Reviewerly/exHarmony huggingface dataset:
VIRTUAL WORLD TECHNOLOGIES IN MANAGING CHILDREN WITH LEARNING DISABILITY

In technological era, virtual reality or augmented reality still remains less popular within the fi eld of learning disabilities. Research shows that children with learning disabilities face various challenges in their dayto- day lives dealing with these disorders, demanding massive solutions. This paper addresses the pros and cons of virtual reality in learning disabilities across different age groups by combining theories of virtual worlds and learning disorders. Exciting research in virtual reality focuses on fi nding out how psychotherapies have benefi ts in learning and education. Upon review, it becomes evident that research in the virtual world along with learning disabilities has not yet been examined from a cohesive perspective, illustrating a lack of alliance that determines a more global understanding of the technological advantages of disabilities. Thus, this paper aims to provide educators with an overview of explanations of the virtual world and to ensure appropriate development of VR/AR applications and special assistance for learning disabilities.
ACTIVITY APPROACH TO THE DESIGN OF DIGITAL EDUCATIONAL ENVIRONMENTS

The problem of digitalization is one of the most relevant, but at the same time debatable areas of modern educational system. The introduction of modern digital technologies into the learning process greatly enhances our ability to create adapted, accessible, individualized, and evolving learning environments, but it also includes many risks. In  V.V. Rubtsov noted that the introduction of new educational technologies requires conducting fundamental and applied psychological and pedagogical research, which precedes introduction into the mass school. Otherwise, the learning process can be turned into a simple «programming» of students’ actions and operations, destroy the whole system of «teacher-class» or «student-student», divide it into separate elements such as «student-computer». Many researchers note that until now the introduction of digital technologies only as a new «external shell» for old purposes, methods, and programs of training, which continue to have the character of «broadcasting» of information and «exercising» skills remain the widespread problem. At present, there are two main positions on the problem of digitization. The fi rst is described in the digital-algorithmic approach, where the execution of a predefi ned program with predefi ned rules is aligned with the correct task solution. This position is no different from the system of programmed learning. The second position is described in the digital-cognitive approach, where a digital program or technology serves as a means of enhancing teacher and students’ ability to communicate with each other, modeling, and presentation of thinking acts and joint actions. The most striking examples of this approach in practice can be presented by methods: «Swimming of Solid» and «Lever», (E.V. Vysotskaya, V.V. Rubtsov, I.M. Ulanovskaya), «Pilot-Locator» (A.G. Kritsky), PL-modifi ed (L.S. Kuravsky, A.A. Margolis, etc.), «Scale» (E.I. Isaev, A.V. Konokotin, V.V. Rubtsov).The key to understanding digitalization in education is to view new technologies not as an end in themselves, but as a means of empowering teachers to organize a system of learning activities that are shared among participants.
ON THE DISTANCE EDUCATION OF CHILDREN AND THE UNRESOLVED ISSUES IN E-LEARNING AT SCHOOL

The development of psychological science has led to the development of artifi cial intelligence and distance learning for children, which require development of all cognitive abilities, thinking, and intelligence in students. The introduction of digital technologies in the educational process is not based on the examination results on the material of school textbooks. What goals do digital technologies teach students? What specifi c and abstract concepts are included in the content of education? Computer use leads to the question of quality criteria for education. There is no school learning computerization model that is based on the psychological analysis of educational activities and students’ abilities. A child in any environment learns, develops and is brought up by adults. A child can learn being a functional psychological system of activity (Shadrikov). Rubinstein showed the connection between thinking and mental abilities. Elkonin identifi ed the learning goal in the learning task and learning actions to which he attributes actions with objects and cognitive actions. Systems of cognitive actions characterize the operational mechanisms of abilities. Shadrikov singled out a universal operating mechanism of abilities – intellectual operations. We need methods of psychological analysis for educational activities and the ability to learn, the ability to desire and want, the ability to feel and experience.
DIDACTICS AND PEDAGOGICAL ANTHROPOLOGY OF DIGITAL EDUCATIONAL ENVIRONMENTS

In the context of the federal experiment on the development of digital educational environments (Decree Government of Russia N ), the issues of fundamental and applied developments in pedagogical anthropology and didactics of digital educational environments are being updated. The aim of the study was to analyze the digital educational environments created in the regions participating in the federal experiment. Teachers and heads of educational organizations from the regions included in the federal experiment on the introduction of digital educational environments took part in the study. Teachers and leaders were participants in the monthly interregional webinar of the Russian Academy of Education “Digital Educational Environment for Human Development”. The participants of the webinar were offered a structured interview, which included  questions to assess the elements of digital educational environment, its opportunities for human development, individualization of education, self-determination in terms of choice, the possibilities of analyzing and refl ecting on personal educational results, the possibilities of productive interaction between participants in educational relations. As a result, a structural and functional analysis of digital educational environments in  regions is presented. Essential changes in the practice of pedagogical activity, ways to organize educational process and cognitive activity of students, ways to construct the interaction of participants in educational relations are revealed. The results are signifi cant for the development of the methodology of modern didactics, for the development of the theory and practice of expertise in education: the assessment of the developing functionality of digital educational environments. To evaluate the developmental functionality of digital educational environments, a special methodology is needed. The model of such methodology has been developed and is currently being tested.
CURRENT DIRECTIONS IN THE DIGITAL TRANSFORMATION OF EDUCATION: PROSPECTS AND NEW OPPORTUNITIES FOR THE DEVELOPMENT OF TRADITIONAL EDUCATION

The development of the direction of digital transformation in education opens up prospects and new opportunities for the development of traditional education, designed to provide assistance, scientifi c and methodological support to subjects of education when introducing various kinds of innovations. The article shows the features of regional methodological services, the main problems and prospects of development in digital transformation in education. Methodological foundations of the research were the general scientifi c approaches: systemic, socio-logical, activity–based and modern methodologies of ecosystem, cluster, metacompetence approaches. Methodological principles that defi ne the requirements for methodological support of teachers, for work with children in the world of digitalization, such as systematic measures to support all subjects of education; orientation to the result, voluntariness, and accessibility are highlighted. The leading principles are openness and targeting, as they make it possible to attract leaders.
Detection of Dyslexia Through Images of Handwriting using Hybrid AI Approach

Dyslexia is a neurodevelopmental disorder characterized by difficulties with acquiring reading skills, despite the presence of appropriate learning opportunities, sufficient education, and a suitable sociocultural context. Dyslexia negatively affects children’s educational development and their acquisition of language, as well as their writing. Therefore, early detection of dyslexia is of great importance. The prediction of dyslexia through handwriting is an active research field of almost five years’ standing. In this paper, we propose hybrid models (CNN-SVM) and (CNN-RF) to reveal dyslexia through images of handwriting. The paper aimed to develop a CNN model to extract features from images of handwriting where CNN is highly reliable in extracting features from images, and to use SVM as a classifier due to its generalization abilities as well as using random forest (RF) as a classifier in (CNN-RF). The study aimed to combine a deep learning (DL) model and a machine learning (ML) model to improve model performance. Data sets that consisted of 176,673 images of handwriting were used in this study. The hyperparameter of the model was adjusted and examined in order to classify the three categories of handwriting. The CNN model that was built demonstrated an outstanding accuracy rate of 98.71% in effectively categorizing three distinct types of handwriting—99.33% with SVM, and 98.44% in the CNN-RF model. The aim of recognizing dyslexic handwriting through CNN-SVM was successfully attained, and our model outperformed all previous models.
Development of YOLO-based Model for Fall Detection in IoT Smart Home Applications

In smart home applications, effective fall detection is a critical concern to minimize the occurrence of falls leading to injuries, especially for the assistance of elderly individuals. Various methods have been proposed, including both vision-based and non-vision-based approaches. Among these, vision-based approaches have garnered significant attention from researchers due to their practicality and applicability. However, existing vision-based methods face challenges such as low accuracy rates and high computational costs, which still need further exploration to enhance fall detection effectiveness. This study aims to develop a vision-based fall detection system tailored for smart home care applications. The objective of this study is to develop an accurate and lightweight fall detection method that is applicable in IoT platforms. A You Only Look Once (YOLO) based network is trained and tested to identify human falls accurately. The experimental results demonstrate that the developed YOLO-based technique shows promising outcomes for human fall detection and holds potential for integration in the Internet of Things (IoT) enabled smart home applications.
Measuring Surroundings Awareness using Different Visual Parameters in Virtual Reality

Due to the popularity of digital games, there is a growing interest in using games as therapeutic interventions. The ability of games to capture attention can be beneficial to distract patients from pain. In this paper, we investigate the impact of visual parameters (color, shapes, and animation) on users’ awareness of their surroundings in virtual reality. We conducted a user study in which experiments included a visual search task using a virtual reality game. Through the game, the participants were asked to find a target among distraction objects. The results showed that the different visual representations of the target among distraction objects could affect the users’ awareness of their surroundings. The least awareness of the surroundings occurred when the target and distractors shared similar features. Further, the conjunction of low similarity between distractors-distractors and high similarity between target-distractors provided less awareness of the surroundings. Additionally, results revealed that there is a strong positive correlation between search time and awareness of the surroundings. Less awareness of the surroundings while playing a game implies that users are positively engaged in that game. These results offered a set of criteria that can be applied to future virtual reality interventions for medical pain distraction.
Blockchain-Enabled Security Framework for Enhancing IoT Networks: A Two-Layer Approach

The increasing proliferation of Internet of Things (IoT) nodes poses significant security challenges for their network’s communication. Blockchain technology, with its decentralized and distributed nature, has the potential to address these security concerns within IoT networks. LEACH (Low Energy Adaptive Clustering Hierarchy) algorithm and blockchain technology enhance IoT network security, enabling energy-efficient data management and transaction integrity, enhancing network lifespan and protection. This paper presents a security model that combines the LEACH algorithm and blockchain technology to improve IoT networks' security. The LEACH algorithm forms clusters of IoT devices, with a designated cluster head (CH) responsible for data aggregation and forwarding. Our model incorporates blockchain technology's core principles and cryptographic foundations, providing additional security measures. It consists of two main layers: the LEACH clustering-based routing protocol, which forms clusters and layers, and a blockchain simulator module. The LEACH algorithm enhances energy consumption, enables efficient data management within clusters, and ensures the integrity, transparency, and immutability of transactions. Our model is implemented on a simulator, allowing for experimentation and modification to evaluate the performance and effectiveness of the security enhanced IoT network model. Our results demonstrate the effectiveness of the proposed enhanced LEACH algorithm compared to previous algorithms, in which the last node died after 1868 transactions. As well as the results of the pro-posed framework, which record 0.058% of the state rate and 2.75 Throughput. Simulation results are validated with respect to previous algorithms, and it obtained higher accuracy compared to them.
Virtual Reality in Training: A Case Study on Investigating Immersive Training for Prisoners

This study addresses the pressing issue of prison rehabilitation by comparing traditional and Virtual Reality (VR) based training services offered by the General Directorate of Prisons in Saudi Arabia. Utilising Technology Acceptance Model (TAM) metrics such as perceived usefulness, ease of use, and enjoyment, the study evaluates the acceptance of VR technologies across two different headset platforms. Findings reveal that VR-based training services received significantly higher acceptance ratings than traditional methods. Both VR platforms were highly rated in terms of perceived usefulness, ease of use, and enjoyment but showed no significant differences between the headsets. These results indicate that VR-based methods could be more effective, engaging, and safer alternatives in correctional rehabilitation programs. Importantly, this research contributes to the field of Human-Computer Interaction (HCI) by suggesting design frameworks tailored for effective interventions in training and rehabilitative contexts where safety and psychological health are of high concern.
The Impact of Text Generation Techniques on Neural Image Captioning: An Empirical Study

Image captioning is an advanced NLP task that has various practical applications. To meet the requirement of visual information understanding and textual information generation, the encoder-decoder framework has been widely adopted by image captioning models. In this context, the encoder is responsible for transforming an image into vector representation, and the decoder acts as a text generator for yielding an image caption. It is obvious and intuitive that the decoder is crucial for the entire image captioning model. However, there is a lack of comprehensive studies in which the impact of various aspects of the decoder on the image captioning is investigated. To advance the understanding of the impacts of text generation techniques employed by the decoder, we conduct an extensive empirical analysis of three types of language models, two types of decoding strategies and two types of training methods, based on four state-of-the-art image captioning models. Our experimental results demonstrate that the language model affects the performance of image captioning models, while different language models may benefit different image captioning models. In addition, it is also revealed that among the decoding and training strategies under investigation, the beam search, AOA mechanism and the reinforcement learning based training method can generally improve the performance of image captioning models. Moreover, the results also show that the combinational usage of these strategies always outperforms the use of single strategy for the task of image captioning.
Contents list

Permissions Request permissions Contents list J. Anal. At. Spectrom., 2023, 38, 2207 DOI: 10.1039/D3JA90046K This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given. Read more about how to correctly acknowledge RSC content.
MetaFormer Baselines for Vision

MetaFormer, the abstracted architecture of Transformer, has been found to play a significant role in achieving competitive performance. In this paper, we further explore the capacity of MetaFormer, again, by migrating our focus away from the token mixer design: we introduce several baseline models under MetaFormer using the most basic or common mixers, and demonstrate their gratifying performance. We summarize our observations as follows: (1) MetaFormer ensures solid lower bound of performance. By merely adopting identity mapping as the token mixer, the MetaFormer model, termed IdentityFormer, achieves [Formula: see text]80% accuracy on ImageNet-1 K. (2) MetaFormer works well with arbitrary token mixers. When specifying the token mixer as even a random matrix to mix tokens, the resulting model RandFormer yields an accuracy of [Formula: see text]81%, outperforming IdentityFormer. Rest assured of MetaFormer's results when new token mixers are adopted. (3) MetaFormer effortlessly offers state-of-the-art results. With just conventional token mixers dated back five years ago, the models instantiated from MetaFormer already beat state of the art. (a) ConvFormer outperforms ConvNeXt. Taking the common depthwise separable convolutions as the token mixer, the model termed ConvFormer, which can be regarded as pure CNNs, outperforms the strong CNN model ConvNeXt. (b) CAFormer sets new record on ImageNet-1 K. By simply applying depthwise separable convolutions as token mixer in the bottom stages and vanilla self-attention in the top stages, the resulting model CAFormer sets a new record on ImageNet-1 K: it achieves an accuracy of 85.5% at 224 ×224 resolution, under normal supervised training without external data or distillation. In our expedition to probe MetaFormer, we also find that a new activation, StarReLU, reduces 71% FLOPs of activation compared with commonly-used GELU yet achieves better performance. Specifically, StarReLU is a variant of Squared ReLU dedicated to alleviating distribution shift. We expect StarReLU to find great potential in MetaFormer- like models alongside other neural networks. Code and models are available at https://github.com/sail-sg/metaformer.
Fault Detection in Wastewater Treatment Process Using Broad Slow Feature Neural Network With Incremental Learning Ability

Developing a fault detection model for the wastewater treatment process that combines satisfactory accuracy with comparatively low time overhead remains an exceedingly formidable endeavor. Fortunately, the broad slow feature neural network (BSFNN) perfectly embodies the dual advantages mentioned above. The BSFNN utilizes both slow feature windows and enhancement windows to extract significant and slowly varying information characterized by different velocities, which facilitates the learning of nonlinear and dynamic features related to superior monitoring accuracy. Another benefit of the BSFNN model is that it continues to retain the efficiency of the broad learning system with regard to time overhead, which is considerably decreased through employing the pseudoinverse strategy to determine network parameters. The operational environment often undergoes nonstationary dynamic changes in actual wastewater treatment processes. Especially in scenarios where higher monitoring accuracy is demanded or the network structure needs online adjustments to real-time update, and yet network adjustments can be time-consuming, the number of node parameters within incremental windows can be flexibly determined by dynamically adding enhancement nodes, which better obviates the necessity of retraining the entire BSFNN system from scratch, thereby allowing for online real-time adjustments to the structure and fault detection accuracy to achieve the desired performance. A case study using benchmark wastewater treatment platforms demonstrates that the suggested method outperforms advanced fault detection methods.
Contrastive Transformer Learning with Proximity Data Generation for Text-Based Person Search

Given a descriptive text query, text-based person search (TBPS) aims to retrieve the best matched target person from an image gallery. Such a cross-modal retrieval task is quite challenging due to significant modality gap, fine-grained differences and insufficiency of annotated data. To better align the two modalities, most existing works focus on introducing sophisticated network structures and auxiliary tasks, which are complex and hard to implement. In this paper, we propose a simple yet effective dual Transformer model for text-based person search. By exploiting a hardness-aware contrastive learning strategy, our model achieves state-of-the-art performance without any special design for local feature alignment or side information. Moreover, we propose a proximity data generation (PDG) module to automatically produce more diverse data for cross-modal training. The PDG module first introduces an automatic generation algorithm based on a text-to-image diffusion model, which generates new text-image pair samples in the proximity space of original ones. Then it combines approximate text generation and feature-level mixup during training to further strengthen the data diversity. The PDG module can largely guarantee the reasonability of the generated samples that are directly used for training without any human inspection for noise rejection. It improves the performance of our model significantly, providing a feasible solution to the data insufficiency problem faced by such fine-grained visual-linguistic tasks. Extensive experiments on two popular datasets of the TBPS task (i.e., CUHK-PEDES and ICFG-PEDES) show that the proposed approach outperforms state-of-the-art approaches evidently, e.g., improving by 3.88%, 4.02%, 2.92% in terms of Top1, Top5, Top10 on CUHK-PEDES.