Reviewerly/exHarmony huggingface dataset:
Aberration correction for flow velocity measurements using deep convolutional neural networks

Optical imaging-based flow measurement techniques, like particle image velocimetry, are vulnerable to optical distortions caused by inhomogeneous refractive index or fluctuating phase boundaries. These distortions can lead to blurred particle images and uncertain tracer particle position assignment, resulting in a degradation of velocity measurement accuracy. In order to improve the measurement accuracy, adaptive optics system can be applied to correct distortions. For imaging metrology in fluid mechanics, the optical distortions have features of large frequency range, high spatial frequency and large dynamic range. Actuator-based approaches are limited by its performances. In our work, a novel intelligent adaptive optic system was applied to flow measurement, a learning-based aberration correction method without wavefront corrector was demonstrated, which was used to correct distortions in imaging-based flow measurement. A particle image velocimetry setup which can measure wavefront aberration was built to generate training and test dataset for deep neural network, and also the distortion caused PIV image degradation model. The correction performance of the trained neural network was quantitatively evaluated by corrected PIV image quality and flow measurement result.
A Closer Look at Prototype Classifier for Few-shot Image Classification

The prototypical network is a prototype classifier based on meta-learning and is widely used for few-shot learning because it classifies unseen examples by constructing class-specific prototypes without adjusting hyper-parameters during meta-testing. Interestingly, recent research has attracted a lot of attention, showing that training a new linear classifier, which does not use a meta-learning algorithm, performs comparably with the prototypical network. However, the training of a new linear classifier requires the retraining of the classifier every time a new class appears. In this paper, we analyze how a prototype classifier works equally well without training a new linear classifier or meta-learning. We experimentally find that directly using the feature vectors, which is extracted by using standard pre-trained models to construct a prototype classifier in meta-testing, does not perform as well as the prototypical network and training new linear classifiers on the feature vectors of pre-trained models. Thus, we derive a novel generalization bound for a prototypical classifier and show that the transformation of a feature vector can improve the performance of prototype classifiers. We experimentally investigate several normalization methods for minimizing the derived bound and find that the same performance can be obtained by using the L2 normalization and minimizing the ratio of the within-class variance to the between-class variance without training a new classifier or meta-learning.
SCFlow: Optical Flow Estimation for Spiking Camera.

As a bio-inspired sensor with high temporal resolution, Spiking camera has an enormous potential in real applications, especially for motion estimation in high-speed scenes. Optical flow estimation has achieved remarkable success in image-based and event-based vision, but % existing methods cannot be directly applied in spike stream from spiking camera. conventional optical flow algorithms are not well matched to the spike stream data. This paper presents, SCFlow, a novel deep learning pipeline for optical flow estimation for spiking camera. Importantly, we introduce an proper input representation of a given spike stream, which is fed into SCFlow as the sole input. We introduce the \textit{first} spiking camera simulator (SPCS). Furthermore, based on SPCS, we first propose two optical flow datasets for spiking camera (SPIkingly Flying Things and Photo-realistic High-speed Motion, denoted as SPIFT and PHM respectively) corresponding to random high-speed and well-designed scenes. Empirically, we show that the SCFlow can predict optical flow from spike stream in different high-speed scenes, and express superiority to existing methods on the datasets. \textit{All codes and constructed datasets will be released after publication}.
An Improved Text Extraction Approach With Auto Encoder for Creating Your Own Audiobook

As we all know, listening makes learning easier and interesting than reading. An audiobook is a software that converts text to speech. Though this sounds good, the audiobooks available in the market are not free and feasible for everyone. Added to this, we find that these audiobooks are only meant for fictional stories, novels or comics. A comprehensive review of the available literature shows that very little intensive work was done for image to speech conversion. In this paper, we employ various strategies for the entire process. As an initial step, deep learning techniques are constructed to denoise the images that are fed to the system. This is followed by text extraction with the help of OCR engines. Additional improvements are made to improve the quality of text extraction and post processing spell check mechanism are incorporated for this purpose. Our result analysis demonstrates that with denoising and spell checking, our model has achieved an accuracy of 98.11% when compared to 84.02% without any denoising or spell check mechanism.
Analysis on the Construction Status of Educational Administration Management System in Vocational Colleges

In higher vocational colleges, education management is the most core work, which can form a more perfect and regular management for students. The development of education management in higher vocational colleges is inseparable from the education management system. Based on the educational administration management system, through the use of advanced ideas and technologies, it can provide perfect information services for teachers and students in higher vocational colleges, which has a certain significance to improve the teaching level of higher vocational colleges. This paper analyzes the status quo of the educational administration management system in higher vocational colleges, and then further puts forward improvement measures on the status quo, so as to better improve the teaching efficiency and quality of higher vocational colleges.
Making ECMWF Open Data more easily accessible via cloud-based services

&amp;lt;p&amp;gt;The European Centre for Medium-Range Weather Forecasts (ECMWF) is moving gradually towards an open data licence , aiming to make real-time forecast data available under a full, free and open data license by 2025. The introduction of open data policies lead in general to an increase in data requests and a broader user base. Therefore a much larger community of diverse users will be interested in accessing, understanding and using ECMWF Open Data (real-time). While an open data license is an important prerequisite, it does not automatically lead to an increased uptake of open data. In order to increase the uptake of (open) data, Wilkinson et al. (2016) defined the FAIR principles, which emphasize the need to make data better &amp;amp;#8216;findable&amp;amp;#8217;, &amp;amp;#8216;accessible&amp;amp;#8217;, &amp;amp;#8216;interoperable&amp;amp;#8217; and &amp;amp;#8216;reusable&amp;amp;#8217;.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In 2019, we conducted a web-based survey among users of big Earth data to obtain a better understanding of users&amp;amp;#8217; needs in terms of the data they are interested in, the applications they need the data for, the way they access and process data and the challenges they face. The results show that users are in particular interested in meteorological and climate forecast data, but facing challenges related to the growing data volumes, the data heterogeneity and the limited processing capacities. At the same time, survey respondents showed an interest in using cloud-based services in the near future, but expressed the need for an easier data discovery and the interoperability of data systems. Moreover, an ECMWF supported activity that made a subset of ERA5 climate reanalysis data available to the user community of the Google Earth Engine platform, revealed that interoperability of data systems is a growing bottleneck.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Conclusions from both activities are helping ECMWF to define the way forward to make ECMWF Open Data (real-time) better accessible via cloud-based services. In this presentation we would like to share and discuss lessons learned to make open data more easily &amp;amp;#8216;accessible&amp;amp;#8217; and &amp;amp;#8216;interoperable&amp;amp;#8217; and the role cloud-based services play in doing so. We will also cover our future plans.&amp;lt;/p&amp;gt;
Risks of AI Foundation Models in Education.

If the authors of a recent Stanford report (Bommasani et al., 2021) on the opportunities and risks of are to be believed, these models represent a paradigm shift for AI and for the domains in which they will supposedly be used, including education. Although the name is new (and contested (Field, 2021)), the term describes existing types of algorithmic models that are trained on broad data at and fine-tuned (i.e., adapted) for particular downstream tasks, and is intended to encompass large language models such as BERT or GPT-3 and computer vision models such as CLIP. Such technologies have the potential for harm broadly speaking (e.g., Bender et al., 2021), but their use in the educational domain is particularly fraught, despite the potential benefits for learners claimed by the authors. In section 3.3 of the Stanford report, Malik et al. argue that achieving the goal of providing education for all learners requires more efficient computational approaches that can rapidly scale across educational domains and across educational contexts, for which they argue foundation models are uniquely well-suited. However, evidence suggests that not only are foundation models not likely to achieve the stated benefits for learners, but their use may also introduce new risks for harm.
StARformer: Transformer with State-Action-Reward Representations

Reinforcement Learning (RL) can be considered as a sequence modeling task, i.e., given a sequence of past state-action-reward experiences, a model autoregressively predicts a sequence of future actions. Recently, Transformers have been successfully adopted to model this problem. In this work, we propose State-Action-Reward Transformer (StARformer), which explicitly models local causal relations to help improve action prediction in long sequences. StARformer first extracts local representations (i.e., StAR-representations) from each group of state-action-reward tokens within a very short time span. A sequence of such local representations combined with state representations, is then used to make action predictions over a long time span. Our experiments show that StARformer outperforms the state-of-the-art Transformer-based method on Atari (image) and Gym (state vector) benchmarks, in both offline-RL and imitation learning settings. StARformer is also more compliant with longer sequences of inputs compared to the baseline. Our code is available at https://github.com/elicassion/StARformer.
Diseño de un curso virtual para la adquisición de competencias digitales dirigido a docentes universitarios

The objective of this work was to design a virtual course for the acquisition of digital competences leaded to university teachers, based on the Common Framework for Teaching Digital Competence developed by the National Institute of Educational Technologies and Teacher Training. The instructional design was developed according to the ADDIE model and the selected platform to teach the course was Google Classroom. The implementation of this course showed that in adverse contexts, such as the COVID-19 pandemic, it is possible to generate innovative, accessible, and flexible learning opportunities, also supported by international frameworks. The free and open access resources available on the Internet offer high-quality tools for the design of this type of courses.
An Improved Coercion-Resistant E-Voting Scheme

E-voting has gradually replaced the traditional voting methods to make it easier for people to conduct an election. Recently, Liu et al. propose an unconditional secure e-voting scheme using secret sharing and k-anonymity. Their scheme achieves correct tallying results without revealing raw voting information. However, in this paper, we observe that Liu et al.’s scheme cannot achieve coercion resistance in e-voting since the voter can prove the content of his ballot to the colluded candidates. Then, we propose an improved e-voting scheme to cover up the ballot of the voter with masked values. In this way, even if the voter colludes with corresponding candidates, he cannot prove which candidate he has voted for. Moreover, comparing with Liu et al.’s scheme, the security analysis shows that our proposed e-voting scheme achieves these security requirements like the coercion resistance, integrity of ballots, privacy of ballots, multiple-voting detection, and fairness. Through performance analysis, the experimental results show that our proposed e-voting scheme has higher time efficiency. Compared with other schemes, our scheme achieves a complete voting process and obtains the correct tallying result without complex computation and intricate communication process.
Scaled torus principal component analysis

A particularly challenging context for dimensionality reduction is multivariate circular data, i.e., data supported on a torus. Such kind of data appears, e.g., in the analysis of various phenomena in ecology and astronomy, as well as in molecular structures. This paper introduces Scaled Torus Principal Component Analysis (ST-PCA), a novel approach to perform dimensionality reduction with toroidal data. ST-PCA finds a data-driven map from a torus to a sphere of the same dimension and a certain radius. The map is constructed with multidimensional scaling to minimize the discrepancy between pairwise geodesic distances in both spaces. ST-PCA then resorts to principal nested spheres to obtain a nested sequence of subspheres that best fits the data, which can afterwards be inverted back to the torus. Numerical experiments illustrate how ST-PCA can be used to achieve meaningful dimensionality reduction on low-dimensional torii, particularly with the purpose of clusters separation, while two data applications in astronomy (three-dimensional torus) and molecular biology (on a seven-dimensional torus) show that ST-PCA outperforms existing methods for the investigated datasets.
Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition

While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using different fine-tuning strategies. Two baseline methods, vanilla fine-tuning (V-FT) and task adaptive pretraining (TAPT) are first presented. We show that V-FT is able to outperform state-of-the-art models on the IEMOCAP dataset. TAPT, an existing NLP fine-tuning strategy, further improves the performance on SER. We also introduce a novel fine-tuning method termed P-TAPT, which modifies the TAPT objective to learn contextualized emotion representations. Experiments show that P-TAPT performs better than TAPT, especially under low-resource settings. Compared to prior works in this literature, our top-line system achieved a 7.4\% absolute improvement in unweighted accuracy (UA) over the state-of-the-art performance on IEMOCAP. Our code is publicly available.
PUBLIC ART IN EDUCATIONAL AND RESEARCH ENVIRONMENT

Combining educational environment with art has a long tradition within university museums, which are a valuable research resource, responsive to the needs of teaching, research and educational activities. At the same time, today not every school, university or research center has or can afford such museums. The article discusses innovative formats of interaction between educational institutions and artistic practices through public art — contemporary art practice, intended for the unprepared audience and implying the demonstration of art in a public, non-institutional environment. The article reveals the essence of the concept of public art, its evolution from understanding itself as an object to procedural and social practice; indicates the communicative, cultural and symbolic potential of public art in the formation of environments, communities, in the promotion of innovative ideas on the basis of universities or other educational institutions on the example of projects implemented at the National Research University Higher School of Economics, Moscow International University, Skolkovo Innovation Center. The conclusions of the article are the practical recommendations for creating public art projects on the territory of modern educational and research centers, based on communicative, site-specific, socially engaged approach.
Noisy Annotation Refinement for Object Detection

Supervised training of object detectors requires well-annotated large-scale datasets, whose production is costly. Therefore, some efforts have been made to obtain annotations in economical ways, such as cloud sourcing. However, datasets obtained by these methods tend to contain noisy annotations such as inaccurate bounding boxes and incorrect class labels. In this study, we propose a new problem setting of training object detectors on datasets with entangled noises of annotations of class labels and bounding boxes. Our proposed method efficiently decouples the entangled noises, corrects the noisy annotations, and subsequently trains the detector using the corrected annotations. We verified the effectiveness of our proposed method and compared it with the baseline on noisy datasets with different noise levels. The experimental results show that our proposed method significantly outperforms the baseline.
Competence Analysis of Teaching and Teaching Activity as a Reflection on the Quality of Higher Education

The competence approach and innovations in education are one of the levers for the development of quality in academic education. The Strategy for Development of Higher Education 2021 – 2030 (draft) sets out a number of priority areas and goals for the development of higher education. In the present study, these objectives are operationalized through a competency analysis of updated curricula of direction 1.2. “Pedagogy”. The study specifies a number of concepts and scientific statements at the meta-level, so as to outline the operational scientific structure of the competency approach. A qualitative criterion analysis of the teaching activity is made in order to outline the leading approaches in academic teaching, types of competencies set in the curricula, effective methods of teaching and learning.
Building the Integrity of Legislative Members Through Low-Cost Election Process

AbstractLegislative candidates currently face large costs due to direct legislative election.Therefore, only candidates with strong finances have the opportunity to nominate.The situation causes the appearance of less qualified candidates.Instead of building knowledge, ability, and integrity, candidates only focus on building their financial strength.Legislative election violations may occur due to intentional elements or due to negligence.Anyone can commit election violations.Everyone has the potential to become perpetrators of election violations.As an anticipation, Law number 7 of 2017 on General Elections has regulated the election at every stage in the form of obligations and prohibitions with additional sanctions.To date, the legal sanctions are only about removal of names from nominations.
Joint Channel and Weight Pruning for Model Acceleration on Moblie Devices

For practical deep neural network design on mobile devices, it is essential to consider the constraints incurred by the computational resources and the inference latency in various applications. Among deep network acceleration related approaches, pruning is a widely adopted practice to balance the computational resource consumption and the accuracy, where unimportant connections can be removed either channel-wisely or randomly with a minimal impact on model accuracy. The channel pruning instantly results in a significant latency reduction, while the random weight pruning is more flexible to balance the latency and accuracy. In this paper, we present a unified framework with Joint Channel pruning and Weight pruning (JCW), and achieves a better Pareto-frontier between the latency and accuracy than previous model compression approaches. To fully optimize the trade-off between the latency and accuracy, we develop a tailored multi-objective evolutionary algorithm in the JCW framework, which enables one single search to obtain the optimal candidate architectures for various deployment requirements. Extensive experiments demonstrate that the JCW achieves a better trade-off between the latency and accuracy against various state-of-the-art pruning methods on the ImageNet classification dataset. Our codes are available at https://github.com/jcw-anonymous/JCW.
Managing Operational Risk Using Bayesian Networks: A Practical Approach for the Risk Manager

This paper provides a practical approach to construct and learn a Bayesian network model that will enable an operational risk manager communicate actionable operational risk information for informed decision making by senior managers. Bayesian networks and their application in operational risk management has been widely studied; however, literature and research has predominantly focused on their application in modeling and measuring operational risk for capital calculation purposes. We detail the approach to construct and learn a BN model, from an incident database, using the machine learning capabilities in the R package bnlearn. The modeling and the inference capabilities of the Bayesian Networks can be applied to business-as-usual risk management techniques such as loss analysis, scenario analysis, risk assessment, development of key risk indicators, and risk reporting.
Social pedagogy in Kazakhstan: diagnosis of research competence to explore future counselors

The aim ‎of the study was to distinguish the ways and criteria to diagnose the research competence levels of ‎future counselors in the specialty “Social ‎pedagogy and self-knowledge” and to describe diagnostic tools in ‎accordance with each criterion. The research engaged 103 students acquiring master’s degree in social pedagogy at two Kazakh universities. The participants’ research competence was gauged by 4 criteria and assessed by the “Motives definition of the teaching students of higher educational institutions” test, “The attitude to the research competence of future social workers” test; “Significant attitude to research” test, methods of self-assessment of readiness for professional activity, “Intellectual lability” test, “Assessment of the level of development of research skills of students” test, Common European Framework of Reference for English Language, and the “Diagnostics of the reflexive development level” test. The usefulness of diagnostic tools was approbated for assessment of students’ competence in other pedagogical specialties‎.