Reviewerly/exHarmony huggingface dataset:
Optimal Parallel Sorting with Comparison Errors

We present comparison-based parallel algorithms for sorting n comparable items subject to comparison errors. We consider errors to occur according to a well-studied framework, where the comparison of two elements returns the wrong answer with a fixed probability. In the persistent model, the result of the comparison of two given elements, x and y, always has the same result, and is independent of all other pairs of elements. In the non-persistent model, the result of the comparison of each pair of elements, x and y, is independent of all prior comparisons, including for x and y. It is not possible to always correctly sort a given input set in the persistent model, so we study algorithms that achieve a small maximum dislocation and small total dislocation of the elements in the output permutation. In this paper, we provide parallel algorithms for sorting with comparison errors in the persistent and non-persistent models. Our algorithms are asymptotically optimal in terms of their span, work, and, in the case of persistent errors, maximum and total dislocation. The main results are algorithms for the binary-forking parallel model with atomics, but we also provide algorithms for the CREW PRAM model. Our algorithms include a number of novel techniques and analysis tools, including a PRAM-to-binary-forking-model simulation result, and are the first optimal parallel algorithms for the persistent model and the non-persistent model in the binary-forking parallel model with atomics. In particular, our algorithms have O(log n) span, O(n log n) work, and, in the case of the persistent model, O(log n) maximum dislocation and O(n) total dislocation, with high probability. We achieve similar results for the CREW PRAM model, which are the first optimal methods for the persistent model and the first optimal results for the non-persistent model with reasonable constant factors in the performance bounds.
IREX: Iterative Refinement and Explanation of classification models for tabular datasets

Tabular datasets, collections of rows and columns, are fundamental in data analysis in basically all areas of research. Self-report questionnaires are a very common and useful tool for gathering data from users, patients, or customers. Often, experts can label each item of these questionnaires as a measure of a given condition or behavior, e.g., mental health conditions such as depression. Considering this, many artificial intelligence techniques, particularly those related to machine learning, have been proposed to analyze the data provided by these tools. However, self-report questionnaires can be very extensive, which often affects the quality of the responses, complicates the data analysis, and renders them more time-consuming. In this paper, the software IREX is presented. IREX iteratively refines tabular datasets, such as self-report questionnaires, while providing an explanation of a given classification model.
Location-Sensitive String Problems in MPC

A suffix tree is a trie-like data structure that stores every suffix of an input string of length n. Finding the Suffix Tree of a given string is a well-studied and classic problem. A compressed suffix tree is constructible in O(n) time using the well-known algorithm of McCreight (JACM, 1976). Suffix trees alongside with hashing are two powerful tools in solving location-sensitive string problems. Many well-studied fundamental string problems such as String Matching, Longest Palindrome Substring (LPS), Longest Common Substring (LCS), and Longest Common Prefix (LCP) queries are location-sensitive and have linear time solutions via reductions to suffix tree.
Brief Announcement: A Parallel Architecture for Dynamic Approximate Membership

We present the first parallel architecture for a dynamic approximate membership data-structure (i.e., a filter) that supports insertions, deletions, and approximate membership queries. Our architecture borrows techniques from PRAM emulation to obtain a parallel filter based on two levels of fingerprint-dictionaries. A key component in the architecture is a special-purpose wide-word processor we designed to support operations over small dictionaries. We implemented this architecture on an FPGA running at 100MHz. The implementation stores up to 1.44 million keys, has a false-positive rate less than 0.3%, receives batches 16 of operations per cycle, preserves sequential order, and runs with a stable throughput of over a billion operations per second with respect to several benchmarks.
Fast Dynamic Programming in Trees in the MPC Model

We present a deterministic algorithm for solving a wide range of dynamic programming problems in trees in O(log D) rounds in the massively parallel computation model (MPC), with O(nδ) words of local memory per machine, for any given constant 0 < δ < 1. Here D is the diameter of the tree and n is the number of nodes---we emphasize that our running time is independent of n.
Movement strategies selection of a mobile robot to avoid obstacles

The movement of a mobile robot in a non-deterministic environment is accompanied by maneuvering due to the need to avoid a collision with a fixed or moving obstacle. Under certain conditions, several obstacles, including moving ones, may appear in the near zone of the robot. In this case, trajectory-planning algorithms based on a “static” approach to the position of obstacles will not be effective; their application may lead to collisions. Finding the path of movement in conditions of dynamic obstacles is an urgent task that researchers are trying to solve using various methods. The article proposes a method for planning the actions and movements of a mobile robot in non-deterministic conditions, in the presence of fixed and moving obstacles. The processes of planning the trajectories of a mobile robot in the presence of obstacles in the near zone are considered. Methods and algorithms for making decisions on avoiding obstacles based on the estimated collision probabilities in the forecast interval are proposed.
The role of theater architecture in music education

The article examines the role of theater architecture in music education. It is determined that the study of theatrical architecture is an important part of obtaining musical education, especially for those who plan to become a professional musician. As part of music education, students can study theater architecture and its impact on musical performances. It should also be noted that music education necessarily includes the development of the general culture of students, for this reason, the architectural features of theater buildings can contribute to the development of the general culture of students.
Algorithms for extracting lines, paragraphs with their properties in PDF documents

The article discusses the algorithms for detecting and extracting lines, paragraphs with their properties and attributes in PDF documents, analyses the structure of PDF-file and its objects. Due to special operators in objects the PDF documents content is saved as symbols or symbol groups. The position of such groups on the page also remains identical. The main challenge that we face, while extracting paragraphs from the PDF document is the complex format that is able to retain various types of information and can be created in several ways.
Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.
Solvability of Peg Solitaire on Graphs is NP-Complete

Peg solitaire is a single-player board game. The goal of the game is to remove all but one peg from the game board. Peg solitaire on graphs is a peg solitaire played on arbitrary graphs. A graph is called solvable if there exists some vertex s such that it is possible to remove all but one peg starting with s as the initial hole. In this paper, we prove that it is NP-complete to decide if a graph is solvable or not.
Superposition of the Similarity Images by Contour

Superposition of the similarity images is implemented by the methodology with divided estimation of parameters. The offsets along the coordinate axes are estimated in the Cartesian coordinate system. The scale and the rotate are estimated in the log-polar coordinate system. The accurate estimation of parameters of similarity models (offsets, scale and rotate) is achieved by the iteration processing. Optimization of the processing time is achieved by contour comparison instead of the image comparison. The test data for experiment is image with the freight car. The decreasing of the processing time for the modified methodology of “contour comparison” was estimated by comparison with the source methodology of “image comparison”.
VOTING BEHAVIOR IN 2022 NATIONAL ELECTION: A CAUSAL-COMPARATIVE AND CORRELATIONAL STUDY

The core of this study is the voting behavior of college students in the 2022 elections. It made use of the causal-comparative and correlational research designs to compare the voting behaviors of the respondents in terms of age and sex, and to determine the strength and direction of the relationship between the variable characteristics of the political candidates and the variable political process with respect to their influence on voting behavior. It included as respondents 41 purposively chosen Social Studies students of Notre Dame of Midsayap College. They were 18 to 23 years old, mostly females, and were all registered voters. Data were gathered through an online survey. Findings revealed that having a degree was considered by the respondents as a very important personal characteristic for the presidential and vice-presidential candidates, while being a member of a well-known family was considered by them as unimportant. Being honest and trustworthy was also considered by the respondents as a very important leadership characteristic for the said candidates. Meanwhile, being clear in their priorities is a political process that is very much likely to influence the respondents in choosing their president and vice-president while the giving of money or goods to voters is unlikely to influence them. Similarly, candidates being talked about in their locality is a political process that is likely to influence the respondents in choosing those candidates. The endorsement of candidates by a local leader is a political process that is likely to influence them in choosing those candidates, while endorsement by a church is very unlikely to influence them. Results bared that there is no significant difference on the voting behavior of the respondents with respect to their age and sex. Finally, there is a moderately strong positive relationship between the characteristics of the political candidates and the political process as to their influence on the voting behavior of the respondents, and such a relationship is highly significant.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0005/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
Impact of data analytics on reporting quality of forensic audit: a study focus in Malaysian auditors

The primary objective of this research is to study the impact of data analytics on reporting quality of forensic audit, a study focussed on auditors in Malaysia. In this research, the researcher intends to discover the impact caused by emerging technology, in particular the data analytics, in audit profession, as audit field is the most complex field where tremendous volume of data, and research is conducted to investigate a certain situation, to report findings. The researcher uses the primary method to conduct the data. In this research, four different variables that causes impact to data analytics usage in forensic audit reporting quality, are tested. The data was collected from forensic audit and accounting service providers, operating around Malaysia. To analyse the findings, statistical tool, Statistical Package of the Social Sciences (SPSS) is used, to further analyse the responses, and to derive at a conclusion relating to impact of data analytics to reporting quality of forensic audits. The findings discovered that all the variables have significant relationship with the data analytics and its impact to the forensic audit reporting quality, among forensic auditors and practitioners in Malaysia. The conclusion has been discussed in this research which caters for and provides confidence and data privacy, for users of data analytic tools for their forensic audit work. This research creates the awareness of the use of data analytics in order to assist forensic auditors in their routine data gathering and analysis, in preparing reports, statistics and trend analysis etc, by saving their efforts and time, to focus more on performing further analysis and findings based on case or audit assignment.
Multiclass Support Vector Data Description in Extreme Learning Machine Feature Space

Abstract Support vector data description (SVDD) method aims to address the one-class classification (OCC) problem to find a hypersphere-shaped description of target data set. For extending SVDD to multiclass classification while remaining the ability of detecting outliers, we propose a novel multiclass SVDD scheme which can be used in effective feature mapping and meta-class separation based on the extreme learning machine algorithm (ELM-MSVDD). Accordingly, the imprecise data difficult to distinguish in specific classes is classified to a meta-class,the meta-class is defined by the disjunction of these specific classes, this operation can reduce the error rate effectively. Experimental results of our ELM-MSVDD method show well performance on the datasets from the UCI machine learning library and radar signal source recognition. Meanwhile, our proposed method provide a theoretical and practical support for other relevant pattern recognition field.
Correlates of human development in Africa: Evidence across gender and income group

Gender inequality is high in Africa compared to other regions of the world. This disparity jeopardises the efforts targeted at improving human development and economic growth in the continent. This study provides empirical evidence on the drivers of human development in Africa across gender and country income groups, covering 54 countries over the period 1990 to 2019. It employed the Mean Group estimator for analysis to accommodate slope heterogeneity and cross-sectional dependence. The paper used Human Development Index (HDI) to proxy human development, while HDI across gender was employed to capture gender differences in human development. Country income grouping followed the World Bank country classification into low, lower-middle and upper-middle-income economies. The findings showed more determinants of HDI when disaggregated across gender than otherwise. The drivers of HDI are similar across gender for each country income group, with infrastructure, particularly ICT shown to have a positive effect in the long run for all country groups. For lower-middle and upper-middle income countries, fertility rate induced a significant negative effect on HDI and gender human development but only in the long run. However, the effect was insignificant for male and female HDI in low-income countries. Moreover, an increase in real Gross Domestic Product (GDP) promotes human development regardless of a country's income classification. A larger positive effect of GDP was observed on the HDI for low-income countries. Therefore, in the bid to improve human development in Africa, efforts should continually be intensified towards promoting GDP growth regardless of the level of growth in macroeconomic income. Policies targeted at reducing fertility rates and increasing ICT are also encouraged as these will improve HDI and close the gap in HDI across gender.
Document-level Relation Extraction Based on Heterogeneous Graph Convolutional Network and Local Semantic Fusion

Abstract Document-level relation extraction is a fundamental task of many downstream applications such as knowledge graph and has gained improvement through document graph and sequence models. These methods always utilize the whole document as an essential global feature while ignoring the discrimination of entity representation. Focusing on local semantic feature, a novel model named GCNLEF based on graph convolutional network is proposed in this paper. In the presented method, a heterogeneous graph containing mention nodes and sentence group nodes is constructed first. Then multi-hop path reasoning is presented to infer the relations between entities. Experimental results on DocRED show that the proposed model can achieve 61.45 F 1 score with 90 epochs, improved by 0.15 F 1 score compared with the state-of-the-art method ATLOP.
Regularizing Binary Neural Networks via Ensembling for Efficient Person Re-Identification

This study aims to leverage Binary Neural Networks (BNN) to learn binary hash codes for efficient person re-identification (ReID). BNNs, which use binary weights and activations, show promise in speeding up the inference time in deep models. However, BNNs typically suffer from performance degradation mainly due to the discontinuity of the binarization operation. Proxy functions have been proposed to calculate the gradients in the backward propagation, but they lead to the gradient mismatch problem. In this study, we propose to address the gradient mismatch problem by designing a multi-branch ensemble model consisting of many weak hash code learners. Specifically, our design aggregates the gradients from multiple branches, which allows a better approximation of the gradients and regularizes the network. Our model adds little computational cost to the baseline BNN since a vast amount of network parameters are shared between the weak learners. Combining the efficiency of the BNNs and hash code learning, we obtain an effective ensemble model which is efficient both in feature extraction and ranking phases. Our experiments demonstrate that the proposed model outperforms a single BNN by more than %20 using nearly the same amount of floating point operations. Moreover, the proposed model outperforms a conventional ensemble of BNN by more than %7 while being nearly 10x and 2x more efficient in terms of CPU consumption and memory footprint, respectively. We explore the performance of BNNs for efficient person ReID as one of the first systems available in the literature. Moreover, we adopt the proposed ensemble model for further validation of the image classification task and observe that our method effectively regularizes BNNs, providing robustness to hyperparameter selection and producing more consistent results under different settings.
Digital Image Representation Model Enriched with Semantic Web Technologies: Visual and Non-Visual Information

The types of content of digital images, visual (syntactic and semantic) and non-visual, cause the complexity of their representation. Considering these contents separately hinders digital image retrieval because this creates a gap between the contents of the image and its representation. Therefore, this work aims to present a representation model of visual and non-visual information of digital images, with semantic enrichment through the Semantic Web technologies. For that, a qualitative methodology with a bibliographical approach was used. Theoretical subsidies of the topics addressed were sought, and it has an applied focus since it proposes a model and its exemplification. The developed model depicts the representation image process and allows the semantic enrichment of the data. This enrichment facilitates the retrieval in multiple contexts with technologies that favor the use of the data through inferences. Also, a use case with digital medical images is presented, demonstrating the feasibility of the proposal. It is concluded that the representation of visual and non-visual content aims to improve the way images are retrieved in digital information environments. The junction of the content and the context of images should be considered, even though search mechanisms usually treat this separately due to the disaggregation of image representation itself.