Reviewerly/exHarmony huggingface dataset:
Factors Influencing Intentions to use Digital Learning during COVID-19 Outbreak in Sindh: An Empirical Study

This study aims to determine the intention of the students towards the use of digital learning during the COVID-19 outbreak as it impacts to cover the syllabus and face the learning management at risk. The rising dilemma of the COVID-19 urged the world communities to adopt the Digital system in the learning sphere to mitigate the psychological stress of the pandemic as Pakistan also moved to meet the criteria accordingly. The current research investigates the university students’ perception to apply the digital learning system in the way to regulate their online classes among the graduate to the Ph.D. level in Sindh. For the discovering the opinion of the digital learning users the behavioral determinants as perceived ease of use (PEOU), perceived usefulness (PU), social influence (SI), perceived quality (PQ), and intention to use e-learning (IEL) have been proposed and these constructs are used to envisage the students intentional level to use the wireless technology in their online classes in a Pakistan point of view. The data was collected through the students using an online survey of the Mehran University of Engineering &amp; Technology Jamshoro, in which 249 respondents participated via their online feedback to fill the questionnaires. The theoretical approach pursues to encircle the technological acceptance model and unified theory of acceptance and use of technology to find out the users intention to use the digital learning and the data analysis was carried out through utilizing SPSS version 24 and the hypotheses were tested through the regressions and correlations. The given results highlight that all proposed factorsreveal the significant and positive relationship with dependent variable. The limitation of the prevailing study is concerned with the use of wireless technology in educational sector within a particular institution and findings could assist the students to adopt the online learning during the COVID-19 outbreak. The overall findings lead towards proposed framework and its practical implications.
A Multiple Imputation Approach for Handling Missing Data in Classification and Regression Trees

Decision trees (DTs) is a machine learning technique that searches the predictor space for the variable and observed value that leads to the best prediction when the data are split into two nodes based on the variable and splitting value. The algorithm repeats its search within each partition of the data until a stopping rule ends the search. Missing data can be problematic in DTs because of an inability to place an observation with a missing value into a node based on the chosen splitting variable. Moreover, missing data can alter the selection process because of its inability to place observations with missing values. Simple missing data approaches (e.g., listwise deletion, majority rule, and surrogate split) have been implemented in DT algorithms; however, more sophisticated missing data techniques have not been thoroughly examined. We propose a modified multiple imputation approach to handling missing data in DTs, and compare this approach with simple missing data approaches as well as single imputation and a multiple imputation with prediction averaging via Monte Carlo Simulation. This study evaluated the performance of each missing data approach when data were MAR or MCAR. The proposed multiple imputation approach and surrogate splits had superior performance with the proposed multiple imputation approach performing best in the more severe missing data conditions. We conclude with recommendations for handling missing data in DTs.
Mask Recognition with Computer Vision in the Age of a Pandemic

&#x0D; &#x0D; &#x0D; In the current age of coronavirus, monitoring and enforcing correct mask-wearing regulation in public spaces is of para- mount importance. Specifically, there is a need to monitor whether people wear masks and whether they wear them cor- rectly. However, there is a lack of automated systems to rec- ognize correct mask-wearing compliance. In this paper, we propose a computer-vision-based solution to the problem of mask-wearing monitoring. In particular, we propose a convo- lutional neural network to recognize images of people wear- ing masks correctly, people wearing masks incorrectly, and people not wearing masks at all. Our proposed model is shown to predict correct mask-wearing practices with over 98% accuracy. The model can be easily deployed as an auto- mated system to screen people entering indoor spaces, and can replace current manual, time-consuming, temperature- screening practices. Such applications can serve as an im- portant tool to help reduce transmission rates during the cur- rent pandemic.&#x0D; &#x0D; &#x0D;
Visual Techniques to Reduce Cybersickness in Virtual Reality

Cybersickness is a unpleasant phenomenon caused by the visually induced impression of ego-motion while in fact being seated. To reduce its negative impact in VR experiences, we analyze the effectiveness of two techniques - peripheral blurring and field of view reduction - through an experiment in an interactive race game environment displayed with a commercial head-mounted display with integrated eye tracker. To measure the level of discomfort experienced by our participants, we utilize self-report and physiological measurements. Our results indicate that, among both techniques, reducing the displayed field of view up to 10 degrees is most efficient to mitigate cybersickness.
Phenomenon of Education: Philosophical and Methodological Aspects of Research

The purpose of the work is to identify in the study of education, as a socio-cultural phenomenon, its functional and structural-organizational features at different historical stages of development and to analyze the relation of the concepts of "education", "science" and "intelligence". Methodology and research methods used in the study are the following: comparative analysis method, systematic approach, functional and cultural approaches and philosophical reflection. Key findings are the following: strategy of introducing new standards in the educational process in connection with the transition to digital reality requires changing the educational methodology and creating a new format for the development of teaching methods. The relation between the concepts of "methodology", "education" and "science" are ambiguous, their use is contextual. The main method of analysis of the study of functionality is philosophical reflection, which reveals their meaning and purpose. As a result of the study of the functional, structural and organizational features of education, the necessary object (material or spiritual) of research is created, where the relation between science and education is revealed. The question of the relation of research methodology and methods of the educational process becomes a question of the interactions of spirituality and science, values and knowledge.
Fractal mathematical over extended finite fields Fp[x]/(f(x))

In this paper, we have defined an algorithm for the construction of iterative operations, based on dimensional projections and correspondence between the properties of extended fields, with respect to modular reduction. For a field with product operations R(x) ⊗ D(x), over finite fields, GF[(pm)n−k]. With Gp[x]/(g(f(x)), whence the coefficient of the g(x) is replaced after a modular reduction operation, with characteristic p. Thus, the reduced coefficients of the generating polynomial of G contain embedded the modular reduction and thus simplify operations that contain basic finite fields. The algorithm describes the process of construction of the GF multiplier, it can start at any stage of LFSR; it is shift the sequence of operation, from this point on, thanks to the concurrent adaptation, to optimize the energy consumption of the GF iterative multiplier circuit, we can claim that this method is more efficient. From this, it was realized the mathematical formalization of the characteristics of the iterative operations on the extended finite fields has been developed, we are applying a algorithm several times over the coefficients in the smaller field and then in the extended field, concurrent form.
Dual-attention Guided Dropblock Module for Weakly Supervised Object Localization

Attention mechanisms is frequently used to learn the discriminative features for better feature representations. In this paper, we extend the attention mechanism to the task of weakly supervised object localization (WSOL) and propose the dual-attention guided dropblock module (DGDM), which aims at learning the informative and complementary visual patterns for WSOL. This module contains two key components, the channel attention guided dropout (CAGD) and the spatial attention guided dropblock (SAGD). To model channel interdependencies, the CAGD ranks the channel attentions and treats the top-k attentions with the largest magnitudes as the important ones. It also keeps some low-valued elements to increase their value if they become important during training. The SAGD can efficiently remove the most discriminative information by erasing the contiguous regions of feature maps rather than individual pixels. This guides the model to capture the less discriminative parts for classification. Furthermore, it can also distinguish the foreground objects from the background regions to alleviate the attention misdirection. Experimental results demonstrate that the proposed method achieves new state-of-the-art localization performance.
Topic space trajectories

Abstract The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep track of research topics and their progress. In this task, researchers can be supported by automated publication analysis. Yet, many such methods result in uninterpretable, purely numerical representations. As an attempt to support human analysts, we present topic space trajectories , a structure that allows for the comprehensible tracking of research topics. We demonstrate how these trajectories can be interpreted based on eight different analysis approaches. To obtain comprehensible results, we employ non-negative matrix factorization as well as suitable visualization techniques. We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues. In addition to a thorough introduction of our method, our focus is on an extensive analysis of the results we achieved. Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work. An advantage in these applications over previous methods lies in the good interpretability of the results obtained through our methods.
Automatic Personality Traits Perception Using Asymmetric Auto-Encoder

On account of an increase in the human-computer interface applications, the study of automatic personality perception has become more and more prevalent than speech signal processing in recent years. These studies have shown that personality traits derived from psychology theories mainly affect acoustic features. However, some obstacles remain in the automatic personality perception classification, and the most important one is to extract the features related to each personality trait. Previous studies have shown that the personality effect differs from one acoustic feature to the others. Additionally, there are many features one can extract from speech signals. Curse of dimensionality in features also makes the classification difficult. This paper aimed to introduce and examine a novel and efficient automatic feature extraction method to classify the well-known big five personality traits. In this regard, three data augmentation methods for increasing data samples were examined. Afterwards, 6,373 statistical features were extracted from the nonverbal features of the SSPNet Speaker Personality Corpus. Finally, an innovative stacked asymmetric auto-encoder was utilized to extract useful features automatically to improve classification results. Compared with the conventional stacked auto-encoder and convolutional neural network, the proposed method exhibited an average improvement of 12.40%(10.14%) and 14.36%(1.42%) in terms of the unweighted average recall (accuracy), respectively. In comparison with other published works, classification results also revealed a notable average enhancement (11.78%) for unweighted average recall for all five traits and an average improvement of 5.1% for accuracy in two out of five personality traits.
Lightweight Cryptography for Network-on-Chip Data Encryption

System-on-chip (SoC) is the main processor for most recent applications such as the Internet of things (IoT). SoCs are composed of multiple blocks that communicate with each other through an integrated router. Data routing from a block to another poses many challenges. The network-on-chip (NoC) was used for the transmission of data from a source to a destination with high reliability, high speed, low power consumption, and low hardware occupation. An NoC is composed of a router, network links (NL), and network interface (NI). The main component of the NoC, the NI, is composed of an input/output FIFO, a finite state machine (FSM), pack, and depack modules. Data transmission from a block to another poses a security problem such as secret information extraction. In this paper, we proposed a data encryption framework for NoC based on a light encryption device (LED) algorithm. The main advantages of the proposed algorithm are to reduce the implementation area and to achieve high speed while reducing the power consumption. The proposed encryption framework was simulated Verilog/VHDL on the Xilinx ISE and implemented on the Xilinx Virtex 5 XC5VFX200T. The obtained results have shown that the proposed framework has a smaller area and higher speed compared to existing works. The proposed algorithm has reduced the NI implementation area and enhanced the network performance in terms of speed and security.
Experiential ownership and body ownership are different phenomena

Body ownership concerns what it is like to feel a body part or a full body as mine, and has become a prominent area of study. We propose that there is a closely related type of bodily self-consciousness largely neglected by researchers-experiential ownership. It refers to the sense that I am the one who is having a conscious experience. Are body ownership and experiential ownership actually the same phenomenon or are they genuinely different? In our experiments, the participant watched a rubber hand or someone else's body from the first-person perspective and was touched either synchronously or asynchronously. The main findings: (1) The sense of body ownership was hindered in the asynchronous conditions of both the body-part and the full-body experiments. However, a strong sense of experiential ownership was observed in those conditions. (2) We found the opposite when the participants' responses were measured after tactile stimulations had ceased for 5 s. In the synchronous conditions of another set of body-part and full-body experiments, only experiential ownership was blocked but not body ownership. These results demonstrate for the first time the double dissociation between body ownership and experiential ownership. Experiential ownership is indeed a distinct type of bodily self-consciousness.
Propositional Encodings of Acyclicity and Reachability by Using Vertex Elimination

We introduce novel methods for encoding acyclicity and s-t-reachability constraints for propositional formulas with underlying directed graphs, based on vertex elimination graphs, which makes them suitable for cases where the underlying graph has a low directed elimination width. In contrast to solvers with ad hoc constraint propagators for graph constraints such as GraphSAT, our methods encode these constraints as standard propositional clauses, making them directly applicable with any SAT solver. An empirical study demonstrates that our methods do often outperform both earlier encodings of these constraints as well as GraphSAT especially when underlying graphs have a low directed elimination width.
Work in Progress: Flipped classroom as a pedagogical model in virtual education in networking courses with the Moodle Learning Management System against COVID 19

The COVID-19 pandemic has impacted the whole world in different areas. Education has also been greatly affected at all educational levels. Peru declared a state of emergency in the first quarter of 2020. Universities in Peru have implemented an adaptation plan for the educational service. Everything seems to indicate that after the pandemic, things will not be the same again and that the virtuality of education is here to stay. Therefore, it is necessary to identify good practices that help to enhance the advantages of remote teaching. For this reason, we decided to apply the flipped classroom pedagogical model with the Moodle Learning Management System platform as an alternative for the virtual teaching of the communication networks course. In this article the preliminary results of the research are presented.
Internet Technologies in Distance Education

In modern education, a new format of distance learning is developing more and more rapidly - massive open online courses (MOOCs), this is especially important in times of quarantines and remote learning. The aim of the research is to study the attitude of teachers and students to MOOCs and their readiness to implement this practice in the educational process. 2145 students and 1050 teachers of higher educational institutions of the Russian Federation voluntarily took part in the study. The analysis of the survey data was made in the STATISTICA system. It has been found that more than 30% of respondents have an idea of MOOCs. It has also been determined that 31% of students and 28% of teachers are positive about the introduction of MOOCs in the educational process of universities. In addition, it has been determined that 31% of students and 23% of teachers plan to take MOOCs. Overall, the survey results showed a fairly good acceptance of MOOCs by respondents. From this, it can be concluded that in the near future, taking into account the reorganization of the educational process, MOOCs may become a worthy alternative to traditional education. It is for this that the results of this study can be used by the administration of the universities that have taken part in the survey, in order to further inform students and teachers about MOOCs. There is also a need to reorganize, revise, implement and provide an opportunity for students to study through such a form of distance learning as MOOCs.
Alternative assessment as a method to motivate natural-science-profile students to learn English

The present requirements of the labour market determine the need for high-quality foreign-language schooling of students of extralinguistic faculties whose competitiveness directly depends on the level of proficiency in a foreign language. In this regard, motivation is considered as one of the key factors securing success in learning a foreign language. According to the authors, alternative assessment of students’ learning progress aims to increase their motivation for learning a foreign language. The study involved 180 undergraduates of Vyatka State University majoring in Biology, Chemical technology and Ecology. The following methods were used in the course of the research: analysis, synthesis, generalisation, observation, experimental training, questioning, analysis of quantitative and qualitative results of the research. Pearson’s criterion χ2 was used for statistical processing of the results. The authors identified the following forms and methods of alternative assessment: electronic portfolio (iPortfolio), work in pairs (We Create Activity), group work project (iLearn &amp; Teach Project), technical report (Technical report), imitation of a job interview (Mock job interview), that are approximated to real situations in the future engineers’ professional sphere and match the undergraduates’ professional interests. It was empirically proved that alternative assessment increases the students’ level of intrinsic motivation to learning a foreign language, as well as the level of motivation for mastering a profession and acquiring due knowledge. The control experiment results point at significant statistical differences between the ascertaining and the control stages of the experiment (χ2 = 17.69; at χ2crit = 5.99). The significance of the research lies in the used approach “assessment as learning” to solving the problem of increasing the undergraduates’ motivation for the study of a foreign language. This approach not only provided for the natural-science-profile students’ orientation towards professional interests and due motivation, but also made it possible to improve the learners’ communicative competence in the English language, overcome psychological barriers, develop cognitive activity and critical reasoning.
The Value Function with Regret Minimization Algorithm for Solving the Nash Equilibrium of Multi-Agent Stochastic Game

In this paper, we study the value function with regret minimization algorithm for solving the Nash equilibrium of multi-agent stochastic game (MASG).To begin with, the idea of regret minimization is introduced to the value function, and the value function with regret minimization algorithm is designed.Furthermore, we analyze the effect of discount factor to the expected payoff.Finally, the single-agent stochastic game and spatial prisoner's dilemma (SDP) are investigated in order to support the theoretical results.The simulation results show that when the temptation parameter is small, the cooperation strategy is dominant; when the temptation parameter is large, the defection strategy is dominant.Therefore, we improve the level of cooperation between agents by setting appropriate temptation parameters.
Disambiguation of Medical Abbreviations in French with Supervised Methods

Abbreviations are very frequent in medical and health documents but they convey opaque semantics. The association with their expanded forms, like Chronic obstructive pulmonary disease for COPD, may help their understanding. Yet, several abbreviations are ambiguous and have expanded forms possible. We propose to disambiguate the abbreviations in order to associate them with the proper expansion for a given context. We treat the problem through supervised categorization. We create reference data and test several algorithms. The descriptors are collected from lexical and syntactic contexts of abbreviations. The training is done on sentences containing expanded forms of abbreviations. The test is done on corpus built manually, in which the meaning of abbreviations is defined according to their contexts. Our approach shows up to 0.895 F-measure on training data and 0.773 on test data.