Reviewerly/exHarmony huggingface dataset:
Alignment Integration Network for Salient Object Detection and Its Application for Optical Remote Sensing Images

Salient object detection has made substantial progress due to the exploitation of multi-level convolutional features. The key point is how to combine these convolutional features effectively and efficiently. Due to the step by step down-sampling operations in almost all CNNs, multi-level features usually have different scales. Methods based on fully convolutional networks directly apply bilinear up-sampling to low-resolution deep features and then combine them with high-resolution shallow features by addition or concatenation, which neglects the compatibility of features, resulting in misalignment problems. In this paper, to solve the problem, we propose an alignment integration network (ALNet), which aligns adjacent level features progressively to generate powerful combinations. To capture long-range dependencies for high-level integrated features as well as maintain high computational efficiency, a strip attention module (SAM) is introduced into the alignment integration procedures. Benefiting from SAM, multi-level semantics can be selectively propagated to predict precise salient objects. Furthermore, although integrating multi-level convolutional features can alleviate the blur boundary problem to a certain extent, it is still unsatisfactory for the restoration of a real object boundary. Therefore, we design a simple but effective boundary enhancement module (BEM) to guide the network focus on boundaries and other error-prone parts. Based on BEM, an attention weighted loss is proposed to boost the network to generate sharper object boundaries. Experimental results on five benchmark datasets demonstrate that the proposed method can achieve state-of-the-art performance on salient object detection. Moreover, we extend the experiments on the remote sensing datasets, and the results further prove the universality and scalability of ALNet.
Security Analysis of Imperfect Gaussian Modulation Caused by Amplitude Modulator in Continuous–Variable Quantum Key Distribution

Continuous-variable quantum key distribution (CV–QKD) is a system that provides secret keys for symmetric key systems. In the application of CV–QKD, the practical security of the system is crucial. In this article, we investigate the practical security issues caused by non–ideal Gaussian modulation, which is caused by fitting defects of the amplitude modulator’s (AM) modulation curve. We provide the effect of fitting error on parameter estimation. We also give the relationship between the fitting order and the secret key rate. The simulation results indicate that the system is completely unable to communicate during first–order fitting. During second–order fitting, the system’s performance decreases by more than half. During third–order fitting, the system’s performance will be consistent with the ideal. Therefore, to ensure the performance of the CV–QKD system, the fitting order must be at least three or higher.
Combining Classifiers for Deep Learning Mask Face Recognition

This research proposes a single network model architecture for mask face recognition using the FaceNet training method. Three pre-trained convolutional neural networks of different sizes are combined, namely InceptionResNetV2, InceptionV3, and MobileNetV2. The models are augmented by connecting an otherwise fully connected network with a SoftMax output layer. We combine triplet loss and categorical cross-entropy loss to optimize the training process. In addition, the learning rate of the optimizer is dynamically updated using the cosine annealing mechanism, which improves the convergence of the model during training. Mask face recognition (MFR) experimental results on a custom MASK600 dataset show that proposed InceptionResNetV2 and InceptionV3 use only 20 training epochs, and MobileNetV2 uses only 50 training epochs, but to achieve more than 93% accuracy than the previous works of MFR with annealing. In addition to reaching a practical level, it saves time for training models and effectively reduces energy costs.
Recommendations for Landslide Early Warning Systems in Informal Settlements Based on a Case Study in Medellín, Colombia

Fatalities from landslides are rising worldwide, especially in cities in mountainous regions, which often expand into the steep slopes surrounding them. For residents, often those living in poor neighborhoods and informal settlements, integrated landslide early warning systems (LEWS) can be a viable solution, if they are affordable and easily replicable. We developed a LEWS in Medellín, Colombia, which can be applied in such semi-urban situations. All the components of the LEWS, from hazard and risk assessment, to the monitoring system and the reaction capacity, were developed with and supported by all local stakeholders, including local authorities, agencies, NGO’s, and especially the local community, in order to build trust. It was well integrated into the social structure of the neighborhood, while still delivering precise and dense deformation and trigger measurements. A prototype was built and installed in a neighborhood in Medellín in 2022, comprising a dense network of line and point measurements and gateways. The first data from the measurement system are now available and allow us to define initial thresholds, while more data are being collected to allow for automatic early warning in the future. All the newly developed knowledge, from sensor hardware and software to installation manuals, has been compiled on a wiki-page, to facilitate replication by people in other parts of the world. According to our experience of the installation, we give recommendations for the implementation of LEWSs in similar areas, which can hopefully stimulate a lively exchange between researchers and other stakeholders who want to use, modify, and replicate our system.
Integration of approaches to employer - college interaction using information environment tools in the context of vocational training

Problem statement . The research is devoted to the interaction in the training of highly qualified personnel using the means of the information environment. The integration of employers and the college considered as an innovative guideline for the development and formation of future specialists in the real sector of the economy. The authors deal with the issue of information media tools that take into account current trends and innovations in the preparation and formation of college students. Methodology . The analysis of foreign and domestic works on this problem is made. With the help of open resources, the integration of interaction between employers and the college using the means of the information environment is considered. Results . The approaches of employer - college interaction are analyzed using the means of the information environment on the example of the Moscow region in order to increase the professional training of college graduates. Conclusion . Modern requirements in the training of specialists require the college to integrate more widely with employers, who should become interested participants in the educational process. The college has a task - to prepare professional personnel in demand in the labor market. For the successful implementation of this task, it is necessary jointly implement modern approaches in the training of future specialists.
Definition of reflection characteristics of educational process participants with artificial intelligence application

Problem statement . Artificial intelligence (AI) conversational tools like chat-bots, virtual assistants and dialog trainers begin to apply in education. However, its efficiency wasn't explored because of novelty and lack of related application experience. In this research an approach to conversations based on AI is considered as means to define reflection of educational process participants. And definition results of reflection are compared between an AI conversational tool and an expert's assessment in the educational process. Methodology . Opportunities of conversational simulations based on AI were analysed for reflection assessment. Behavioural markers of reflection in communication were developed as well as assessment procedures in online mode with AI simulations and in offline mode with an expert assessment. Research was provided as a part of the volunteer’s competition. There were 65 participants of the research, students of schools and universities. Statistical processing of the results was performed using Pearson's criteria. Results. Weak correlation was detected between AI and expert assessment. Conclusion. Suggestions were offered about AI assessment improvement for increasing assessment precision of reflection of educational process participants from the methodological point of view as well as from AI algorithms development.
Digital transformation of quality management of educational business processes

Problem statement. Educational institutions are actively moving towards digitalization processes. These processes have to be implemented not from scratch, but taking into account the databases, information systems and technologies already applied at the informatization stage and used. Therefore, issues related to the concept of digital transformation of educational organizations become relevant. The study aimed to present the general principles and approaches to the implementation of the digital transformation of educational business processes, formed on the basis of studying domestic and foreign experience, as well as on the basis of the author's experience of participating in the informatization and digitalization of educational institutions in Russia and Kazakhstan. Methodology. The methods for systematizing the approaches of domestic and foreign researchers to the definition of the concept of digital transformation, problems, experience and prospects for the digitalization of educational organizations, methods of mathematical and computer modeling, methods of development of information management systems, methods of quality management system were used. Results. The experience and principles of continuous digital transformation of the quality management of educational business processes at the Siberian State University of Geosystems and Technologies are presented. The distinctive features of digital transformation are described: documentation support for educational programs, publishing process, intermediate certification, digital footprint formation, teaching workload management for teachers, classroom fund, system of local regulations, executive discipline of employees (“Executor Calendar”). As a result of the digital transformation of the quality management of educational business processes, an increase in target indicators and an increase in the efficiency of educational processes were noted. Conclusion. Digital transformation should develop the positive results of the informatization stage. Using the principles of the process approach, standardization and documentation of processes within the quality management system simplifies the setting of the goal and objectives of digital transformation. The principles of ensuring demand and process control allow us to consider the digital transformation of process quality management as an indicative feedback control system. The tasks of digital transformation should include: continuous improvement of the activities of an educational organization, optimization of procedures for achieving its target indicators, prevention of various inconsistencies. Digitalization processes must be continuous, taking into account the constantly changing external and internal challenges and expectations of the education system and society.
Turning a CLIP modal into image-text matching

Image-text matching (ITM) benefits from Large-scale Contrastive Language-Image Pre-training (CLIP) method that achieves higher accuracy. However, the CLIP method learns by contrasting global visual and textual features, which inevitably leads to the problem of mismatching in the image-text process due to the lack of inter-modal fine-grained information. Therefore, in this work, we propose a method called Turning a CLIP Model into Image-Text Matching (CIT) that focuses on combining fine-grained information between modalities to convert the CLIP model into a more efficient ITM model. The CIT method effectively improves the image-text matching accuracy of existing CLIP model and does not require additional pre-training. We demonstrate the effectiveness of our method through experiments with a range of state-of-the-art methods on two widely used datasets.
Framework design of reconfigurable convolution neural network acceleration system for edge computing

In this paper, we propose a reconfigurable framework optimized for resource-constrained platforms to accelerate CNNs using the high concurrency and data-proximate characteristics of edge computing devices. The framework is designed from three aspects: control flow, data flow, and storage flow. To address the impact of memory access cost on network computation efficiency, we introduce a parallel ping-pong data scheduling framework to compensate for it. Experimental results show that the system can support convolutional kernel operations of any size under structural constraints, and the computational efficiency is improved 2.28 times compared to CNN acceleration systems based on traditional data scheduling schemes.
A HYBRID RECOGNITION METHOD VIA KELM WITH CPSO FOR MMG-BASED UPPER-LIMB MOVEMENTS CLASSIFICATION

Mechanomyography (MMG) is a low-frequency signal emitted during muscle contraction; it can overcome the inherently unreliable defects of electromyography (EMG) and electroencephalography (EEG). For MMG-based movement pattern recognition, this paper proposes an innovative kernel extreme learning machine (KELM) based on the chaotic particle swarm optimization (CPSO), namely CPSO–KELM. By using CPSO–KELM in MMG-based movement pattern recognition, the classification accuracy of upper-limb movement has been improved, and the results can be better applied to the control of passive rehabilitation training of the upper-limb exoskeleton, which can provide help for the upper extremity rehabilitation of stroke patients. In this paper, MMG which is used for pattern recognition research, is collected by accelerometers when the subjects performed seven types of upper-limb rehabilitation movements. After filtering and segmentation, six time-domain features are extracted for the MMG of each channel, then kernel principal component analysis (KPCA) and principal component analysis (PCA) are used to reduce the feature dimensions. By using different classifiers to build classification models, the average recognition accuracies of movement classification under different processing methods are obtained; it is found that for most classifiers, the recognition rate of MMG after KPCA dimensionality reduction is better than that of PCA, and the overall recognition rate of upper-limb movements using the CPSO–KELM classifier can reach 97.1%, which is better than support vector machine (SVM), back-propagation neural network (BPNN), linear discriminant algorithm (LDA) and other MMG common classifiers in recognition accuracy. Moreover, the experimental analysis shows that compared with genetic algorithm (GA) and particle swarm optimization (PSO), CPSO has faster convergence and smaller training error, and the final recognition accuracy proves that the performance of CPSO–KELM is better than those of GA–KELM and PSO–KELM.
Didactics and pedagogical anthropology of digital educational environments

Problem statement. In the context of the scientific and technological development of the country, the implementation of the federal experiment on the development of digital educational environments, the introduction of new legal regimes for digital transformations of industries, including education, the issues of fundamental and applied developments in pedagogical anthropology and didactics of digital educational environments are being updated. The development of expert tools for assessing the developing functionality of digital educational environments is becoming relevant. The aim of the study is to analyze digital means of pedagogical activity, to systematize changes in didactic principles. Methodology. Analysis of the development of didactics, pedagogical anthropology in the context of the use of digital didactic tools is based on the study of digital educational environments created during the federal experiment. The study of digital didactic tools used by teachers was organized during the annual interregional webinar of the Russian Academy of Education. According to the research methodology, the requirements for the preparation of reports by webinar participants were based on the structured interview method. Results. The frontiers of changes in didactics and pedagogical anthropology in the conditions of digital educational environments are characterized. Essential changes in the practice of pedagogical activity, ways of organizing the educational process, ways of constructing the interaction of participants in educational relations are revealed. The results presented in the publication are significant for the development of the methodology of modern didactics and pedagogical anthropology and the theory and practice of expertise in education in terms of assessing the developmental functionality of digital educational environments. Conclusion. To evaluate the results of the federal experiment on the introduction of digital educational environments, a proven methodology for the examination of the developing functionality of digital educational environments is needed.
Future mathematics teachers’ implementation of research activities based on dynamic mathematics software GeoGebra

Problem statement. The digital transformation of higher education significantly affects the organization of pedagogical universities students’ research activities. Therefore, two important problems can be identified. Firstly, the matter of discovering information technology tools and directions for their use for the implementation of research work of future mathematics teachers studying in 1-2 courses. And secondly, the problem of choosing a mathematical topic, the solution of which with the help of digital technologies would lead to some interesting results that have elements of novelty. The purpose of this study is to identify the line of research of a mathematical nature, the implementation of which relies heavily on the dynamic mathematics software GeoGebra’s features. Methodology. Some features of the GeoGebra dynamic mathematics software and its application in geometric studies are analyzed. To study geometric figures, methods of visualization of mathematical objects and of experimental mathematics are used. Results. Some possibilities of dynamic mathematics software applicability in research and development of pedagogical universities’ mathematical profile students are considered. In these circumstances, the emphasis is on research related to the study of spatial curves and their planar projections, the construction of which naturally relies on the principles of displaying three-dimensional objects on a screen. The role of dynamic mathematics software in such studies mainly lies in the visualization of considered geometric objects, which makes it possible to identify possible properties of the constructed curves, and then, using available mathematical apparatus, to confirm their validity. Conclusion. The direction of designing and studying geometric objects using the GeoGebra dynamic mathematics software, which formed the basis of several research works of undergraduate students, is presented.
A transmission line image defogging algorithm based on improved transmission estimation and color bias correction

UAV inspection of overhead transmission lines is a common means for power grid companies to conduct defect detection, but in bad weather, the performance of the target detection algorithm deployed on the onboard equipment often decreases significantly. Based on the classical atmospheric light scattering model, the atmospheric light estimation is first done more accurately by separating the sky area. In terms of transmission estimation, the synthetic transmission is estimated from comprehensive information loss and image texture information weighting, and finally the color bias is corrected to realize the target image fog processing. The experimental results show that the proposed algorithm can significantly improve the clarity of transmission line images in foggy environment.
Autoencoder-enabled model portability for reducing hyperparameter tuning efforts in side-channel analysis

Abstract Hyperparameter tuning represents one of the main challenges in deep learning-based profiling side-channel analysis. For each different side-channel dataset, the typical procedure to find a profiling model is applying hyperparameter tuning from scratch. The main reason is that side-channel measurements from various targets contain different underlying leakage distributions. Consequently, the same profiling model hyperparameters are usually not equally efficient for other targets. This paper considers autoencoders for dimensionality reduction to verify if encoded datasets from different targets enable the portability of profiling models and architectures. Successful portability reduces the hyperparameter tuning efforts as profiling model tuning is eliminated for the new dataset, and tuning autoencoders is simpler. We first search for the best autoencoder for each dataset and the best profiling model when the encoded dataset becomes the training set. Our results show no significant difference in tuning efforts using original and encoded traces, meaning that encoded data reliably represents the original data. Next, we verify how portable is the best profiling model among different datasets. Our results show that tuning autoencoders enables and improves portability while reducing the effort in hyperparameter search for profiling models. Lastly, we present a transfer learning case where dimensionality reduction might be necessary if the model is tuned for a dataset with fewer features than the new dataset. In this case, tuning of the profiling model is eliminated and training time reduced.
A Note on the Regularity of Images Generated by Convolutional Neural Networks

The regularity of images generated by a class of convolutional neural networks, such as the U-net, generative networks, or the deep image prior, is analyzed. In a resolution-independent, infinite dimensional setting, it is shown that such images, represented as functions, are always continuous and, in some circumstances, even continuously differentiable, contradicting the widely accepted modeling of sharp edges in images via jump discontinuities. While such statements require an infinite dimensional setting, the connection to (discretized) neural networks used in practice is made by considering the limit as the resolution approaches infinity. As a practical consequence, the results of this paper in particular provide analytical evidence that basic regularization of network weights (also known as weight decay) might lead to oversmoothed outputs.