglement play a key role in quantum information tasks, and they are easily disturbed by the noise. We construct a family of multipartite states without genuine entanglement and distillability sudden death across every bipartition, respectively. They are realized by establishing the noise as the multipartite high-dimensional Pauli channels. Further, we construct a locally unitary channel as another noise such that the multipartite Greenberger–Horne–Zeilinger state becomes the Dür’s multipartite state. We also show that the quantum information masking still works under the noise we constructed and thus show a novel quantum secret sharing scheme under noise. The evolution of a family of three-qutrit genuinely entangled states distillable across every bipartition under noise is also investigated.
Kernel Density Estimation through Density Constrained Near Neighbor Search

In this paper we revisit the kernel density estimation problem: given a kernel K(x, y) and a dataset of n points in high dimensional Euclidean space, prepare a data structure that can quickly output, given a query q, a (1+ ε)-approximation to μ:=[1/(|P|)]Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p∈P</sub> K(p, q). First, we give a single data structure based on classical near neighbor search techniques that improves upon or essentially matches the query time and space complexity for all radial kernels considered in the literature so far. We then show how to improve both the query complexity and runtime by using recent advances in data-dependent near neighbor search. We achieve our results by giving an new implementation of the natural importance sampling scheme. Unlike previous approaches, our algorithm first samples the dataset uniformly (considering a geometric sequence of sampling rates), and then uses existing approximate near neighbor search techniques on the resulting smaller dataset to retrieve the sampled points that lie at an appropriate distance from the query. We show that the resulting sampled dataset has strong geometric structure, making approximate near neighbor search return the required samples much more efficiently than for worst case datasets of the same size. As an example application, we show that this approach yields a data structure that achieves query time μ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-(1+0(1))/4</sup> and space complexity μ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-(1+0(1))</sup> for the Gaussian kernel. Our data dependent approach achieves query time μ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-0.173-0(1)</sup> and space μ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-(1+0(1))</sup> for the Gaussian kernel. The data dependent analysis relies on new techniques for tracking the geometric structure of the input datasets in a recursive hashing process that we hope will be of interest in other applications in near neighbor search.
The Diversification Benefits of Cryptocurrencies in Multi-Asset Portfolios: Cross-Country Evidence

Using a sample of 21 developing and developed countries, we analyze whether a welldiversified investor of traditional assets (stocks, bonds, real estate, and commodities) may benefit from investing in cryptocurrencies. Country-specific analyses indicate that cryptocurrencies usually fit in the tangent portfolio (maximum Sharpe ratio) but no – or very little – in the minimum variance portfolio (MVP). Out-of-sample analysis indicates that even global portfolios that already benefits from international diversification may enjoy investing marginally in cryptocurrencies: mean-variance optimal and naive with cryptocurrencies outperformed otherwise identical portfolios in terms of risk-adjusted returns. Besides, exchange rate movements do not drive this better performance – it occurs for both local (all returns denominated in the local currency) and global perspectives (all returns in U.S. Dollars). We also find that cryptocurrencies’ diversification benefits occur both before and after the COVID-19 pandemics, with the 1/N portfolio with cryptocurrencies presenting the higher risk-adjusted returns. Our paper adds to the literature by analyzing the marginal effects of adding cryptocurrencies on a sample of developing and developed economies and considering up-to-date data following the COVID-19 crisis.
Data-Efficient Histopathology Image Analysis with Deformation Representation Learning

Histopathological examination of tissue biopsies plays a fundamental role in disease assessment. Automatic histopathology image analysis requires substantial task-specific annotations, which are often expensive and laborious in realworld scenarios. This insufficient annotation of data limits the generalization ability of supervised learning models. To address this challenge, we propose a self-supervised Deformation Representation Learning (DRL) framework to learn semantic features from unlabeled data. As a novel paradigm, our approach utilizes deformation as supervisory signals based on two critical features, i.e., local structure heterogeneity and global context homogeneity. Given an original histopathology image and its deformed counterpart, there exists a moderate difference in local structures. In contrast, due to the transformation-invariance, both images share a similar global context compared with other images. Specifically, an encoder network is trained to distinguish the local inconsistency by measuring the mutual information and maintain the global consistency with noise contrastive estimation. Extensive experiments on public histopathology image datasets show that the learned representations are generalizable for various downstream tasks, such as transfer learning on segmentation and semi-supervised classification. Our approach achieves superior results over other self-supervised methods and the ImageNet pre-trained model, and it reveals the ability as a novel pre-training scheme in histopathology image analysis.
Parallel Dense Binary Stereo Matching Using CUDA

This paper addresses the problem of dense stereo matching from two rectified images without prior information about the structure of the scene. CUDA is proposed to exploit the data independence inherently present in local stereo matching to handle the accuracy-time trade-off. To address edge fattening, the parallel implementation utilizes an outstanding aggregation technique that is followed by a hybrid matching metric which is proved to enhance the matching accuracy. By porting the whole pipeline to the GPU, a speedup ranging from 34× to 108× is achieved without compromising the accuracy of the resulting disparity map. The Middlebury benchmark and the latest dataset are used to evaluate the results.
Is Face Recognition Sexist? No, Gendered Hairstyles and Biology Are.

Recent news articles have accused face recognition of being biased, sexist or racist. There is consensus in the research literature that face recognition accuracy is lower for females, who often have both a higher false match rate and a higher false non-match rate. However, there is little published research aimed at identifying the cause of lower accuracy for females. For instance, the 2019 Face Recognition Vendor Test that documents lower female accuracy across a broad range of algorithms and datasets also lists Analyze cause and effect under the heading What we did not do. We present the first experimental analysis to identify major causes of lower face recognition accuracy for females on datasets where previous research has observed this result. Controlling for equal amount of visible face in the test images reverses the apparent higher false non-match rate for females. Also, principal component analysis indicates that images of two different females are inherently more similar than of two different males, potentially accounting for a difference in false match rates.