Reviewerly/exHarmony huggingface dataset:
Endpoint Device Risk-Scoring Algorithm Proposal for Zero Trust

The rapid expansion of remote work following the COVID-19 pandemic has necessitated the development of more robust and secure endpoint device security solutions. Companies have begun to adopt the zero trust security concept as an alternative to traditional network boundary security measures, which requires that every device and user be considered untrustworthy until proven otherwise. Despite the potential benefits of implementing zero trust, the stringent security measures can inadvertently lead to low availability by denying access to legitimate users or limiting their ability to access necessary resources. To address this challenge, we propose a risk-scoring algorithm that balances confidentiality and availability by evaluating the user’s impact on resources. Our contributions include (1) summarizing the limitations of existing risk scoring systems in companies that implement zero trust, (2) proposing a dynamic importance metric that measures the importance of resources accessible to users within zero trust systems, and (3) introducing a risk-scoring algorithm that employs the dynamic importance metric to enhance both security and availability in zero trust environments. By incorporating the dynamic importance metric, our proposed algorithm provides a more accurate representation of risk, leading to better security decisions and improved resource availability for legitimate users. This proposal aims to help organizations achieve a more balanced approach to endpoint device security, addressing the unique challenges posed by the increasing prevalence of remote work.
Discriminative estimation of probabilistic context-free grammars for mathematical expression recognition and retrieval

Abstract We present a discriminative learning algorithm for the probabilistic estimation of two-dimensional probabilistic context-free grammars (2D-PCFG) for mathematical expressions recognition and retrieval. This algorithm is based on a generalization of the H-criterion as the objective function and the growth transformations as the optimization method. For the development of the discriminative estimation algorithm, the N -best interpretations provided by the 2D-PCFG have been considered. Experimental results are reported on two available datasets: Im2Latex and IBEM . The first experiment compares the proposed discriminative estimation method with the classic Viterbi-based estimation method. The second one studies the performance of the estimated models depending on the length of the mathematical expressions and the number of admissible errors in the metric used.
An Enhanced Version of MDDB-GC Algorithm: Multi-Density DBSCAN Based on Grid and Contribution for Data Stream

With the continuous enrichment of big data technology application scenarios, the clustering analysis of a data stream has become a research hotspot. However, the existing data stream clustering algorithms usually have some defects, such as inability to cluster arbitrary shapes, difficulty determining some important parameters, and “static” clustering. In this study, a novel algorithm MDDSDB-GC is innovated. It selected MDDB-GC as the original algorithm that cannot deal with a data stream. In MDDSDB-GC, the calculation methods of contribution, grid density, and migration factor are effectively improved, and other parts are adjusted accordingly. The experiments show that MDDSDB-GC retains the advantage of MDDB-GC and obtains the ability to cluster an analysis for a data stream. At the same time, it effectively overcomes the above conventional defects, and its overall performance is better.
Academic and career motivation of future engineers through foreign language teaching and learning

The paper discusses the issues of academic and career motivation of engineering university students. This motivation deals with the development of cognitive motives and interest in the future profession. Although there are many publications focusing on this topic, most of them consider academic and career motivation of students in relation to the future profession through professional courses, practice in the workplace, or project-based learning. Only few publications consider humanities as the tool to develop this motivation. This paper aims at analyzing academic and career motivation of engineering students through foreign language teaching and learning based on literature review and a poll of the faculty members. The results show that academic and career motivation of engineering students througha foreign language integrates language specific and profession specific issues, as the foreign language course implies the development of intercultural and professional competencies to train students for their future professional communication. The authors define the following prerequisites necessary to enhance the students motivation including problem-based teaching and learning combined with active methods in class as well as specific teaching and learning materials related to the future engineering profession. The authors conclude that the process and results of academic and career motivation influence social and professional self-directed development of students, a foreign language course is a good tool, and further research into topic is relevant.
Results of teaching mathematics in a subject-based digital educational environment

The paper presents the results of teaching mathematics to schoolchildren in a subject-based digital educational environment built according to the model proposed by the author. The aim of the study is to experimentally verify the effectiveness of the process of teaching mathematics to schoolchildren in a subject-based digital educational environment. The study required the use of a whole range of methods, such as observation, questioning, interpretation, tabular presentation of empirical data and their processing, static data processing and comparative analysis of the results. Two groups of students participated in the pedagogical experiment: the first group was an experimental group that studied mathematics in a subject-based digital educational environment, the second group was students who studied mathematics in a traditional form. In this study, the assumption was tested that the students of the experimental group show on average the same result of knowledge as the control group. To determine the absence of differences between the two experimental distributions, the students t-test was used. As a result, an alternative hypothesis was adopted that the students of the experimental group showed on average a higher level of knowledge than the participants of the control group. Therefore, it is possible to draw a conclusion about the effectiveness of the learning process based on the subject digital educational environment built according to the model proposed by the author.&#x0D; The value of this research lies in the fact that the author offers a theoretically sound model of the subject digital educational environment, the effectiveness of which has been experimentally tested. The instrumental approach of the model allows the teacher to build a digital educational environment taking into account the peculiarities of the taught subject according to his author's plan.
Moodle tools for teaching foreign languages at distance learning department

The paper investigates the use of the Moodle system tools for teaching the subject of Foreign Languages (English) at the faculty of distance and part-time education of the Institute of Oil and Gas Technologies (INGT) of Samara State Technical University (SSTU). The analysis was carried out on the content material of Moodle electronic course for the specialty Petroleum engineering 21.03.01, 2020-2021 years. The course structure, tools for presenting educational material for practical classes and independent work, as well as assessment tools for intermediate and final monitoring control were analyzed. The study showed that the content of the course is presented logically and divided into modules in accordance with the main educational program and the Federal State Educational Standard of the Russian Federation. The Page tool was chosen to present the material for practical classes, which ensures the availability and clarity of educational material provided. Tasks for independent work and study are implemented via the Task tool in such a way that the files can be downloaded, then done and uploaded to the system. The effectiveness of the course was analyzed according to the data automatically provided by the system. The following aspects were taken into consideration: online activity of students in the system, the number of downloaded and completed assignments, the number of attempts to take tests, assessment reports based on the results of students` performance. The study has shown that the tools chosen for the course are relevant and provide effective learning. Analysis of program data has suggested that materials and exercises are easily accessible and system navigation has not caused any difficulties for students. Thus, it can be concluded that the Moodle system has proved to be an effective platform for distance learning of part-time students with a low level of language proficiency.
Approximation by the Extended Neural Network Operators of Kantorovich Type

Based on the idea of integral averaging and function extension, an extended Kantorovich-type neural network operator is constructed, and its error estimate of approximating continuous functions is obtained by using the modulus of continuity. Furthermore, by introducing the normalization factor, the approximation property of the new version of the extended Kantorovich-type neural network (normalized extended Kantorovich-type neural network) operator is obtained in Lp[−1,1]. The numerical examples show that this newly proposed neural network operator has a better approximation performance than the classical one, especially at the endpoints of a compact interval.
HE-SNA: an efficient cross-platform network alignment scheme from privacy-aware perspective

Abstract User alignment across online social network platforms (OSNPs) is a growing concern with the rapid development of internet technology. In reality, users tend to register different accounts on multiple OSNPs, and the network platforms are reluctant to share network structure and user’s information due to business interest and privacy protection, which brings great obstacles to cross-platform user alignment. In view of this, we propose a homomorphic encryption-based social network alignment (HE-SNA) algorithm from the perspective of privacy leakage. Specifically, we first consider the OSNPs as a system containing multiple social networks, that each participant of OSNPs owns part of the network, i.e., a separate private sub-network. Then, encryption, fusion and decryption operations of the alignment information are performed by two third-party servers using HE scheme, which can protect the privacy information of sub-networks effectively. Finally, each sub-network uses the fused alignment information sent back from the third-party server for user alignment. Experimental results show that the HE-SNA method can provide a sum of locally trained models to third-party servers without leaking the privacy of any single sub-network. Moreover, the HE-SNA achieves a promising network alignment performance than only using the structural information and alignment data of single private sub-network while protecting its topology structure information.
Virtual reality is not always a cure-all: evidences from a quasi-experiment of EFL business speaking courses

English speaking has been seen as a challenge for many English as a foreign language (EFL) learners, since they have limited chances to practice English-speaking skills. Therefore, it is essential to provide learners with adequate English-speaking contexts. To address this issue, this study integrated virtual reality (VR) into English speaking classes and aimed to explore the effects of VR on English oral proficiency, speaking anxiety, and emotions, as well as students’ perceptions. Forty university students from two classes were assigned as an experimental group and a control group. The students in the experimental group practiced English speaking with the VR tools, while those in the control group used PowerPoint. Data analysis revealed conflicting results. While ANCOVAs disclosed no significant difference between the two groups regarding their post-intervention oral proficiency, speaking anxiety, and emotions, qualitative analysis of semi-structured interviews indicated that the independent VR environment could reduce their speaking anxiety, and VR tools could act as effective tools to practice speaking before formal situations in real life. Furthermore, three themes characterized students’ perceptions: “training simulation,” “learning without time or space limitations,” and “providing a safe learning environment.” Finally, interpretations of such conflicting results are offered and implications are proposed.
Consistency in the formation of culture-general competencies of economics students in the blended learning environment

The purpose of the study is to analyze the effectiveness of the formation of culture-general competencies of economics students in the context of the blended learning environment. The following results have been obtained. A blended learning system to teach Econometrics has been introduced at two Russian universities and one Kazakh university A questionnaire has been developed, and a sociological survey of economics students of the three universities has been conducted to assess the effectiveness of the formation of culture-general competencies in the context of blended learning of Econometrics. The analysis of the research results confirmed a number of hypotheses: H1 “Blended learning contributes to the formation of professional identity”, H2 “Blended learning contributes to the formation of communication skills”, H3 “Blended learning contributes to the formation of self-organization and self-education skills”, H4 “Blended learning does not lead to a decrease in academic performance”. The practical significance of the results obtained involves the fact that they make it possible to effectively implement blended learning technologies for the development of the competencies of economics students.
Satellite and Aerial Image Restoration Using Deep Reinforcement Learning

In this paper, we present a deep reinforcement learning-based method for effectively denoising satellite and aerial imagery data. Noise of various kinds and with varying noise levels contaminates satellite imagery data. The image’s quality and readability suffer when there is noise present. Therefore, it is crucial to create a network that can effectively and efficiently remove noise from the image while also preserving its quality and signal components. This paper evaluates the denoising capabilities of the deep reinforcement learning system. The proposed network is trained using the training set from the “dataset of object detection in aerial images (DOTA) dataset,” and its hyperparameters were adjusted for optimum performance. The training set from the aforementioned dataset was used to train the proposed network. The trained network was given the test set of unseen images for denoising. Statistical denoising, a common denoising technique, was used on the test dataset, and the outcomes were assessed. The same unseen images were also given to existing CNN-based denoising algorithms like denoising using CNN (DnCNN), U-shaped DnCNN (UDnCNN), and dilated U-shaped DnCNN (DUDnCNN), designed specifically for image denoising. Runtime and structural similarity index (SSIM) as well as peak signal-to-noise ratio have both been used as evaluation metrics to compare the effectiveness of various approaches. It is discovered that, when comparing the performance of various systems, the suggested system outperforms both statistical- and CNN-based denoising in terms of the evaluation metrics, PSNR and SSIM.
An Efficient and Accurate Cross-Domain Object Detection Method Using One-Level Feature and Domain Adaptation

To improve the accuracy of cross-domain object detection, the existing unsupervised domain adaptation (UDA) object detection methods mostly use Feature Pyramid Network (FPN), multiple Region Proposal Network (RPN), and multiple domain classifier, but these methods lead to complex network structures, slow model convergence, and low detection efficiency. To solve the above problems, this paper proposes an Efficient and Accurate Cross-domain Object Detection Method Using One-level Feature and Domain Adaptation (OFDA). This method realizes one-level feature object detection through feature fusion and divide-and-conquer technology; realizes overfitting feature suppression and unsupervised domain adaptation through domain-specific suppression and domain feature alignment technology; realizes background feature suppression through the Objectness branch, which replaces the time-consuming Region Proposal Network (RPN) structure and improves the efficiency of unsupervised adaptive detection. The paper verifies the feasibility and superiority of the proposed method by the comparative experiments and ablation experiments of multiple datasets. The proposed OFDA method not only improves the efficiency of object detection, but also ensures the accuracy of cross-domain detection.
A Weight-Based Cutoff Resampling Method for Accelerated Particle Filtering

The particle filter is known to be a powerful tool for the estimation of moving targets guided by nonlinear dynamics and sensors. The filter, however, is known to suffer from degeneracy — a feature of one particle gathering all the weight, thus causing the filter to completely diverge. Degeneracy problems become more evident when the sensors are accurate and/or the target maneuvers greatly. The resampling step in the particle filter is critical because it avoids degeneracy of particles by eliminating the wasteful use of particles that do not contribute to the posterior probability density function. The conventional resampling methods, despite being unbiased in approximating the posterior density, involve exhaustive and sequential communication within the particles and thus are computationally expensive. Hence conventional resampling is a major bottleneck for fast implementation of particle filters for real-time tracking. In this paper, we propose a new approach of filtering that requires resampling of only a minimum number of the most important particles that contribute to the posterior density. Minimizing the resampling operation to over a few important particles substantially accelerates the filtering process. We show the merits of the proposed method via simulations using a nonlinear example.
Real-time depth completion based on LiDAR-stereo for autonomous driving

The integration of multiple sensors is a crucial and emerging trend in the development of autonomous driving technology. The depth image obtained by stereo matching of the binocular camera is easily influenced by environment and distance. The point cloud of LiDAR has strong penetrability. However, it is much sparser than binocular images. LiDAR-stereo fusion can neutralize the advantages of the two sensors and maximize the acquisition of reliable three-dimensional information to improve the safety of automatic driving. Cross-sensor fusion is a key issue in the development of autonomous driving technology. This study proposed a real-time LiDAR-stereo depth completion network without 3D convolution to fuse point clouds and binocular images using injection guidance. At the same time, a kernel-connected spatial propagation network was utilized to refine the depth. The output of dense 3D information is more accurate for autonomous driving. Experimental results on the KITTI dataset showed that our method used real-time techniques effectively. Further, we demonstrated our solution's ability to address sensor defects and challenging environmental conditions using the p-KITTI dataset.
“dynmix”: An R package for the estimation of dynamic finite mixtures

“dynmix” is an R package allowing to estimate dynamic mixture models with state-space components and with normal regression components. Additionally it implements clustering with normal mixture. The Quasi-Bayesian methods of estimations are mostly implemented. However, the “dynmix” functions allows also for approximations based on the Kerridge inaccuracy. Mixtures can be used as approximations of non-linear models, and they are able to describe quite a wide class of real systems. They are also useful for describing systems that switch their behaviour between several (finite) regimes. In particular, this can be useful in economics, industry, medicine, etc. For instance, in modelling the driving style and the fuel consumption of a car, or the car queue length at a crossroad. Theoretically, using large enough number of components they are able to approximate an arbitrary system with multimodality, non-linearity, etc. The novelty of “dynmix” comes from allowing for recursive (dynamic) estimations, dynamic nature of model switching, implementation of fully Bayesian approach (with approximations only on the numerical level). Moreover, it seems to fill a gap for full package open-source implementation of such algorithms.
EVALUATING THE AD-HOC IMPACT OF FISCAL POLICY AND GOVERNANCE QUALITY IN ATTRACTING THE OFFICIAL DEVELOPMENT ASSISTANCE: A TESTIMONIAL FROM EAST AFRICAN COUNTRIES

Numerous studies have exclusively focused on foreign direct inflow attraction whereas the relative attraction of official development assistance (ODA) has relatively seldom been addressed, particularly in the context of spillover attraction in East Africa. In contrast to FDI, official development assistance, requires lesser complex frameworks for donors although the idiosyncrasy of philanthropic grants for developing nations such Africa is more unlikely without a cardinal governmental strategy. Accordingly, in this paper we examine how adequate fiscal policy in East Africa contributes to increase of official development assistance (I), we also addressee how governance quality facilitates the barrier for ODA inflow which in turn provides a decent inflow for recipient state. To provide empirical answers, the study employed panel cointegration test Ols test to explore the impact and relationship, similarly a granger causality test was used to observe future effect. According to the findings, fiscal policy manifested a negative long run association with official development assistance. Which brings the light how East Africa region are still stagnating in the sense of failing to implement adequate fiscal policy. This poor budget implications with a limited portfolio fails the assurance of providing a concise distribution at different public sectoral which in turns alters potential external aid. At the same time, the finding in this article also demonstrates that aid may have adverse fiscal policy-related repercussions that are not just harmful but also ineffectual. Channeling help to nations with "poor policies" does not solve the problem: funding consistently weakens positive policies that already exist while encouraging aid dependency. On the other hand, Governance factors positively attracts ODA spillover. This Panacea of Governmental quality for ODA materialization in the long-run can be attributed on how ODA is attracted toward nations with greater transparency level.