Reviewerly/exHarmony huggingface dataset:
The Pandemic as a Challenge to the Development of University Networks in Russia: Differentiation or Collaboration?

As an inevitable result of Russia’s higher education policies of the past two decades, new university leaders in and outside of Moscow and St. Petersburg have emerged, and vertical differentiation has increased. Inequality of educational potential has a strong regional dimension, exerting a considerable delayed impact on regional socioeconomic development. Differences in universities’ resources affected their ability to adapt their instructional, research, and administrative processes to change during the pandemic, thus broadening the education and research quality gap in higher education. Some regions may face an increased outflow of youth talent to leading universities or just any colleges based in Moscow and St. Petersburg, which will certainly weaken the socioeconomic growth prospects of Russia’s regions.The pandemic accelerated the debate over this problem and demonstrated readiness of universities for joint efforts. Groundwork was laid for deploying a policy to create a cooperative network of universities and their stakeholders so as to reduce institutional differentiation and promote exchange of experience and competence among universities.This paper investigates into the main characteristics of vertical differentiation in Russian higher education that had been in place when the pandemic broke out and determined whether universities succeeded or failed in switching to distance learning. Furthermore, lockdown measures and their economic impact on different types of universities are analyzed. Finally, we discuss possible avenues and specific considerations for expanding cross-institutional collaboration and engaging stakeholders inuniversity development.
Analysis of the Pros and Cons of Students' Online Courses during the Epidemic and the Prospects for the Development of English Online Platform in Secondary Vocational School

Facing the severe epidemic situation, China's education department has put forward an online teaching emergency plan of "Stopping going to school while keeping teaching online", so that many students in secondary vocational school can avoid close contact back to school and protect their lives and health. At the same time, it guarantees the teaching progress in the special period. Teachers in secondary vocational school prefer to choose DingTalk software to develop online teaching models. In the teaching of the vocational English network platform, online teaching is a reform of the teaching form based on information technology and the Internet. Teachers can use the DingTalk platform to interact with students, so that the online teaching method meets the individual needs of students. It takes good account of students with different English foundations and levels, and greatly stimulates students' interest in learning. This paper analyzes the advantages and disadvantages of online teaching and explores the online teaching model.
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems

We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
A Fast and Accurate Few-Shot Detector for Objects with Fewer Pixels in Drone Image

Unmanned aerial vehicles (UAVs) are important in modern war, and object detection performance influences the development of related intelligent drone application. At present, the target categories of UAV detection tasks are diversified. However, the lack of training samples of novel categories will have a bad impact on the task. At the same time, many state-of-the-arts are not suitable for drone images due to the particularity of perspective and large number of small targets. In this paper, we design a fast few-shot detector for drone targets. It adopts the idea of anchor-free in fully convolutional one-stage object detection (FCOS), which leads to a more reasonable definition of positive and negative samples and faster speed, and introduces Siamese framework with more discriminative target model and attention mechanism to integrate similarity measures, which enables our model to match the objects of the same categories and distinguish the different class objects and background. We propose a matching score map to utilize the similarity information of attention feature map. Finally, through soft-NMS, the predicted detection bounding boxes for support category objects are generated. We construct a DAN dataset as a collection of DOTA and NWPU VHR-10. Compared with many state-of-the-arts on the DAN dataset, our model is proved to outperform them for few-shot detection tasks of drone images.
Analysis of Developers of Online Courses on Ukrainian Platforms of MOOC

The article examines the developers of courses on Ukrainian platforms of mass open online courses Prometheus, EdEra, Open University of Maidan, their quantitative analysis in various thematic areas. It is noted that education remains the main way to acquire new knowledge and form the necessary competencies. The main advantages of open educational resources, their classification, the reasons for the growing popularity in modern society are highlighted. It was found that mass open online courses are a logical continuation of distance learning courses, have their differences and features. However, some problematic issues related to the promotion of mass open online courses that need to be addressed are considered. An analysis of the number of online courses on Ukrainian-language platforms and their thematic areas. As of March 2020, there are 235 online courses on all considered platforms. The same topics for all platforms were law, training, education, media, economics, health. It was found that the most active developers of online courses on the platform of the Open University of Maidan were public and charitable organizations. Educational online platforms and international organizations are most actively involved in the creation of online courses on the EdEra resource. On the Prometheus platform, the largest number of courses was created at the initiative of international organizations, institutions of higher and secondary education
ICTs and Project-Based Learning (PBL) in EFL: Pre-service Teachers’ Attitudes and Digital Skills

Although there is a rich body of literature about the implementation of ICTs in foreign language learning, none has investigated the technological attitudes and digital skills of pre-service teachers thanks to the adoption of a Project-Based Learning (PBL) methodology. This study analyses the attitudes and digital skills of pre-service teachers through the implementation of ICT-based projects in the EFL classroom. For this purpose, 120 teacher candidates at the University of Alicante (Spain) participated in this experiment. First, all students were administered a pretest to measure the degree of technological affinity and digital skills. Then, they were divided into smaller groups (cooperative learning) in order to design and create several ICT-based projects aimed at teaching English as a foreign language to children and young learners. All the participants later presented their projects and evaluated the results (peer-evaluation) from a technical and pedagogical perspective. Finally, they completed a post-test based on their self-perceived learning outcomes in relation to ICT integration. The results revealed significant learning gains in digital and higher-order thinking skills (create, analyse, evaluate). Teaching candidates show a positive attitude towards the integration of ICTs in the EFL classroom but demand better training as regards the adoption of new technologies, particularly given the current situation and the need for increased on-line teaching.
A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

Zero-watermarking is one of the solutions for image copyright protection without tampering with images, and thus it is suitable for medical images, which commonly do not allow any distortion. Moment-based zero-watermarking is robust against both image processing and geometric attacks, but the discrimination of watermarks is often ignored by researchers, resulting in the high possibility that host images and fake host images cannot be distinguished by verifier. To this end, this paper proposes a PCET- (polar complex exponential transform-) based zero-watermarking scheme based on the stability of the relationships between moment magnitudes of the same order and stability of the relationships between moment magnitudes of the same repetition, which can handle multiple medical images simultaneously. The scheme first calculates the PCET moment magnitudes for each image in an image group. Then, the magnitudes of the same order and the magnitudes of the same repetition are compared to obtain the content-related features. All the image features are added together to obtain the features for the image group. Finally, the scheme extracts a robust feature vector with the chaos system and takes the bitwise XOR of the robust feature and a scrambled watermark to generate a zero-watermark. The scheme produces robust features with both resistance to various attacks and low similarity among different images. In addition, the one-to-many mapping between magnitudes and robust feature bits reduces the number of moments involved, which not only reduces the computation time but also further improves the robustness. The experimental results show that the proposed scheme meets the performance requirements of zero-watermarking on the robustness, discrimination, and capacity, and it outperforms the state-of-the-art methods in terms of robustness, discrimination, and computational time under the same payloads.
Research on Fast Pedestrian Detection Algorithm Based on Autoencoding Neural Network and AdaBoost

In order to solve the problem of low accuracy of pedestrian detection of real traffic cameras and high missed detection rate of small target pedestrians, this paper combines autoencoding neural network and AdaBoost to construct a fast pedestrian detection algorithm. Aiming at the problem that a single high-level output feature map has insufficient ability to express pedestrian features and existing methods cannot effectively select appropriate multilevel features, this paper improves the traditional AdaBoost algorithm structure, that is, the sample weight update formula and the strong classifier output formula are reset, and the two-input AdaBoost-DBN classification algorithm is proposed. Moreover, in view of the problem that the fusion video is not smoothly played, this paper considers the motion information of the video object, performs pixel interpolation by motion compensation, and restores the frame rate of the original video by reconstructing the dropped interframe image. Through experimental research, we can see that the algorithm constructed in this paper has a certain effect.
Augmented Reality in Assembly Systems: State of the Art and Future Perspectives

Abstract Assembly represents a fundamental step in manufacturing, being a time-consuming and costly process, on which the final quality of the product mostly depends. Augmented Reality (AR) may represent a key tool to assist workers during assembly, thanks to the possibility to provide the user of real-time instructions and information superimposed on the work environment. Many implementations have been developed by industries and academic institutions for both manual and collaborative assembly. Among the most remarkable examples of the last few years are applications in guidance of complex tasks, training of personnel, quality control and inspection. This keynote paper aims to provide a useful survey by reviewing recent applications of AR in assembly systems, describing potential advantages, as well as current limitations and future perspectives.
Full Surround Monodepth From Multiple Cameras

Self-supervised monocular depth and ego-motion estimation is a promising approach to replace or supplement expensive depth sensors such as LiDAR for robotics applications like autonomous driving. However, most research in this area focuses on a single monocular camera or stereo pairs that cover only a fraction of the scene around the vehicle. In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs. Using generalized spatio-temporal contexts, pose consistency constraints, and carefully designed photometric loss masking, we learn a single network generating dense, consistent, and scale-aware point clouds that cover the same full surround <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> field of view as a typical LiDAR scanner. We also propose a new scale-consistent evaluation metric more suitable to multi-camera settings. Experiments on two challenging benchmarks illustrate the benefits of our approach over strong baselines.
The Design of a Gamified Responsible Use of Social Media

Social media is part of almost everyone's daily life. Its networking facilities redefines the way people connect and interact with each other. However, social media is reported being misused in different ways, especially the millennials. There is a need to elevate the teens' level of empowerment on the responsible use of social media. Other technological innovations like augmented reality (AR) and digital gamification provides pedagogical benefits. Digital gamification in the classroom is a teaching strategy that translates content and delivery into a game using digital technology. On the other hand, AR is an emerging technology to enlarge real-life situations in multimedia. Research shows both technologies increase interactivity well as attention span among the learners. Additionally, these technologies, including social media, are among the many useful tools in teaching twenty first century learners once correctly used. With this, a game-based mobile application was developed to advocate responsible use of social media among teens. The learning content was gamified in augmented reality to provide an innovative teaching and learning way at Silliman University. This paper describes the gamification design of the learning trail on the topic of responsible use of social media. Specifically, it presents the publishing process of the augmented reality mobile application about responsible use of social media. Kuhlmann's 3C Model (Challenge, Choices, Consequences) was utilized to formulate the learning content. It also demonstrates the design phases, game mechanics, and the general evaluation of the learning application. Four challenges were developed. These are (a) be familiar with security and privacy policies, (b) do not express concerns about others, even if you think you are anonymous, (c) respond to digital offenders, (d) do not tell the world about an upcoming vacation. These challenges were translated into graphics and animations. The animated material was compiled, programmed, and published to a server of the mobile application. The gamified learning trail on responsible use of social media is accessible through QR codes leading to the augmented reality interface. The design was validated and found to be relevant and engaging.
An Orthogonal Locality and Globality Dimensionality Reduction Method Based on Twin Eigen Decomposition

Dimensionality reduction is a hot research topic in pattern recognition.Traditional dimensionality reduction methods can be separated into linear dimensionality reduction methods and nonlinear dimensionality reduction methods.Linear dimensionality reduction methods usually utilize Euclidean distances to explore global geometric structures, and nonlinear dimensionality reduction methods can preserve local manifold structures in learned low-dimensional subspaces.However, redundant information and noises of the raw high-dimensional data restrict the classification performance of these methods.To solve the problem, we propose a novel orthogonal dimensionality reduction method based on twin eigen decomposition called orthogonal locality and globality preserving projections (OLGPP).Orthogonality, as a commonly used criterion in pattern recognition, is insensitive to the redundant information and the noises.OLGPP not only combines the advantages of global Euclidean structures and local manifold structures, but also is insensitive to the redundant information and noises of the raw high-dimensional data by embedding the orthogonality criterion.Additionally, the objective function based on twin eigen decomposition can be solved sequentially to obtain analytical solutions.On four well-known datasets, we design extensive experiments to evaluate the performance of OLGPP.From the experimental results, OLGPP has the optimal experimental results, and its average recognition rates are about 10% higher than the classic local dimensionality reduction method (i.e., locality preserving projection), which proves OLGPP is an effective dimensionality reduction method.
Improved Preimage Attacks on 4-Round Keccak-224/256

This paper provides an improved preimage attack method on standard 4-round Keccak-224/256. The method is based on the work pioneered by Li and Sun, who design a linear structure of 2-round Keccak-224/256 with 194 degrees of freedom left. By partially linearizing 17 output bits through the last 2 rounds, they finally reach a complexity of 2207/2239 for searching a 4-round preimage. Yet under their strategy, those 17 bits are regarded as independent bits and the linearization costs a great amount of freedom. Inspired by their thoughts, we improve the partial linearization method where multiple output bits can reuse some common degrees of freedom. As a result, the complexity of preimage attack on 4-round Keccak-224/256 can be decreased to 2192/2218, which are both the best known theoretical preimage cryptanalysis so far. To support the theoretical analysis, we apply our strategy to a 64-bit partial preimage attack within practical complexity. It is remarkable that this partial linearization method can be directly applied if a better linear structure with more freedom left is proposed.
Misuse-Free Key-Recovery and Distinguishing Attacks on 7-Round Ascon

Being one of the winning algorithms of the CAESAR competition and currently a second round candidate of the NIST lightweight cryptography standardization project, the authenticated encryption scheme Ascon (designed by Dobraunig, Eichlseder, Mendel, and Schläffer) has withstood extensive self and third-party cryptanalysis. The best known attack on Ascon could only penetrate up to 7 (out of 12) rounds due to Li et al. (ToSC Vol I, 2017). However, it violates the data limit of 264 blocks per key specified by the designers. Moreover, the best known distinguishers of Ascon in the AEAD context reach only 6 rounds. To fill these gaps, we revisit the security of 7-round Ascon in the nonce-respecting setting without violating the data limit as specified in the design. First, we introduce a new superpoly-recovery technique named as partial polynomial multiplication for which computations take place between the so-called degree-d homogeneous parts of the involved Boolean functions for a 2d-dimensional cube. We apply this method to 7-round Ascon and present several key recovery attacks. Our best attack can recover the 128-bit secret key with a time complexity of about 2123 7-round Ascon permutations and requires 264 data and 2101 bits memory. Also, based on division properties, we identify several 60 dimensional cubes whose superpolies are constant zero after 7 rounds. We further improve the cube distinguishers for 4, 5 and 6 rounds. Although our results are far from threatening the security of full 12-round Ascon, they provide new insights in the security analysis of Ascon.
Signal detection with co-channel interference using deep learning

Signal detection using deep learning is a challenging and promising research topic. Several learning-based signal detectors have been proposed to produce significant results. However, most of them have ignored interference in their designs. In this paper, we evaluate the performance of learning-based signal detectors in the presence of co-channel interference under different channel conditions. Specifically, fully connected deep neural network (FCDNN) and convolutional neural network (CNN) are examined as the data-driven signal detector for blind signal detection without knowledge of the channel state information. Several important system parameters, including signal-to-interference ratio, number of interferences and type of interference, are considered. Numerical results show that FCDNN and CNN-based detectors have better performance and robustness to different SIRs conditions than traditional detectors in the presence of interference and FCDNN performs better than CNN when SIR is small and the order of interference modulation is high.
Comparative European DigCompEdu Framework (JRC) and Common Framework for Teaching Digital Competence (INTEF) through expert judgment

Digital technology, as one of the elements of the Knowledge Society, assumes a determining role in educational systems, where the role of teachers is fundamental. Along these lines, different institutions support the importance of teaching digital skills, proposing models and conceptual frameworks. In them, the knowledge and skills that teachers must develop are classified, combining technological, professional, organizational and pedagogical capacities, with different dimensions and descriptors. The objective of this article is to compare and evaluate the feasibility of the DigCompEdu European Digital Competence Framework for Teachers (JRC) and the Common Framework for Teaching Digital Competence (INTEF). To do this, it is decided to carry out a theoretical reflection on said frameworks and a subsequent Delphi study with the coefficient of expert competence (CEC). 335 people participated, of which 275 (82.1%) were considered experts (CEC ≥ 0.8). The results indicate that, although they are very similar, the European DigCompEdu Teaching Digital Competence Framework is the most endorsed by experts. Finally, the idea that both are viable proposals to develop teaching digital skills through personalized training itineraries is presented.