Reviewerly/exHarmony huggingface dataset:
Pruning Multilayered ELM Using Cholesky Factorization and Givens Rotation Transformation

Extreme learning machine is originally proposed for the learning of the single hidden layer feedforward neural network to overcome the challenges faced by the backpropagation (BP) learning algorithm and its variants. Recent studies show that ELM can be extended to the multilayered feedforward neural network in which the hidden node could be a subnetwork of nodes or a combination of other hidden nodes. Although the ELM algorithm with multiple hidden layers shows stronger nonlinear expression ability and stability in both theoretical and experimental results than the ELM algorithm with the single hidden layer, with the deepening of the network structure, the problem of parameter optimization is also highlighted, which usually requires more time for model selection and increases the computational complexity. This paper uses Cholesky factorization strategy and Givens rotation transformation to choose the hidden nodes of MELM and obtains the number of nodes more suitable for the network. First, the initial network has a large number of hidden nodes and then uses the idea of ridge regression to prune the nodes. Finally, a complete neural network can be obtained. Therefore, the ELM algorithm eliminates the need to manually set nodes and achieves complete automation. By using information from the previous generation’s connection weight matrix, it can be evitable to re-calculate the weight matrix in the network simplification process. As in the matrix factorization methods, the Cholesky factorization factor is calculated by Givens rotation transform to achieve the fast decreasing update of the current connection weight matrix, thus ensuring the numerical stability and high efficiency of the pruning process. Empirical studies on several commonly used classification benchmark problems and the real datasets collected from coal industry show that compared with the traditional ELM algorithm, the pruning multilayered ELM algorithm proposed in this paper can find the optimal number of hidden nodes automatically and has better generalization performance.
Learning gene regulatory networks using gaussian process emulator and graphical LASSO

Large amounts of research efforts have been focused on learning gene regulatory networks (GRNs) based on gene expression data to understand the functional basis of a living organism. Under the assumption that the joint distribution of the gene expressions of interest is a multivariate normal distribution, such networks can be constructed by assessing the nonzero elements of the inverse covariance matrix, the so-called precision matrix or concentration matrix. This may not reflect the true connectivity between genes by considering just pairwise linear correlations. To relax this limitative constraint, we employ Gaussian process (GP) model which is well known as computationally efficient non-parametric Bayesian machine learning technique. GPs are among a class of methods known as kernel machines which can be used to approximate complex problems by tuning their hyperparameters. In fact, GP creates the ability to use the capacity and potential of different kernels in constructing precision matrix and GRNs. In this paper, in the first step, we choose the GP with appropriate kernel to learn the considered GRNs from the observed genetic data, and then we estimate kernel hyperparameters using rule-of-thumb technique. Using these hyperparameters, we can also control the degree of sparseness in the precision matrix. Then we obtain kernel-based precision matrix similar to GLASSO to construct kernel-based GRN. The findings of our research are used to construct GRNs with high performance, for different species of Drosophila fly rather than simply using the assumption of multivariate normal distribution, and the GPs, despite the use of the kernels capacity, have a much better performance than the multivariate Gaussian distribution assumption.
Research on Penetration Path Planning Method on Near Sea Bottom Based on the Sonar Detection Blind Zone

Abstract To improve the survivability and penetration probability of unmanned undersea vehicle(UUV) on near sea bottom combat missions, the factors leading to sonar detection blind zone are analyzed, and the detection blind zone produced by underwater terrain obscuration is mainly studied. A detection range calculation method based on DEM and acoustic ray is proposed, and the simulation study of the near sea bottom penetration path planning is carried out by using terrain blind zone and invasive weed optimization algorithm. Simulation results show the effectiveness of the proposed algorithm. This method can be applied to path planning of the UUV on near sea bottom penetration, which helps to improve Combat effectiveness and Operational effectiveness of UUV.
Students' organism state in conditions of distance learning

In the course of distance learning, the student is located remotely from his teacher, as well as from the educational institutions themselves. Such education is carried out mostly with the use of technologies for studying. Online learning contributes to improving the education quality due to the active implementation of the worldwide educational resources and a large share of independent work. A manifestation of negative impact of such education is stress caused not only by worries about coronavirus situation, but also by changes in the education field. Distance learning implies strict self-control, independence and consciousness of the student, ability to restrain their emotional states.
Workflow Models to Establish Software Baselines in SSMEs

Capability Maturity Model Integration (CMMI) is used for Software Process Improvement (SPI) worldwide. Research reveals that CMMI adoption needs a lot of resources in terms of training, funds, and professional workers. Software Small & Medium Enterprises (SSMEs) cannot, however, reserve resources for the purpose. One of the challenges of CMMI adoption is that CMMI identifies “What-to-Do” as a requirement to fulfill and leaves “How-To-Do” to implementers. Implementation of Configuration Management Process Area (CM-PA), being one of the umbrella activities, presents more obstacles generally to the software industry and particularly to SSMEs as compared to other PAs. Establish software baselines is the first Specific Goal (SG-1) needed by CMMI for the effective implementation of CM-PA. Workflow Models (WFMs), an identified set of activities performed in a logical sequence to accomplish a specific practice with probable actors and potential work products, were designed for implementation of its three contributing Specific Practices (SPs). The proposed WFMs were evaluated through the Expert Panel Review (EPR) process. Additionally, a case study was conducted to validate the proposed models. The results of the EPR/Case Study showed that the proposed WFMs are useful, easy to use, supportive in the achievement of SG-1, and applicable to SSMEs. It is important to mention that this research work adds to the empirical software engineering body of knowledge as well as contribute to the implementation of CM-PA.
Bellman Filtering for State-Space Models

We present a new filter for state-space models based on Bellman's dynamic programming principle, allowing for nonlinearity, non-Gaussianity and degeneracy in the observation and/or state-transition equations. The proposed Bellman filter is a direct generalisation of the (iterated and extended) Kalman filter, but remains equally inexpensive computationally. Under suitable conditions we prove that the Bellman-filtered states are (a) stable over time and (b) contractive in quadratic mean to a small region around the true state at every time step. Constant parameters are estimated by numerically maximising a filter-implied log-likelihood decomposition. Simulation studies reveal that the Bellman filter performs on par with state-of-the-art simulation-based techniques, while requiring a fraction of the computational cost, being straightforward to implement and offering full scalability to higher dimensional state spaces. In an empirical application involving multidimensional, nonlinear and degenerate state dynamics, the Bellman filter outperforms the particle filter in terms of both parameter estimation and filtering.
Protection of Copyright Owners and Authors Rights in the Internet: A Comparative Analysis of IP Law in Sri Lanka and EU Directives

Protection of copyright of original intellectual Creations has become an uphill task under the prevailing highly connected and complicated global environment with the digitization and the internet. This paper scrutinizes the effectiveness of the copyright protection of the current legal regime in Sri Lanka in a comparative analysis with the European Union Directives in order to seek the answer for the research gap to conclude whether the Sri Lankan Intellectual Property (IP) regime is sufficient to safeguard the rights of the copyright owners and authors with the rapid involvement of the internet.This study is expedient due to lack of investigation in the Sri Lankan academia. The exploration rested on secondary sources and data was analyzed by a qualitative approach. Materials included national and international legislation, academic and media data. The study stood on the comparative legal analysis, integrated legal interpretation and modeling. In addition to that, key informants in the field were interviewed as a supplementary primary source of information.The research findings suggest the free riders are exploiting the existing loopholes of the legal systems and law enforcement areas. Therefore, a special emphasis should be paid to the protection of intellectual property rights on the internet. The article identifies major approaches in legislation and practice on EU regulations and explores a number of current options: IP enforcement as a harmonized and regulated or not regulated by separate rules of law, Digital Single Market etc.. The research findings laid grounds for preliminary recommendations on legal drafting with regard to IP status as that of harmonized legal personality. It could be concluded that the Sri Lankan IP Legislation consist of many lope holes to safe guard their copyright of the Owners and Authors due to the territorial nature of enforcement, lack of harmonization and lack of proper regulations and awareness. In order to prevent those problems it is suggested various options such as development and amendments of the national legislation, introduce IT-IP regulations, introduction of the data privacy Act etc. further research could be recommended to discover the challenges for IP law in artificial Intelligence.
Optimization of statistical processing of clinical and laboratory data in acute poisoning

The article describes a specialized software application developed by the authors that summarizes the long-term experience of analyzing clinical and laboratory data in clinical toxicology using Microsoft’s Visual Basic for Applications object-oriented programming tools in Excel. The main purpose of the application is a significant (5-10 times) acceleration of the implementation of the complex of the most commonly used statistical algorithms. The application calculates descriptive sample characteristics, identifies deviations of sample distributions from the normal law by several generally accepted criteria, performs statistical comparison of data matrices by several criteria, and automatically generates a number of tables ready for placement in scientific texts for various purposes in Microsoft Word format. The presentation of the output data makes it easy to verify the results of calculations by comparing them with the output forms of the Statistica application software package. The acceleration of obtaining the final result is achieved: 1 – due to the absence of the need to move data from Excel to the package of applied statistical programs; 2 – as a result of automating the determination of task parameters by means of special color markup; 3 – by combining a limited number of the most commonly used statistical methods into one immutable complex; 4 – as a result of the software implementation of statistical comparison of all possible combinations of data matrices involved in the study for a set of indicators as a single operation that does not require user intervention; 5 – due to the application’s use of syntactic transformations of output data and automatic table formation by filling in ready-made templates with calculation results.
The Design of a Virtual Reality Image Distortion Method for mobile devices

Abstract How to predict a more accurate next frame according to the current frame is still a problem. In this study, a method of generating intermediate frames in VR is proposed, which can effectively reduce the image jitter in VR games and increase the user experience; on the other hand, the inverse matrix method proposed in this study has a small amount of calculation and a fast calculation speed, and can complete the calculation in a short time. Therefore, this method can help us to complete the insertion of frames in the specified time. In this paper, firstly, the algorithm is constructed based on the principle of inverse matrix, and then the simulation experiment is carried out. The experimental results show that the scheme is a viable and effective solution.
Research of Deep Learning-Based Visibility Prediction Model for Foggy Days in Airport

In this paper, we estimate the visibility distance under heavy fog conditions based on the airport video image containing information about the varying process of fog and the observed meteorological data. This article uses data from 00:00:16 to 11:47:48 on March 13, 2020, for an airport. By establishing two regression models, the Multiple Linear Regression (MLR) model and the Multiple Polynomial Regression (MPR) model, to analyzing the relationship between visibility distance and meteorological observation data on the ground, and comparing and evaluating the two models. Experimentally, it was found that MPR models solve relational equations with higher precision, and if it used a higher order of MPR model, it has higher predicted precision. And we establish a deep learning model based on video visibility distance estimation from video data and meteorological observation data of an airport and evaluate the accuracy of the estimated visibility. The experimental results show that the percentage error between the predicted value and the true value is less than 0.25%, which achieves high predictive accuracy and has a very good prediction effect.
Enhancing the Motorcycling Experience with Social Applications: A Study of User Needs

For many riders, the motorcycle is much more than a transportation mode. Riding a motorcycle can be a pleasurable experience in itself, and the motorcycle is frequently a tool of socialization. An evidence for that can be found in the numerous motorcycling communities around the world. However, those communities may not be accessible to everyone, or they may not satisfy everyone. We aim to explore how social applications for motorcyclists could reach more riders and serve a broader range of their needs. To pursue this idea, we conducted focus groups with motorcyclists, exploring their current practices with digital tools as well as any related needs. The results hinted on several aspects that need to be considered in the design of a social application for motorcyclists. Generally, motorcyclists are willing to receive and share information with others, and referred several needs in terms of communication and trip planning. However, they also expressed concerns regarding the use of digital tools while riding, both for safety reasons and because it could disrupt the riding experience. These results are a contribution to inform the design of new social concepts for motorcyclists.
Design and Application of Converged Infrastructure through Virtualization Technology in Grid Operation Control Center in North Eastern Region of India

Modern day grid operation requires multiple interlinked applications and many automated processes at control center for monitoring and operation of grid. Information technology integrated with operational technology plays a critical role in grid operation. Computing resource requirements of these software applications varies widely and includes high processing applications, high Input/Output (I/O) sensitive applications and applications with low resource requirements. Present day grid operation control center uses various applications for load despatch schedule management, various real-time analytics & optimization applications, post despatch analysis and reporting applications etc. These applications are integrated with Operational Technology (OT) like Data acquisition system / Energy management system (SCADA/EMS), Wide Area Measurement System (WAMS) etc. This paper discusses various design considerations and implementation of converged infrastructure through virtualization technology by consolidation of servers and storages using multi-cluster approach to meet high availability requirement of the applications and achieve desired objectives of grid control center of north eastern region in India. The process involves weighing benefits of different architecture solution, grouping of application hosts, making multiple clusters with reliability and security considerations, and designing suitable infrastructure to meet all end objectives. Reliability, enhanced resource utilization, economic factors, storage and physical node selection, integration issues with OT systems and optimization of cost are the prime design considerations. Modalities adopted to minimize downtime of critical systems for grid operation during migration from the existing infrastructure and integration with OT systems of North Eastern Regional Load Despatch Center are also elaborated in this paper.
Variability of the Russian educational axiosphere of the early 20<sup>th</sup> century

Being the subject of interest of many scientists, the evolution of education is considered as a process, as a set of values that are reflected in the works of educators of each era. In this case, the object of the study is the first half of the 20th century, as the most controversial era in terms of values, represented by a wide range of pedagogical ideas and trends. The article analyzes various texts of educators of the early 20th century to form a holistic view concerning the axiological field of education at that time. The applied methods of analysis and synthesis, generalization, abstraction, classification, and modeling, as well as the historical and structural method were dictated by the purpose of the study. The conducted work has resulted in the clarification of the concept of the axiosphere, its components and objective laws, the description of the educational axiosphere of the declared era through the analysis and synergy of values and meanings of educators of that time. The reliability of the result is ensured by the reference to the author’s text of the studied educators, which allowed formulating their values in their own language, so to speak in the first person. The attempt to present the value dominants of different educational figures of the same time as components of a single axiosphere is a fundamentally new approach, since traditionally in the history of education, it is customary to oppose the author’s pedagogical concepts and consider them as autonomous, sometimes contradictory systems.
A Memetic Algorithm for Chinese Traditional Music Composition

Using computers to create art works has been an interesting field of exploring the capability of information technologies for decades. As an important form of art, music represents one of the most significant objects to be generated by artificial intelligence. This paper proposes a novel memetic algorithm to create Chinese traditional music. The approach utilizes relative rules to generate initial music sections with Chinese traditional style, then optimizes and rearranges them in the process of the proposed memetic algorithm. Evaluation results suggest that music samples generated by the proposed algorithm meet the expectations of users in terms of fluency, integrity, national characteristics, and the overall melody aesthetics.
A reversible data hiding approach based on filter selection

In prediction-error-expansion and pixel sorting based reversible data hiding (RDH) works, only part of the neighboring pixels are used in pixel prediction and local complexity calculation. To fully exploit the local correlations, we propose a novel efficient RDH using filter selection. Firstly, we divide the cover image into multiple non-overlapping layers following the concept of image down-sampling. Then data is embedded in a layer-wise manner. For data embedding, the prediction-error and the local complexity are estimated using the idea of block-based image filtering, the related techniques of which have been widely studied in the area of image processing. Experimental results validate the applicability of the proposed work in the perspectives of both embedding capacity and image quality.
Generativity Revisited. Participatory Design for Self-Organization in Communities

Societal trends, such as the governmental withdrawal from the public realm, increasingly motivates communities to self-organize in taking care of it. As a result, designers have explored the concept of 'generativity' as a quality of design that supports communities in questioning, supporting and giving form to self-organization in the public realm. Nonetheless, a thorough investigation of how to enable generativity in the context of community-based participatory design (PD) is lacking. When designers give form to generativity, they intend to allow people to 'self-organize' by transforming and using infrastructures through and for debating and creating public matters, without assistance from the infrastructure's original designers. While design for informatics defines generativity with a focus on ‘self’-organized processes, we conclude that generativity in the context of designing for the complex politics of the public realm is a quality that mainly supports communities' ‘co’-organization. We describe the generative quality of design in the community project Betty's Garden and discuss how the specific roles and capabilities that were developed by the community and by us as researchers contributed to this quality.
CF-SSDAE: Symmetric SDAE for Collaborative Filtering Algorithm

Collaborative Deep Learning (CDL) uses the stacked denoising autoencoder to encode information matrix of items, which reduces the dimension of the matrix, but the method does not consider the user information. In order to get the user information, a new acquisition method is proposed: the user information matrix is obtained from the item information matrix and the rating matrix, so that the user information matrix and the item information matrix are in the same space. And a symmetric SDAE for collaborative filtering algorithm(CF-SSDAE) is proposed, which uses the two stacked denoising autoencoders to simultaneously train the user information matrix and the item information matrix to obtain the feature matrix of users and items. In order to compare with the state-of-the-art algorithms, experiments show that our algorithm has higher accuracy on the CiteULike dataset.