Reviewerly/exHarmony huggingface dataset:
Theoretical foundations of the formation of project competence of future teachers as an urgent pedagogical problem

This article is devoted to the formation of the project competence of future teachers in university environment. Theoretical analysis of the concept of «project» allowed to determine its structure. When defining the concept of «project activity» the author identified the role of a teacher and a student in implementation of the project. Based on the analysis of the research, the distinctive features of the project method and project-based learning were identified. On their basis, the need to address issues of project competence of future teachers is motivated by their implementation in higher educational institutions, as well as indicators and requirements for the training of pedagogical personnel are characterized.
Object Tracking by Google Cloud API and Data Alignment for Front/rear Car DVR Footages

Until recently, dashboard cameras have been used primarily by law enforcement agencies around the world. With their widespread use among private users, they have recently become almost ubiquitous. Driven by the potential of their application, especially in the insurance and security industries, this rise has been contributing to the advancement of research in the fields of video analytics, leading to the development of new anomaly detection techniques [1]. In this paper we exploit the potential of the Google Video Intelligence APIs in order to perform a standard object tracking operation on front/rear videos produced by car DVRs. This technology allowed us to save the intervals in which the detection occurred, used for subsequent data alignment, a very under-researched problem in this field. Our new approach for alignment is based on calculating the period of time that objects in the video are outside the field of view of both dashcams, using the.gpx file attached to each pair of front/rear videos. after this was accomplished, the individual detections were compared by constructing a fictitious continuous interval from the rcalculated blind time. the results differ according to the traffic levels within the videos. the best performances were achieved in low traffic situations where the calculation of the blind time interval is considerably more accurate, given the nearly constant speed held by the dashcam mounted car.
Super-Resolution Reconstruction of Deep Residual Network with Multi-Level Skip Connections

The Fast Super-Resolution Convolutional Neural Network algorithm (FSRCNN) is difficult to extract deep image information due to the small number of convolution layers and the correlation lack between the feature information of adjacent convolutional layers. To solve this problem, a deep residual network super-resolution reconstruction method with multi-level skip connections is proposed. Firstly, a residual block with multi-level skip connections is designed to solve the problem that the characteristic information of adjacent convolutional layers lacks relevance. A deep residual network with multi-level skip connections is constructed on the basis of the residual block. Then, the deep residual network connected to the multi-level skip is trained by using the adaptive gradient rate strategy of Stochastic Gradient Descent (SGD) method and the network super-resolution reconstruction model is obtained. Finally, the low-resolution image is input into the deep residual network super-resolution reconstruction model with the multi-level skip connections, and the residual eigenvalue is obtained by the residual block connected the multi-level skip connections. The residual eigenvalue and the low resolution image are combined and converted into a high resolution image. The proposed method is compared with the bicubic, A+, SRCNN, FSRCNN and ESPCN algorithms in the Set5 and Set14 test sets. The proposed method is superior to other comparison algorithms in terms of visual effects and evaluation index values.
Multi-Branch Convolution Network for Few-Shot Classification

Few-shot learning aims to complete the classification by only a small number of samples. In many few-shot learning frameworks, relation network is an end-to-end method, which can identify new categories through a small number of label samples based on metric learning. However, a simple feature extractor is used in this method, which limits the further improvement of the classification accuracy. To solve this problem, this paper proposes a multi-branch convolution network for feature extraction. This method combines the training strategies of multi-scale feature extraction, relation network, receptive field block and meta-learning. Firstly, the multi-scale feature vectors of the input image are extracted from the multi-branch convolution network. Then the feature vectors from the support set and the prediction set are input into the relation model, while the receptive field block is employed to improve the measurement ability of the network. Finally, the classification of the testing samples are realized according to the similarity score. In this paper, the effectiveness of the proposed model is verified on Omniglot and MiniImageNet datasets. The experimental results show that the classification accuracy of the proposed model is higher than that of other classical few-shot learning models.
Comparative Analysis of Various Language Models on Sentiment Analysis for Retail

Understanding what people think about a product/service/movie, etc. has become a crucial part of business as it helps shape businesses and prosper at an exponential rate because no matter what they produce, the success of anything they make entirely depends on its customers/end users as to whether they appreciate it or not. This very task of mining the hidden sentiment within customer reviews is termed sentiment analysis. In this study, we put forward a system that showcases a novel approach for optimizing the analysis of sentiment in textual data using divergent combinations of the miscellaneous hyperparameters obtainable with various Language Models. We experiment on the performance of the Language Models, ALBERT, RoBERTa, and BERT, by varying the method of optimization for a multiclass classification task by pre-processing the customer reviews which gave us our finest model achieving futuristic results. We conducted multiple trials of various Language Models using customer reviews of the Google Play app by varying the optimizer used. We found out that AdamW and Adabound give us the highest accuracies among the three experimented upon.
Manipulating Image Style Transformation via Latent-Space SVM

Deep Neural Networks have been proved as the go-to approach in modeling data distribution in a latent space, especially in Neural Style Transfer (NST), which casts a specific style extracted from a source image to another target image by calibrating the style and content information in a latent space. While existing methods focuses on different ways to extract features that more precisely describe style or content information to improve existing NST pipelines, the latent space of the NST model has not been well-explored. In this paper, we show that different half-spaces in the latent space are actually associated with particular styles of a network’s generated images. The corresponding constraints of these half-spaces can be computed by using linear classifiers, e.g. a Support Vector Machines (SVM). Leveraging the understanding of the relation between half-spaces in the latent space and output style, we propose the Linear Modification for Latent Representations (LMLR), a method that effectively increases or decreases the level of stylizing in the output image for any given NST model. We empirically evaluate our method on several state-of-the-art NST models and show that LMLR can manipulate the level of stylizing in the output image.
Automated Pruning of Neural Networks for Mobile Applications

Pruning is useful method to compress neural networks and further reduce the required computations and thus the inference speed. This work presents an automatic pruning workflow using an measurement based method to determine which portions of the network only contribute little to the total accuracy. Furthermore to increase the pruneability within networks containing residual blocks this work evaluates zero-padding as an useful complement to existing pruning methods. With zero-padding added to the pruning, we enable the automatic pruning process to also choose layers for pruning which would otherwise not be possible or only possible with removing additional filters which might contribute to the total accuracy. Zero-padding therefore adds the removed channels back into the original output feature map in a manner that the shapes remain identical, but the computations are saved. Using this method we achieved a speedup of up to 21% on CPU based platforms and 5-6% on GPU based execution on a MobileNetV2. The pruned network became comparable to an original network with an applied depth multiplier with only little additional retraining time.
Online Object-Oriented Semantic Mapping in Triger Classification Environment

Creating and maintaining an accurate representation of the environment is an essential capability for every mobile robot. Especially, semantic information plays an important role in mobile robot navigation and other operations. Some scholars are studying different forms of expression of the environment, including geometric, semantic and other forms. In this paper, we present a semantic mapping framework. Our system is capable of online mapping and object updating given object detections from RGB-D data. The map can provides 2D representations, the locations and labels of the mapped objects. To undo wrong data association, we perform a judgment whether to intersect when updating object shapes. Furthermore, we maintain a merge step to deal with part of object detections and keep the map updated. Our mapping system is highly accurate and efficient. We evaluated our approach in the simulated triger classification environments using turtlebot3 robot. As the experimental results demonstrate, our system is able to generate maps that are close to the simulated environment.
A Tracking Method Based on Lightweight Feature Extraction Network for Tracking Robot

This paper proposes to use lightweight feature extraction network as backbone of tracking network applied in embeded system of tracking robot, which achieves speed improvement and similar accuracy as compared with other mainstream tracking algorithms. Firstly, since there are unbalance between hard and easy samples on training dataset, data augmentation is proposed to increase hard samples in training phase. Secondly, modified Ghostnet is used as feature extraction network of tracking network, which dramatically decreases the float point operation and model parameters and achives realtime speed to meet the demand of embeded systems. Thirdly, focal loss is used as classification loss function instead of BCE loss, because BCE fuction cannot take unbalance between position and negative samples into consideration, and DIOU loss is taken as regression loss function to train tracking network, which can regress predicted box close to ground truth box. Experimental resutls show that the proposed method has better performance than traditional methods.
Maximising the Transferability of Interaction Techniques for Immersive Technologies

A key factor encompassing immersive technologies is interaction, and the input methods employed to manipulate an interface. Nevertheless, barriers surrounding interaction and usability continue to hinder the development and potential of immersive technologies, with researchers having difficulties taking applications from laboratory environments to real use cases [5]. Therefore, the proposed PhD research aims to identify how natural input methods are best implemented in regards to commonplace tasks in immersive environments, and uncover how user approaches adapt depending on changes in environmental factors, the context of interaction and the hardware employed.
Salient Region Detection of Aerial Sea Detection Image Based on Fusion Algorithm

Based on the attention mechanism of human vision, an image salient region detection model is proposed by fusing multi-scale and log amplitude spectrum. The model considers the characteristics of target and background at the same time, calculates the spectral residuals at different scales, and uses “center surround difference operation” for cross scale combination. According to feature integration, multi-scale feature maps are superimposed to get the final saliency map. The simulation results show that compared with the traditional detection algorithm, the improved algorithm in this paper has obvious improvement in the detection effect and accuracy of the salient region, and reduces the misjudgment rate. The salient region detection for the aerial sea detection image has certain anti-interference and applicability.
Memory Using Data Generator in Continual Learning for Remote Sensing Scene Classification

Deep learning models suffer from catastrophic forgetting and collapse dramatically when they are subjected to continual learning process. To overcome this handicap, we propose a novel continual learning approach based on previously seen data auto-generation sub-networks. The proposed model continually learns a set of sequential classification tasks or classes, where each classification task includes a certain number of remote sensing scenes or classes. The proposed neural networks architecture is composed of two trainable sub-networks. The first module adjusts its weights by minimizing the discrimination between the land-cover classes error during the new task learning. In parallel, the second module attempts to learn how to reproduce the task data by discovering the latent data structure of the new task dataset. Experiments are conducted on Merced dataset. The experimental results confirm the outperformance and robustness of the proposed model.
Methods and Algorithms for Generating Sustainable Cognitive Systems Based on Thematic Category Hierarchies for the Development of Heterogeneous Information Resources in Technological and Social Spheres

The research and development results are presented that implement semantic clustering methods based on vector models, extraction of conceptual structures in a multilingual mode, and realization of semantic Internet navigation within the specified domains. Automatic construction of high-quality sustainable topic hierarchies is designed to solve a wide range of artificial intelligence tasks, including the automatic construction of reviews of mass media sources, identifying misinformation, extremist activities, advertisement targeting, and forecasting prospective directions of science and technology development. As the Internet grows around the world, users write comments in different languages. Multilingual methods of tuning neural networks training have been developed to analyze text data in different languages.
A Model for Zero-shot Text Multi-labeling Using Semantics-based Labels

We introduce a transformer-based method to associate relevant tags to text passages or blocks such as categories to pages of a website, marking sections in an article, or social postings subject tagging. In contrast with traditional multi-label formulations, the proposed approach uses semantic definitions of the tags available during training, and the model outputs a binary prediction of whether the described category applies to a document or not. The transformer-based model learns the semantics of the definition of a tag, and therefore works for tags not seen during training. Performance on domain-specific datasets can be further improved via transfer learning after fine-tuning with relatively little additional labeled data required.
Towards Social Interaction between 1st and 2nd Person Perspectives on Bodily Play

Bodily play, which is a productive social interaction for bonding social relationships, has positive impacts on self-efficacy, acute cognitive benefit, and emotion. However, most bodily play encourages players to enjoy their own experiences. There are limited researches on sharing players' perspectives to enhance players' empathy for understanding others. Thus, we propose an asymmetric two-person game in an immersive environment. This bodily play, which supports perspective-taking via the integration with the first- and second-perspectives, has a collaborative interface that allows users to share their physiological and emotional perspectives. Initial testing of the system shows that players can not only understand well the feeling and problems encountered by each other through sharing perspectives and information but also increases the social closeness of players and stimulates empathy after the interplay.
Navigation Patterns of a Hybrid Scanning Agent using Uninformed and Informed Search Algorithms for Reactive and Deliberative Behaviors

Hybrid agent architectures enable effective control layering the agent functions according to the sophistication they require. In this study, assuming a partially observable and deterministic environment, reactive and deliberative behaviors of a hybrid scanning agent were controlled using some uninformed and informed search algorithms, respectively. As the reactive layer decided the agent actions online in an unknown environment, a map of the environment was constructed in parallel. When the reactive layer failed to find a proper action, the deliberative layer proposed a solution offline using the constructed map so that the agent could continue its scanning task. The navigation patterns that were produced with the adapted algorithms in the reactive and deliberative layers were analyzed. The results showed that depth-first search (DFS) and breadth-first search (BFS) algorithms can be used as reactive motion planners for scanning an environment in zigzag and spiral patterns. Simulations in 25 grid-based environments with different sizes and varying percentages of obstacles yielded that running A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm as a deliberative planner, the agent could completely scan the environments with equal successes for all different modes of the developed scanning algorithms. The horizontal mode of the DFS-based scanning algorithm had the least rescan rate on average.
SUBATOMIC INFERENCES: AN INFERENTIALIST SEMANTICS FOR ATOMICS, PREDICATES, AND NAMES

Abstract Inferentialism is a theory in the philosophy of language which claims that the meanings of expressions are constituted by inferential roles or relations. Instead of a traditional model-theoretic semantics, it naturally lends itself to a proof-theoretic semantics, where meaning is understood in terms of inference rules with a proof system. Most work in proof-theoretic semantics has focused on logical constants, with comparatively little work on the semantics of non-logical vocabulary. Drawing on Robert Brandom’s notion of material inference and Greg Restall’s bilateralist interpretation of the multiple conclusion sequent calculus, I present a proof-theoretic semantics for atomic sentences and their constituent names and predicates. The resulting system has several interesting features: (1) the rules are harmonious and stable; (2) the rules create a structure analogous to familiar model-theoretic semantics; and (3) the semantics is compositional, in that the rules for atomic sentences are determined by those for their constituent names and predicates.
An Innovative Approach to Cluster Interaction of Social Institutions in the Regional Educational Space: Fostering the Culture of Health Among Students

Integration of the resources and capabilities from various social institutions within the regional educational space of a particular region is one possible way to improve the activities of educational organizations aimed to foster a culture of health among children and adolescents. The cluster approach was used to implement the administrative and managerial aspects of the joint activities among various organizations in the region to foster a culture of health among students. The innovative cluster interaction theory for the social institutions within the regional educational space aimed to nurture students’ health culture developed and justified by the authors has the following components: theoretical and methodological foundations; a set of regularities in joint activities among the subjects of cluster interaction in the regional educational space aimed to foster a culture of health among students; a set of principles governing the clusters; a cluster interaction model for the social institutions with theoretical and methodological, content and technological, and evaluation and results blocks. The model gives the insights into the theory and provides efficient joint activities of the cluster interaction subjects in the regional educational space aimed to foster a culture of health among the students. The study defines the concepts “cluster of social institutions in the regional educational space aimed to foster students’ health culture” and “cluster interaction of social institutions in the regional educational space aimed to foster students’ health culture”. Experimental studies reliably confirm the validity of the developed and implemented innovative approach.
Three-Dimensional Cubic Barcodes

We consider three-dimensional cubic barcodes, consisting of smaller cubes, each built from one of two possible materials and carry one bit of information. To retrieve the information stored in the barcode, we measure a 2D projection of the barcode using a penetrating wave such as X-rays, either using parallel-beam or cone-beam scanners from an unknown direction. We derive a theoretical representation of this scanning process and show that for a known barcode pose with respect to the scanner, the projection operator is linear and can be easily inverted. Moreover, we provide a method to estimate the unknown pose of the barcode from a single 2D scan. We also propose coding schemes to correct errors and ambiguities in the reconstruction process. Finally, we test our designed barcode and reconstruction algorithms with several simulations, as well as a real-world barcode acquired with an X-ray cone-beam scanner, as a proof of concept.
Increasing Landsat 5 TM Spatial Resolution to 15 M Using a Super-Resolution Deep Learning Model Trained with Pan-Sharpened Landsat 7 ETM+ DATA

In this work, we aim to recover the information at 15 m spatial resolution from Landsat 5 TM (L5 TM) data with 30 m spatial resolution using a super-resolution deep learning model. The model is designed to predict a pan-sharpened Landsat 7 ETM+ (L7 ETM+) image at 15 m resolution from a L5 TM image at 30 m spatial resolution. For the model training, we used images of L5 TM and L7 ETM+ from the same region and at a time interval of acquisition < 10 days. Our results show that the model achieves to improve the spatial resolution of the L5 TM even with a modest sample for training, constituted only of 4225 couples of L5 TM and L7 ETM+ images of size 128 ×128 pixels from the Landsat tile 216068. We also found that L5 TM emulated reflectance values at 15 m spatial resolution were more comparable to the values and ranges of L7 ETM+ reflectance than the original L5 TM reflectance. With an improved dataset for training, this model could be used to produce a dataset of images spatially and radiometrically harmonized of L5 TM and L7 ETM+ at 15 m spatial resolution.