Reviewerly/exHarmony huggingface dataset:
Mathematical Problems as a Means of Developing Students’ Research Skills in the Context of School Education Content Updating

Objective: The main goals of school mathematical education are the development of intellectual students, the formation of thought qualities characteristic of mathematical activities and the acquisition of specific mathematical knowledge, the skills and skills necessary for practical application, and the formation of research skills. Theoretical Framework: The problem and purpose of the study is to identify the possibilities of problem-searching tasks in mathematics as a way for school students to develop research skills in updated content situations. Method: A method has been developed to build selected skills based on the corresponding system. It has been established that collective or group forms are most effective in creating problem situations in the classroom. Group or individual forms are most effective for testing a hypothesis and finding a solution to a problematic task. Result and Conclusion: The article also highlights the main blocks of research skills of schoolchildren and problem-search tasks in algebra and geometry developed for these blocks and methods for the formation of these skills. The basic principles for constructing a system of tasks focused on the formation of each block and each skill that we have chosen are determined. Research Implications: Implementation of these goals necessitates updating the system of school mathematical education, which is designed to ensure a harmonious combination of the interests of the individual and society. Originality/value: It contributed to the activation of students’ activities and their positive motivation for learning activities carried out in the classroom and at home.
On the Relationship Between Crowdsourced Sentiments and Mobility Trends During COVID-19: A Case Study of Kyoto

COVID-19 has significantly changed people’﻿s attitudes and behavior in Japan. In this study, we firstly conducted a sentiment analysis on the contents of tweets posted on Twitter to understand the changes in people’s emotions due to the COVID-19 pandemic and related restriction policies. ML-ask, an open-source analysis system for textual input in Japanese, is used for this sentiment analysis. Its emotion identification is lexicon-based and can identify ten different emotions including joy, sadness, dislike, and anger. Secondly, we investigated the impacts of such “crowdsourced” sentiments on mobility and activity participation by regression models. Publicly available, also crowdsourced, Google’s COVID-19 Community Mobility Reports are used as dependent variables, and the identified tweet sentiments are used as independent variables together with other conventional variables such as dummies for policies, weekends, and holidays. As a result, it was confirmed that the changes in mobility and activity participation could be explained more accurately by the counts of tweet sentiments. The model fit was significantly improved if sentiments are added as variables, compared to the cases in which only the number of tweets or the number of daily new cases was used. In addition, we test the effect of time lag. More specifically, the model fit was higher when using the averaged emotion counts of the past 7 days than using the count of emotions on the same day. We conclude that using tweet sentiments offers the feasibility to improve the prediction levels of urban mobility.
Drivers and barriers on implementing XR technologies in the construction industry in Turkey

AbstractThis study presents drivers and barriers on construction industry extended reality (XR) implementations in Turkey. It aims to investigate the current state and future directions of XR opportunities for architecture offices and construction companies in a developing country. A quantitative approach was employed which involves conducting a survey, performing statistical analyses, and verification of the hypotheses. The data were analyzed by SPSS and Excel using the frequency, mean score, standard deviation (SD), and one sample t-test. Thirty-three different companies participated in the survey with a return rate of 56.89%. The results were considered statistically significant since all the p-values were below 0.05. The findings revealed that even though 60% of the companies are not using XR, 60% companies have near future plans on implementing them. According to the results, XR was considered as beneficial and highly motivating for the near future, yet it needs to be widespread for companies to keep themselves abreast of information and communication technology trends. Finally, by considering the findings obtained from the results this study reaches a classification of drivers as 'increased efficiency: time-cost-quality' and 'increased communication and coordination' while barriers as 'technical limitations', 'high costs', and 'unqualified professional'.Keywords: Extended reality (XR)virtual reality (VR)augmented reality (AR)construction industrytrend analysis Disclosure statementNo potential conflict of interest was reported by the author(s).Notes1 https://www.statista.com/statistics/1096765/global-market-spend-on-xr-technologies-by-industry/ [accessed 30 December 2021].2 https://www.businesswire.com/news/home/20220601005690/en/Augmented-Reality-AR-in-the-Construction-Industry-2022-Thematic-Research-Report–-Market-to-Grow-to-150-Billion-by-2030–-ResearchAndMarkets.com3 https://yfk.csb.gov.tr/bakanligimizda-yapi-bilgi-modellemesi-egitimi-gerceklestirildi-haber-260532 [accessed 30 December 2021].4 https://www.researchgate.net/publication/313739443_Virtual_and_Augmented_Reality_on_the_5G_Highway [accessed 9 June 2022].5 https://digiplaceproject.eu/finalbooklet/ [accessed 13 January 2022].6 http://www.arkiv.com.tr/proje?&filtre[yil]=2020-2029&filtre[mimarlik-odulleri]=&filtre[tur]=5 [accessed 9 June 2022].7 https://www.yapikatalogu.com/blog/turkiye-de-sosyal-medyayi-ve-dijital-dunyayi-en-iyi-kullanan-mimarlik-ofisleri_288 [accessed 9 June 2022].8 https://www.checkmarket.com/sample-size-calculator/ [accessed 9 June 2022].9 See note 4 above.10 http://www.emfexplained.info/?ID=25916 [accessed 9 June 2022].11 https://www.enkasystems.com/ [accessed 30 December 2021].12 http://www.limaktechnology.com/Default/Hakkimizda# [accessed 30 December 2021].13 https://tavtechnologies.aero/en-EN/about-us [accessed 30 December 2021].14 Survey Questions, https://qfreeaccountssjc1.az1.qualtrics.com/survey-builder/SV_6Aaoe8vNvRyfWjY/edit
Design of CSD based bi-orthogonal wavelet filter bank for medical image retrieval

Two-channel wavelet filter-banks (FBs) are universally used in many applications of signal processing. In this work, design of a bi-orthogonal wavelet FB with low complexity is suggested. The proposed design is based on canonical signed digit (CSD) implementation of the bi-orthogonal wavelet FB. CSD technique has been applied to the FB to eliminate the power consuming multipliers and to reduce the number of shifters and adders in the implementation of bi-orthogonal FB. The suggested FB design is suitable for the application of high speed signal processing applications. In addition, the proposed bi-orthogonal wavelet FB is implemented on hardware using Field Programmable Gate Array (FPGA) board Kintex-7 from Xilinx. It is found that the suggested bi-orthogonal wavelet FB attained low digital hardware requirement as compared to well-known rationalized existing bi-orthogonal wavelet FBs. The efficacy of the suggested wavelet FB is demonstrated on medical image retrieval application. NEMA, OASIS, and EXACT09, three publicly accessible medical image datasets, are utilized to measure the efficacy of image retrieval. It is shown that the suggested bi-orthogonal wavelet FB performs better than the bi-orthogonal wavelet FBs existing that are being used.
A novel blind tamper detection and localization scheme for multiple faces in digital images

Abstract Face image manipulation detection (FIMD) is a research area of great interest, widely applicable in fields requiring data security and authentication. Existing FIMD schemes aim to identify manipulations in digital face images, but they possess individual strengths and limitations. Most schemes can only detect specific manipulations under certain conditions, leading to variable success rates across different images. The literature lacks emphasis on detecting manipulations involving multiple faces. This paper introduces a novel blind tamper detection and localization scheme specifically designed for multiple faces in digital images. The proposed multiple faces manipulation detection (MFMD) scheme consists of two stages: face detection and selection, and image watermarking. Through extensive experiments, the MFMD scheme's performance has been evaluated on various multiple‐face images, considering embedding capacity, payload, watermarked image quality, time complexity, and manipulation detection ability. The results demonstrate the MFMD scheme's efficacy in detecting different types of manipulations for multiple faces in images. Furthermore, the watermarked images exhibit high visual quality, even when multiple faces are present. The scheme's efficiency recommends it for practical applications, especially in sharing personal images over unsecured networks. This research advances FIMD techniques by addressing the neglected area of multiple‐face manipulation detection. With improved accuracy, faster processing times, and resilience against various manipulations, the MFMD scheme offers valuable capabilities for enhancing data security and authentication in real‐world scenarios.
Content and Originality of Professional and Pedagogical Skills of a Teacher in Primary General Education

The article discusses the content and originality of the professional and pedagogical skills of a teacher of primary general education for the implementation of the State educational standard of school general education of the Kyrgyz Republic of a new generation. It is indicated that the effectiveness of the pedagogical activity of a teacher of primary general education is determined by the level of mastering the necessary professional knowledge, skills, pedagogical skills and personal qualities and properties. Methodological training acts in unity with organizational training and relationships with wards. Along with the elements of the educational process, the professional readiness of a teacher includes the formation of spiritual sensitivity, warmth, care and joy, which are moral qualities. The teacher also needs a complex of social and human qualities: activity, conviction, maturity of thinking, adherence to principles, and more. A teacher can develop only on the basis of a high general and pedagogical culture, a steady need for self-education.
ODSmoothGrad: Generating Saliency Maps for Object Detectors

Techniques for generating saliency maps continue to be used for explainability of deep learning models, with efforts primarily applied to the image classification task. Such techniques, however, can also be applied to object detectors, not only with the classification scores, but also for the bounding box parameters, which are regressed values for which the relevant pixels contributing to these parameters can be identified. In this paper, we present ODSmoothGrad, a tool for generating saliency maps for the classification and the bounding box parameters in object detectors. Given the noisiness of saliency maps, we also apply the SmoothGrad algorithm [12] to visually enhance the pixels of interest. We demonstrate these capabilities on one-stage and two-stage object detectors, with comparisons using classifier-based techniques.
Densely Connected Transformer With Linear Self-Attention for Lightweight Image Super-Resolution

Image super-resolution (SR) is the process of restoring high-resolution (HR) images from low-resolution (LR) ones. Recent Transformer-based SR methods have achieved impressive results by utilizing the self-attention (SA) mechanism, which allows modeling long-range dependencies among input features in spatial dimensions. However, the computational complexity of SA increases quadratically with respect to the feature size, which makes Transformer-based methods inefficient. Additionally, despite the success of dense connections in CNN-based methods, they have not been fully explored in Transformer-based methods. In this paper, we propose a novel approach for lightweight SR, called Densely Connected Transformer with Linear Self-Attention (DCTLSA) network. Our method addresses the efficiency issue of SA by designing a new linear SA, which calculates the similarities in spatial dimension with linear complexity. Moreover, we leverage dense connections to integrate multiple levels of features and provide rich information for SR. Our experimental results demonstrate that DCTLSA outperforms state-of-the-art lightweight SR methods in terms of SR performance, model complexity, and inference speed. The code of the proposed method is available at https://github.com/zengkun301/DCTLSA.
Hamming Similarity and Graph Laplacians for Class Partitioning and Adversarial Image Detection

Researchers typically investigate neural network representations by examining activation outputs for one or more layers of a network. Here, we investigate the potential for ReLU activation patterns (encoded as bit vectors) to aid in understanding and interpreting the behavior of neural networks. We utilize Representational Dissimilarity Matrices (RDMs) to investigate the coherence of data within the embedding spaces of a deep neural network. From each layer of a network, we extract and utilize bit vectors to construct similarity scores between images. From these similarity scores, we build a similarity matrix for a collection of images drawn from 2 classes. We then apply Fiedler partitioning to the associated Laplacian matrix to separate the classes. Our results indicate, through bit vector representations, that the network continues to refine class detectability with the last ReLU layer achieving better than 95% separation accuracy. Additionally, we demonstrate that bit vectors aid in adversarial image detection, again achieving over 95% accuracy in separating adversarial and non-adversarial images using a simple classifier.
Geometry Enhanced Reference-based Image Super-resolution

With the prevalence of smartphones equipped with a multi-camera system comprising multiple cameras with different field-of-view (FoVs), images captured by two or three cameras now share a portion of the FoV that are compatible with reference-based super-resolution (RefSR). In this work, we propose a novel RefSR model that utilizes geometric matching methods to enhance its performance in two aspects. First, we integrate geometric matching maps to improve feature fusion. Second, we train the matching modules equipped in the RefSR models under the supervision of accurate geometric matching maps to increase their robustness. Our experimental results demonstrate the effectiveness and state-of-the-art performance of the proposed method.
OTST: A Two-Phase Framework for Joint Denoising and Remosaicing in RGBW CFA

RGBW, a newly emerged type of Color Filter Array (CFA), possesses strong low-light photography capabilities. RGBW CFA shows significant application value when low-light sensitivity is critical, such as in security cameras and smartphones. However, the majority of commercial image signal processors (ISP) are primarily designed for Bayer CFA, research pertaining to RGBW CFA is very rare. To address above limitations, in this study, we propose a two-phase framework named OTST for the RGBW Joint Denoising and Remosaicing (RGBW-JRD) task. For the denoising stage, we propose Omni-dimensional Dynamic Convolution based Half-Shuffle Transformer (ODC-HST) which can fully utilize image’s long-range dependencies to dynamically remove the noise. For the remosaicing stage, we propose a Spatial Compressive Transformer (SCT) to efficiently capture both local and global dependencies across spatial and channel dimensions. Experimental results demonstrate that our two-phase RGBW-JRD framework outperforms existing RGBW denoising and remosaicing solutions across a wide range of noise levels. In addition, the proposed approach ranks the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> place in MIPI 2023 RGBW Joint Remosaic and Denoise competition.
EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation

The ubiquitous multi-camera setup on modern autonomous vehicles provides an opportunity to construct surround-view depth. Existing methods, however, either perform independent monocular depth estimations on each camera or rely on computationally heavy self attention mechanisms. In this paper, we propose a novel guided attention architecture, EGA-Depth, which can improve both the efficiency and accuracy of self-supervised multi-camera depth estimation. More specifically, for each camera, we use its perspective view as the query to cross-reference its neighboring views to derive informative features for this camera view. This allows the model to perform attention only across views with considerable overlaps and avoid the costly computations of standard self-attention. Given its efficiency, EGA-Depth enables us to exploit higher-resolution visual features, leading to improved accuracy. Furthermore, EGA-Depth can incorporate more frames from previous time steps as it scales linearly w.r.t. the number of views and frames. Extensive experiments on two challenging autonomous driving benchmarks nuScenes and DDAD demonstrate the efficacy of our proposed EGA-Depth and show that it achieves the new state-of-the-art in self-supervised multi-camera depth estimation.
A Short-Term Power Prediction Method Based on Temporal Convolutional Network in Virtual Power Plant Photovoltaic System

In the virtual power plant architecture based on edge computing, short-term photovoltaic power prediction is carried out by using multi-source data of photovoltaic power plants stored in photovoltaic intelligent edge terminal (PVIET), which can achieve efficient dispatching and management of renewable energy by the control and coordination center, thus ensuring the safe and stable operation of the power grid. Therefore, this paper proposes a short-term photovoltaic power prediction method that combines improved grey relation analysis (IGRA), efficient channel attention module (ECANet), and temporal convolutional network (TCN). The IGRA algorithm can fully explore the correlation between predicted days and historical days using meteorological environmental data from multiple sources, while the deep TCN model combined with ECANet can effectively avoid the drawbacks of traditional photovoltaic power prediction models. Finally, using the real case of Alice Spring in Australia, the proposed method (IGRA-ECA-TCN) is compared with other hybrid models in different seasons and weather conditions, and the mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R2) are selected as the evaluation indicators of algorithm performance. The results indicate that IGRA-ECA-TCN has stronger adaptability and higher accuracy.
PanopticRoad: Integrated Panoptic Road Segmentation Under Adversarial Conditions

Segmentation becomes one of the most important methods for scene understanding. Segmentation plays a central role in recognizing things and stuff in a scene. Among all things and stuff in a scene, the road guides vehicles in the cities and highways. Most segmentation models, i.e., semantic, instance, and panoptic segmentation, have focused on images with clear daytime weather conditions. Few papers have tackled nighttime vision under adversarial conditions, i.e., fog, rain, snow, strong illumination, and disaster events. Moreover, further segmentation of road conditions like dry, wet, and snow is still challenging under such invisible conditions. Weather impacts not only visibility but also roads and their surrounding environment, causing vital disasters with obstacles on the road, i.e., rocks and water. This paper proposes PanopticRoad with five Deep Learning-based modules for road condition segmentation under adversarial conditions: DeepReject/Scene/Snow/Depth/Road. Integration of them helps refine the failure of local road conditions where weather and physical constraints are applied. Using foggy and heavy snowfall nighttime road images and disaster images, the superiority of PanopticRoad is demonstrated over state-of-the-art panoptic-based and adaptive domain-based Deep Learning models in terms of stability, robustness, and accuracy.
LDFA: Latent Diffusion Face Anonymization for Self-driving Applications

In order to protect vulnerable road users (VRUs), such as pedestrians or cyclists, it is essential that intelligent transportation systems (ITS) accurately identify them. Therefore, datasets used to train perception models of ITS must contain a significant number of vulnerable road users. However, data protection regulations require that individuals are anonymized in such datasets. In this work, we introduce a novel deep learning-based pipeline for face anonymization in the context of ITS. In contrast to related methods, we do not use generative adversarial networks (GANs) but build upon recent advances in diffusion models. We propose a two-stage method, which contains a face detection model followed by a latent diffusion model to generate realistic face in-paintings. To demonstrate the versatility of anonymized images, we train segmentation methods on anonymized data and evaluate them on non-anonymized data. Our experiments reveal that our pipeline is better suited to anonymize data for segmentation than naive methods and performes comparably with recent GAN-based methods. Moreover, face detectors achieve higher mAP scores for faces anonymized by our method compared to naive or recent GAN-based methods. Code is available at https://github.com/KITMRT/latent_diffusion_face_anonymization.
ProgDTD: Progressive Learned Image Compression with Double-Tail-Drop Training

Progressive compression allows images to start loading as low-resolution versions, becoming clearer as more data is received. This increases user experience when, for example, network connections are slow. Today, most approaches for image compression, both classical and learned ones, are designed to be non-progressive. This paper introduces ProgDTD, a training method that transforms learned, non-progressive image compression approaches into progressive ones. The design of ProgDTD is based on the observation that the information stored within the bottleneck of a compression model commonly varies in importance. To create a progressive compression model, ProgDTD modifies the training steps to enforce the model to store the data in the bottleneck sorted by priority. We achieve progressive compression by transmitting the data in order of its sorted index. ProgDTD is designed for CNN-based learned image compression models, does not need additional parameters, and has a customizable range of progressiveness. For evaluation, we apply ProgDTD to the hyperprior model, one of the most common structures in learned image compression. Our experimental results show that ProgDTD performs comparably to its non-progressive counterparts and other state-of-the-art progressive models in terms of MS-SSIM and accuracy.