Reviewerly/exHarmony huggingface dataset:
Corporate employee training in partnership with higher education institutions

In the presented work, the issues of the organization of in-house training based on the partnership of a commercial company and an educational organization of vocational education are considered. Continuing education helps professionals climb the corporate ladder and achieve success at work, and also allows corporations to save on expensive external hiring by promoting from within. The article substantiates that in modern conditions, when the gap in the skills that the employer expects from employees and their actual level is quite deep, corporate human resource management systems face the need to take advantage of corporate training, including that implemented on the basis of bilateral cooperation between industrial and academic structures. It is concluded that in conditions of talent shortage, it is extremely important for companies to adopt strategies that promote lifelong learning, professional development and retraining of the workforce.
Perspectives: Creating Inclusive and Equitable Hybrid Meeting Experiences

With the shift to hybrid meetings in work spaces, there is an increasing need to create a more inclusive hybrid meeting experience where people meeting together in a room interact with those joining remotely. This paper describes a design exploration, implementation, and evaluation of Perspectives, a novel hybrid meeting system that aimed to create an inclusive and equitable space for hybrid meetings. Perspectives digitally composites everyone into a virtual room so that each person has a unique but spatially consistent viewpoint into the meeting. The user study compared Perspectives with three commercially available UX designs for hybrid meetings: Gallery, Together Mode, and Front Row. Results from this study revealed key benefits of Perspectives, including supporting natural interactions, creating a strong sense of co-presence, and reducing cognitive load. Results from the study also helped iterate on the design principles of Perspectives, which offer important insights on supporting hybrid meetings.
Philosophical reminiscences in the work of the artist Mikhail Kapustin

The author examines the art history problem of the influence of philosophical trends on the art that is relevant to them on the example of the work of the St. Petersburg ceramic artist Mikhail Yuryevich Kapustin and his philosophical reflections revealed in the plastic search for impressive images. His worldview was formed in a whirlwind of contemporary philosophical trends, scientific and technological discoveries, new trends in culture and contemporary art. The article reveals the regular change of one philosophical direction to the next, often alternative, and how artists reacted to this in their work, finding new ideals and expanding expressive possibilities in the visual arts. The purpose of the article is an attempt to decipher the in-depth meanings of the artist's works. The subject of research is philosophical reminiscences in the author's sculptural and graphic works of the ceramic artist. The main themes of his artwork are Man, Earth, and Time. The study has a novelty, because its materials are original and are offered for publication for the first time. In domestic and foreign art criticism there was no scientific study of the work of M. Kapustin. The works of M. Kapustin are large-scale, peculiar ceramic (sculptural and graphic) compositions of epic imagery, represent a compressed formula of an idea in a tense volume of form. The logical chain of his creative process as a ceramic artist goes as follows: from the idea towards the artistic image, material, technology, and finally – work. The results and research methods in this article can be used in art criticism for contribution in writing monographic works about the work of other artists.
Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning

In this study, we propose a digital over-the-air computation (OAC) scheme for achieving continuous-valued (analog) aggregation for federated edge learning (FEEL). We show that the average of a set of real-valued parameters can be calculated approximately by using the average of the corresponding numerals, where the numerals are obtained based on a balanced number system. By exploiting this key property, the proposed scheme encodes the local stochastic gradients into a set of numerals. Next, it determines the positions of the activated orthogonal frequency division multiplexing (OFDM) subcarriers by using the values of the numerals. To eliminate the need for precise sample-level time synchronization, channel estimation overhead, and channel inversion, the proposed scheme also uses a non-coherent receiver at the edge server (ES) and does not utilize a pre-equalization at the edge devices (EDs). We theoretically analyze the MSE performance of the proposed scheme and the convergence rate for a non-convex loss function. To improve the test accuracy of FEEL with the proposed scheme, we introduce the concept of adaptive absolute maximum (AAM). Our numerical results show that when the proposed scheme is used with AAM for FEEL, the test accuracy can reach up to 98% for heterogeneous data distribution.
A Prediction Model Based Energy Efficient Data Collection for Wireless Sensor Networks

Many real-time applications make use of advanced wireless sensor networks (WSNs). Because of the limited memory, power limits, narrow communication bandwidth, and low processing units of wireless sensor nodes (SNs), WSNs suffer severe resource constraints. Data prediction algorithms in WSNs have become crucial for reducing redundant data transmission and extending the network's longevity. Redundancy can be decreased using proper machine learning (ML) techniques while the data aggregation process operates. Researchers persist in searching for effective modelling strategies and algorithms to help generate efficient and acceptable data aggregation methodologies from preexisting WSN models. This work proposes an energy-efficient Adaptive Seagull Optimization Algorithm (ASOA) protocol for selecting the best cluster head (CH). An extreme learning machine (ELM) is employed to select the data corresponding to each node as a way to generate a tree to cluster sensor data. The Dual Graph Convolutional Network (DGCN) is an analytical method that predicts future trends using time series data. Data clustering and aggregation are employed for each cluster head to efficiently perform sample data prediction across WSNs, primarily to minimize the processing overhead caused by the prediction algorithm. Simulation findings suggest that the presented method is practical and efficient regarding reliability, data reduction, and power usage. The results demonstrate that the suggested data collection approach surpasses the existing Least Mean Square (LMS), Periodic Data Prediction Algorithm (P-PDA), and Combined Data Prediction Model (CDPM) methods significantly. The proposed DGCN method has a transmission suppression rate of 92.68%, a difference of 22.33%, 16.69%, and 12.54% compared to the current methods (i.e., LMS, P-PDA, and CDPM).
Experimental Demonstration of Quantum Steering Swapping with Gaussian Entangled States

Abstract As a key element in quantum repeaters, entanglement swapping establishes entanglement between two independent entangled states. Besides entanglement, quantum steering has been identified as an essential quantum resource and has broad applications in quantum communication. Although entanglement swapping has been realized experimentally, quantum steering swapping still remains a challenge since the condition of quantum steering is stronger than that of entanglement. In this article, quantum steering swapping between a three‐mode and a two‐mode Gaussian entangled state in a lossy channel is demonstrated. By choosing the optimum gain in the classical channel that corresponds to the maximum transmission distance, the one‐way and two‐way steerabilities in a new three‐mode Gaussian state located in distant nodes are achieved. The obtained collective steerability from two users to one user has potential application in quantum secret sharing. The results make a crucial step toward applications of quantum steering in asymmetric quantum information processing.
Swinv2-Imagen: hierarchical vision transformer diffusion models for text-to-image generation

Abstract Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation. Google’s Imagen follows this research trend and outperforms DALLE2 as the best model for text-to-image generation. However, Imagen merely uses a T5 language model for text processing, which cannot ensure learning the semantic information of the text. Furthermore, the Efficient UNet leveraged by Imagen is not the best choice in image processing. To address these issues, we propose the Swinv2-Imagen, a novel text-to-image diffusion model based on a Hierarchical Visual Transformer and a Scene Graph incorporating a semantic layout. In the proposed model, the feature vectors of entities and relationships are extracted and involved in the diffusion model, effectively improving the quality of generated images. On top of that, we also introduce a Swin-Transformer-based UNet architecture, called Swinv2-Unet, which can address the problems stemming from the CNN convolution operations. Extensive experiments are conducted to evaluate the performance of the proposed model by using three real-world datasets, i.e. MSCOCO, CUB and MM-CelebA-HQ. The experimental results show that the proposed Swinv2-Imagen model outperforms several popular state-of-the-art methods.
Digitalising Social Protection Systems for Achieving the Sustainable Development Goals: Insights from Zimbabwe

Social protection systems, a target of the United Nation’s (UN) Sustainable Development Goals (SDGs), are intended to reduce extreme poverty, build human capital, and protect against risks to sustainable livelihoods and well-being. As social protection systems are by their nature inherently complex, multi-faceted, and socially embedded, it is inevitable that tensions will emerge between their design and implementation, representing design-reality gaps. These tensions present an excellent opportunity for cross-disciplinary research, by understanding how best to bridge these design-reality gaps. In this qualitative, interpretivist case study, we situate our work on the ground with the actors involved in the design, implementation, and use of a social protection system in Zimbabwe. We find interaction failures amongst some users; design-reality gaps around network access and ICT policy implementation; as well as mixed views regarding transparency and accountability of ICT. Our findings provide rich insights from ICT users in the global south and underscore the importance of the co-creation of IS interventions together with communities to ensure technologies consider social, political, economic, and network realities. We conclude by providing directions for future research.
Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Breast cancer is the most common cancer among women globally and presents a significant challenge due to its rising incidence and fatality rates. Factors such as cultural, socioeconomic, and educational barriers contribute to inadequate awareness and access to healthcare services, often leading to delayed diagnoses and poor patient outcomes. Furthermore, fostering a collaborative approach among healthcare providers, policymakers, and community leaders is crucial in addressing this critical women's health issue, reducing mortality rates, alleviating, and the overall burden of breast cancer. The main goal of this review is to explore various techniques of machine learning algorithms to examine high accuracy and early detection of breast cancer for the safe health of women.
Automatic Identification of Glomerular in Whole-Slide Images Using a Modified UNet Model

Glomeruli are interconnected capillaries in the renal cortex that are responsible for blood filtration. Damage to these glomeruli often signifies the presence of kidney disorders like glomerulonephritis and glomerulosclerosis, which can ultimately lead to chronic kidney disease and kidney failure. The timely detection of such conditions is essential for effective treatment. This paper proposes a modified UNet model to accurately detect glomeruli in whole-slide images of kidney tissue. The UNet model was modified by changing the number of filters and feature map dimensions from the first to the last layer to enhance the model's capacity for feature extraction. Moreover, the depth of the UNet model was also improved by adding one more convolution block to both the encoder and decoder sections. The dataset used in the study comprised 20 large whole-side images. Due to their large size, the images were cropped into 512 × 512-pixel patches, resulting in a dataset comprising 50,486 images. The proposed model performed well, with 95.7% accuracy, 97.2% precision, 96.4% recall, and 96.7% F1-score. These results demonstrate the proposed model's superior performance compared to the original UNet model, the UNet model with EfficientNetb3, and the current state-of-the-art. Based on these experimental findings, it has been determined that the proposed model accurately identifies glomeruli in extracted kidney patches.
Video SAR Target Detection and Tracking Method Based on Yolov5+Bytetrack

Video Synthetic Aperture Radar (SAR) technology can acquire high frame rate image sequences of the observed scene, and use the shadow features formed by ground moving targets such as vehicles in the image sequence to achieve dynamic target state perception. In this paper, a lightweight Yolov5s deep learning network is used for vehicle target shadow detection, and image enhancement and attention mechanisms are introduced to significantly improve the detection accuracy of vehicle targets, achieving an accuracy rate of 95.6% and greatly reducing the false alarm rate. Furthermore, five target tracking algorithms are compared, and it is verified that the Bytetrack algorithm can achieve better dynamic target data association and tracking performance than SORT and its three improved methods.
DMF-Net: a deep multi-level semantic fusion network for high-resolution chest CT and X-ray image de-noising

Abstract Medical images such as CT and X-ray have been widely used for the detection of several chest infections and lung diseases. However, these images are susceptible to different types of noise, and it is hard to remove these noises due to their complex distribution. The presence of such noise significantly deteriorates the quality of the images and significantly affects the diagnosis performance. Hence, the design of an effective de-noising technique is highly essential to remove the noise from chest CT and X-ray images prior to further processing. Deep learning methods, mainly, CNN have shown tremendous progress on de-noising tasks. However, existing CNN based models estimate the noise from the final layers, which may not carry adequate details of the image. To tackle this issue, in this paper a deep multi-level semantic fusion network is proposed, called DMF-Net for the removal of noise from chest CT and X-ray images. The DMF-Net mainly comprises of a dilated convolutional feature extraction block, a cascaded feature learning block (CFLB) and a noise fusion block (NFB) followed by a prominent feature extraction block. The CFLB cascades the features from different levels (convolutional layers) which are later fed to NFB to attain correct noise prediction. Finally, the Prominent Feature Extraction Block(PFEB) produces the clean image. To validate the proposed de-noising technique, a separate and a mixed dataset containing high-resolution CT and X-ray images with specific and blind noise are used. Experimental results indicate the effectiveness of the DMF-Net compared to other state-of-the-art methods in the context of peak signal-to-noise ratio (PSNR) and structural similarity measurement (SSIM) while drastically cutting down on the processing power needed.
STUDENTS' PERCEPTION TOWARD INSTAGRAM USAGE IN WRITING SKILL

The goal of this research is to describe students' attitudes toward using Instagram to improve their writing ability and interest. In this study, the descriptive quantitative method was used. The information was gathered from senior high school students, and a total of 104 students took part. The goal of this study was to examine students' perceptions of writing skills in using the Instagram application and as an English learning platform, as well as students' understanding and interest in using Instagram application. Data were gathered using questionnaires distributed via Google Forms to high school students and interviews. For tallying the data analysis, the data analysis used SPSS and Excel software. This study finds that using Instagram can help senior high school students improve their writing skills.
A network traffic identification method based on AutoEncoder - a feature selection algorithm

Abstract Traffic identification methods consider a large number of traffic features, resulting in low identification efficiency. To address the efficiency problem of traffic recognition, this paper proposes an efficient network traffic recognition method, AutoEncoder-based traffic recognition (AE-NTI). The method first preprocesses the original dataset and converts it into a two-dimensional grayscale image. Then, feature selection is performed by an improved feature selection algorithm based on AutoEncoder. The algorithm consists of a feature scorer, which globally scores all features, and a feature selector, which selects the highest scoring features to reconstruct the original data. Finally, the convolutional neural network structure is adjusted so that the network traffic can be identified. The experimental results show that the method has significantly improved in recognition accuracy and model fitting speed.
Dataset for the assessment of presence and performance in an augmented reality environment for motor imitation learning: A case-study on violinists

This dataset comprises motion capture, audio, and questionnaire data from violinists who underwent four augmented reality training sessions spanning a month. The motion capture data was meticulously recorded using a 42-marker Qualisys Animation marker set, capturing movement at a high rate of 120 Hz. Audio data was captured using two condenser microphones, boasting a bit depth of 24 and a sampling rate of 48 kHz. The dataset encompasses recordings from 2 violin orchestra section leaders and 11 participants. Initially, we collected motion capture (MoCap) and audio data from the section leaders, who performed 2 distinct musical pieces. These recordings were then utilized to create 2 avatars, each representing a section leader and their respective musical piece. Subsequently, each avatar was assigned to a group of violinists, forming groups of 5 and 6 participants. Throughout the experiment, participants rehearsed one piece four times using a 2D representation of the avatar, and the other piece four times using a 3D representation. During the practice sessions, participants were instructed to meticulously replicate the avatar's bowing techniques, encompassing gestures related to bowing, articulation, and dynamics. For each trial, we collected motion capture, audio data, and self-reported questionnaires from all participants. The questionnaires included the Witmer presence questionnaire, a subset of the Makransky presence questionnaire, the sense of musical agency questionnaire, as well as open-ended questions for participants to express their thoughts and experiences. Additionally, participants completed the Immersive Tendencies questionnaire, the Music Sophistication Index questionnaire, and provided demographic information before the first session commenced.
Blockchain Powered IoT Platform for Autonomous Drone Operations in Smart Farming for Environment Sustainability

Abstract A blockchain based platform can be decentralized, meaning that it is not controlled by a single entity or organization. This can help to cut down the risk of data manipulation or fraud, and can also make the platform more resilient to cyber-attacks or other security threats. By automating many of the processes involved in data collection and analysis, blockchain technology can assist farmers to decrease their costs and increase profitability. Architecture for blockchain powered IoT platform for autonomous drone operations in smart farming is proposed. It has three layers which include data acquistion and encryption in IoT layer and creation and management of ledger in the blockchain layer and service provider layer that include user interaction and farming software. Agriculture integrates many of the new automation technologies already in their routine. The proposed architecture enriches the functioning of farming without affecting the current framework. Overall, the blockchain powered IoT platform for autonomous drone operations in smart farming can help farmers improve their efficiency, increase transparency and security, reduce costs, and ultimately achieve more environment sustainability and profitable farming practices.
GenAIR: Exploring Design Factor of Employing Generative AI for Augmented Reality

Generative Artificial Intelligence (GenAI) has emerged as a fundamental component of intelligent interactive systems, enabling the automatic generation of multimodal media content. The continuous enhancement in the quality of Artificial Intelligence-Generated Content (AIGC), including but not limited to images and text, is forging new paradigms for its application, particularly within the domain of Augmented Reality (AR). Nevertheless, the application of GenAI within the AR design process remains opaque. This paper aims to articulate a design space encapsulating a series of criteria and a prototypical process to aid practitioners in assessing the aptness of adopting pertinent technologies. The proposed model has been formulated based on a synthesis of design insights garnered from ten experts, obtained through focus group interviews. Leveraging these initial insights, we delineate potential applications of GenAI in AR.
Wind Turbine Surface Defect Detection Method Based on YOLOv5s-L

In order to solve the problems of low efficiency, time consumption and high costs in the detection of defects on wind turbine surfaces in industrial scenarios, an improved YOLOv5 algorithm for wind turbine surface defect detection is proposed, named YOLOv5s-L. Firstly, the C3 module of YOLOv5s is replaced with the C2f module, which is more abundant in gradient flow, to enhance the ability of feature extraction and feature fusion. Secondly, the Squeeze and Excitation (SE) module is embedded in the YOLOv5 Backbone network to filter out redundant feature information and retain important feature information. Thirdly, the weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced to replace the FPN + PAN, which can achieve a higher level of feature fusion while keeping the weight light. Finally, the Focal Loss function is used to replace the CIOU Loss function of the YOLOv5 algorithm to optimize the training model and improve the accuracy of the algorithm. The experimental results show that, compared with the traditional YOLOv5 algorithm, the average precision mAP is improved by 1.9%, and the frame rate FPS can reach 145 F/s without increasing the model parameters; it can satisfy the requirements for real-time, accurate detection on mobile devices. This method provides effective support for surface defect detection of wind turbines and provides reference for intelligent wind farm operation and maintenance.