Reviewerly/exHarmony huggingface dataset:
Pandemic Lessons: Story of Cooperation and Competition in Russian Education

Abstract This chapter examines how main actors such as policymakers, school teams, and Edtech companies faced the pandemic challenges and whether they cooperated with each other. The analysis demonstrates that while before COVID-19, Russian schools and Edtechs rarely cooperated with each other, the partnership developed in response to the necessity of an emergency transition to distance learning. The government attempted to establish a nationwide infrastructure for distance learning and the vetting of educational content during the initial stages of the pandemic, however, this strategy was not implemented. Since the government did not immediately react to the situation, schools were forced to cope with the transition themselves. EdTech helped students, teachers, and regions deal with the crisis. After the pandemic, EdTech companies found themselves in a situation of increased government regulation, to which they reacted differently: some companies preferred to focus on B2C formats, while others responded with investments in the B2G sector. The school-Edtech partnership might be one of the most far-reaching positive changes of the pandemic for education, but our analysis shows this lesson has rather not been learned.
Deep learning-enabled computational microscopy and diffractive imaging

In this talk, I will discuss diffractive optical networks designed by deep learning to all-optically implement various complex functions as the input light diffracts through spatially-engineered surfaces. These diffractive processors designed by deep learning have various applications, e.g., all-optical image analysis, feature detection, object classification, computational imaging and seeing through diffusers, also enabling task-specific camera designs and new optical components for spatial, spectral and temporal beam shaping and spatially-controlled wavelength division multiplexing. These deep learning-designed diffractive systems can broadly impact (1) all-optical statistical inference engines, (2) computational camera and microscope designs and (3) inverse design of optical systems that are task-specific. In this talk, I will give examples of each group, enabling transformative capabilities for various applications of interest, e.g., autonomous systems, defense/security, telecommunications as well as biomedical imaging and sensing.
Reduction of influence caused by mirror tilting in phase measuring deflectometry

In the conventional deflectometry, since the observation target is a virtual image generated by the mirror, measuring is highly sensitive to the titling angle of mirror. That means it is hard to use this method to measure the displacement. Here, we proposed a new type of deflectometry method, in which the observation target is the surface of the observed mirror. Using an imaging lens, the measurement fringes are projected on the surface of mirror. The result show that the proposed method is lower sensitive to the titling angle of the mirror compared to the conventional deflectometry.
Pengaruh Model Pembelajaran Kooperatif Tipe Two Stay Two Stray (TS-TS) Terhadap Keaktifan Belajar Pendidikan Agama Kristen Siswa Kelas VIII SMP Negeri 4 Tarutung Tahun Pembelajaran 2023/2024

The purpose of this study is to determine the positive and significant influence of the use of the Two Stay Two Stray (TS-TS) type cooperative learning model on the learning activity of PAK students of grade VIII SMP Negeri 4 Tarutung Learning Year 2023/2024. The research method used is quantitative research method. The population is all grade VIII students of SMP Negeri 4 Tarutung for the 2023/2024 learning year totaling 125 people and a sample of 62 people was determined using random sampling techniques. Data was collected using a positive closed questionnaire of 34 items, namely 18 items for variable X and 16 items for variable Y. The results of data analysis showed that there was a positive and significant influence on the use of the Two Stay Two Stray (TS-TS) type cooperative learning model on the learning activity of PAK students of grade VIII SMP Negeri 4 Tarutung Learning Year 2023/2024: 1) Test analysis requirements: a) positive relationship test obtained rxy value = 0.624 &gt; rtable(a=0,05,n=62) = 0,254. ­b) Test the relationship of significant obtained tcount values &gt; ttabel(a=0,05,dk=n-2=60)= 2,000. 2) Test the influence: a) Test the regression equation, obtained regression equation. b) Regression coefficient of determination test (r2) = 38,9%. 3) Test the hypothesis using the F test obtained Fcalculate &gt; Ftable=(a=0.05,dk numerator k=20,dk denominator=n-2=62-2=60) which is 38.16 &gt; 1.39. Thus, Ha is accepted and H0 is rejected.
Applying Human-in-the-Loop to construct a dataset for determining content reliability to combat fake news

Annotated corpora are indispensable tools to train computational models in Natural Language Processing. However, in the case of more complex semantic annotation processes, it is a costly, arduous, and time-consuming task, resulting in a shortage of resources to train Machine Learning and Deep Learning algorithms. In consideration, this work proposes a methodology, based on the human-in-the-loop paradigm, for semi-automatic annotation of complex tasks. This methodology is applied in the construction of a reliability dataset of Spanish news so as to combat disinformation and fake news. We obtain a high quality resource by implementing the proposed methodology for semi-automatic annotation, increasing annotator efficacy and speed, with fewer examples. The methodology consists of three incremental phases and results in the construction of the RUN dataset. The annotation quality of the resource was evaluated through time-reduction (annotation time reduction of almost 64% with respect to the fully manual annotation), annotation quality (measuring consistency of annotation and inter-annotator agreement), and performance by training a model with RUN semi-automatic dataset (Accuracy 95% F1 95%), validating the suitability of the proposal.
A Contrastive Learning Network for Performance Metric and Assessment of Physical Rehabilitation Exercises

Human activity analysis in the legal monitoring environment plays an important role in the physical rehabilitation field, as it helps patients with physical injuries improve their postoperative conditions and reduce their medical costs. Recently, several deep learning-based action quality assessment (AQA) frameworks have been proposed to evaluate physical rehabilitation exercises. However, most of them treat this problem as a simple regression task, which requires both the action instance and its score label as input. This approach is limited by the fact that the annotations in this field usually consist of healthy or unhealthy labels rather than quality scores provided by professional physicians. Additionally, most of these methods cannot provide informative feedback on a patient's motion defects, which weakens their practical application. To address these problems, we propose a multi-task contrastive learning framework to learn subtle and critical differences from skeleton sequences to deal with the performance metric and AQA problems of physical rehabilitation exercises. Specifically, we propose a performance metric network that takes triplets of training samples as input for score generation. For the AQA task, the same contrast learning strategy is used, but pairwise training samples are fed into the action quality assessment network for score prediction. Notably, we propose quantifying the deviation of the joint attention matrix between different skeleton sequences and introducing it into the loss function of our learning network. It is proven that considering both score prediction loss and joint attention deviation loss improves physical exercises AQA performance. Furthermore, it helps to obtain informative feedback for patients to improve their motion defects by visualizing the joint attention matrix's difference. The proposed method is verified on the UI-PRMD and KIMORE datasets. Experimental results show that the proposed method achieves state-of-the-art performance.
Indexing Structures for the Efficient Multi-Resolution Visualization of Big Graphs

Nowadays there is a great interest in the visualization of graphs for making easier their navigation, inspection and visual analysis. However, graphs can be quite large and their rendering on web browsers can lead to a dark cloud of points that is difficult to visually explore. Even if several approaches have been proposed for substituting clusters of related vertices with aggregated meta-nodes for reducing the size of the visualized graph, they usually consider the graph in main memory and do not adopt efficient data structures for extracting parts of it from disk. In this paper, we propose indexing structures and algorithms for the representation of the graph at different resolutions and for making faster the passage from a representation to another. An extensive experimental analysis has been conducted to assess the quality of the proposed solution.
Augmentation Techniques for Adult-Speech to Generate Child-Like Speech Data Samples at Scale

Technologies such as Text-To-Speech (TTS) synthesis and Automatic Speech Recognition (ASR) have become important in providing speech-based Artificial Intelligence (AI) solutions in today’s AI-centric technology sector. Most current research work and solutions focus largely on adult speech compared to child speech. The main reason for this disparity can be linked to the limited availability of children’s speech datasets that can be used in training modern speech AI systems. In this paper, we propose and validate a speech augmentation pipeline to transform existing adult speech datasets into synthetic child-like speech. We use a publicly available phase vocoder-based toolbox for manipulating sound files to tune the pitch and duration of the adult speech utterances making them sound more child-like. Both objective and subjective evaluations are performed on the resulting synthetic child utterances produced to validate that adult speech samples are successfully tuned. For the objective evaluation, the similarities of the selected top adults’ speaker embeddings are compared before and after the augmentation to a mean child speaker embedding. The average adult voice is shown to have a cosine similarity of approximately 0.87 (87%) relative to the mean child voice after augmentation, compared to a similarity of approximately 0.74 (74%) before augmentations. Mean Opinion Score (MOS) tests were also conducted for the subjective evaluation, with average MOS scores of 3.7 for how convincing the samples are as child-speech and 4.6 for how intelligible the speech is, for the best set of augmentation parameters. Finally, ASR models trained with the augmented speech are tested against a baseline set of ASR experiments showing some modest improvements over the baseline model trained (i.e., finetuned) with only adult speech.
SA$^{2}$Net: Ship Augmented Attention Network for Ship Recognition in SAR Images

Maritime surveillance is extensively concerned by worldwide authorities, in which ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental component. Though some development has been achieved in the SAR ship recognition task, two areas remain inadequately explored, which are the comprehensive utilization of multi-scale features and the deployment of the prior knowledge of the ship shape. In this paper, a novel ship augmented attention network (SA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net) for ship recognition is proposed, which comprehensively utilizes the multi-scale features and integrates the ship shape prior to the end-to-end network. On one hand, due to the unequal effects of different scales, a scale attention module (SAM) is proposed to adaptively select and assign weights to desired feature scales while disregarding irrelevant scales. Moreover, a feature weaving module (FWM) is constructed to merge semantic and detailed features produced by the high-to-low backbone, enriching representations across all scales of ship targets. On the other hand, in order to incorporate the priory knowledge of the ship shape into the network, we develop a feature augmentation module (FAM) to further boost the ship recognition accuracy. This module can provide rectangular receptive fields that align with the shape of ships, wherein a limitation encountered with traditional square convolutions. Comprehensive experiments on representative three- and six-category OpenSARShip tasks and seven-category FUSAR-Ship tasks show that our SA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Net demonstrates superior performance when compared to the current state-of-the-art methods.
RGB-Depth Structure Similarity for Self-supervised Monocular Depth Estimation

Monocular depth estimation is a fundamental technique for robots to perceive the real (unseen) scene. Supervised methods rely on large-scale datasets with groundtruth (GT) depth labels, which cannot be well generalized to other scenes. A dominant solution is to directly train the model on target scenes in self-supervised way with pseudo depth labels (e.g. generated by stereo matching). However, pseudo depth labels are often unreliable especially near object boundaries. It may disturb the training of the model and consequently decrease the depth quality in the inference. In this paper, we investigate the structure similarity of RGB-Depth based on Gaussian kernels, because the structure of RGB image is always reliable. Such RGB-Depth structure similarity measurement is then used to improve the self-supervised depth estimation in two aspects. It is first utilized to measure the confidence of pseudo depth labels and filter unreliable pixels. It is then utilized to limit the structure of predicted depth maps in the loss. Experiments on the KITTI Eigen Splits datasets verify that the proposed method achieves better or comparable quantitative results and always achieves better visual results with clear depth boundaries compared with five recent baselines.
An Image Enhancement Method for Gas Leak Detection Based on Infrared Imaging <sup>*</sup>

Infrared imaging technology is a useful tool for detecting gas leaks due to its significant advantages in detection spacial range, efficiency, and visualization. However, the original infrared images of gas leak traces often suffer from low contrast due to a small thermal radiation intensity difference between the gas plume and the scene. So it is difficult to directly separate them in the grayscale histogram distribution. Traditional enhancement methods lack targeted optimizations for traces of leaking gas. Therefore, enhancing the trace of gas leaks is crucial to the performance of gas leak detection systems based on infrared imaging. In this paper, we propose an image enhancement method based on guided filtering, the visibility restoration algorithm. The proposed method comprises three stages: image decomposition, base and detail layer enhancement, and image fusion output. We selected three infrared images of gas leakage taken in different scenes for experiments. There are obvious differences in the original images taken from the three scenes. We compare the image enhancement result of Histogram Equalization(HE), Contrast Limited Adaptive Histogram Equalization (CLAHE) and our proposed method. The image enhanced by our proposed has the highest values of quantitative indicators: average gradient(AG) and Enhancement Measure Evaluation(EME); which are three times higher than the original image. By visual observation, the gas leak plume in the images enhanced by our proposed method are obvious significantly.
Digital IC Functionality Duplication using Neural Networks

In this paper, we propose a method for duplicating digital circuits using neural networks (NNs). Our approach involves training a common neural network model on the inputs and outputs of existing digital circuits, creating a digital twin. This digital twin can be used to predict the outputs of a digital I'C, only using its inputs and this has been verified using counter circuits, and shift register circuits. Moreover, we have successfully validated the functionality duplication of digital logic in a robotic arm equipped with servo motors, further validating the utility of our approach.
PICO: An Approach to Build Dataset for Customer Service Chatbot

Data resource building is one of the main parts in training deep learning-based chatbot. To address this, it is a time-consuming and expensive task, especially in specific domains such as customer service. In this paper, we propose a method to build dataset for customer service chatbot in the mobile phone domain. We applied a "light annotation" and extended it adapting to online service by continuous operations. And we call the approach as PICO (Planning - Implementation - Continuous Operations). Actual use indicates our approach can reduce costs and secure the quality. The rate and trend of offline evaluation results with the datasets are very close to online sampling analysis results, which further indicates the built datasets are well representative and reliable. We believe the approach can be extended to other consumer service domains.
Quantum Computing Application Opportunities in Military Scenarios

Quantum Information Science (QIS) is potentially a game-changing technology for the future military operational environment. Being significantly dependent on the capabilities of current quantum computers, it is still very immature; however, evolving rapidly and increasing the technical and knowledge potential of industrial and state actor players that invest in it. Investing in this technology can lead the NATO Alliance to gain quantum superiority and outperform its adversaries in technological development in diverse military domains. This article presents possible applications of QIS in the military domain in the form of use cases. We have formulated these use cases after analysis of today's capabilities of quantum computing technology and its prospects for the future of the quantum information domain. Our article concludes with initial recommendations for the early adoption of quantum technology. This paper was presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY - the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.
A Data Mesh Approach for Enabling Data-Centric Applications at the Tactical Edge

Effectively managing, sharing, and analyzing large volumes of data in real time is essential for making informed decisions, predicting potential threats, and adapting to changes on the battlefield. It therefore represents a critical capability for military operations. However, implementing effective analytics at the tactical edge requires to address the challenges of Denied, Degraded, Intermittent, and Limited (DDIL) networks, particularly in terms of bandwidth and processing capability constraints. Additionally, implementing such a system presents other challenges such as ensuring the security, trustworthiness, integrity, and privacy of data in motion and at rest. The accurate analysis of the vast amounts of data generated at the tactical edge requires a dedicated IT infrastructure shareable designed to operate in an unpredictable and changing environment while still ensuring the availability and reliability of the data. To address these challenges, we propose a middleware architecture based on a data mesh approach, which is designed to adapt to the demands of modern tactical networks by providing a secure and efficient data-centric storage solution for (big) data. Our proposed middleware is based on a distributed domain-driven approach to serve “data as a product”, facilitating data management and analysis, and providing a flexible and robust solution for developing data-driven services. “This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200RSY — the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023”
Detection of Previously Unknown Advanced Persistent Threats Through Visual Analytics with the MASFAD Framework

With the rapid evolution of the Internet and the prevalence of sophisticated adversarial cyber threats, it has become apparent that an equally rapid development of new Situation Awareness techniques is needed. The vast amount of data produced everyday by Intrusion Detection Systems, Firewalls, Honeypots and other systems can quickly become insurmountable to analyze by the domain experts. To enhance the human - machine interaction, new Visual Analytics systems need to be implemented and tested, bridging the gap between the detection of possible malicious activity, identifying it and taking the necessary measures to stop its propagation. The detection of previously unknown, highly sophisticated Advanced Persistent Threats (APT) adds a higher degree of complexity to this task. In this paper, we discuss the principles inherent to Visual Analytics and propose a new technique for the detection of APT attacks through the use of anomaly and behavior-based analysis. Our ultimate goal is to define sophisticated cyber threats by their defining characteristics and combining those to construct a pattern of behavior, which can be presented in visual form to be explored and analyzed. This can be achieved through the use of our Multi-Agent System for Advanced Persistent Threat Detection (MASFAD) framework and the combination of highly-detailed and dynamic visualization techniques. This paper was originally presented at the NATO Science and Technology Organization Symposium (ICMCIS) organized by the Information Systems Technology (IST) Panel, IST-200 RSY - the ICMCIS, held in Skopje, North Macedonia, 16–17 May 2023.
Transfer Learning Based Multi-Perception Safety Strategy for Human-Robot Collaboration

Human-robot collaboration (HRC) is vital to adapt to the significant change in manufacturers’ demands for automation, and safety is a primary and major challenge that must be addressed for it. In this paper, a muti-perception safety strategy and framework are introduced to ensure human safety while trying to avoid reducing the work efficiency of the robot by taking human activity intentions and human-robot distance into account. To realize the safety strategy, an LSTM-CNN based neural network is built for human activity classification. To improve the generalization ability and performance of the network with data scarcity for existing deep learning-based human activity recognition methods, transfer learning-enabled activity recognition is proposed. Based on the studies, a feasible security system is implemented in the human-robot collaboration scenario.