Reviewerly/exHarmony huggingface dataset:
MODELS OF ORGANIZATION OF TEACHING STUDENTS AT THE UNIVERSITY: BASIC ASSUMPTIONS, ADVANTAGES AND LIMITATIONS

Assessment of the quality of higher education and its management is not possible without answers to the following questions: what students should learn at the university; how the studying process at the university should be organized to achieve certain educational outcomes; what the role of students in the educational process is. Some researchers attempted to answer these questions through developing conceptual assumptions related to building student-university relationships. However, today there is no systematic analysis of such conceptual models, which makes it difficult to compare them and choose the optimal one for the university according to its external conditions and resources. The article aims to analyze the models of organization of learning at the university and building relations between teachers and students. There are presented models, conceptualizing students as consumers, active learners, and partners. We also suggest the classification of the models by two criteria: 1) students’ activity in the learning process and 2) students’ participation in decision-making and creating educational products. The paper provides understanding which principles foreign universities use to interact with students and teachers as well as to develop educational policy. The article might be practically useful for the executives of Russian universities to help them decide on the educational strategies; of no less use would it be for the teachers to make them deeper understand their relations with students.
One Bit is All It Takes: A Devastating Timing Attack on BLISS’s Non-Constant Time Sign Flips

Abstract As one of the most efficient lattice-based signature schemes, and one of the only ones to have seen deployment beyond an academic setting (e.g., as part of the VPN software suite strongSwan), BLISS has attracted a significant amount of attention in terms of its implementation security, and side-channel vulnerabilities of several parts of its signing algorithm have been identified in previous works. In this paper, we present an even simpler timing attack against it. The bimodal Gaussian distribution that BLISS is named after is achieved using a random sign flip during signature generation, and neither the original implementation of BLISS nor strongSwan ensure that this sign flip is carried out in constant time. It is therefore possible to recover the corresponding sign through side-channel leakage (using, e.g., cache attacks or branch tracing). We show that obtaining this single bit of leakage (for a moderate number of signatures) is in fact sufficient for a full key recovery attack. The recovery is carried out using a maximum likelihood estimation on the space of parameters, which can be seen as a statistical manifold. The analysis of the attack thus reduces to the computation of the Fisher information metric.
The effect of total quality management on the financial performance by moderating organizational culture

This study aims to learn the effect of the Total Quality Management dimensions, Top Management Support, Customer Focus, Process Management, Employees Participation, Employees' Empowerment, and Continuous Improvement, on financial performance by moderating organizational culture. The study population includes nine transport companies listed on the Amman Stock Exchange until the end of 2019. Alia Company and Royal Jordanian Airlines were excluded since its financial data formed extreme values when compared with the values of the rest of the companies. The study uses Multiple Linear Regression to test the hypotheses. The study found a statistically significant effect at the level of significance (α &lt; 0.05) for both top management support, customer focus, employees’ participation and empowerment, by moderating the organizational culture on financial performance measured by return on asset for the Jordanian transport companies listed on the Amman Stock Exchange. In light of the statistical results, the study presented several recommendations, the most important of which was to increase the top management interest in cultivating a profound organizational culture towards learning, development, mastering performance and improving the quality of its services to attract customers, and distinguish the organization from other organizations, in a way that reflects positively on developing its financial performance.
Comparative Analysis of Threat Modeling Methods for Cloud Computing towards Healthcare Security Practice

Healthcare organizations consist of unique activities including collaborating on patients care and emergency care.The sector also accumulates high sensitive multifaceted patients' data such as text reports, radiology images and pathological slides.The large volume of the data is often stored as Electronic Health Records (EHR) which must be frequently updated while ensuring higher percentage up-time for constant availability of patients' records.Healthcare as a critical infrastructure also needs highly skilled IT personnel, Information and Communication Technology (ICT) and infrastructure with regular maintenance culture.Fortunately, cloud computing can provide these necessary services at a lower cost.But with all thees enormous benefits of cloud computing, it is characterized with various information security issues which is not enticing to healthcare.Amid many threat modelling methods, which of them is suitable for identifying cloud related threats towards the adoption of cloud computing for healthcare?This paper compared threat modelling methods to determine their suitability for identifying and managing healthcare related threats in cloud computing.Threat modelling in pervasive computing (TMP) was identified to be suitable and can be combined with Attack Tree (AT), Attack Graph (AG) and Practical Threat Analysis (PTA) or STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service and elevation of privilege).Also Attack Tree (AT) could be complemented with TMP, AG and STRIDE or PTA.Healthcare IT security professionals can hence rely on these methods in their security practices, to identify cloud related threats for healthcare.Essentially, privacy related threat modeling methods such as LINDDUN framework, need to be included in these synergy of cloud related threat modelling methods towards enhancing security and privacy for healthcare needs.
A general framework for multimode Gaussian quantum optics and photo-detection: Application to Hong–Ou–Mandel interference with filtered heralded single photon sources

The challenging requirements of large scale quantum information processing using parametric heralded single photon sources involve maximizing the interference visibility while maintaining an acceptable photon generation rate. By developing a general theoretical framework that allows us to include a large number of spatial and spectral modes together with linear and non-linear optical elements, we investigate the combined effects of spectral and photon number impurity on the measured Hong–Ou–Mandel interference visibility of parametric photon sources, considering both threshold and number resolving detectors, together with the effects of spectral filtering. We find that for any degree of spectral impurity, increasing the photon generation rate necessarily decreases the interference visibility, even when using number resolving detection. While tight spectral filtering can be used to enforce spectral purity and increased interference visibility at low powers, we find that the induced photon number impurity results in a decreasing interference visibility and heralding efficiency with pump power, while the maximum generation rate is also reduced.
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks

We study the problem of clustering nodes in a dynamic graph, where the connections between nodes and nodes' cluster memberships may change over time, e.g., due to community migration. We first propose a dynamic stochastic block model that captures these changes, and a simple decay-based clustering algorithm that clusters nodes based on weighted connections between them, where the weight decreases at a fixed rate over time. This decay rate can then be interpreted as signifying the importance of including historical connection information in the clustering. However, the optimal decay rate may differ for clusters with different rates of turnover. We characterize the optimal decay rate for each cluster and propose a clustering method that achieves almost exact recovery of the true clusters. We then demonstrate the efficacy of our clustering algorithm with optimized decay rates on simulated graph data. Recurrent neural networks (RNNs), a popular algorithm for sequence learning, use a similar decay-based method, and we use this insight to propose two new RNN-GCN (graph convolutional network) architectures for semi-supervised graph clustering. We finally demonstrate that the proposed architectures perform well on real data compared to state-of-the-art graph clustering algorithms.
Online Digital Image Stabilization for an Unmanned Aerial Vehicle (UAV)

The Unmanned Aerial Vehicle (UAV) video system uses a portable camera mounted on the robot to monitor scene activities. In general, UAVs have very little stabilization equipment, so getting good and stable images of UAVs in real-time is still a challenge. This paper presents a novel framework for digital image stabilization for online applications using a UAV. This idea aims to solve the problem of unwanted vibration and motion when recording video using a UAV. The proposed method is based on dense optical flow to select features representing the displacement of two consecutive frames. K-means clustering is used to find the cluster of the motion vector field that has the largest members. The centroid of the largest cluster was chosen to estimate the rigid transform motion that handles rotation and translation. Then, the trajectory is compensated using the Kalman filter. The experimental results show that the proposed method is suitable for online video stabilization and achieves an average computation time performance of 47.5 frames per second (fps).
Twitter Fake Account Detection and Classification using Ontological Engineering and Semantic Web Rule Language

Nowadays, Twitter has become one of the fastest-growing Online Social Networks (OSNs) for data sharing frameworks and microblogging. It attracts millions of users worldwide where subscribers communicate with each through posts and messages known as "tweets". The open structure and behaviour of Twitter cause it to be vulnerable to attacks from fake accounts and a large number of automated software, known as 'bots'. Bots are regarded to be malicious as they send spam to users of social networks over the internet. Data security and privacy are among the most critical issues of social network users, as the protection and fulfilment of these requirements strengthen the network's interest and, ultimately, its credibility. To overcome these issues, we need to build an efficient model to detect and classify fake twitter accounts. This paper presents a new approach with dual functions, namely to identify and classify the twitter bots based on ontological engineering and Semantic Web Rule Language (SWRL) rules. Web Ontology Language (OWL), Semantic Web Rule Language (SWRL) rules, and reasoners are deployed to inductively learn the rules that distinguish a fake account (bot) from a real one, as well as to classify fake accounts into fake followers or spam bot. Our approach could properly identify the false account with an accuracy of (97%) in the first stage, after which these fake accounts were classified into spam or fake follower bots with an accuracy rate of (94.9%). Furthermore, it has been found that he ontology classifier is a more interpretable model that offers straightforward and human-interpretable decision rules, as compared to other machine learning classifiers.
Read and Reason with MuSeRC and RuCoS: Datasets for Machine Reading Comprehension for Russian

The paper introduces two Russian machine reading comprehension (MRC) datasets, called MuSeRC and RuCoS, which require reasoning over multiple sentences and commonsense knowledge to infer the answer.The former follows the design of MultiRC, while the latter is a counterpart of the ReCoRD dataset.The datasets are included in RussianSuperGLUE, the Russian general language understanding benchmark.We provide a comparative analysis and demonstrate that the proposed tasks are relatively more complex as compared to the original ones for English.Besides, performance results of human solvers and BERT-based models show that MuSeRC and RuCoS represent a challenge for recent advanced neural models.We thus hope to facilitate research in the field of MRC for Russian and prompt the study of multi-hop reasoning in a cross-lingual scenario.
Exploring the Value of Personalized Word Embeddings

In this paper, we introduce personalized word embeddings, and examine their value for language modeling.We compare the performance of our proposed prediction model when using personalized versus generic word representations, and study how these representations can be leveraged for improved performance.We provide insight into what types of words can be more accurately predicted when building personalized models.Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4.7% relative reduction in perplexity.Additionally, we show that a language model using personalized word embeddings can be effectively used for authorship attribution.
Academic Performance and Cognitive Load in Mobile Learning

&lt;p class="0abstract"&gt;The study considers the influence of cognitive load on the achievement of educational goals in the concept of mobile learning. It has been proved that the effectiveness of educational programs in a mobile learning environment is achieved by harmonizing cognitive load and structuring the educational content. The experiment was performed in order to identify pedagogical tools to ensure the effective use of the mental resources of students and to reduce their cognitive load when studying educational materials. The experiment was performed within the course of National education as part of the educational programs of Sechenov First Moscow State University (Russia) and Abai Kazakh National Pedagogical University (Kazakhstan). Traditional classroom learning was supplemented with electronic educational resources and a mobile learning program developed based on the Storage ISpring Cloud mobile learning platform with a focus on the study of national values of good manners and education. The experimental groups were offered three different approaches to studying the discipline in a mobile learning environment. The observation of the learning process helped us to identify the key aspects of effective educational practice in a mobile learning environment, namely: high-quality educational content; thematic and temporal structuring of the educational process; distribution of cognitive load in the educational course; introduction of tools to encourage student attention; active participation of students in the educational process; synergy of knowledge accumulation and delivery in the learning environment; thematic social interaction of the members of the educational group. The results of the experiment confirmed the constructive influence of pedagogical tools aimed at the reduction of cognitive load of students on the effectiveness of educational programs in a mobile learning strategy.&lt;/p&gt;
Joint Event Extraction with Hierarchical Policy Network

Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence.As a result, these efforts fail to utilize information interactions among event triggers, event arguments, and argument roles, which causes information redundancy.In view of this, we propose to exploit the role information of the arguments in an event and devise a Hierarchical Policy Network (HPNet) to perform joint EE.The whole EE process is fulfilled through a two-level hierarchical structure consisting of two policy networks for event detection and argument detection.The deep information interactions among the subtasks are realized, and it is more natural to deal with multiple events issue.Extensive experiments on ACE2005 and TAC2015 demonstrate the superiority of HPNet, leading to state-of-the-art performance and is more powerful for sentences with multiple events.
JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models against Commonsense Validation and Explanation

In this paper, we describe our team's (JUSTers) effort in the Commonsense Validation and Explanation (ComVE) task, which is part of SemEval2020.We evaluate five pre-trained Transformer-based language models with various sizes against the three proposed subtasks.For the first two subtasks, the best accuracy levels achieved by our models are 92.90% and 92.30%, respectively, placing our team in the 12 th and 9 th places, respectively.As for the last subtask, our models reach 16.10 BLEU score and 1.94 human evaluation score placing our team in the 5 th and 3 rd places according to these two metrics, respectively.The latter is only 0.16 away from the 1 st place human evaluation score.
Quantum criticality of the Rabi-Stark model at finite frequency ratios

In this paper, we analyze the quantum criticality of the Rabi-Stark model at finite ratios of the qubit and cavity frequencies in terms of the energy gap and the order parameter, as well as the fidelity, if the Stark coupling strength is the same as the cavity frequency. The critical exponents are derived analytically. The energy gap and the length critical exponents are different from those in the quantum Rabi model and the Dicke model. A finite-size scaling analysis for the order parameter and the fidelity susceptibility is also performed. The universal scaling behaviors are demonstrated, and several finite-size exponents then can be extracted. Furthermore, universal critical behavior also can be established in terms of the bosonic Hilbert space truncation number, and the corresponding critical scaling exponents are found. Interestingly, the critical correlation length exponents in terms of the photonic truncation number as well as the equivalently effective length scales are different in the Rabi-Stark model and the quantum Rabi model, suggesting they belong to different universality classes. The second-order quantum phase transition is convincingly corroborated in the Rabi-Stark model at finite frequency ratios; by contrast, it emerges only at the infinite frequency ratio in the original quantum Rabi model without the Stark coupling.
Incorporating Syntax and Frame Semantics in Neural Network for Machine Reading Comprehension

Machine reading comprehension (MRC) is one of the most critical yet challenging tasks in natural language understanding(NLU), where both syntax and semantics information of text are essential components for text understanding.It is surprising that jointly considering syntax and semantics in neural networks was never formally reported in literature.This paper makes the first attempt by proposing a novel Syntax and Frame Semantics model for Machine Reading Comprehension (SS-MRC), which takes full advantage of syntax and frame semantics to get richer text representation.Our extensive experimental results demonstrate that SS-MRC performs better than ten state-of-the-art technologies on machine reading comprehension task.
Speaker-change Aware CRF for Dialogue Act Classification

Recent work in Dialogue Act (DA) classification approaches the task as a sequence labeling problem, using neural network models coupled with a Conditional Random Field (CRF) as the last layer.CRF models the conditional probability of the target DA label sequence given the input utterance sequence.However, the task involves another important input sequence, that of speakers, which is ignored by previous work.To address this limitation, this paper proposes a simple modification of the CRF layer that takes speaker-change into account.Experiments on the SwDA corpus show that our modified CRF layer outperforms the original one, with very wide margins for some DA labels.Further, visualizations demonstrate that our CRF layer can learn meaningful, sophisticated transition patterns between DA label pairs conditioned on speaker-change in an end-to-end way.
News authentication and tampered images: evaluating the photo-truth impact through image verification algorithms

<h2>Abstract</h2> Photos have been used as evident material in news reporting almost since the beginning of Journalism. In this context, manipulated or tampered pictures are very common as part of informing articles, in today's misinformation crisis. The current paper investigates the ability of people to distinguish real from fake images. The presented data derive from two studies. Firstly, an online cross-sectional survey (N = 120) was conducted to analyze ordinary human skills in recognizing forgery attacks. The target was to evaluate individuals' perception in identifying manipulated visual content, therefore, to investigate the feasibility of "crowdsourced validation". This last term refers to the process of gathering fact-checking feedback from multiple users, thus collaborating towards assembling pieces of evidence on an event. Secondly, given that contemporary veracity solutions are coupled with both journalistic principles and technology developments, an experiment in two phases was employed: a) A repeated measures experiment was conducted to quantify the associated abilities of Media and Image Experts (N = 5 + 5) in detecting tampering artifacts. In this latter case, image verification algorithms were put into the core of the analysis procedure to examine their impact on the authenticity assessment task. b) Apart from conducting interview sessions with the selected experts and their proper guidance in using the tools, a second experiment was also deployed on a larger scale through an online survey (N = 301), aiming at validating some of the initial findings. The primary intent of the deployed analysis and their combined interpretation was to evaluate image forensic services, offered as real-world tools, regarding their comprehension and utilization by ordinary people, involved in the everyday battle against misinformation. The outcomes confirmed the suspicion that only a few subjects had prior knowledge of the implicated algorithmic solutions. Although these assistive tools often lead to controversial or even contradictory conclusions, their experimental treatment with the systematic training in their proper use boosted the participants' performance. Overall, the research findings indicate that the scores of successful detections, relying exclusively on human observations, cannot be disregarded. Hence, the ultimate challenge for the "verification industry" should be to balance between forensic automations and the human experience, aiming at defending the audience from inaccurate information propagation.