Reviewerly/exHarmony huggingface dataset:
AI-Assisted Risk Based Two Factor Authentication Method (AIA-RB-2FA)

Authentication, forms an important step in any security system to allow access to resources that are to be restricted. In this paper, we propose a novel artificial intelligence-assisted risk-based two-factor authentication method. We begin with the details of existing systems in use and then compare the two systems viz: Two Factor Authentication (2FA), Risk-Based Two Factor Authentication (RB-2FA) with each other followed by our proposed AIA-RB-2FA method. The proposed method starts by recording the user features every time the user logs in and learns from the user behavior. Once sufficient data is recorded which could train the AI model, the system starts monitoring each login attempt and predicts whether the user is the owner of the account they are trying to access. If they are not, then we fallback to 2FA.
Teaching Chemical Disciplines Using Electronic Learning and Distance Education Technologies

Research relevance: in 2020 year, due to COVID-19 conditions, educational institutions of all levels began to be forced to organize the educational process using e-learning and distant learning technologies. This process was not easy for students nor for teachers. It was especially difficult to study natural sciences, for example, chemistry from a distance. Research materials and methods: e-learning is based on distant learning technologies, electronic libraries, scientific materials in electronic format, as well as on use of information and telecommunication networks and many programs for remote communication with students. Research results: organization of learning process with using e-learning and distant learning technologies has some advantages. Conclusions: in distant learning of chemical disciplines, the course consists mainly of same stages as in traditional education: activation of fundamental chemical concepts; explanation of topic, expected results and new material; reflection or generalization, student assessment and homework.
Realities and Prospects of Digital Transformation of Additional Education for Children in Russia

The relevance of the research is due to the development of the digital space and the processes of restructuring the entire education system. Currently, a modern educational environment is being created in Russia to create conditions for the digital transformation of education with access to individual learning trajectories of students. A special place in modern education is occupied by the additional education of children. Additional educational activities are carried out based on target models for the development of regional systems in the regions of Russia. Different approaches to implementation in the field of digitalization are being developed in each subject of the Russian Federation. In this regard, it becomes relevant to assess the readiness of institutions of additional education for children in the regions to the challenges of modern digital society. The purpose of the publication is to identify the current state and prospects of digital transformation of additional education for children in Russia (on the example of the Komi Republic), considering the key features of the development of the modern digital industry. The leading methodological approaches to the study of the problem under study are system-pragmatist, personality-oriented, and differentiated. The methods of socio-historical and theoretical-methodological analysis, evaluation and generalization of the best practices of institutions for additional education of children, analysis of normative and program documents, and comparative analysis of static data were used in the course of the research. The main trends in the digital transformation of additional education for children have been identified. Promising directions for the development of additional education for children have been identified, considering the key approaches to the implementation of the National Technological Initiative and the National Project "Education". End-to-end technologies of the digital industry have been identified as a result of the research and the correspondence of the directions of educational programs in modern models for additional education of children to these technologies has been determined. The experience of educational activities in modern centers of additional education of the Komi Republic has been summarized: "Kvantorium", "IT-cube", "House of scientific collaboration". The practical significance of the research lies in the possibility of using the obtained data to improve the processes of digital transformation of additional technical education. The research materials can be in demand by managers, methodologists, teachers of the system of additional education for children when developing the concept of development and planning educational activities.
AND/OR search techniques for chance constrained motion primitive path planning

Motion primitive planning under parametric uncertainty may be modeled as a chance-constrained Markov Decision Process (CCMDP). Single-query solutions to CCMDPs can be obtained by searching the And/Or graph representing the state–action space of the system. The Risk-bounded AO* (RAO*) algorithm has been proposed as a solution method for this problem, but it scales poorly to MDPs resulting from a motion primitive discretization because it has no mechanism to prioritize expansion of AND nodes. This paper describes an induced heuristic for state–action pairs that can be rapidly computed by leveraging the properties of motion primitives; its value can be used to prioritize AND nodes for more efficient search. Search is further accelerated by leveraging shared symmetry in constraints and dynamics to move almost all computation necessary to enforce convex polytope constraints offline. The performance improvements are demonstrated with path planning problems involving a Dubins Car and a nonlinear aircraft model.
Unsupervised cross-media Hashing retrieval based on multi-head attention network

The cross-media Hash retrieval encodes different media data into a common binary Hash space, which can effectively measure the correlation between different modal samples. In order to further improve the retrieval performance, this paper proposes an unsupervised cross-media Hash retrieval method based on multi-head attention network. First, we use a multi-head attention network to generate a Hash code matrix, which makes the images and texts match better. Second, an auxiliary similarity matrix is constructed to integrate the original neighborhood information from different modalities. Through the collaborative learning of auxiliary similarity matrix and Hash code matrix, our method can capture the potential correlations between different modalities and within the same modality. In addition, we design two loss functions to train the model, and adopt strategies of batch normalization and replacing Hash code generation functions to optimize the model, which greatly improves the training speed of the model. Experiments on three datasets show that the average performance of our method is significantly higher than many state-of-the-art unsupervised methods, which fully proves the effectiveness and superiority of our method.
TRIPP: Transparent Resource Provisioning for Multi-Tenant CPU-GPU based Cloud Environments

Cloud Warehouses have been expanding their computational resources to cover the growing offloading of tenants’ applications. Currently, cloud nodes integrate heterogeneous resources, such as CPU and GPU, so they can exploit different types and levels of parallelism available in the applications. However, heterogeneous cloud nodes bring challenges to the software development process, since the programmer must be aware of each device’s specifications, analyze and distribute the code over the available devices. Even though OpenCL supports transparent programming on heterogeneous devices, softening the programmer’s burden, the choice of target device is still the programmer’s responsibility. Given that, this work proposes a framework for the execution of OpenCL applications on a multi-tenant CPU-GPU cloud environment, responsible for transparently scheduling the applications to the best available device, without any interaction from the programmer. The framework has the goal of optimizing resource provisioning, reducing makespan and energy consumption. Considering the execution of the PolyBench benchmark suite, the framework shows reduction on makespan of $3.4 \times$ and energy savings of 33% when compared to the GPU standalone execution.
Attitude of University Students towards e-learning in Times of Pandemic

The aim of this article is to know the attitude of university students towards e-learning having age and gender as sociodemographic variables. It describes important aspects about virtual education, connectivism, e-learning model and factors about attitude towards e-learning, b-learning and its advantages. In this basic, descriptive level study, the scale of attitudes towards e-learning proposed by Mehra and Omidian (2012) consisting of 92 items (distributed on a dichotomous yes/no scale) was used, with a reliability, through Kr-20, of 0.995. The sample comprised a total of 330 medical students from a private university from the first to the fifth cycle of the degree. It was demonstrated that e-learning is a tool at the service of the teaching-learning processes that seeks interaction between the teacher and the student, developing cognitive, collaborative and self-regulatory skills in the university student. The conclusions of the research were that, regarding the sex of the students, 38.5% of the women perceived an unfavorable level and 34.2% of the men, a favorable level of attitude towards e-learning. As for the intervening variable age, 23% of the students fewer than 21 years of age perceived an unfavorable level of attitude towards e-learning. In the ease of use dimension, 44.4% of the students perceived an unfavorable level. Finally, 40.3% perceived an unfavorable level; 34.8% perceived a fair level and 24.8% a favorable level of attitude towards e-learning.
A Deep Learning Approach to Extract Integrated Meaningful Keywords from Social Network Posts with Images, Texts and Hashtags

Using the social network services, users might create different types of content including numeric, textual and non-textual data objects. In the past, social network service providers mainly focus on numeric and textual content to understand their users and to provide them with related information or advertisements. However, the information behind the non-textual content has not been well considered. This research aims at extracting integrated meaningful keywords by jointly considering photo, text descriptions and hashtags to better reflect the meaning of the user-posted content. A deep learning approach with convolutional neural network methods that integrate ResNet-50 and Word2Vec models, as well as Dijkstra’s algorithm is proposed to extract the meaningful keywords. The well-trained ResNet-50 and Word2Vec models are applied respectively to gain the predicted classification labels of the image and to identify the co-occurrences among predicted classification labels of image, segmented words of text descriptions and hashtags. A multistage graph weighted with the pairs of co-occurrences of image, segmented words and hashtags is built and then, the Dijkstra’s algorithm is adapted to extract consistent keywords of the posted content with maximized cumulated weights. A simplified example is provided to illustrate the proposed approach for acquiring the integrated information embedded in the image, text and hashtags.
Zero Trust-Based Adaptive Authentication using Composite Attribute Set

Rapid evolution of internet-oriented applications has increased the threats to confidential data. Single-factor authentication approaches are no longer sufficient to ensure user credibility. Multi-factor authentication schemes are also not tamper-proof. A Zero Trust, adaptive authentication-based approach that uses the user's past behavior can offer protection in this scenario. This paper proposes a system that collects a composite attribute set that includes the user behavior, attributes of the application through which the user is requesting access, and the device used. The enhanced collection allows the creation of detailed context that allows granular variance calculation and risk score.
The Rain Prediction in Australia Based Big Data Analysis and Machine Learning Technology

To prevent wildfires, Australian meteorological data over a ten-year period were analysed to determine the days with the highest risk of uncontrolled fires using machine learning algorithms and statistical data.
Sparse sampling and reconstruction for freeform surface based on low-rank matrix completion

Abstract The coordinate measuring machine (CMM) becomes an extensive and effective method for high precision inspection of free-form surfaces due to its ability to measure complex and irregular surfaces. Sampling strategy and surface restoration method have an important influence on the efficiency and precision of CMM. In this paper, a sparse sampling strategy and surface reconstruction method for free-form surfaces based on low-rank matrix completion (LRMC) is proposed. In this method, the free-form surface is sampled randomly with uniform distribution in the cartesian coordinate system to obtain sparse sampling points, and then optimizes the scanning path to obtain the shortest path through all measurement points, and finally, the LRMC algorithm based on alternating root mean square prop was used to reconstruct the surface with high precision. The simulation and experimental results show that under the premise of ensuring accuracy, the number of sampling points is greatly reduced and the measurement efficiency is greatly improved.
A Targeted Adversarial Attack Method against Person Re-identification Model

In recent years, with the development of feature extraction and metric learning of deep neural network, the performance of person re-identification (Re-ID) based on deep learning has been greatly improved. However, the vulnerability of deep neural network is also affecting the security of person Re-ID model, and the adversarial examples become a common way to attack the deep models. In this paper, we propose a targeted adversarial attack method against person Re-ID by adding imperceptible noise on the query examples for the generation of adversarial examples, so that the retrieval results of query examples returned from the model are consistently biased towards the specific pedestrian. To test the effectiveness of the proposed method, several experiments are conducted on the Market-1501 dataset, and the results show that the targeted adversarial attack method can effectively attack the retrieval results of person Re-ID model.
Research on Cooperative Detection Technology of Networked Radar Based on Data Fusion

With the development of science and technology, the electronic warfare environment is becoming increasingly complicated, making the traditional combat platform relying on a single radar no longer meet the needs of air defense in modern battlefield. In order to solve this problem, this paper proposes a cooperative detection technology of networked radar based on data fusion. Through multi-radar cooperative combat and multi-source radar data fusion, the resource sharing and functional complementarity between the radars in the network are realized, and the overall combat effectiveness of radar system is effectively improved. Simulation results show that the cooperative detection technology of networked radar based on data fusion can not only expand the space-time coverage of radar monitoring, but also improve the detection and tracking performance of radar system, so it is of great significance in improving the overall combat effectiveness.
Shifting Paradigms in Language Learning and Teaching

The main focus of this chapter is to discuss how dramatically teaching and learning are changing, largely as a result of developments in technology. These changes have brought about changes in the roles of the teachers, of learners and even of the technologies themselves. Teachers not only have to manage their teaching environments, but they also need to manage their technological skills and the emotional load that goes along with the pressures of maintaining digital literacy. Learners are faced with having greater expectations to use technology, while it is expected that they are already skilled in using technologies for learning purposes. It explores the possible future directions of education, where teachers and learners need to consider not only what information needs to be learned, but also what information is acceptable to be referenced, along with the need to develop skills in evaluating information from the enormous amount of available resources. It also explores the view of teaching and learning in formal and informal contexts and looks at how mobile technologies have impacted both of these learning situations.
Mesoscopic and macroscopic quantum correlations in photonic, atomic and optomechanical systems

This paper reviews the progress that has been made in our knowledge of quantum correlations at the mesoscopic and macroscopic level. We begin by summarizing the Einstein-Podolsky-Rosen (EPR) argument and the Bell correlations that cannot be explained by local hidden variable theories. It was originally an open question as to whether (and how) such quantum correlations could occur on a macroscopic scale, since this would seem to contradict the correspondence principle. The purpose of this review is to examine how this question has been answered over the decades since the original papers of EPR and Bell. We first review work relating to higher spin measurements which revealed that macroscopic quantum states could exhibit Bell correlations. This covers higher dimensional, multiparticle and continuous-variable EPR and Bell states where measurements on a single system give a spectrum of outcomes, and also multipartite states where measurements are made at multiple separated sites. It appeared that the macroscopic quantum observations were for an increasingly limited span of measurement settings and required a fine resolution of outcomes. Motivated by this, we next review correlations for macroscopic superposition states, and examine predictions for the violation of Leggett-Garg inequalities using dynamical quantum systems. These results reveal Bell correlations for coarse-grained measurements which need only distinguish between macroscopically distinct states, thus bringing into question the validity of certain forms of macroscopic realism. Finally, we review progress for massive systems, including Bose-Einstein condensates and optomechanical oscillators, where EPR-type correlations have been observed between massive systems. Experiments are summarized which support the predictions of quantum mechanics in mesoscopic regimes.
A Process Reverse Engineering Approach Using Process and Observation Ontology and Probabilistic Relational Models: Application to Processing of Bio-composites for Food Packaging

Designing new processes for bio-based and biodegradable food packaging is an environmental and economic challenge. Due to the multiplicity of the parameters, such an issue requires an approach that proposes both (1) to integrate heterogeneous data sources and (2) to allow causal reasoning. In this article, we present POND (Process and observation ONtology Discovery), a workflow dedicated to answering expert queries on domains modeled by the Process and Observation Ontology (PO $$^2$$ ). The presentation is illustrated with a real-world application on bio-composites for food packaging to solve a reverse engineering problem, using a novel dataset composed of data from different projects.
Robust hand gesture recognition based on mWLD and LTeP descriptors

Hand gesture recognition has been widely used to develop human-computer interfaces that are both efficient and intuitive. In this context, we propose in this paper a hand gesture recognition system based on two descriptors, namely, Local Ternary Pattern (LTeP) and multi-scale Weber Local Descriptor (mWLD), and for classification task, we used Support Vector Machine (SVM) and Radial Basis Neural Network (RBNN) classifiers. Two user independent datasets were used to evaluate the performance of our system, the first one is Jochen Triesch’s static hand posture dataset and the second is Sebastien Marcel’s dynamic gesture dataset. Obtained results show that LTeP descriptor performs better for static gestures achieving 91.67% recognition rate for Triesch’s dataset, whereas mWLD performed better for Marcel’s dataset where a 100% recognition rate was achieved.
Musical Representation of The Religious Message in Zoltán Gárdonyi’s Három Nagyheti Kép (Three Holy Week Events)

"Zoltán Gárdonyi (1906-1986) composed his work entitled Három Nagyheti Kép (Three Holy Week Events) in 1966. At that time, he was a professor at the Liszt Ferenc Academy of Music, in Budapest. He composed the work for organ and a string ensemble (1st violin, 2nd violin, viola, cello, double bass). The work has three movements, as the title Három Nagyheti Kép (Three Holy Week Events) suggests. This paper analyses the work in question in detail, from a harmonic and formal point of view. Keywords: Gárdonyi Zoltán, Három Nagyheti Kép, Three Holy Week Events, organ, strings, harmony, forms, structure, religious message "
Research on recognition of painted faces

In the field of public safety surveillance, suspects may disguise themselves by painting their faces, challenging the face recognition system. The well-developed ordinary, two-dimensional image face recognition methods rely mainly on the texture features of the face images, which leaves the traditional face recognition system relatively vulnerable to the camouflage attacks. We set our sights on three-dimensional (3D) alternatives. A 3D facial recognition method is proposed which extracts 3D features of each individual that are not related to face texture. Considering actual requirements, we applied binocular stereo matching to obtain 3D face point clouds. Hereafter, we included a spatial representation for classification and recognition of 3D face, based on which we then adopted a multicascade classifier. By reconstructing, extracting features, and identifying multiple 3D faces, comparison experiment results demonstrate that our proposed method discerned 3D faces correctly, defeated the face camouflage attacks effectively, and showed promising application prospects for public safety surveillance.
Strategic evaluation in times of Covid 19: Perspectives, changes and recommendations for the improvement of the learning experience

Faced with the social isolation caused by COVID 19, educational institutions adopted changes in order to generate functional learning spaces in relation to new educational environments. In this context, the purpose of the study focuses on the characterization of the perspectives, strategic uses and changes originated in the evaluation of learning in Higher Education. For this, data collection and analysis methods based on the qualitative approach were used. The results, derived from the content analysis and coding, show that the evaluation experience must be flexible, diversified, and oriented to provide feedback and support during the learning process.
Multi-level threshold segmentation based on LSHADE

In order to solve the problems in traditional multi-threshold image segmentation methods, a Multi-threshold image segmentation method based on LSHADE is proposed. On the basis of SHADE, LSHADE uses a linear population size reduction method to continuously reduce the population size to improve the performance of the algorithm. First, perform multi-threshold image segmentation based on the between-class variance and entropy, and compare the segmentation results of the two methods. Then using PSNR and objective function values as evaluation criteria, comparing LSHADE with other algorithms. The results show that the segmentation speed and segmentation accuracy have been improved.