Reviewerly/exHarmony huggingface dataset:
Crypto Currency as an Object and Instrument of Committing Crimes

The article examines the legal status of cryptocurrencies in Russia. The authors study the civil and criminal law aspects of this phenomenon. The legislation does not define the legal status of cryptocurrencies, while the theory of law and court practice have not yet worked out a unified approach to it. The authors present an overview of the existing positions regarding this issue and suggest that cryptocurrency should be regarded as other property. It is stated that whatever position is incorporated into law, in real life cryptocurrency is in global circulation and has a great potential. As for criminal law, the authors show that it is relatively independent from civil law when dealing with issues within its object. It is suggested that, for the purposes of criminal law, the available instruments should be adjusted to enable a prompt reaction to the crimes committed at the present moment. The authors analyze the possibility of recognizing cryptocurrencies as an object and instrument of committing crimes within the framework of current legislation. Using the examples different crimes, the authors demonstrate that cryptocurrency could act as an instrument of committing them. It is also stated that cryptocurrency could be recognized as an object of some crimes. To support their position, the authors present such arguments as crimes of terrorism, illegal trade in drugs and psychoactive substances, economic crimes and some others. They also discuss cryptocurrency as an object of theft. It is proven that there is a practical necessity to recognize cryptocurrency as an object of theft. Criticism of this approach by some researchers is also discussed. The authors, however, show that it is possible to solve this problem positively. They conclude that current Russian and foreign legislation should be amended and should define the legal status of cryptocurrencies; however, the goal of criminal law could and should be achieved even before these amendments are adopted.
An introduction of Gaussian processes and deep Gaussian processes and their applications to speech processing

Gaussian process (GP) is a distribution of functions, which can be used for a machine learning framework. GP regression has characteristics of Bayesian model, which can predict uncertainty of outputs, and kernel methods, which enables nonlinear function with a small number of parameters. In this paper, we first describe the basic of GP regression, and introduce recent notable advances of GP. Specifically, we focus on stochastic variational GP that is an approximation method available for a huge amount of training data, and explain a GP-based deep architecture model called deep Gaussian process. Since GP regression is a general-purpose machine learning framework, there are many applications. In this paper, we introduce GP-based applications to speech information processing including speech synthesis.
DESIGN OF THE EDUCATIONAL ENVIRONMENT FOR STEM-ORIENTED LEARNING

The article examines the impact of market requirements on the educational system, technological development of society and changes in the economic development of the country. The main directions of changes in European educational policy and their formalization in the framework of competences have been reviewed. The considered practices of organizing educational space in Ukraine and other countries allowed determining the structure of the interaction of participants in the educational environment of STEM-oriented training: administrative and managerial component, cooperation between education and business structures, scientific and methodological support, technological factor, popularization and human factor. The article gives a definition of the educational environment of STEM-oriented learning. According to each of the points of this structure, the peculiarities of their implementation in Ukraine have been presented: steps to implementation at legislative level have been taken, a list of measures aimed at popularizing and supporting STEM-education has been developed, a way of the dissemination of these ideas has been outlined and the list of already existing developments and implementation practices has been made. The conditions required for the development of educational STEM-environment have been defined. The article presents the model of the educational environment of STEM-oriented learning developed at the STEM-school of Kherson State University. The description of the project activity of the teacher and students in the context of the creation of educational environment of STEM-oriented learning is given. Also, the article offers examples of interdisciplinary projects. Their implementation requires that the teacher have a good theoretical foundation in mathematics, physics, technology and programming.
Robotic Control of Dynamic and Static Gesture Recognition

In recent years, gesture recognition control has become an important research topic in the field of intelligent robots at home and abroad, and it is a very flexible human-computer interaction method. At present, the use of gesture recognition in industrial production to control robots is mostly a gesture-oriented teaching method, lacking a systematic description of dynamic gestures and static gestures, making it difficult for the robot to understand the complete intention expressed by the operator. The static gesture real-time recognition is centered and the computer vision control in the complex environment realizes the space movement and posture movement of the robot. This way of using the human body language to directly control the robot movement, the human experience, the intention and the manipulator's high efficiency The combination of sustainability and other advantages can accomplish tasks that cannot be accomplished by people or robots alone.
PolarTag

Visual tags (e.g., barcodes, QR codes) are ubiquitous in modern day life, though they rely on obtrusive geometric patterns to encode data, degrading the overall user experience. We propose a new paradigm of passive visual tags which utilizes light polarization to imperceptibly encode data using cheap, widely-available components. The tag and its data can be extracted from background scenery using off-the-shelf cameras with inexpensive LCD shutters attached atop camera lenses. We examine the feasibility of this design with real-world experiments. Initial results show zero bit errors at distances up to 3.0~m, an angular-detection range of \ang110, and robustness to manifold ambient light and occlusion scenarios.
Research Collaborations and Quality in Research: Foes or Friends?

Collaboration is the cornerstone of nowadays research. Successful collaborative research and high research quality go hand in hand. Collaborative research needs to build on common and upfront expectations for the quality of its outputs. This is necessary to enable a trustful research environment where all are committed to contribute and can share the rewards. A governance and leadership are critical for this to happen as well as a policy for openness and for effective data sharing. Collaborative research is often large-scale research: to be successful it needs good research practice as an enabler. Collaborative projects are ideal vehicles to promote high research quality, among other by enabling the delivery of results of high external validity and the development and implementation of standards. Robustness of results increases when confirmed by combining different methods and tools and even more when results are obtained while sharing and learning different approaches and languages of science. When doing collaborative research, there is the best opportunity to combine the different experience and expertise of all partners by design to create a more efficient and effective environment conductive for high-quality research. Using as example the public-private partnership type of projects created by the Innovative Medicines Initiative, the chapter covers the key aspects of the complex relationship between collaborative research and quality of research providing insights on the critical factors for delivering both a successful collaboration in research and robust high-quality research outputs.
Disentangling Adaptive Gradient Methods from Learning Rates

We investigate several confounding factors in the evaluation of optimization algorithms for deep learning. Primarily, we take a deeper look at how adaptive gradient methods interact with the learning rate schedule, a notoriously difficult-to-tune hyperparameter which has dramatic effects on the convergence and generalization of neural network training. We introduce a "grafting" experiment which decouples an update's magnitude from its direction, finding that many existing beliefs in the literature may have arisen from insufficient isolation of the implicit schedule of step sizes. Alongside this contribution, we present some empirical and theoretical retrospectives on the generalization of adaptive gradient methods, aimed at bringing more clarity to this space.
A Variational Approximations-DIC Rubric for Parameter Estimation and Mixture Model Selection Within a Family Setting

Mixture model-based clustering has become an increasingly popular data analysis technique since its introduction over fifty years ago, and is now commonly utilized within a family setting. Families of mixture models arise when the component parameters, usually the component covariance (or scale) matrices, are decomposed and a number of constraints are imposed. Within the family setting, model selection involves choosing the member of the family, i.e., the appropriate covariance structure, in addition to the number of mixture components. To date, the Bayesian information criterion (BIC) has proved most effective for model selection, and the expectation-maximization (EM) algorithm is usually used for parameter estimation. In fact, this EM-BIC rubric has virtually monopolized the literature on families of mixture models. Deviating from this rubric, variational Bayes approximations are developed for parameter estimation and the deviance information criteria (DIC) for model selection. The variational Bayes approach provides an alternate framework for parameter estimation by constructing a tight lower bound on the complex marginal likelihood and maximizing this lower bound by minimizing the associated Kullback-Leibler divergence. The framework introduced, which we refer to as VB-DIC, is applied to the most commonly used family of Gaussian mixture models, and real and simulated data are used to compared with the EM-BIC rubric.
Revisiting EXTRA for Smooth Distributed Optimization

EXTRA is a popular method for dencentralized distributed optimization and has broad applications. This paper revisits EXTRA. First, we give a sharp complexity analysis for EXTRA with the improved $O\left(\left(\frac{L}{\mu}+\frac{1}{1-\sigma_2(W)}\right)\log\frac{1}{\epsilon(1-\sigma_2(W))}\right)$ communication and computation complexities for $\mu$-strongly convex and $L$-smooth problems, where $\sigma_2(W)$ is the second largest singular value of the weight matrix $W$. When the strong convexity is absent, we prove the $O\left(\left(\frac{L}{\epsilon}+\frac{1}{1-\sigma_2(W)}\right)\log\frac{1}{1-\sigma_2(W)}\right)$ complexities. Then, we use the Catalyst framework to accelerate EXTRA and obtain the $O\left(\sqrt{\frac{L}{\mu(1-\sigma_2(W))}}\log\frac{ L}{\mu(1-\sigma_2(W))}\log\frac{1}{\epsilon}\right)$ communication and computation complexities for strongly convex and smooth problems and the $O\left(\sqrt{\frac{L}{\epsilon(1-\sigma_2(W))}}\log\frac{1}{\epsilon(1-\sigma_2(W))}\right)$ complexities for non-strongly convex ones. Our communication complexities of the accelerated EXTRA are only worse by the factors of $\left(\log\frac{L}{\mu(1-\sigma_2(W))}\right)$ and $\left(\log\frac{1}{\epsilon(1-\sigma_2(W))}\right)$ from the lower complexity bounds for strongly convex and non-strongly convex problems, respectively.
Blockchain and real estate: <i>Dopo di Noi</i> project

Purpose This paper aims at investigating the possibility of effectively implementing the blockchain technology in the real estate environment, specifically applied to the Trust legal instrument in Dopo di Noi (After Us) project, which is intended to guarantee assistance to persons with severe disabilities. Design/methodology/approach The paper is focused on how to apply the blockchain to the tool of Trust, analyzing the main features and characteristics of this technology. Findings The paper proposes two potential solutions for managing the Trust tool in the real estate sector, specifically within the Dopo di Noi project. The first simpler proposal is based on timestamping application. The second one radically changes the classical Trust model and introduces an automatization level in the process. Social implications The paper presents potential applications of the blockchain technology within the framework of Dopo di Noi project, which allows among other features, legal and tax facilitation for the institution of Trusts to benefit persons with severe disabilities. Originality/value This paper highlights the potentiality of the combination of the blockchain technology and the real estate environment and applies the blockchain technology to the Dopo di Noi project. Specifically, with the second solution, the paper proposes a platform that gathers, in a single network, various elements of the blockchain technology, such as timestamping, smart property, smart contract, and links them in order to provide services to persons with severe disabilities.
Immersive Virtual Reality Environment for Construction Detailing Education Using Building Information Modeling (BIM)

Construction site visits are real-life practical experience where the AEC students’ conceptual knowledge is developed and serves as an extension of in-class learning tools. Nevertheless, very low rates of construction site visits have been reported worldwide due to certain limitations, such as the limited visit time, the lack of visit objective, potential hazards, etc. In addition, site activities may also meet specific class needs. During a construction site visit, students mainly learn through their own observation, not by involvement in the site work or having the possibility to take decisions. Besides, students have only the chance to attend one phase of construction. Virtual reality (VR)—as a game‐friendly, interactive, and immersive technology—may facilitate virtual construction site visits to meet learning needs and provide the learner with a near-real experiential learning environment where he/she can “learn by doing” in a zero-risk environment. In this essence, this paper describes “VRConDet” project which builds on the VR technology as a medium and Building Information Modeling (BIM) as a source of technical information taking into consideration adults’ active learning and gamification of learning materials. “VRConDet” is a computer-assisted learning (CAL) conceptual framework for construction detailing that aims at enhancing the learning experience and learning outcomes for construction education within architecture curricula. This paper focuses on “VRConDet” system architecture and the design of its diverse, scalable, and adaptive modules according to the correspondent complexity and intended learning outcomes of construction education. The results of this first phase feed into a second phase of VR environment development and the validation thereof.
QSW_MPI: A framework for parallel simulation of quantum stochastic walks

QSW_MPI is a Python package developed for time-series simulation of continuous-time quantum stochastic walks. This model allows for the study of Markovian open quantum systems in the Lindblad formalism, including a generalisation of the continuous-time random walk and continuous-time quantum walk. Consisting of a Python interface accessing parallelised Fortran libraries utilising sparse data structures, QSW_MPI is scalable to massively parallel computers, which makes possible the simulation of a wide range of walk dynamics on directed and undirected graphs of arbitrary complexity. Program Title: QSW_MPI CPC Library link to program files: http://dx.doi.org/10.17632/r8jsx4xxgv.1 Licensing provisions: GPLv3 Programming language: Python 3 + Fortran 2003 External routines/libraries: NumPy [1], SciPy [2], MPI for Python [3], h5py[4] Nature of problem: QSW_MPI provides a framework for the simulation of quantum stochastic walks on arbitrary graphs (directed/undirected, weighted/unweighted). Solution method: A parallel distributed-memory implementation of the matrix exponential via a truncated Taylor series expansion with scaling and squaring [5]. Additional comments including restrictions and unusual features: QSW_MPI will provide support for the simulation of multiple quantum walkers in a future version. References: [1] T. Oliphant, NumPy: A guide to NumPy, 2006, published: USA: Trelgol Publishing. [2] E. Jones, T. Oliphant, P. Peterson, SciPy: Open source scientific tools for Python (2001). [3] L. Dalcìn, R. Paz, M. Storti, J. D’Elia, MPI for Python: Performance improvements and MPI-2 extensions, Journal of Parallel and Distributed Computing 68 (5) (2008) 655–662. doi:10.1016/j.jpdc.2007.09. 005. [4] A. Collette, Python and HDF5, O’Reilly, 2013. [5] A. Al-Mohy, N. Higham, Computing the Action of the Matrix Exponential, with an Application to Exponential Integrators, SIAM Journal on Scientific Computing 33 (2) (2011) 488–511. doi:10.1137/100788860.
Turbidity factor coefficient on the estimation of hourly solar radiation in Depok City, Indonesia

Indonesia is a country located on the equator and is a tropical country that has two climates namely rainy and sunny. Indonesia has a long solar radiation intensity time that started from 8 AM until 16 PM, which the effective time is along 5 h on peak solar time. The calculation of solar radiation intensity is needed to know the efficiency power applicable to solar power technology. This paper will discuss the estimation of hourly solar radiation compared with measured data in the Department of Mechanical Engineering Universitas Indonesia, Depok City, Jawa Barat Province, Indonesia. The estimation of hourly solar radiation is based on Liu and Jordan’s empirical models. The comparison of empirical model with measured data in order to know the influence of solar radiation parameters like turbidity factor coefficients and climate conditions used to calculate the solar radiation at the studied site. The turbidity factor coefficient assumed as a normal sky condition with the value is 0.88 and 0.26, respectively for A and B. These values were chosen based on the cloud cover data from the measurement that show there is no cloud cover during the day.
Identifying Challenges for Information Organization in Language Archives: Preliminary Findings

Language archives are repositories of linguistic data about a selected set of languages, typically including recordings, transcripts, translations, and linguistic annotations. Digital accessibility of primary language data, particularly that of endangered languages, has long been recognized as necessary for research reproducibility, production of pedagogical materials, and typological discovery, though their potential currently lies dormant because these resources are rarely accessed by linguists or language communities. Reasons for the under-utilization of language archives include the lack of data standardization and decreased focus on metadata quality. The present work seeks to elucidate the issues facing language archive managers and users through two steps: content analysis of information organization in language archives, and semi-structured interviews with language archive managers and users. Primary challenges identified include lacking institutional support and a range of factors which impact authority control in language archives.
Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments

We present a novel POMDP problem formulation for a robot that must autonomously decide where to go to collect new and scientifically relevant images given a limited ability to communicate with its human operator. From this formulation we derive constraints and design principles for the observation model, reward model, and communication strategy of such a robot, exploring techniques to deal with the very high-dimensional observation space and scarcity of relevant training data. We introduce a novel active reward learning strategy based on making queries to help the robot minimize path "regret" online, and evaluate it for suitability in autonomous visual exploration through simulations. We demonstrate that, in some bandwidth-limited environments, this novel regret-based criterion enables the robotic explorer to collect up to 17% more reward per mission than the next-best criterion.
A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application

The increasing integration of photovoltaic (PV) power plants into power systems demands a high accuracy of yield prediction and measurement. With this aim, different global horizontal irradiance (GHI) estimations based on new-generation geostationary satellites have been recently proposed, providing a growing number of solutions and databases, mostly available online, in addition to the many ground-based irradiance data installations currently available. According to the specific literature, there is a lack of agreement in validation strategies for a bankable, satellite-derived irradiance dataset. Moreover, different irradiance data sources are compared in recent contributions based on a diversity of arbitrary metrics. Under this framework, this paper describes a characterization of metrics based on a principal component analysis (PCA) application to classify such metrics, aiming to provide non-redundant and complementary information. Therefore, different groups of metrics are identified by applying the PCA process, allowing us to compare, in a more extensive way, different irradiance data sources and exploring and identifying their differences. The methodology has been evaluated using satellite-based and ground-measured GHI data collected for one year in seven different Spanish locations, with a one-hour sample time. Data characterization, results, and a discussion about the suitability of the proposed methodology are also included in the paper.
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay

Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.