Reviewerly/exHarmony huggingface dataset:
Bayesian Personalized Ranking for Novelty Enhancement

Novelty enhancement of recommendations is typically achieved through a post-filtering process applied on a candidate set of items. While it is an effective method, its performance heavily depends on the quality of a baseline algorithm, and many of the state-of-the-art algorithms generate recommendations that are relatively similar to what the user has interacted with in the past. In this paper we explore the use of sampling as a means of novelty enhancement in the Bayesian Personalized Ranking objective. We evaluate the proposed extensions on the MovieLens 20M dataset, and show that the proposed method can be successfully used instead of two-step reranking, as it offers comparable and better accuracy/novelty tradeoffs, and more unique recommendations.
Understanding Evolutionary Coupling by Fine-Grained Co-Change Relationship Analysis

Frequent co-changes to multiple files, i.e., evolutionary coupling, can demonstrate active relations among files, explicit or implicit. Although evolutionary coupling has been used to analyze software quality, there is no systematic study on the categorization of frequent co-changes between files which may used for characterizing various quality problems. In this paper, we report an empirical study on 27,087 co-change commits of 6 open-source systems with the purpose of understanding the observed evolutionary coupling. We extracted fine-grained change information from version control system to investigate whether two files exhibit particular kinds of co-change relationships. We consider code changes on 5 types of program entities (i.e., field, method, control statement, non-control statement, and class) and identified 6 types of dominating co-change relationships. Our manual analysis showed that each of the 6 types can be explained by structural coupling, semantic coupling, or implicit dependencies. Temporal analysis further shows that files may exhibit different co-change relationships at different phases in the evolution history. Finally, we investigated co-changes among multiple files by combining co-change relationships between related file pairs and showed with live examples that rich information embedded in the fine-grained co-change relationships may help developers to change code at multiple locations. Moreover, we analyzed how these co-change relationship types can be used to facilitate change impact analysis and to pinpoint design problems.
Two-Stage RGB-Based Action Detection Using Augmented 3D Poses

In this paper, a novel approach for action detection from RGB sequences is proposed. This concept takes advantage of the recent development of CNNs to estimate 3D human poses from a monocular camera. To show the validity of our method, we propose a 3D skeleton-based two-stage action detection approach. For localizing actions in unsegmented sequences, Relative Joint Position (RJP) and Histogram Of Displacements (HOD) are used as inputs to a k-nearest neighbor binary classifier in order to define action segments. Afterwards, to recognize the localized action proposals, a compact Long Short-Term Memory (LSTM) network with a de-noising expansion unit is employed. Compared to previous RGB-based methods, our approach offers robustness to radial motion, view-invariance and low computational complexity. Results on the Online Action Detection dataset show that our method outperforms earlier RGB-based approaches.
Preparing for InSight: Evaluation of the Blind Test for Martian Seismicity

In December 2018, the NASA InSight mission deployed a seismometer on the surface of Mars.In preparation for the data analysis, in July 2017 the Mars Quake Service initiated a blind test, in which participants were asked to detect and characterize seismicity embedded in a one Earth year long synthetic dataset of continuous waveforms.Synthetic data were computed for a single station, mimicking the streams that will be available from InSight as well as the expected tectonic and impact seismicity, and noise conditions on Mars (Clinton et al. 2017).In total, 84 teams from 20 countries registered for the blind test and 11 of them submitted their results in early 2018.The collection of documentations, methods, ideas and codes submitted by the participants exceeds 100 pages.The teams proposed well established as well as novel methods to tackle the challenging target of building a global seismicity catalogue using a single station.This paper summarizes the performance of the teams, and highlights the most successful contributions.
FPGA-based real-time autocorrelator and its application in dynamic light scattering

Digital correlators play a significant role in dynamic light scattering (DLS) technology, which characterizes particle size distribution. We present a field programable gate array (FPGA)-based digital correlator that can be applied to process DLS data. To satisfy the DLS requirements in the FPGA logic with limited resources, a multiple lag time period (multi-τ) method is employed that does not require storing the full dataset in memory. Moreover, the device directly accepts the transistor–transistor logic (TTL) signal from the photon counting detector by measuring the time intervals between photon events and calculates the autocorrelation functions in real time. Furthermore, we derive estimates for the error arising from the use of the multi-correlator. We implement all the necessary operations in a single Xilinx FPGA chip with a lag time from 10 ns to 45 min, including a highly optimized photon counter.
Russian Higher Education in Conditions of Professional Standardization: Experience, Challenges, Risks

The authors of the article are the experts of Federal Educational and Methodological Union (FEMU) in the training direction “Linguistics and Literature”, who have worked out three subsequent versions of State Educational Standards, including the last version of Federal State Educational Standard (FSES 3++) and Samples of Basic Educational Programs (SBEP) in the training direction of higher education (HE) “Philology”. The given article provides an overview of the main stages of Russian education standardization within the period of 1990–2010 and its further adaptation to the market requirements. The experts focus their attention on the problems of working out and implementing since 2012 the Professional Standards essential for carrying out different kinds of professional activities. The given investigation summarizes the problems discussed in substantive methodological literature that the Russian higher education has to deal with in the conditions of professional standardization. The authors of the article analyze in detail the experience of the FEMU with the view to display the variety of approaches suggested by the authors of FSES HE in correlation with professional standards – from the broadest approach implying the conformity of FSES HE with dozens of professional standards to the narrowest trend designating that a graduate student’s professional activity is only restricted to scientific and pedagogical spheres. Consequently, there is a threat that the variety of approaches may cause in future certain difficulties in coordinating the educational processes in higher educational institutions and in case of uncontrolled extrapolation may result in the downfall of the unified Russian educational system. In the concluding part of the investigation the authors share their know-how in correlating the Educational Standards in “Philology” (HE) with professional standards, dwell on their positive experience of aligning the competence model of the graduate student in FSES and SBEP with the norms of professional standards.
The Landscape of Rights and Licensing Initiatives for Data Sharing

Over the last twenty years, a wide variety of resources have been developed to address the rights and licensing problems inherent with contemporary data sharing practices. The landscape of developments is this area is increasingly confusing and difficult to navigate, due to the complexity of intellectual property and ethics issues associated with sharing sensitive data. This paper seeks to address this challenge, examining the landscape and presenting a Version 1.0 directory of resources. A multi-method study was pursued, with an environmental scan examining 20 resources, resulting in three high-level categories: <em>standards, tools, and community initiatives</em>; and a content analysis revealing the subcategories of <em>rights, licensing, metadata &amp; ontologies</em>. A timeline confirms a shift in licensing standardization priorities from open data to more nuanced and technologically robust solutions, over time, to accommodate for more sensitive data types. This paper reports on the research undertaking, and comments on the potential for using license-specific metadata supplements and developing data-centric rights and licensing ontologies.
Make the most out of your SIMD investments: counter control flow divergence in compiled query pipelines

Increasing single instruction multiple data (SIMD) capabilities in modern hardware allows for the compilation of data-parallel query pipelines. This means GPU-alike challenges arise: control flow divergence causes the underutilization of vector-processing units. In this paper, we present efficient algorithms for the AVX-512 architecture to address this issue. These algorithms allow for the fine-grained assignment of new tuples to idle SIMD lanes. Furthermore, we present strategies for their integration with compiled query pipelines so that tuples are never evicted from registers. We evaluate our approach with three query types: (i) a table scan query based on TPC-H Query 1, that performs up to 34% faster when addressing underutilization, (ii) a hashjoin query, where we observe up to 25% higher performance, and (iii) an approximate geospatial join query, which shows performance improvements of up to 30%.
Analysis of Radar Emitter Signal Sorting and Recognition Model Structure

Aiming at the problem that there are some defects and shortcomings of traditional radar emitter signal sorting model structure, the structure of radar signal sorting model based on intra-pulse characteristic parameters is analyzed. Although the model is more complete than the early recognition model, there are still some shortcomings in the face of the increasingly complex electromagnetic environment and the emergence of complex radar signal sources. This article has studied this.
An exploration-enhanced elephant herding optimization

Purpose The purpose of this paper is to propose an enhanced elephant herding optimization (EEHO) algorithm by improving the exploration phase to overcome the fast-unjustified convergence toward the origin of the native EHO. The exploration and exploitation of the proposed EEHO are achieved by updating both clan and separation operators. Design/methodology/approach The original EHO shows fast unjustified convergence toward the origin specifically, a constant function is used as a benchmark for inspecting the biased convergence of evolutionary algorithms. Furthermore, the star discrepancy measure is adopted to quantify the quality of the exploration phase of evolutionary algorithms in general. Findings In experiments, EEHO has shown a better performance of convergence rate compared with the original EHO. Reasons behind this performance are: EEHO proposes a more exploitative search method than the one used in EHO and the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Operator γ is added to EEHO assists to escape from local optima, which commonly exist in the search space. The proposed EEHO controls the convergence rate and the random walk independently. Eventually, the quantitative and qualitative results revealed that the proposed EEHO outperforms the original EHO. Research limitations/implications Therefore, the pros and cons are reported as follows: pros of EEHO compared to EHO – 1) unbiased exploration of the whole search space thanks to the proposed update operator that fixed the unjustified convergence of the EHO toward the origin and the proposed separating operator that fixed the tendency of EHO to introduce new elephants at the boundary of the search space; and 2) the ability to control exploration–exploitation trade-off by independently controverting the convergence rate and the random walk using different parameters – cons EEHO compared to EHO: 1) suitable values for three parameters (rather than two only) have to be found to use EEHO. Originality/value As the original EHO shows fast unjustified convergence toward the origin specifically, the search method adopted in EEHO is more exploitative than the one used in EHO because of the balanced control of exploration and exploitation based on fixing clan updating operator and separating operator. Further, the star discrepancy measure is adopted to quantify the quality of exploration phase of evolutionary algorithms in general. Operator γ that added EEHO allows the successive local and global searching (exploration and exploitation) and helps escaping from local minima that commonly exist in the search space.
Using K-Means Algorithm for Description Analysis of Text in RSS News Format

This article shows the use of different techniques for the extraction of information through text mining. Through this implementation, the performance of each of the techniques in the dataset analysis process can be identified, which allows the reader to recommend the most appropriate technique for the processing of this type of data. This article shows the implementation of the K-means algorithm to determine the location of the news described in RSS format and the results of this type of grouping through a descriptive analysis of the resulting clusters.
Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks

Large-scale deep neural networks are both memory and computation-intensive, thereby posing stringent requirements on the computing platforms. Hardware accelerations of deep neural networks have been extensively investigated. Specific forms of binary neural networks (BNNs) and stochastic computing-based neural networks (SCNNs) are particularly appealing to hardware implementations since they can be implemented almost entirely with binary operations.&#x0D; Despite the obvious advantages in hardware implementation, these approximate computing techniques are questioned by researchers in terms of accuracy and universal applicability. Also it is important to understand the relative pros and cons of SCNNs and BNNs in theory and in actual hardware implementations. In order to address these concerns, in this paper we prove that the “ideal” SCNNs and BNNs satisfy the universal approximation property with probability 1 (due to the stochastic behavior), which is a new angle from the original approximation property. The proof is conducted by first proving the property for SCNNs from the strong law of large numbers, and then using SCNNs as a “bridge” to prove for BNNs. Besides the universal approximation property, we also derive an appropriate bound for bit length M in order to provide insights for the actual neural network implementations. Based on the universal approximation property, we further prove that SCNNs and BNNs exhibit the same energy complexity. In other words, they have the same asymptotic energy consumption with the growth of network size. We also provide a detailed analysis of the pros and cons of SCNNs and BNNs for hardware implementations and conclude that SCNNs are more suitable.
Scalable Distributed Genetic Algorithm Using Apache Spark (S-GA)

In this era of big data with facilities for advanced real-time data acquisition, the solutions to large-scale optimization problems are strongly desired. Genetic Algorithms are efficient optimization algorithms that have been successfully applied to solve a multitude of complex problems. The growing need for large-scale optimization, and inherent parallel evolutionary nature of the algorithms calls for new solutions exploiting existing parallel, in-memory, distributed computing frameworks like Apache Spark. In this paper, we present an algorithm for Scalable Genetic Algorithms using Apache Spark (S-GA). S-GA makes liberal use of rich APIs offered by Spark. We have tested S-GA on several numerical benchmark problems for large-scale continuous optimization containing up to 3000 dimensions, 3000 population size, and one billion generations. S-GA presents a variant of island model and minimizes the materialization and shuffles in RDDs for minimal and efficient network communication. At the same time it maintains the population diversity by broadcasting the best solutions across partitions after specified Migration Interval. We have tested and compared S-GA with the canonical Sequential Genetic Algorithm (SeqGA). S-GA has been found to be more scalable and it can scale up to large dimensional optimization problems while yielding comparable results.
MATRIX MULTIPLIER OF POLYNOMIALS MODULO ANALYSIS STARTING WITH THE LOWER ORDER DIGITS OF THE MULTIPLIER

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community.Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары.Геология жəне техникалық ғылымдар сериясы" ғылыми журналының
ANALYSIS OF ERROR DISTRIBUTION DENSITY IN DETERMINING THE COORDINATES OF EARTHING EARTH IN THE METHODS OF SPHERES AND ELLIPSOIDS

Objectives The purpose of the study is to obtain an expression for determining the coordinates of the earthquake focus using the ellipsoid method, as well as testing the possibility of using the method using the figures of the second order of the ellipsoid during the initial determination of the coordinates of the earthquake hypocenter. Method A comparative analysis of the probability density of errors in the hypocentral zone of the earth's surface, in combination with various spheres, ellipsoid and hyperboloid and ellipsoids, is carried out. Result Obtained an expression for determining the coordinates of the earthquake focus by the method of ellipsoids, as well as the density of the distribution of error probabilities in the determination of the earthquake hypocenter the in calculations by the method of spheres, by the combined method of spheres, hyperboloid and ellipsoid, and also by the method of ellipsoids. Conclusion Methods used to determine the coordinates of the hypocenter ellipsoid, have large errors in comparison with the method of areas. This can explained by the fact that in determining the coordinates of the hypocenter in the sphere method, three errors are used in determining the difference in the travel times of seismic waves, while in the ellipsoid method and the combined method of the sphere of the ellipsoid and hyperboloid have four errors, which introduces final errors in the distribution. All the obtained dependences of the error distribution have the form close to the Cauchy distribution.
Voices from the Teams - Impacts on Autonomy in Large-Scale Agile Software Development Settings

Forming autonomous, self-organizing, cross-functional teams in software development is becoming more common even in larger organizations, and many organizations are implementing the Scaled Agile Framework. When autonomous teams need to work together, they must sacrifice some level of autonomy since work needs to be coordinated with other teams, which could be a threat to team performance. This study presents how perceived autonomy has changed by listening to the voices from the teams in three large organizations. Although several respondents did not express any experienced changes to autonomy at all, others put forth important changes. The practices where several teams gather in joint events are important arenas in both positive and negative aspects. The arenas give teams a better overview and a sense of being empowered in using their veto right to stop overload of planned work. However, more detailed planning in every single team could cause less ability to switch work between teams and a sense of suffocation due to detailed routines and practices.
WTMED at MEDIQA 2019: A Hybrid Approach to Biomedical Natural Language Inference

Natural language inference (NLI) is challenging, especially when it is applied to technical domains such as biomedical settings.In this paper, we propose a hybrid approach to biomedical NLI where different types of information are exploited for this task.Our base model includes a pre-trained text encoder as the core component, and a syntax encoder and a feature encoder to capture syntactic and domain-specific information.Then we combine the output of different base models to form more powerful ensemble models.Finally, we design two conflict resolution strategies when the test data contain multiple (premise, hypothesis) pairs with the same premise.We train our models on the MedNLI dataset, yielding the best performance on the test set of the MEDIQA 2019 Task 1.
Diffusion Tensor Image segmentation based on multi-atlas Active Shape Model

Active Shape Model (ASM) has been successfully applied in the segmentation of Diffusion Tensor Magnetic Resonance Image (DT-MRI, referred to as DTI) of brain. However, due to multiple anatomical structure types, irregular shapes, small gray-scale and large amount of these images, perfect segmentation performance could not be achieved. Especially, it is sensitive to initial values with high computational complexity. In this paper, we introduce the gray information of multiple atlases and the prior information of target shapes into the ASM and propose the Multi-Atlas Active Shape Model (referred to as MA-ASM) approach for DTI segmentation. It was evaluated in a manually labeled database with 7 Region of Interest (ROI)s for each of 20 subjects. In comparison with the state of art method of STAPLE (Simultaneous Truth Performance Level Estimation), the proposed algorithm was closer to the manual segmentation shape by subjective visual effects, and had higher overlap rates and lower error detection rates on quantitative analysis than STAPLE.
Accurate Shoulder Joint Angle Estimation Using Single RGB camera for Rehabilitation

Motion capture system offer the best interface to understand the trajectory development, which has benefited the research in animation, navigation, health care, etc. The existing human motion capture systems are based on lab-like environment requiring markers, which are computationally costly and time-consuming. MS Kinect is a marker-less technology introduced recently in the gaming industry. In this paper, we have undertaken a comparison study of accuracy in the estimation of shoulder joint angle between machine-learning-based RGB camera and MS Kinect. The results obtained are in time-domain plots for estimating shoulder angle. The results have shown a mean error of 9.29° and 5.3° (and RMS of 3.5° and 1°) in the sagittal and coronal planes respectively. The inaccuracy reported is in the range enough to assure its utility for use in stroke rehabilitation.