Reviewerly/exHarmony huggingface dataset:
Interdisciplinary problems of mathematical content as a means of teaching programming to secondary school pupils

Abstract The article discusses the main provisions of a practice-oriented approach to teaching programming in a school computer science course. The practice-oriented approach is implemented using a system of practice-oriented problems of mathematical content. The article reveals the classification of practice-oriented problems. Particular attention is paid to Interdisciplinary problems of mathematical content. The article describes the methodology, the main stages and the results of the experiment, during which the effectiveness of the system of practice-oriented problems of mathematical content in the process of formation of pupils’ ability to solve programming problems was tested. Using the methods of mathematical statistics, it was proved that the system of practice-oriented problems of mathematical content is more effective than the system of problems given in school textbooks on computer science. During the experiment, the influence of the system of practice-oriented tasks on the formation of pupils’ ability to solve programming problems was also investigated. It was proved that the use of the system of practice-oriented programming problems more effectively affects the formation of the ability to solve programming problems. For pupils of the experimental group, the differences between the initial level of formation of the ability to solve programming problems and the final level were significant, while for pupils of the control group, they were insignificant. The results of the experiment confirm the positive influence of the system of practice-oriented problems of mathematical content on the formation of pupils’ ability to solve programming problems.
Entanglement dynamics of non-linearly coupled spin-boson model using hierarchical equation of motion approach

Abstract One of the most intensively studied problems in the field of open quantum systems is the spin-boson model. In this paper, we consider an extension of the spin-boson model with non-linear coupling. We solve the entanglement dynamics of two independent spins quadratically coupled to a bath of harmonic oscillators, using numerically ”exact” approach of hierarchical equations of motion (HEOM). Without Markovian approximation, generalized influence functional formulation by Hu et.al is used to describe the environmental effect of noisy quadratic bath in high-temperature regime. Wotters concurrence is then applied to numerically solved reduced density matrix to visualize the entanglement dynamics. By adjusting the environmental parameters, we vary the degree of non-markovianity and found the elongated time of entanglement.
Face Attribute Analysis Method Based on Self-Supervised Siamese Network

Face attribute analysis, which is a challenging and popular task in the vision field, has been widely used in various fields including intelligent security, human-computer interaction, and targeted promotion. However, in practical applications, people are not always facing the camera, and their head postures would affect the accuracy of attribute prediction. In addition, some attributes are inherently more difficult to predict due to the image noise, motion blur, and light variation. To improve the accuracy of face attribute analysis, a self-supervised Siamese multi-task convolutional neural network (SS-MCNN) is proposed in this paper. First, a Siamese network model is built for multi-task joint training. Second, a sparse self-supervised loss function is designed to learn the common features of Siamese contrastive data. Finally, the proposed SS-MCNN improves the performance on 40 face attributes analysis with an average accuracy of 91.42% on CelebA dataset. For age estimation task, the model also achieves good results with a mean absolute error (MAE) of 3.29 and 3.10 on the AFAD and MORPH II test sets.
Building robust morphing attacks for face recognition systems

In this paper, a method to build a robust morphing attack to a face verification system will be presented. The proposed method has been developed to investigate the robustness and the impact of morphing attacks to face recognition systems. In this kind of attack, an impostor accesses a face recognition system (FRS) which compares its real-time image with a stored morphed image, built with the impostor and a legitimate user. The attack succeeds when the FRS accepts the impostor and accesses the system. The current approach offers a method to build a robust attack to the FRS, in the sense that the morphed image will be closest to the decision threshold. Morphing attacks are usually evaluated only with images in which both subjects contributed in the same way to the morphing images. The image database considered, FRA V Database, was made up of 200 images. Likewise, two stages were carried out. The first stage was designed to build a baseline reference: an FRS system (trained only with legitimate users) was tested with morphed images. One contribution of this paper is that this test, which usually only considers a 50% fusion between two images has been enriched and some fusion contributions has been considered. Tests have been conducted with 20%, 40%, 50%, 60% and 80% contribution of each image to the morphed image. The comparison of the equal error rate (EER) achieved will show which contribution defines the best plausible attack. Notice that the attack that achieves the best rates with minimum disturbance of the images. The second stage consisted of the reinforcement of the FRS, training it with the contribution set defined in the previous stage. The outcomes obtained achieved improved by 3% of the EER scores.
Designing a Digital Educational Service Based on Didactic Tasks

The development of software products for the education sector is characterized by specific results requirements. Digital services for education are subject to special requirements both from society and legislation, and from participants in educational relations. Introduction of new digital technologies in the educational process should not be the aim itself. The use of digital educational resources and services should contribute to the achievement of didactic and educational tasks. When designing educational software products, it is, first of all, necessary to consider the didactic tasks that the teacher pursues in his/her work. This article proposes an approach to the development of an educational web service based on the selection of software solutions for the didactic tasks to be solved.
GRL goal analysis using zero-sum game theory

In Requirements Engineering (RE), goal models are used to represent stakeholder objectives, which are also known as system requirements or system goals. Stakeholder requirements are of two types: functional requirements and non-functional requirements. Goal models are analysed to find out suitable functional requirements amongst ensemble of all functional requirements. RE literature has addressed both qualitative and quantitative methods for performing goal analysis. Recently, operation research techniques have been used for performing optimal goal analysis. The existing optimisation approaches focus on maximising objective functions. But real world problems involve optimisation of both maximising and minimising objective functions simultaneously. In this study, a game theory based approach is used for solving simultaneous optimisation of both maximum and minimum objective functions in goal models. The proposed approach is applied to Goal-Oriented Requirement Language (GRL) framework which is perceived as a standard for goal-modelling. The practicality of the proposed approach was assessed by running the case studies in a simulated environment using JAVA Eclipse combined with IBM Cplex tool. The results showed that the proposed approach aids in the analysis of goals in goal models with opposing objective functions.
Few-Shot Learning in Object Classification using Meta-Learning with Between-Class Attribute Transfer

We present a novel framework for the problem of transfer learning between few-shot source and target domains, using synthetic attributes in addition to convolutional neural networks that are pre-trained on larger image corpora. In these corpora, no labeled instances of the target domains are present, though they may contain instances of their superclasses. Using probabilistic inference over predicted classes and inferred attributes, we developed a meta-learning ensemble method that builds upon that of [10]. This paper introduces the new framework BCAT (Between-Class Attribute Transfer), adapting inter-class attribute transfer designed for zero-shot learning (ZSL), combined with fusing transfer learning and probabilistic priors, and thereby extending and improving upon existing deep meta-learning models for FSL. We show how probabilistic learning architectures can be adapted to use state-of-the-field deep learning components in this framework. We applied our technique to four baseline convnet-based FSL ensembles and boosted accuracy by up to 6.24% for 1-shot learning and up to 4.11% for 5-shot learning on the mini-ImageNet dataset, the best result of which is competitive with the current state of the field; using the same technique, we improved accuracy by up to 7.83% for 1-shot learning and up to 3.67% for 5-shot learning on the tiered-ImageNet dataset.
Technology Strategy for Meticulous Governance of Smart Cities: Memory Bus Width Aware Pruning for Efficient Image Super-Resolution Implementation in FPGA

Abstract The mega-city governance is based on the aggregation, collation, development and application of multi-scene data, and efficient access to scene data is a key link to promote the meticulous governance of smart cities. Super-resolution technique is the process of upscaling and improving the details within an image. In this paper, we implement a 16-layer residual neural network (ResNet) for the efficient image super-resolution in FPGA. We discover that the memory access is the major performance bottleneck of this implementation. To reduce the memory access overhead, we design a pruning algorithm with the consideration of the memory bus width. Since the memory used in our design generates 256 bits for each access, the proposed pruning algorithm drops the kernel by aligning this bit width. That is, all kernels in one layer are ranked by its L1-norm and we drop the kernels out of the 256 bits. The experimental results show that the proposed method reduces the number of weights by 50% compared with the baseline. As a result, the inference speed can be enhanced by 3 times.
Swarm Intelligence Approach for Feature Selection Problem

Classification problems have been part of numerous real-life applications in fields of security, medicine, agriculture, and more. Due to the wide range of applications, there is a constant need for more accurate and efficient methods. Besides more efficient and better classification algorithms, the optimal feature set is a significant factor for better classification accuracy. In general, more features can better describe instances, but besides showing differences between instances of different classes, it can also capture many similarities that lead to wrong classification. Determining the optimal feature set can be considered a hard optimization problem for which different metaheuristics, like swarm intelligence algorithms can be used. In this paper, we propose an adaptation of hybridized swarm intelligence (SI) algorithm for feature selection problem. To test the quality of the proposed method, classification was done by k-means algorithm and it was tested on 17 benchmark datasets from the UCI repository. The results are compared to similar approaches from the literature where SI algorithms were used for feature selection, which proves the quality of the proposed hybridized SI method. The proposed method achieved better classification accuracy for 16 datasets. Higher classification accuracy was achieved while simultaneously reducing the number of used features.
AVES: A data-driven approach for airman certification

Airman certification training relies on post-flight debriefing conducted between flight instructor and student, which is a review of the flight using observations from hand-written notes and the collective memory of the two persons on board. Data-driven visualizations have yet to be fully incorporated into the flight training process. The Airman Visual Evaluation System (AVES) applies a data-driven approach to support airman certification training by processing data recorded from common avionics systems such as the Garmin G1000. Maneuvers, e.g. steep turns, are decomposed into a hierarchical definition including task, sub-task, and related components according to available data. Each level in the hierarchy defines a related event detectable within the data. At the top-level of the hierarchy are points where an instructor may wish to review with a student while lower levels of the hierarchy support the review with additional information. Performance measurements are associated with each event to be captured from the data. Visual analytics approaches are applied to convey the events and related performance information including 2D and 3D views of flight replay, event timelines, performance related visualizations. The visualizations are provided in an interactive application to convey the information. User studies are conducted to identify and measure value within airman certification programs.
Reparameterizing Residual Unit for Real-time Maritime Low-light image Enhancement

Video surveillance is critical in the maritime industry. However, the inescapable low-light situation places a limitation on video surveillance advancement. At the same time, the high precision of deep learning brings high computational and memory requirements to its training and inference stages. However, high precision and high resource consumption are the characteristics of deep learning. To more effectively deploy the learning-based low-light enhancement method on the terminal device, we adopted the reparameterization technology in the enhancer model to reduce the number of additional calculations (named RepMConv). Specifically, we use linear combinations of inconsistent kernel sizes in the training phase and fold them back to normal convolutions in the inference phase. Convolution kernels with different sizes can effectively extract enhancer’s significant edge and texture information by providing different receptive fields. We first embed RepMConv into the residual block to improve the learning efficiency of the residual block. Finally, we complete our enhancer network in a multi-scale structure of encoder-decoder. Experimental results show that our proposed Enhancer can achieve high-quality maritime low-light image enhancement while maintaining breakneck inference speed.
Telecommunication Distance Education Systems: New Perspectives

the main issues of creation and operation of the telecommunications regional network of the Russian State University for the Humanities are considered. The main components of the system that ensure the planning, management and functioning of a higher educational institution in modern conditions are highlighted. The functions of the system, its information and technical support based on the use of the Internet and satellite channels are determined. The directions of modernization of the considered telecommunications regional system are proposed, ensuring its development and effective use in emergencies: sanctions, pandemics, etc.
Using the Arduino Platform to Organize Project Activities in Technoparks

The paper discusses the possibilities of using the Arduino microprocessor platform in educational institutions for the organization of project activities. The main functions, properties and capabilities of the Arduino platform are described. The directions of using microprocessor platforms in educational technoparks are determined. Examples of projects implemented on the basis of Arduino in the VGSPU technopark are given.
SiamAVOT: A High-performance Siamese Network Tracker for Aerial Visual Object Tracking

Abstract Aerial vision object tracking technology plays an increasingly critical part in intelligent perception and task cognition when aircraft perform various reconnaissance missions. However, the particularity in object types and visual angles in aerial vision imagery render the tracking process more prone to deformation, scale variation, motion blur and occlusion, making this technique very challenging. A high-performance Siamese network tracker for aerial visual object tracking, SiamAVOT, is proposed to deal with this problem in this paper. First, we utilize the SiamCAR infrastructure to introduce the Bottleneck Attention Module (BAM) attention mechanism after its backbone network for fusion of low and high level features to improve recognition for deformation object features. Then, we change to Distance-IoU (DIoU) loss for the bounding box regression during the training process to improve the network’s ability to predict scale variation. Finally, a Kalman filter online learning module that integrates time and space trajectory information is designed to solve the object motion blur and occlusion disappearance problem in the inference process. The proposed SiamAVOT achieves leading performance on the UAV123 and AVOT40 aerial datasets and can run in real-time at 72 FPS.
A transformer-based deep learning model for evaluation of accessibility of image descriptions

Images have become an integral part of digital and online media and they are used for creative expression and dissemination of knowledge. To address image accessibility challenges to the visually impaired community, adequate textual image descriptions or captions are provided, which can be read through screen readers. These descriptions could be either human-authored or software-generated. It is found that most of the image descriptions provided tend to be generic, inadequate, and often unreliable making them inaccessible. There are tools, methods, and metrics used to evaluate the quality of the generated text, but almost all of them are word-similarity-based and generic. There are standard guidelines such as NCAM image accessibility guidelines to help write accessible image descriptions. However, web content developers and authors do not seem to use them much, possibly due to the lack of knowledge, undermining the importance of accessibility coupled with complexity and difficulty understanding the guidelines. To our knowledge, none of the quality evaluation techniques take into account accessibility aspects. To address this, a deep learning model based on the transformer, a most recent and most effective architecture used in natural language processing, which measures compliance of the given image description to ten NCAM guidelines, is proposed. The experimental results confirm the effectiveness of the proposed model. This work could contribute to the growing research towards accessible images not only on the web but also on all digital devices.
Neither Naked Nor Afraid: Survivalist Misunderstandings of Thoreau’s <i>Walden</i>

The Journal of Popular CultureEarly View Original Article Neither Naked Nor Afraid: Survivalist Misunderstandings of Thoreau’s Walden Kate Huber, Corresponding Author Kate Huber khuber1@uco.edu Search for more papers by this author Kate Huber, Corresponding Author Kate Huber khuber1@uco.edu Search for more papers by this author First published: 20 June 2022 https://doi.org/10.1111/jpcu.13138Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Faster R-CNN with Generative Adversarial Occlusion Network for Object Detection

Abstract: Performing object detection on partially occluded objects is a challenging task due to the amount of variation in location, scale, and ratio present in real-world occlusion. A typical solution to this problem is to provide a large enough dataset with ample occluded samples for feature learning. However, this is rather costly given the amount of time and effort involved in the data collection process. In addition, even with such a dataset, there is no guarantee that it covers all possible cases of common occlusion in the real world. In this paper, we propose an alternate approach that utilizes the power of adversarial learning to reinforce the training of common object detection models. More specifically, we propose a Generative Adversarial Occlusion Network (GAON) capable of generating partially shaded training samples that are challenging for the object detector to classify. We demonstrate the efficacy of such an approach by conducting experiments on the Faster R-CNN detector, and the results indicate the superiority of our approach in improving the model's performance on occluded inputs.
Some Applications of the Training Programs on the Transport Task

Training programs can play an important role in the learning process. They provide support for the learning process on a par with traditional teaching aids. The article discusses training programs for solving transport tasks by the cost criterion and by the time criterion. The possibilities of programs for independent work of students and carrying out control activities are described. The results of the statistical analysis of the using the training program to prepare for the performance of the control task are presented.
Behavior testing of load forecasting models using BuildChecks

In recent years, machine learning (ML) models have been widely developed for building systems. For example, a number of ML models have been developed to predict the load demand of a building. Current ML models commonly report snap-shot accuracy only. Practitioners have difficulties in understanding how a model behaves in usage, i.e., model accuracy may change during model usage. This raises concerns in the ML-model deployment.