Reviewerly/exHarmony huggingface dataset:
Publishing houses and publishing organizations in Siberia and the Far East as popularizers of the historical and cultural heritage of the region

Due to the rapid globalization in the second half of the 20th century the problems of preserving and studying the historical and cultural heritage of certain territories of national states have become especially relevant and socially significant. Since the book is a form of presentation of cultural heritage values, the memorial function responsible for preserving the heritage of certain territories and making population familiar to it is of fundamental importance for regional book culture. The purpose of the study presented in the article is to reveal ways to implement the memorial function of the regional book culture in Siberia and the Far East in the 2000–2010s. As a methodological basis, the theoretical model of book culture proposed by K. Migoń was used, who distinguishes the local, regional, national, supranational, cultural-civilizational and world “cultural circles of the book”. The activities of publishing houses and publishing organizations in Siberia and the Far East are treated within the local and regional “circles” as a part of the national Russian book culture. The article shows the main actors of modern local history book publishing in Siberia and the Far East: these are the majority of regional publishing houses and those publishing organizations whose activities are institutionally related to the preservation of historical and cultural heritage (museums, archives and libraries). Methods for popularizing their products are presented. For the first time, examples of product placement in advertising local history literature are identified and presented. The author concludes that Internet is becoming the main channel for the dissemination and popularization of regional local history book production, that opens the access to a wider circle of readers of historical and cultural heritage of Siberia and the Far East.
Potential negative implications of Libra cryptocurrency

Cryptocurrencies have been in the center of interest of both scientific and professional public for over ten years.Due to the volatile exchange rate against convertible currencies, investors predominantly use cryptocurrencies as an instrument of speculative investment, while their use in the payments is at a negligible level.On June 18, 2019, the Internet giant Facebook announced the creation of a consortium of financial and technology companies aimed at establishing a global cryptocurrency with stable value called Libra.It is planned to create an open blockchain through a new programming language, which will serve development teams in the future for creation of smart contracts.The subject of research in the paper will be the potential operational performances of Libra concept.The basis for the research will be a white paper published by Facebook and knowledge about the functioning of other cryptocurrencies, notably Bitcoin.The aim of the paper is to highlight the expected economic and security implications of Libra concept.
Representation of graphs for storing in relational databases

The article is devoted to the problem of representing graphs in the form that is most suitable for their recording in relational databases and for subsequent efficient extracting and processing. The article analyzes various ways to describe graphs, such as adjacency and, incidence matrices, and adjacency lists. Each of them is reviewed from the point of view of their compliance with normal forms to assess the possibility of using a particular method when developing databases for storing graphs. It is shown that for such a task, each of these methods has a large number of disadvantages that lead to low efficiency of both data storing and processing. The article suggests the way to represent graphs in the form of a relational list of edges corresponding to the third normal form and allowing to eliminate the disadvantages of other methods.
Stochastic Activation Actor Critic Methods

Stochastic elements in reinforcement learning (RL) have shown promise to improve exploration and handling of uncertainty, such as the utilization of stochastic weights in NoisyNets and stochastic policies in the maximum entropy RL frameworks. Yet effective and general approaches to include such elements in actor-critic models are still lacking. Inspired by the aforementioned techniques, we propose an effective way to inject randomness into actor-critic models to improve general exploratory behavior and reflect environment uncertainty. Specifically, randomness is added at the level of intermediate activations that feed into both policy and value functions to achieve better correlated and more complex perturbations. The proposed framework also features flexibility and simplicity, which allows straightforward adaptation to a variety of tasks. We test several actor-critic models enhanced with stochastic activations and demonstrate their effectiveness in a wide range of Atari 2600 games, a continuous control problem and a car racing task. Lastly, in a qualitative analysis, we present evidence of the proposed model adapting the noise in the policy and value functions to reflect uncertainty and ambiguity in the environment.
A Top-K Query Scheme With Privacy Preservation for Intelligent Vehicle Network in Mobile IoT

The emergence of mobile Internet of Things (IoT) has brought much more convenience to our daily life than ever before, modern intelligent vehicles are usually equipped with various mobile IoT components or devices. However, when we are enjoying the convenience from the rapid development of mobile IoT services, our privacy can be misused by attackers in an easier way. In this paper, we devise a query scheme with intelligent vehicle privacy guarantees, which enables a vehicle to acquire accurate query service from the service provider (SP) without providing its explicit private information, such as location or query interest privacy. And more than that, we introduce network coding for the first time to make our scheme applicable to a more sophisticated top-K query by multiple vehicles cooperation in mobile IoT. Furthermore, we also consider the unnecessary reveal issue of service data at SP side since the data is its asset. Performance analysis and experiments verify the validity of our scheme and demonstrate a better accuracy and efficiency compared with existing solutions in mobile IoT scenario.
AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

With the growth of knowledge graphs, entity descriptions are becoming extremely lengthy. Entity summarization task, aiming to generate diverse, comprehensive and representative summaries for entities, has received an increasing interest recently. In most previous methods, features are usually extracted by the hand-crafted templates. Then the feature selection and multi-user preference simulation take place, depending too much on human expertise. In this paper, a novel integration method called AutoSUM is proposed for automatic feature extraction and multi-user preference simulation to overcome the drawbacks of previous methods. There are two modules in AutoSUM: extractor and simulator. The extractor module operates automatic feature extraction based on a BiLSTM with a combined input representation including word embeddings and graph embeddings. Meanwhile, the simulator module automates multi-user preference simulation based on a well-designed two-phase attention mechanism (i.e., entity-phase attention and user-phase attention). Experimental results demonstrate that AutoSUM produces the state-of-the-art performance on two widely used datasets (i.e., DBpedia and LinkedMDB) in both F-measure and MAP.
Music computer in teaching the “Listening to Music” course

The “Listening to Music” course is an important link in the music education of a child. The “Listening to Music” course fosters love and interest in music, teaches to understand its language, and develops emotional responsiveness in children. The article considers new opportunities in teaching the “Listening to Music” course, which appeared along with a music computer, and which is more often used in the children's music school and meets the requirements of modern pedagogy. The article describes the capabilities of music and computer programs aimed at developing creative abilities of children. The authors examined new forms of work in the classroom using a music computer. The article proves that using a musical computer opens up broad prospects for employing creative tasks at children's music and art schools.
Exploring Controllable Text Generation Techniques

Neural controllable text generation is an important area gaining attention due to its plethora of applications. Although there is a large body of prior work in controllable text generation, there is no unifying theme. In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules. The control of attributes in the generation process requires modification of these modules. We present an overview of different techniques used to perform the modulation of these modules. We also provide an analysis on the advantages and disadvantages of these techniques. We further pave ways to develop new architectures based on the combination of the modules described in this paper.
Modelling and Control of Proposed Two Dodecacopter Systems

Nowadays, the use of multi-propellers has been increased, because of some basic advantages such as vertical take-off and landing, simple mechanical structure. In addition, different usage fields such as carrying an object and making video records over the cities. This paper exposes design and control of two different multi propeller systems and both systems have 12 rotors to carry more load. In first, all motors are arranged at identical angles in the same plane. In the other, the motors are mounted in two groups, eight and four in two different planes, the lower and upper. Flight performances of these two different UAVs were compared under disturbing effect. It was observed that the flight performances were close in the simulation study. Although the average and maximum of the position errors for three axes in the first model was less than the other, for the same propeller dimensions, the first model must have larger dimensions than the other in the context of the smallest vehicle dimensions.
Observations on the Linear Order of Program Code Reading Patterns in Programmers with Dyslexia

The software engineering industry is increasingly aware of the role and value of neurodiverse engineers within the workforce. One motivation is the alignment between skills needed for software development and the processing strengths of individuals with autistic spectrum conditions. One aspect of neurodiversity is dyslexia, typically presenting in individuals through a range of reading deficiencies. In this paper we build on recent work which has sought to investigate if programmers with dyslexia read program code in a way which is different from programmers without dyslexia. The particular focus of this analysis is the nature of saccadic movement and patterns of linearity when reading code. A study is presented in which the eye gaze of 28 programmers (14 with dyslexia and 14 without) was recorded using an eye tracking device while reading and understanding three on-screen Java programs. Using insights from the wider dyslexia literature, hypotheses are formulated to reflect the expected saccadic gaze behaviour of programmers with dyslexia. A range of existing metrics for linearity of program reading are adapted and used for statistical analysis of the data. Results are consistent with recent work elsewhere and indicate that programmers with dyslexia do not exhibit patterns of linearity significantly different from the control group. Non-linear gaze is shown to be approximately 40% of all saccadic movement. Some preliminary insights are offered based on the data available, suggesting that the extent of non-linear reading when comprehending program code might complement the processing and problem solving style of the programmer with dyslexia.
Modeling Document Interactions for Learning to Rank with Regularized Self-Attention

Learning to rank is an important task that has been successfully deployed in many real-world information retrieval systems. Most existing methods compute relevance judgments of documents independently, without holistically considering the entire set of competing documents. In this paper, we explore modeling documents interactions with self-attention based neural networks. Although self-attention networks have achieved state-of-the-art results in many NLP tasks, we find empirically that self-attention provides little benefit over baseline neural learning to rank architecture. To improve the learning of self-attention weights, We propose simple yet effective regularization terms designed to model interactions between documents. Evaluations on publicly available Learning to Rank (LETOR) datasets show that training self-attention network with our proposed regularization terms can significantly outperform existing learning to rank methods.
Optimal Topology Search for Fast Model Averaging in Decentralized Parallel SGD

Distributed training of deep learning models on high-latency systems necessitates decentralized parallel SGD solutions. However, existing solutions suffer from slow convergence because of hand-crafted topologies. The question arises, “for decentralized parallel SGD, is it possible to learn a topology that provides faster model averaging compared to the hand-crafted counterparts?”.By leveraging spectral properties of the graph, we formulate the objective function for finding the topology that provides fast model averaging. Since direct optimization of the objective function is infeasible, we employ a local search algorithm guided by the objective function. We show through extensive empirical evaluation on image classification tasks that the model averaging based on learned topologies leads to fast convergence. An equally important aspect of the decentralized parallel SGD is the link weights for sparse model averaging. In contrast to setting weights via Metropolis-Hastings, we propose to use Laplacian link weights on the learned topologies, which provide a significant lift in performance.
Fact or Fiction: Verifying Scientific Claims

We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that supports or refutes a given scientific claim, and to identify rationales justifying each decision. To study this task, we construct SciFact, a dataset of 1.4K expert-written scientific claims paired with evidence-containing abstracts annotated with labels and rationales. We develop baseline models for SciFact, and demonstrate that these models benefit from combined training on a large dataset of claims about Wikipedia articles, together with the new SciFact data. We show that our claim verification system is able to identify plausible evidence for 23 / 36 claims relevant to COVID-19 on the CORD-19 corpus. Our results and experiments strongly suggest that our new task and data will support significant future research efforts.
Robust Encodings: A Framework for Combating Adversarial Typos

Despite excellent performance on many tasks, NLP systems are easily fooled by small adversarial perturbations of inputs. Existing procedures to defend against such perturbations are either (i) heuristic in nature and susceptible to stronger attacks or (ii) provide guaranteed robustness to worst-case attacks, but are incompatible with state-of-the-art models like BERT. In this work, we introduce robust encodings (RobEn): a simple framework that confers guaranteed robustness, without making compromises on model architecture. The core component of RobEn is an encoding function, which maps sentences to a smaller, discrete space of encodings. Systems using these encodings as a bottleneck confer guaranteed robustness with standard training, and the same encodings can be used across multiple tasks. We identify two desiderata to construct robust encoding functions: perturbations of a sentence should map to a small set of encodings (stability), and models using encodings should still perform well (fidelity). We instantiate RobEn to defend against a large family of adversarial typos. Across six tasks from GLUE, our instantiation of RobEn paired with BERT achieves an average robust accuracy of 71.3% against all adversarial typos in the family considered, while previous work using a typo-corrector achieves only 35.3% accuracy against a simple greedy attack.
Graph Spectral Feature Learning for Mixed Data of Categorical and Numerical Type

Feature learning in the presence of a mixed type of variables, numerical and categorical types, is an important issue for related modeling problems. For simple neighborhood queries under mixed data space, standard practice is to consider numerical and categorical variables separately and combining them based on some suitable distance functions. Alternatives, such as Kernel learning or Principal Component do not explicitly consider the inter-dependence structure among the mixed type of variables. In this work, we propose a novel strategy to explicitly model the probabilistic dependence structure among the mixed type of variables by an undirected graph. Spectral decomposition of the graph Laplacian provides the desired feature transformation. The Eigen spectrum of the transformed feature space shows increased separability and more prominent clusterability among the observations. The main novelty of our paper lies in capturing interactions of the mixed feature type in an unsupervised framework using a graphical model. We numerically validate the implications of the feature learning strategy
Out-of-Sample Representation Learning for Multi-Relational Graphs

Many important problems can be formulated as reasoning in knowledge graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already observed entities. This is true for both attributed graphs(where each entity has an initial feature vector) and non-attributed graphs (where the only initial information derives from known relations with other entities). For out-of-sample reasoning, where one needs to make predictions for entities that were unseen at training time, much prior work considers attributed graph. However, this problem is surprisingly under-explored for non-attributed graphs. In this paper, we study the out-of-sample representation learning problem for non-attributed knowledge graphs, create benchmark datasets for this task, develop several models and baselines, and provide empirical analyses and comparisons of the proposed models and baselines.
Component element ratio analysis of the McCreedy West Fe–Ni–Cu sulfide deposit, North Range of the Sudbury igneous complex

This article is motivated by the need to reveal the scientific untenability of the current, widely used, ‘standard’ approach to interpreting exploration borehole assay data from magmatic Ni–Cu sulfide deposits such as the McCreedy West deposit at Sudbury. This scientifically untenable methodology involves recalculating assay data to 100% sulfides, using metal tenor diagrams such as Ni100 versus Cu100 plots, and appealing to R factors to interpret differences between magmatic sulfide deposits from different locations. Therefore, component element ratio diagrams (Ni/S vs. Cu/S), requiring no assumptions or recalculations, are used to evaluate the fractional crystallization path of the parent sulfide melt that gave rise to the McCreedy West deposit. This methodology reveals that two compositionally distinct pyrrhotites, and sulfide–sulfide liquid immiscibility were involved in the formation of this deposit; observations previously regarded as controversial. Additionally, it allows the distribution coefficients for Ni and Cu between pyrrhotite and the parent sulfide melt to be estimated directly, and a compositional vector toward more fractionated ore to be developed. These observations are confirmed using two independent research quality data sets. It is concluded that the current, ‘standard’ method involving the recalculation of assay data should be rejected and that the component element ratio analysis methodology be adopted in order to correctly interpret the fractionation of magmatic sulfide deposits.
Mining Locally Trending High Utility Itemsets

High utility itemset mining consists of identifying all the sets of items that appear together and yield a high profit in a customer transaction database. Recently, this problem was extended to discover trending high utility itemsets (itemsets that yield an increasing or decreasing profit over time). However, an important limitation of that problem is that it is assumed that trends remain stable over time. But in real-life, trends may change in different time intervals due to specific events. To identify time intervals where itemsets have increasing/decreasing trends in terms of utility, this paper proposes the problem of mining Locally Trending High Utility Itemsets (LTHUIs) and their Trending High Utility Periods (THUPs). Properties of the problem are studied and an efficient algorithm named LTHUI-Miner is proposed to enumerate all the LTHUIs and their THUPs. An experimental evaluation shows that the algorithm is efficient and can discover insightful patterns not found by previous algorithms.
Learning hierarchical behavior and motion planning for autonomous driving

Learning-based driving solution, a new branch for autonomous driving, is expected to simplify the modeling of driving by learning the underlying mechanisms from data. To improve the tactical decision-making for learning-based driving solution, we introduce hierarchical behavior and motion planning (HBMP) to explicitly model the behavior in learning-based solution. Due to the coupled action space of behavior and motion, it is challenging to solve HBMP problem using reinforcement learning (RL) for long-horizon driving tasks. We transform HBMP problem by integrating a classical sampling-based motion planner, of which the optimal cost is regarded as the rewards for high-level behavior learning. As a result, this formulation reduces action space and diversifies the rewards without losing the optimality of HBMP. In addition, we propose a sharable representation for input sensory data across simulation platforms and real-world environment, so that models trained in a fast event-based simulator, SUMO, can be used to initialize and accelerate the RL training in a dynamics based simulator, CARLA. Experimental results demonstrate the effectiveness of the method. Besides, the model is successfully transferred to the real-world, validating the generalization capability.
Big Data retrieval techniques based on Hash Indexing and MapReduce approach with NoSQL Database

As the size of the data grows enormous day by day, there are challenges in storing, sorting and quick accessibility of the data. In order to overcome these challenges indexing of Big Data were made predominant so that these data can be ordered, addressed and located easily. Though there are lot of techniques to index data and map them, each has its own advantages and issues over its performance across various kinds of data. Two different techniques for Big Data retrieval namely MapReduce, a way of simplifying a huge collection into some useful aggregation values and Hash indexing, which is a method of generating key and storing the value of the tuples so that the data are addressed by the generated key on its tuples is compared using NoSQL database. An analysis is made to examine the retrieval efficiency of the data which are of varying size from the whole dataset and limiting the data to be retrieved using predicates through search queries is performed. The comparison is made using both singleton and distributed NoSQL MongoDB.