Reviewerly/exHarmony huggingface dataset:
Bitcoin dilemma: Is popularity destroying value?

I measure the amount of congestion in the Bitcoin network by the time to execute a trade and find that congestion is harming Bitcoin's ability to function as a method of trade. The average time to execute a Bitcoin transaction increased from 30 min in 2016 to 413 min in 2018. I find that the congestion is associated with higher transaction fees and reduced volume. The congestion is also creating a distortion in Bitcoin price. The relationship between congestion and price is strongest during the business hours of East Asia.
Blockchain technology and cryptocurrencies - legal and tax aspects

The main objective of the paper is to provide an answer to the question whether new technologies such as blockchain that enter various spheres of public life are safe for users and what impact they have on national legislations. Cryptocurrencies, which are based on blockchain technology, can be used as a means of payment, investment or capital accumulation. Therefore, blockchain becomes more and more popular. The main research method used in the paper consists of the analysis of legislation and jurisprudence as well as linguistic and purposive interpretation which affects the functioning of blockchain and cryptocurrencies. The introductory part of the paper contains a general historical outline and basic principles related to blockchain technology. Real and potential threats posed by the discussed technology are also discussed in the paper as well as the question whether governments or, more broadly, the international community, offer sufficient level of protection against risks related to the use of new technologies such as blockchain and cryptocurrencies. Using blockchain to streamline logistic activities or to speed up transactions is useful in itself, but in the hands of private entities the technology may pose a risk of losing funds if not properly secured. The financial market is and should be supervised and controlled by the state which is the guarantor of economic freedom. Another vital question is whether legislators keep up with the advances in technology. It seems that the development of blockchain technology triggers development of new legal regulations. The more blockchain technology enters everyday life, the more legal documents appear in the form of case law, legal provisions, opinions etc. The role of these documents is to regulate, define and specify new technologies as they appear.
On the list decodability of Rank Metric codes

Let $k,n,m \in \mathbb{Z}^+$ integers such that $k\leq n \leq m$, let $\mathrm{G}_{n,k}\in \mathbb{F}_{q^m}^n$ be a Delsarte-Gabidulin code. Wachter-Zeh proven that codes belonging to this family cannot be efficiently list decoded for any radius $\tau$, providing $\tau$ is large enough. This achievement essentially relies on proving a lower bound for the list size of some specific words in $\mathbb{F}_{q^m}^n \setminus \mathrm{G}_{n,k}$. In 2016, Raviv and Wachter-Zeh improved this bound in a special case, i.e. when $n\mid m$. As a consequence, they were able to detect infinite families of Delsarte-Gabidulin codes that cannot be efficiently list decoded at all. In this article we determine similar lower bounds for Maximum Rank Distance codes belonging to a wider class of examples, containing Generalized Gabidulin codes, Generalized Twisted Gabidulin codes, and examples recently described by the first author and Yue Zhou. By exploiting arguments suchlike those used in the above mentioned papers, when $n\mid m$, we also show infinite families of generalized Gabidulin codes that cannot be list decoded efficiently at any radius greater than or equal to $\left\lfloor \frac{d-1}2 \right\rfloor+1$, where $d$ is its minimum distance. Nonetheless, in all other examples belonging to above mentioned class, we detect infinite families that cannot be list decoded efficiently at any radius greater than or equal to $\left\lfloor \frac{d-1}2 \right\rfloor+2$, where $d$ is its minimum distance. Finally, relying on the properties of a set of subspace trinomials recently presented by McGuire and Mueller, we are able to prove that any rank metric code of $\mathbb{F}_{q^m}^n$ of order $q^{kn}$ with $n$ dividing $m$, such that $4n-3$ is a square in $\mathbb{Z}$ and containing $\mathrm{G}_{n,2}$, is not efficiently list decodable at some values of the radius $\tau$.
A Modular 3D-Printed Inverted Pendulum

Here we describe a simple modular 3D-printed design for an inverted pendulum system that is driven using a stepper motor operated by a microcontroller. The design consists of a stainless-steel pole that acts as the pendulum, which is pivoted at one end and attached to a cart. Although in its inverted configuration the pendulum is unstable without suitable control, if the cart travels backwards and forwards appropriately it is possible to balance the pole and keep it upright. The pendulum is intended for use as a research and teaching tool in the fields of control engineering and human sensori-motor control. We demonstrate operation of the design by implementing an observer-based state feedback controller, with augmented positional state of the cart and integral action, that can balance the pole in its unstable configuration and also maintains the cart at its starting position. When the controller is running, the pendulum can resist small disturbances to the pole, and it is possible to balance objects on its endpoint.
Safety Functions in the Software Quality Assurance of NPP Safety Important Systems

One of the most important tasks in the software complexes quality assurance is the procedure of forming requirements to a developed or modified system and their subsequent verification. The most essential errors are done at the first life cycle stages – these are errors in determining requirements, selecting the architecture, high-level design. The faults of safety critical software may considerably damage the equipment or properties, as well as to lead to essential detriment of the environment and human victims. Increasing requirements to the software quality of NPP (nuclear power plant) safety important systems at all stages of the life cycle is concerned with increasing the software complexity and functionality and has led to the necessity of developing approaches to justify both the system’s safety and the safety of software involved in the systems make-up. The paper considers an approach based on the “safety functions” the implementing of which is verified in sequel. This approach is used under the software verification of upper level systems of automated process control systems and may be applied for the fault tolerance analysis, information-and cyber-security of soft-and hardware complexes.
Multilayer feedforward neural networks with backpropagation model on immunotherapy dataset

Multilayer Feedforward Neural Network is one of the linear or nonlinear mathematical model between the set of input vectors and their corresponding output vectors of the real-world problem. In this paper, MAMFFN has been used as a mathematical model as delimited as mathematical approach that develops the Multilayer Feedforward Neural Network with Backpropagation to overcome from the complexity of the conventional model. Also, the proposed model applied on various real-world problems and obtained results are compared with existing results on those problems.
Multi-class Categorization of User-Generated Content in a Domain Specific Medium: Inferring Product Specifications from E-Commerce Marketplaces

A “marketplace” is an e-commerce medium where product and inventory information is provided by varying third parties, whereas catalog service is hosted, and payments are processed by the marketplace operator. As a result of increasing use of marketplaces, e-commerce capabilities can now be accessed by everyone. Consequently, both the number of merchants and products have been growing exponentially. Such growth raises some problems including “Does product description reflect specifications of the real one?”, “Does the seller really own the product?”, “Is this product legal for purchasing online?”, “Is this product listed under correct category?”. These problems can lead to penalties or complete close-down of the merchant as e-commerce business is regulated in most countries. We propose a methodology to detect an accurate product category from user-generated content on e-commerce marketplaces, so that proactive removal of certain products can be automated. We present our methodology as a complete system that incorporates data collection, cleaning, and categorization. In this work, we transform unstructured text into vector representations of words during machine-learning-ready dataset preparation stage. We train ML models by a large corpus of text which includes more than half a million product descriptions. Finally, we compare our results in alternate classification algorithms and varying methodologies of vector representations. We showed that accurate predictions of text categories reaching an F-score of 0.82 can be obtained from user-generated text that may contain typos, special punctuation, and abbreviations, and comes from a non-moderated e-commerce medium.
Nonlinear Dimensionality Reduction in Texture Classification: Is Manifold Learning Better Than PCA?

This paper presents a comparative analysis of algorithms belonging to manifold learning and linear dimensionality reduction. Firstly, classical texture image descriptors, namely Gray-Level Co-occurrence Matrix features, Haralick features, Histogram of Oriented Gradients features and Local Binary Patterns are combined to characterize and discriminate textures. For patches extracted from several texture images, a concatenation of the image descriptors is performed. Using four algorithms to wit Principal Component Analysis (PCA), Locally Linear Embedding (LLE), Isometric Feature Mapping (ISOMAP) and Laplacian Eigenmaps (Lap. Eig.), dimensionality reduction is achieved. The resulting learned features are then used to train four different classifiers: k-nearest neighbors, naive Bayes, decision tree and multilayer perceptron. Finally, the non-parametric statistical hypothesis test, Wilcoxon signed-rank test, is used to figure out whether or not manifold learning algorithms perform better than PCA. Computational experiments were conducted using the Outex and Salzburg datasets and the obtained results show that among twelve comparisons that were carried out, PCA presented better results than ISOMAP, LLE and Lap. Eig. in three comparisons. The remainder nine comparisons did not presented significant differences, indicating that in the presence of huge collections of texture images (bigger databases) the combination of image feature descriptors or patches extracted directly from raw image data and manifold learning techniques is potentially able to improve texture classification.
Research on Optimization Model of Storage Capacity Based on the Consortium Blockchain

To solve the problem of increasing nodes data in blockchain, we propose an optimization model of storage capacity based on the consortium blockchain (OMSCCB). We have divided blockchain network into groups by the characteristics of partial decentralization of the consortium blockchain in the OMSCCB. In each group, the full node is selected according to the hardware performance and the accumulated reliability evaluation of the node. The full node is responsible for storing the blockchain data, and other ordinary nodes segment storage of the blockchain data according to the consistent hashing. And then, we have designed a replacement strategy of full node to ensure that the OMSCCB can resume normal operation when the network is attacked. The experimental results show that the storage cost of the system can be reduced effectively, and the reliability of the system can be guaranteed at the same time.
Spatiotemporal Analysis of “Jello Effect” in Drone Videos

A computational method for analyzing the “jello effect” caused by high-frequency vibrations of the camera with a rolling shutter mechanism is demonstrated. We use an optical flow model to analyze the “jello effect” in drone videos. Motion vectors and hue-saturation-value color map are used to visualize the spatiotemporal patterns. Our observations show that poor insulation between the drone structures and the camera creates a persistent “jello effect.” An impact of a projectile on the drone can cause a brisk “jello effect” where the magnitude of the shearing, rotational, and translational movements are proportional to the energy of the impact. By scaling the motion vectors in the optical flow, we are able to amplify the movements in the video to observe subtle distortions that are invisible to the naked eye. Our approach combines optical flow and visualization methods for real-time video analytics of drone footage. The study explores inferring physical interactions through non-ideal effects on digital systems. Applications include near real-time drone situational awareness.
Agile Process Improvement in Retrospectives

Working in iterations and repeatedly improving team workflows based on collected feedback is fundamental to agile software development processes. Scrum, the most popular agile method, provides dedicated retrospective meetings to reflect on the last development iteration and to decide on process improvement actions. However, agile methods do not prescribe how these improvement actions should be identified, managed or tracked in detail. The approaches to detect and remove problems in software development processes are therefore often based on intuition and prior experiences and perceptions of team members. Previous research in this area has focused on approaches to elicit a team's improvement opportunities as well as measurements regarding the work performed in an iteration, e.g. Scrum burn-down charts. Little research deals with the quality and nature of identified problems or how progress towards removing issues is measured. In this research, we investigate how agile development teams in the professional software industry organize their feedback and process improvement approaches. In particular, we focus on the structure and content of improvement and reflection meetings, i.e. retrospectives, and their outcomes. Researching how the vital mechanism of process improvement is implemented in practice in modern software development leads to a more complete picture of agile process improvement.
Robust alternating optimisation for extrinsic calibration of RGB‐D cameras

RGB-depth (RGB-D) cameras are widely used for 3D reconstruction or human–computer interaction. To simultaneously acquire colour and depth images of an object in widely separated viewpoints, multiple RGB-D cameras are required. To fuse the colour and depth information of individual RGB-D cameras in the reference frame, the RGB-D cameras need to be fully calibrated. Among many steps of extrinsic calibration between different RGB-D cameras, bundle adjustment has been either slow or error-prone in the presence of false correspondences across the views. This Letter presents an accurate and efficient bundle adjustment method based on alternating optimisation of a robust cost function. Through experiments, the authors show that the proposed method can dramatically reduce computation time while preserving state-of-the-art accuracy.
Treant: Training Evasion-Aware Decision Trees

Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force prediction errors. In this paper we focus on evasion attacks against decision tree ensembles, which are among the most successful predictive models for dealing with non-perceptual problems. Even though they are powerful and interpretable, decision tree ensembles have received only limited attention by the security and machine learning communities so far, leading to a sub-optimal state of the art for adversarial learning techniques. We thus propose Treant, a novel decision tree learning algorithm that, on the basis of a formal threat model, minimizes an evasion-aware loss function at each step of the tree construction. Treant is based on two key technical ingredients: robust splitting and attack invariance, which jointly guarantee the soundness of the learning process. Experimental results on three publicly available datasets show that Treant is able to generate decision tree ensembles that are at the same time accurate and nearly insensitive to evasion attacks, outperforming state-of-the-art adversarial learning techniques.
Analyze Facial Expression Recognition Based on Curvelet Transform via Extreme Learning Machine

This paper aims to investigate the key factors of facial expression recognition based on local curvelet transform for real-time training data. Local curvelet transform (LCT) is the application of curvelet transform that benefits from useful features extracted by curvelet transform and reduces the computation cost of using all curvelet coefficients. The reduction of computation is through calculating the representative features, instead of directly using all curvelet coefficients. The representative features are mean, standard deviation and entropy. This approach has been reported to achieve impressively 0.9445 and 0.9486 accuracy on JAFFE and Cohn-Kanade datasets. However, there are many factors influencing the final performance, in which these factors have not been thoroughly studied. Our investigation has shown that these factors could result up to almost 10% difference and their effects are thoroughly studied.
Post-editese: an Exacerbated Translationese

Post-editing (PE) machine translation (MT) is widely used for dissemination because it leads to higher productivity than human translation from scratch (HT). In addition, PE translations are found to be of equal or better quality than HTs. However, most such studies measure quality solely as the number of errors. We conduct a set of computational analyses in which we compare PE against HT on three different datasets that cover five translation directions with measures that address different translation universals and laws of translation: simplification, normalisation and interference. We find out that PEs are simpler and more normalised and have a higher degree of interference from the source language than HTs.
The Assessment Method of Network Security Situation Based on Improved BP Neural Network

With the popularity of the Internet and the emergence of cloud computing, network security issues have become increasingly prominent. In view of the low efficiency and poor reliability of the existing network security situation assessment methods, this paper proposes a quantitative assessment method based on an improved BP neural network. Aiming at the disadvantages of slow convergence speed, easy oscillation, and local minimum in BP neural network, this paper optimized the algorithm by combining Cuckoo search algorithm, introducing momentum factor and adaptive learning rate. The simulation results show that the improved CS-BPNN algorithm in this paper has fast convergence rate and high evaluation accuracy, which provides a new method for network situation assessment.
The Interaction of Universities with Schools in the Higher Education System on the Example of the Department of Human Resourse Management of the State University of Management

The need for close cooperation between the University and the school is now becoming one of the key objectives of the educational programs. The aim of the study is to analyze the options and models of interaction between the University and schools. The article deals with the specifi c activities of vocational guidance work of the University with schools on the example of the Department of “personnel management” of the State University of management: Olympiad students in the framework of the annual personnel forum, visiting master classes in sponsored schools, open days. The analysis of the positive aspects of mutual cooperation both for the partner University and for sponsored schools from the point of view of the quality of educational programs and practical experience. Possible models of joint interaction between school and University are studied: options when the Central link is a school or university and in case of equal partnership.
TüKaSt at SemEval-2019 Task 6: Something Old, Something Neu(ral): Traditional and Neural Approaches to Offensive Text Classification

We describe our system (TüKaSt) submitted for Task 6: Offensive Language Classification, at SemEval 2019. We developed multiple SVM classifier models that used sentence-level dense vector representations of tweets enriched with sentiment information and term-weighting. Our best results achieved F1 scores of 0.734, 0.660 and 0.465 in the first, second and third sub-tasks respectively. We also describe a neural network model that was developed in parallel but not used during evaluation due to time constraints.
Machine Learning at the Edge: a few applicative cases of Novelty Detection on IIoT gateways

In the paper we present the design and development of the Machine Learning (ML) modules for two case studies. In both cases we developed a ML model to learn the system's normal behavior so to identify whichever abnormal condition may arise. Such a framework is usually referred to as Anomaly Detection (also known as Fault Detection or Novelty Detection). Our models succeeded at identifying the injected anomalies. In addition, no anomalies were observed when the model was fed with normal data. The results are discussed considering the trade-off between type of sensors, learning algorithm, training effort, computational demands.
Research on abnormal detection of one-class support vector machine based on ensemble cooperative semi-supervised learning

Abstract In order to promote the accuracy of anomaly detection model under the condition of only a small number of labeled samples and large number of unlabeled samples, abnormal detection of One-class Support Vector Machine(SVM) based on ensemble cooperative Semi-supervised Learning is proposed. A kind of One-class SVM model which bring supervision with a small number of abnormal samples can classify samples with max interval. The semi-supervised learning methods easily suffer from the low accuracy because the mistake labeled sample are chosen as training sample set. Refer to the semi-supervision method of Tri-training, the K-Nearest Neighbour(KNN) and Naive Bayes classifier are used to uses to assist the One-class SVM based on ensemble cooperative Semi-supervised learning method which can classify the large number of unlabeled samples as accurate as possible. The weight is also given after ensemble cooperative Semi-supervised Learning. Then the proposed semi-supervised One-class SVM would be trained with the result and used to classify test samples. The experimental results on UCI dataset show that the proposed algorithm achieves higher classification accuracy with less labeled samples and it improves generalization performance and reduces the labelling cost.
FORMAL REPRESENTATIONS OF DEPENDENCE AND GROUNDEDNESS

Abstract We study, in an abstract and general framework, formal representations of dependence and groundedness which occur in semantic theories of truth. Our goals are (a) to relate the different ways in which groundedness is defined according to the way dependence is represented and (b) to represent different notions of dependence as instances of a suitable generalisation of the mathematical notion of functional dependence.