Reviewerly/exHarmony huggingface dataset:
Data sharing practices in open access mode: a study of the willingness to share data in different disciplines

Introduction. Considering the problems of making research data freely available despite many initiatives and legislation, this study explores the possible differences and similarities of research fields in understanding and enacting open access use and sharing of research data. Method. An online survey was distributed and 671 responses of researchers from Lithuania and Finland were surveyed. Analysis. Chi-squared tests were used to compare proportions across questions groups. Bonferroni corrections applied to p-values were used to reduce the risk of type I errors in the post-hoc analysis of Chi-squared tests. Chi-squared post-hoc tests using adjusted residuals were calculated. Results. Willingness to share research data in open access mode was higher in natural, biomedical and technological sciences than in humanities and social sciences. Although the principle of open research data is supported, there are considerable differences in the character of research data, and the ethical concerns and data management policies between different research fields. Conclusions. Changing culture of data sharing is much harder than publication of new initiatives and legislation. By comparing results with the data from same survey from UK, France, and Turkey we find out that researcher in Finland and Lithuania are more conservative regarding open research data.
Structural optimization of integrated non-volatile photonic memory towards high storage density and low energy consumption

Photonic memory provides optics the opportunity for upgrading data storage hardware and shows great promise in future computing hardware due to its innate superiority in speed, power consumption and bandwidth. Chalcogenide phase-change materials (PCMs) such as Ge 2 Sb 2 Te 5 (GST) are one of the most promising non-volatile storage materials and have been used for on-chip photonic integration circuits (PIC). Here, we explore the transmission contrast and the thermal distribution produced by different spatial structures of GST cells in photonic memory including geometric shapes, vertical positions and array distributions. We design the geometric shape of GST cells in photonic memory that matches the optical field distribution profile, causing a more than 20% increase in transmission contrast. We then set the vertical position of GST cells embedded in waveguides where the GST cell is overlapping most with the waveguide propagation mode, increasing the transmission contrast by more than 50%. Engineering the surrounding cladding layer is also an effective method to improve the transmission contrast (&gt;10%). Beyond that, selecting a suitable size of the gap can obtain a better balance between the storage capacity and the thermal management in uniform GST arrays while the non-uniform array can further improve the thermal uniformity. Our studies indicate that spatial distribution optimization of light storage media is necessary to develop low-energy-consumption on-chip PIC.
A Neural Network Architecture for Program Understanding Inspired by Human Behaviors

Program understanding is a fundamental task in program language processing.Despite the success, existing works fail to take human behaviors as reference in understanding programs.In this paper, we consider human behaviors and propose the PGNN-EK model that consists of two main components.On the one hand, inspired by the "divide-and-conquer" reading behaviors of humans, we present a partitioningbased graph neural network model PGNN on the upgraded AST of codes.On the other hand, to characterize human behaviors of resorting to other resources to help code comprehension, we transform raw codes with external knowledge and apply pre-training techniques for information extraction.Finally, we combine the two embeddings generated from the two components to output code embeddings.We conduct extensive experiments to show the superior performance of PGNN-EK on the code summarization and code clone detection tasks.In particular, to show the generalization ability of our model, we release a new dataset that is more challenging for code clone detection and could advance the development of the community.Our codes and data are publicly available at
Interaction of phonological biases and frequency in learning a probabilistic language pattern

We examine how learning a phonological rule in an artificial language interacts with morphological and lexical learning. We exposed adult participants to an artificial language in which noun plurals were marked by one of two prefix forms (ba- or ni-), one of which also triggered a velar palatalization rule (e.g., singular kimu, plural ni-chimu). In some conditions, the rule additionally created homophony. We also manipulated the relative frequency of the two prefix variants. The results showed that participants shifted away from using the rule-triggering prefix (ni-), but only when it was already the less frequent prefix. We attribute this effect to a paradigm uniformity bias leading participants to avoid phonological alternations (particularly in the stem). When the rule created homophony between lexical items, participants were less able to learn the rule, but it did not affect their choice of prefix. We attribute this effect to homophony avoidance interfering with participants' ability to extract the phonological generalization.
An analysis of SPARQL usage for information retrieval in heterogeneous domains through various tools

SPARQL is a Query Language and a protocol popularly used in web semantics for the information retrieval. It is standard language declared by World Wide Web Consortium for resource description framework. Due to the exponential popularity of semantic web, bigdata, cloud computing, graph databases and linked open data, SPARQL Queries are playing a vital role in the stated domain for managing data with different aspects. This paper summarizes SPARQL usage in various domains touching the latest emerging technologies in computer science and various other fields. A brief analysis has been done of various tools on which SPARQL protocol can be executed for different purposes and it has been analyzed that using such protocols will generate tremendous results in different application areas towards integration of semantic web with different technologies like cloud computing, Linked Open Data (LOD) , Graph Databases and others.
Moneo

Cloud-based AI infrastructure is becoming increasingly important, especially on large-scale distributed training. To improve its efficiency and serviceability, real-time monitoring of the infrastructure and workload profiling are proved to be the effective approach empirically. However, cloud environment poses great challenges as service providers cannot interfere with their tenants' workloads or touch user data, thus previous instrumentation-based monitoring approach cannot be applied, nor does the workload trace collection.
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations

Target speech extraction is a technique to extract the target speaker's voice from mixture signals using a pre-recorded enrollment utterance that characterize the voice characteristics of the target speaker. One major difficulty of target speech extraction lies in handling variability in ``intra-speaker'' characteristics, i.e., characteristics mismatch between target speech and an enrollment utterance. While most conventional approaches focus on improving {\it average performance} given a set of enrollment utterances, here we propose to guarantee the {\it worst performance}, which we believe is of great practical importance. In this work, we propose an evaluation metric called worst-enrollment source-to-distortion ratio (SDR) to quantitatively measure the robustness towards enrollment variations. We also introduce a novel training scheme that aims at directly optimizing the worst-case performance by focusing on training with difficult enrollment cases where extraction does not perform well. In addition, we investigate the effectiveness of auxiliary speaker identification loss (SI-loss) as another way to improve robustness over enrollments. Experimental validation reveals the effectiveness of both worst-enrollment target training and SI-loss training to improve robustness against enrollment variations, by increasing speaker discriminability.
Time-to-event modeling for hospital length of stay prediction for COVID-19 patients

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six proposed survival models is evaluated and compared based on clinical data from COVID-19 patients.
Generation of User Interfaces and Code from User Stories

Coding is time-consuming and confusing more than any other single process in a software development project. This research addresses the above issue and proposes the transition from a computation independent model (CIM) to a code whose aim is to generate cross-platform mobile and Web applications with the CRUD functionalities (create, read, update, and delete). The implementation of the model-driven architecture (MDA) approach resides in the generation of cross-platform applications, through model-to-text transformations with ACCELEO. Our automation approach is based on the agile requirement to generate an Ecore file representing the platform-independent model (PIM). Subsequently, the source code and interfaces are automatically built. The CRUD application is generated for each class of an Ecore file. These output applications are carried out using the Flutter framework. In our development process, the automation of transformations from one step to another leads to productivity gains and a reduction in overall costs.
Detector-Informed Batch Steganography and Pooled Steganalysis

We study the problem of batch steganography when the senders use feedback from a steganography detector. This brings an additional level of complexity to the table due to the highly non-linear and non-Gaussian response of modern steganalysis detectors as well as the necessity to study the impact of the inevitable mismatch between senders' and Warden's detectors. Two payload spreaders are considered based on the oracle generating possible cover images. Three different pooling strategies are devised and studied for a more comprehensive assessment of security. Substantial security gains are observed with respect to previous art - the detector-agnostic image-merging sender. Close attention is paid to the impact of the information available to the Warden on security.
Exploring the Relationship between High-Performance Work Systems, Employee Commitment, and Firm Performance (A Multi Mediation Model)

The study explored the effects of High-Performance Work System practices (HPWS -AMO model) on the employee and organizational performance and mediating role of employee commitments between these two.Hypotheses were tested with a sample of 205 employee-supervisor dyads working in public sector banks and insurance companies of Sargodha and Lahore.The proposed direct and indirect relationships among study variables were tested using structural equation modeling (SEM).Results showed that ability and opportunity enhancing HPWS practices had a positive effect on the employee and organizational performance.Affective, continuance and occupational commitment are partially mediated between ability and opportunity enhancing HPWS practices and performance.In contrast, normative commitment did not show meditation on the relationship between HPWS practices and employee performance; however, it partially mediated the link between ability and motivation, enhancing HPWS practices and subjective organizational performance.This study suggests that developing a system of HPWS practices in the organizations will not only help the organizations to flourish and stay competitive but will also help in eliciting the right employee attitudes by enhancing employee commitment.
Two-pass Decoding and Cross-adaptation Based System Combination of End-to-end Conformer and Hybrid TDNN ASR Systems

Fundamental modelling differences between hybrid and end-to-end (E2E) automatic speech recognition (ASR) systems create large diversity and complementarity among them. This paper investigates multi-pass rescoring and cross adaptation based system combination approaches for hybrid TDNN and Conformer E2E ASR systems. In multi-pass rescoring, state-of-the-art hybrid LF-MMI trained CNN-TDNN system featuring speed perturbation, SpecAugment and Bayesian learning hidden unit contributions (LHUC) speaker adaptation was used to produce initial N-best outputs before being rescored by the speaker adapted Conformer system using a 2-way cross system score interpolation. In cross adaptation, the hybrid CNN-TDNN system was adapted to the 1-best output of the Conformer system or vice versa. Experiments on the 300-hour Switchboard corpus suggest that the combined systems derived using either of the two system combination approaches outperformed the individual systems. The best combined system obtained using multi-pass rescoring produced statistically significant word error rate (WER) reductions of 2.5% to 3.9% absolute (22.5% to 28.9% relative) over the stand alone Conformer system on the NIST Hub5'00, Rt03 and Rt02 evaluation data.
The impact of gold ore mining on total lead (Pb) concentration in some mining and residential communities in Zamfara State, Nigeria

Illegitimate artisanal gold mining quite often results in elevated lead (Pb) concentrations in mining and residential areas close to ore mining sites, which exposes the communities to varying degrees of health and environmental challenges.Here,mining and residential soils from Abare, Dareta and Bagega, Anka local government,Zamfara state's exposed communities, were examined for total Pb concentration.Three replicates of soil from four different soil fields in mining and residential areas were carefully selected for sampling from the surface layer 0 to 21 cm.All thesoil samples were digested and analysed using flame atomic absorption spectrophotometry (Varian model-AA240FS). The total Pb concentration in the mining areas ranges from 466.01-729.13 mg/kg (Abare), 151.34-168.21 mg/kg (Bagega) and 108.89-258.88 mg/kg (Dareta), while those from the residential areas ranges from 326.28-391.38mg/kg (Abare), 67.74-79.44 mg/kg (Bagega) and 17.58-43.40mg/kg (Dareta).This study revealed that the Pb concentration in all mining areas exceeded the standard ofthe department of petroleum resources of Nigeria's 85 mg/kg threshold (DPR).According to the study, the only residential communities with Pb concentrations above the DPR threshold are those in Abare.Based on the computed degree of contamination indices in those areas using the single pollution index (SPI) and Nemerow composite pollution index (NCPI), the study revealed the residential sites are within the safety limit, whereas the mining sites are slightly contaminated. Nevertheless, more studies are needed to determine the Pb speciation in those areas.
Conformer Based Elderly Speech Recognition System for Alzheimer’s Disease Detection

Early diagnosis of Alzheimer's disease (AD) is crucial in facilitating preventive care to delay further progression. This paper presents the development of a state-of-the-art Conformer based speech recognition system built on the DementiaBank Pitt corpus for automatic AD detection. The baseline Conformer system trained with speed perturbation and SpecAugment based data augmentation is significantly improved by incorporating a set of purposefully designed modeling features, including neural architecture search based auto-configuration of domain-specific Conformer hyper-parameters in addition to parameter fine-tuning; fine-grained elderly speaker adaptation using learning hidden unit contributions (LHUC); and two-pass cross-system rescoring based combination with hybrid TDNN systems. An overall word error rate (WER) reduction of 13.6% absolute (34.8% relative) was obtained on the evaluation data of 48 elderly speakers. Using the final systems' recognition outputs to extract textual features, the best-published speech recognition based AD detection accuracy of 91.7% was obtained.
Coal Identification Based on Reflection Spectroscopy and Deep Learning: Paving the Way for Efficient Coal Combustion and Pyrolysis

Coal plays an indispensable role in the world's energy structure. Coal converts chemical energy into energy such as electricity, heat, and internal energy through combustion. To realize the energy conversion of coal more efficiently, coal needs to be identified during the stages of mining, combustion, and pyrolysis. On this basis, different categories of coal are used according to industrial needs, or different pyrolysis processes are selected according to the category of coal. This paper proposes an approach combining deep learning with reflection spectroscopy for rapid coal identification in mining, combustion, and pyrolysis scenarios. First, spectral data of different coal samples were collected in the field and these spectral data were preprocessed. Then, an identification model combining a multiscale convolutional neural network (CNN) and an extreme learning machine (ELM), named RS_PSOTELM, is proposed. The effective features in the spectral data are extracted by the CNN, and the feature classification is realized utilizing the ELM. To enhance the identification performance of the model, we utilize a particle swarm optimization algorithm to optimize the parameters of the ELM. Experimental results show that RS_PSOTELM achieves 98.3% accuracy on the coal identification task and is able to identify coal quickly and accurately, providing a low-cost, efficient, and reliable approach for coal identification during the mining and application phases, as well as paving the way for efficient combustion and pyrolysis of coal.
Identifying the degree of influence of various factors on students’ academic performance based on their self-assessment, taking into account students’ gender

Introduction. The article examines the influence of various factors on the academic performance of university students. The purpose of the article is to identify the degree of influence of various factors on first-year university students’ academic performance taking into account their gender. The study is based on students’ self-assessment. Materials and Methods. The methodological basis of the research was the articles of Russian and foreign scholars devoted to the factors affecting the academic performance of university students (A. Alhadabi, H. W. Brink, Y. García, H. G. Hidajat, E. V. Krekhovets, F. Moradi, N. S. Nurieva, A. O. Ryazhkin, V. N. Shlyapnikov, D. R. Valeeva, S. D. Vaulin, S. V. Yaroshevskaya, I. Živčić-Bećirević and etc.). The empirical study was carried out at Tyumen State University. The sample consisted of 232 first-year students of various fields of study, including 101 males and 131 females. Results. The analysis of the scholarly literature on the research problem enabled the authors to identify the factors that influence the academic performance of university students and propose a classification of these factors. Based on students’ self-assessment, the degree of influence of each of the selected factors on their academic performance was determined. Significant differences in the assessment of the influence of a few factors on academic performance related to the students’ gender were revealed. According to the self-assessment of students, the competence of academic staff has the strongest influence on their academic performance. Within the framework of the proposed classification of factors, the most important for students was a group of factors related to their personal characteristics. Factors related to randomness and academic fraud were ranked among the least important. Intrinsic motivation as a factor influencing academic performance turned out to be more important than extrinsic positive and extrinsic negative motivation. Conclusions. The experimental data show that women, to a greater extent than men, associate their academic performance with external factors (teachers’ competence, learning environment, study facilities), or with their innate personality traits (intellectual abilities, memory characteristics and attention, emotional balance). For men, a more significant factor in academic performance is relying on their own strengths and expectations of success from their efforts. At the same time, their academic performance is more determined by the social environment at the university.
Machine Learning Using a Simple Feature for Detecting Multiple Types of Events From PMU Data

This paper describes simple and efficient machine learning (ML) methods for efficiently detecting multiple types of power system events captured by PMUs scarcely placed in a large power grid. It uses a single feature from each PMU based on a rectangle area enclosing the event in a given data window. This single feature is sufficient to enable commonly used ML models to detect different types of events quickly and accurately. The feature is used by five ML models on four different data-window sizes. The results indicated a tradeoff between the execution speed and detection accuracy in variety of data-window size choices. The proposed method is insensitive to most data quality issues typical for data from field PMUs, and thus it does not require major data cleansing efforts prior to feature extraction.
System analysis and information system development for a smart grid

Because the systemic model of any organization is organized by matching an operating system and a decision system through an information system, the implementation of the information system and the effectiveness of its exploitability is an unavoidable operation. Indeed, its modeling allows us to have a tool of analysis and assistance in decision-making. The purpose of this paper is to present an application of a system approach based on the OOPP method for modeling an information system of an industrial company.
JPV-Net: Joint Point-Voxel Representations for Accurate 3D Object Detection

Voxel and point representations are widely applied in recent 3D object detection tasks from LiDAR point clouds. Voxel representations contribute to efficiently and rapidly locating objects, whereas point representations are capable of describing intra-object spatial relationship for detection refinement. In this work, we aim to exploit the strengths of both two representations, and present a novel two-stage detector, named Joint Point-Voxel Network (JPV-Net). Specifically, our framework is equipped with a Dual Encoders-Fusion Decoder, which consists of the dual encoders to extract voxel features of sketchy 3D scenes and point features rich in geometric context, respectively, and the Feature Propagation Fusion (FP-Fusion) decoder to attentively fuse them from coarse to fine. By making use of the advantages of these features, the refinement network can effectively eliminate false detection and provide better accuracy. Besides, to further develop the perception characteristics of voxel CNN and point backbone, we design two novel intersection-over-union (IoU) estimation modules for proposal generation and refinement, both of which can alleviate the misalignment between the localization and the classification confidence. Extensive experiments on the KITTI dataset and ONCE dataset demonstrate that our proposed JPV-Net outperforms other state-of-the-art methods with remarkable margins.