Reviewerly/exHarmony huggingface dataset:
Joint think locally and globally: Communication-efficient federated learning with feature-aligned filter selection

Federated learning, as a distributed machine learning framework that can protect data privacy, has recently received increasing attention. In federated learning, a shared global model is obtained through parameter interaction, which leads to frequent parameter communication during the training process. The limited bandwidth of IoT and edge devices that deploy federated learning further affects communication and learning efficiency. In this paper, an enhanced federated learning technique is presented by proposing a feature-aligned filter selection method. Besides, it is believed that the training gap between the global model and the local model on each node should be focused on during the training process. Then, we can select the local contribution parameters to improve the communication efficiency while ensuring the performance of the shared global model. Therefore, the Geometric Median of each layer in the global model is adopted as the criterion to select important filters in the local model, and then the parameters interact with other nodes to achieve efficient communication. The results under a variety of experimental settings demonstrate that our proposed federated learning scheme can effectively enhance communication efficiency and ensure the performance of the global model. Compared with the state-of-the-art methods, a maximum of 6.5× improvements in communication efficiency can be obtained.
Super-Resolution Techniques in Photogrammetric 3D Reconstruction from Close-Range UAV Imagery

Current Multi-View Stereo (MVS) algorithms are tools for high-quality 3D model reconstruction, strongly depending on image spatial resolution. In this context, the combination of image Super-Resolution (SR) with image-based 3D reconstruction is turning into an interesting research topic in photogrammetry, around which however only a few works have been reported so far in the literature. Here, a thorough study is carried out on various state-of-the-art image SR techniques to evaluate the suitability of such an approach in terms of its inclusion in the 3D reconstruction process. Deep-learning techniques are tested here on a UAV image dataset, while the MVS task is then performed via the Agisoft Metashape photogrammetric tool. The data under experimentation are oblique cultural heritage imagery. According to results, point clouds from low-resolution images present quality inferior to those from upsampled high-resolution ones. The SR techniques HAT and DRLN outperform bicubic interpolation, yielding high precision/recall scores for the differences of reconstructed 3D point clouds from the reference surface. The current study indicates spatial image resolution increased by SR techniques may indeed be advantageous for state-of-the art photogrammetric 3D reconstruction.
Robotic System for Blood Serum Aliquoting Based on a Neural Network Model of Machine Vision

The quality of the diagnostic information obtained in the course of laboratory studies depends on the accuracy of compliance with the regulations for the necessary work. The process of aliquoting blood serum requires immersing the pipette to different depths depending on the boundary level between blood phases. A vision system can be used to determine this depth during automated aliquoting using various algorithms. As part of the work, two recognition algorithms are synthesized, one of which is based on the use of the HSV color palette, the other is based on the convolutional neural network. In the Python language, software systems have been developed that implement the ability of a vision system to recognize blood in test tubes. The developed methods are supposed to be used for aliquoting biosamples using a delta robot in a multirobotic system, which will increase the productivity of ongoing biomedical research through the use of new technical solutions and principles of intelligent robotics. The visualized results of the work of the considered programs are presented and a comparative analysis of the quality of recognition is carried out.
An Adaptive Pedaling Assistive Device for Asymmetric Torque Assistant in Cycling

Dynamic loads have short and long-term effects in the rehabilitation of lower limb joints. However, an effective exercise program for lower limb rehabilitation has been debated for a long time. Cycling ergometers were instrumented and used as a tool to mechanically load the lower limbs and track the joint mechano-physiological response in rehabilitation programs. Current cycling ergometers apply symmetrical loading to the limbs, which may not reflect the actual load-bearing capacity of each limb, as in Parkinson's and Multiple Sclerosis diseases. Therefore, the present study aimed to develop a new cycling ergometer capable of applying asymmetric loads to the limbs and validate its function using human tests. The instrumented force sensor and crank position sensing system recorded the kinetics and kinematics of pedaling. This information was used to apply an asymmetric assistive torque only to the target leg using an electric motor. The performance of the proposed cycling ergometer was studied during a cycling task at three different intensities. It was shown that the proposed device reduced the pedaling force of the target leg by 19% to 40%, depending on the exercise intensity. This reduction in pedal force caused a significant reduction in the muscle activity of the target leg (p < 0.001), without affecting the muscle activity of the non-target leg. These results demonstrated that the proposed cycling ergometer device is capable of applying asymmetric loading to lower limbs, and thus has the potential to improve the outcome of exercise interventions in patients with asymmetric function in lower limbs.
“Transforming” Personality Scale Development: Illustrating the Potential of State-of-the-Art Natural Language Processing

Natural language processing (NLP) techniques are becoming increasingly popular in industrial and organizational psychology. One promising area for NLP-based applications is scale development; yet, while many possibilities exist, so far these applications have been restricted—mainly focusing on automated item generation. The current research expands this potential by illustrating an NLP-based approach to content analysis, which manually categorizes scale items by their measured constructs. In NLP, content analysis is performed as a text classification task whereby a model is trained to automatically assign scale items to the construct that they measure. Here, we present an approach to text classification—using state-of-the-art transformer models—that builds upon past approaches. We begin by introducing transformer models and their advantages over alternative methods. Next, we illustrate how to train a transformer to content analyze Big Five personality items. Then, we compare the models trained to human raters, finding that transformer models outperform human raters and several alternative models. Finally, we present practical considerations, limitations, and future research directions.
Nurturing Italian Geo-palaeontological Heritage with Virtual Palaeontology: Preliminary Report of Its Application in Two Natural History Museums

Abstract In this manuscript, we present a preliminary report on the use of virtual palaeontology methodologies in two natural history museums in central Italy, the Geology and Paleontology Museum of Florence and the Civic Museum of Natural Sciences of Faenza. Despite the differences between the museums (in terms of history, type and size of the collections, etc.), the use of surface and computed tomography (CT) scans has allowed the acquisition of a consistent amount of data to create digital copies of selected specimens and to plan several future projects sparked from the use of these methodologies. Our first step focused on the selection of the scanning sample: type and historically relevant specimen in the case of the Florence museum, and fragile and relevant specimens for the Faenza museum from a local yet internationally known site, Cava Monticino, dated to the Late Miocene. The scanning techniques included the use of three high-resolution scanners, with different specifications, to acquire surface data and a medical scanner to obtain CT scans. The outputs of the surface scans were excellent digital copies of the specimens, whereas tomography allowed the first investigations and visualisations of the presence of embedded bones in the fossiliferous blocks of Cava Monticino. The resulting 3D and raw data represent invaluable resources that the two museums are currently planning to implement in their exhibitions using digital visualisation devices and techniques (e.g. AR web apps, touchscreens) or 3D-printed touchable specimens.
STCN-Net: A Novel Multi-Feature Stream Fusion Visibility Estimation Approach

Low visibility always leads to serious traffic accidents worldwide, although extensive works are studied to deal with the estimation of visibility in meteorology areas, it is still a tough problem.Deep learning-based visibility estimation methods, it has low accuracy due to lacking "specific features" of the foggy images.Meanwhile, physical modelbased visibility estimation methods are only applicable to some specific scenes due to its high requirements for extra auxiliary parameters.Therefore, This paper proposes a novel end-to-end framework named STCN-Net for visibility estimation, which combined the "engineered features" and "learned features" to achieve higher accuracy.Specifically, a novel 3D multi-feature stream Matrix, named DDT, is designed for visibility estimation, which is consisted of a transmittance matrix, a dark channel matrix, and a depth matrix.Unlike traditional deep learning methods which only use convolutional neural networks (CNN) to deal with the input data or images, our method combines CNN and Transformer to process the input data or images.In STCN-Net, Swin-Transformer(Swin-T) module takes the original image as input while the CNN module takes the DDT matrix as input.Moreover, in order to integrate different feature information from the CNN and Swin-T, we embed a Coordinate Attention (CA) module in STCN-Net.Finally, two visibility datasets: Visibility Image Dataset Ⅰ (VID I) and Visibility Image Dataset Ⅱ (VID II) were constructed for evaluation where VID I is a real scene visibility dataset and VID II is a synthetic visibility dataset.The experimental results show that our method has better performance than classical methods on the two datasets.And compared with the runner-up, it has 2.1% more accuracy in VID I and 0.5% in VID II.
Internet of Things with Wearable Devices and Artificial Intelligence for Elderly Uninterrupted Healthcare Monitoring Systems

The advent of recent pandemics has changed the priority given to the healthcare system by each country, and this has changed the thinking of many towards the management of health-related illnesses. The Internet of Things (IoT) interconnects with smart devices in today's Internet and has changed the trend in the next-generation technologies. This comes with various advantages like the connectivity of smart devices with several services to amass a huge amount of data and connectivity. These have revolutionized modern healthcare by assuring economic, social, and technological prospects. There has been an increase in the number of elderly people living or staying alone, and the need of monitoring them remotely increasing exponentially. Hence, the use of IoT-based systems can be used to leverage these challenges. The combination of IoT-wearable devices enabled by Artificial Intelligence can be used to solve some of these problems by monitoring elderly persons remotely and allowing them to conduct their day-to-day activities without any fear. Therefore, this paper proposed IoT-wearable enabled AI to remotely monitor elderly persons in real-time. Various wearable sensors were used to capture elderly physiological signs, the IoT-based cloud database was used to store the captured data, and the AI model was to process the data for effective decision-making. The health status of the elderly gets to the healthcare workers in real-time, thus enabling them to give precautionary advice to save lives. The system will also reduce the workload of medical personnel by monitoring elderly persons in real-time and remotely.
It's not all Bad - Worker Perceptions of Industrial Robots

The current discourse presented by mainstream media towards industrial robots seems to focus on the negative aspects their introduction brings to the workforce. However, it is unclear whether industrial workers share this negative perspective regarding industrial robots. In this paper, we present the results of a survey study (N=94) investigating differences in perception towards industrial robots, depending on the presence or absence of exposure to them in the workplace. Our results show that while workers with robot experience acknowledge that robots can lead to job loss, they also show stronger beliefs in the robots ability to boost new job opportunities. Additionally, we found that first-hand experience with robots in the workplace can positively affect workers perceptions about their advantages. Overall, our findings show that, contrasting the bleak picture drawn by mainstream media, workers exposed to industrial robots developed a more nuanced view of this new technology in the workplace.
Full-Range Virtual Try-On with Recurrent Tri-Level Transform

Virtual try-on aims to transfer a target clothing image onto a reference person. Though great progress has been achieved, the functioning zone of existing works is still limited to standard clothes (e.g., plain shirt without complex laces or ripped effect), while the vast complexity and variety of non-standard clothes (e.g., off-shoulder shirt, word-shoulder dress) are largely ignored. In this work, we propose a principled framework, Re-current Tri-Level Transform (RT-VTON), that performs full-range virtual try-on on both standard and non-standard clothes. We have two key insights towards the framework design: 1) Semantics transfer requires a gradual feature transform on three different levels of clothing representations, namely clothes code, pose code and parsing code. 2) Geometry transfer requires a regularized image deformation between rigidity and flexibility. Firstly, we predict the semantics of the “after-try-on” person by recurrently refining the tri-level feature codes using local gated attention and non-local correspondence learning. Next, we design a semi-rigid deformation to align the clothing image and the predicted semantics, which preserves local warping similarity. Finally, a canonical try-on synthesizer fuses all the processed information to generate the clothed person image. Extensive experiments on conventional benchmarks along with user studies demonstrate that our framework achieves state-of-the-art performance both quantitatively and qualitatively. Notably, RT-VTON shows compelling results on a wide range of non-standard clothes. Project page: https://lzqhardworker.github.io/RT-VTON/.
Detection of Phishing URLs Using Temporal Convolutional Network

Throughout the past few years, phishing attacks have become an increasingly substantial problem for individuals and organizations. In this non-technical attack, the victim is deceived into accessing a malicious URL that downloads a malicious program to access the network or redirects the victim to a page that requests sensitive information. The literature is filled with many research proposals to mitigate this problem however, the dynamic nature and the creativity of the attacker have made it difficult and reoccurring. In this paper, we proposed to use novel deep learning techniques, namely the Temporal convolutional network (TCN) with word embedding, to detect phishing URLs. As a result of our experiments, we found that our model can detect phishing URLs with 98.95% accuracy, 98% precision, 98% recall, and 98% f1-score. This result indicates that our model is effective against phishing attacks.
Social media content classification and community detection using deep learning and graph analytics

Social cybermedia has greatly changed the world’s perspective on information sharing and propagation. These platforms build virtual user communities in the form of followings or followers that can spread positive or negative content. These virtual communities play a vital role in the spread of hate content, which may create a severe law-and-order situation and social unrest in any country. Hate content can include offensive, anti-state, anti-religion, sexist, and racist posts. Classifying such content and identifying influential individuals who participate in these communities are challenging tasks for law enforcement agencies. This study proposes novel deep learning and graph-based approaches to identifying hate content, followed by approaches to detecting communities and exploring social media to detect hate content. Twitter is used as a case study, and tweets are extracted and annotated by linguistic experts to develop a dataset for experimentation and validation. The proposed customized LSTM-GRU model is used to classify hate content into six categories. The developed model offers an accuracy of 98.14% on the obtained dataset. The Girvan–Newman algorithm is employed for Twitter community detection and successfully identifies the most influential individuals. Moreover, the proposed approach precisely detects intraclass communities and highly influential persons. The developed model effectively identifies hate tweets and communities and can be used to monitor social media to identify any potential threat of unrest.
DiOS—An Extended Reality Operating System for the Metaverse

Driven by the recent improvements in device and networks capabilities, extended reality (XR) is becoming more pervasive; industry and academia alike envision ambitious projects, such as the metaverse. However, XR is still limited by the current architecture of mobile systems. This article makes the case for an XR-specific operating system (XROS). An XROS integrates hardware-support, computer vision algorithms, and XR-specific networking as the primitives supporting XR technology. These primitives represent the physical–digital world as a single shared resource among applications. Such an XROS allows for the development of coherent and system-wide interaction and display methods, systematic privacy preservation on sensor data, and performance improvement while simplifying application development.
Object Detection based Approach for an Efficient Video Summarization with System Statistics over Cloud

The tremendous volume of video data generated by industrial surveillance networks presents a number of difficulties when examining such videos for a variety of purposes, including video summarization (VS), analysis, indexing and retrieval. The task of creating video summaries is extremely difficult because of the huge amount of data, redundancy, interleaved views and light variations. Multiple object detection and identification in video is difficult for machines to recognize and classify. To address all such issues, multiple low-feature and clustering-based machine learning strategies that fail to completely exploit VS are recommended. In this work, we achieved VS by embedding deep neural network-based soft computing methods. Firstly, the objects in extracted frames are detected using YOLOv5, and then the frames without objects (useless frames) are removed. Video summary generation occurs with the help of frames containing Objects. To check the quality of the proposed work Summary length, precision, recall, PR curve, and mean average precision (mAP) are used and system resource utilization during the model training are also tracked. As a result, the proposed work was able to identify the most effective video summarization framework with best summary length under varying conditions.
A New Vehicle Tracking System with R-CNN and Random Forest Classifier for Disaster Management Platform to Improve Performance

Smart devices connected to the Internet of Things (IoT) play a big role in the growth of smart cities, which are becoming more technologically advanced over time. Find out how many monitoring systems that are based on artificial intelligence could save people's lives in different situations. Disaster management needs to be better organized in order to save more lives and keep an accurate count of property damage. People who are in trouble can get help quickly, like fire extinguishers, ambulances, and other emergency vehicles. Today, you can also use visual equipment to look at a situation and find actions that can be done again. In the past, this was not the case. With the systems that are out there now, it is possible to find out where an ambulance is. GPS and surveillance cameras can be used to find the location with great accuracy. In the proposed work, a new intervention method is used to pull out features that can be used to find objects and track vehicles. This process uses image object detection and a field of view that takes in the whole NH road (AoV). Region-based convolutional neural network, or R-CNN, is an algorithm that can be useful when doing research. In an emergency, it gives you the right mix of different visual clues to help you find your way. When looking at traffic during times of heavy congestion, the position of the camera and how drivers and passengers interact on the road are both taken into account. Cars can be targeted based on the area where they run and the time differences that come with that. Both the Random Forest algorithm and the Adam optimizer are based on neural networks. They are analysed to see how well classifiers do their jobs. These changes were made to make the system work better as a whole while keeping the same level of effectiveness, even though the monitoring services rotate through 360 degrees.