Reviewerly/exHarmony huggingface dataset:
Facial Feature-Based Microsleep Detection with High Precision Using Deep Learning

Annually, approximately 1.3 million people lose their lives in road traffic accidents, with microsleep being a significant contributing factor. The rise in microsleep-related accidents is attributed to factors like heavy workloads, extended hours, traffic congestion, and substance consumption. These incidents result in fatalities, injuries, property damage, and disability. To combat this trend, we propose a deep learning-based approach to detect microsleep in smart vehicles. Leveraging 772 images data points from Kaggle, we evaluate our model using the intersection of union (IoU) of microsleep traits. Focusing on facial features, our approach predicts microsleep confidence values, utilizing deep learning's capabilities in image recognition. Initial results are promising, with a mean Average Precision (mAP) of 99%, signifying high accuracy in microsleep detection. This level of accuracy holds the potential to significantly enhance road safety by effectively identifying and preventing microsleep-related accidents
Net0Chain: An AI-Enabled Climate and Environmental Risks (CER) Framework for Achieving Net-Zero

This paper presents a comprehensive analysis of climate and environmental risks (CERs) and their significant financial implications. The existing scientific literature lacks adequate recognition and understanding of the financial impacts of CERs, particularly on financial services supporting entities exposed to these risks. The urgency to address these risks becomes paramount for achieving net-zero. To bridge this gap, we propose an AI-enabled framework, Net0Chain, designed to predict and manage the risks associated with climate change. Net0Chain integrates the COBIT 2019 IT Governance model to realize benefits and optimize resources, offering recommendations for achieving net-zero emissions. By implementing a circular economy model, the framework aims to guide financial sectors in translating physical and transition scenarios of climate change into actionable financial strategies. This paper underscores the importance of proactive and informed financial management in the face of growing climate challenges, advocating for a robust approach that aligns with global climate and energy transition targets.
Integration of Extended Reality (XR) in Non-Native Undergraduate Programmes

This paper presents the use of XR technologies in teaching CAD. Immersive experience generated by XR technologies have traditionally found a quicker uptake in visualization industry especially in arts, entertainment and gaming. Current undergraduate (UG) modules in computer science and game development programme provide underpinning modules for learning XR technology. Other non-native UG Programmes such as architecture, engineering (mechanical, aerospace, automotive, Computer Aided Design (CAD)) and construction do not provide these modules as a part of the programme. However, use of XR technology has been widely exploited in such application areas as its use cases are exponentially growing. The proposed study explored the potential benefits of utilizing XR technology in the BSc CAD, BEng (Mechanical and Aircraft) program. The paper presents a review of the current state of the XR use in various industries and XR related pedagogy, followed by a pilot study of introducing Virtual Reality (VR) application to current programme modules at the University of the West of Scotland. This use case is particularly important to collaborative design review using XR technology. The study captures students' and staffs' perception on adapting to this new technology. This paper provides an academic feasibility scenario for integrating XR/VR subject area in non-native UG programmes. The paper also provides an use-case for implementing VR assisted collaborative product review for evaluating a product design and development progress.
Heuristic Routing and Wavelength Allocation for Tidal Traffic in Quantum Secure Metropolitan Area Networks

In quantum key distribution (QKD) metropolitan area networks (MANs), the tidal effect of traffic, which is caused by the imbalanced distribution of users, decreases network resource utilization and leads to an imbalanced distribution of quantum keys. Additionally, there is noise interference when both classical and quantum signals are transmitted together within the same optical fiber. To solve these problems while considering the specific requirements of the services, we propose heuristic routing and wavelength allocation algorithm. Firstly, the routing scheme based on Minimization of Network Load Standard Deviation (MNLSD) is applied to balance traffic, reduce blocking rate, and achieve the goal of balancing secure key rate of QKD system. Based on this, we design a wavelength allocation scheme by Priority Based on Noise Influence (PBNI), which is applied to the scenarios where quantum channels are fixed but classical services dynamically arrive. It achieves noise suppression by flexibly scheduling classical services. Simulation results shows that MNLSD can reduce the standard deviation of link load up to 83.9% and the blocking rate up to 20% compared to benchmark scheme. Additionally, when compared to benchmark scheme, PBNI can improve the secure key rate of QKD system up to 127.3%.
DMCNET: Data-Driven Multi-Pilot Convolution Neural Network for Mimo-Ofdm Receiver

This paper focuses on studying the deep learning (DL) application of neural networks to solve the reception of single-antenna OFDM signals. Specifically, in multi-antenna scenarios, the channel model is more complex compared to single-antenna cases. By leveraging the characteristics of DL, such as automatic learning of parameters using deep neural networks, we treat the reception process of MIMOOFDM signals as a black box and utilize neural networks to accomplish the signal reception task. Moreover, we propose the data-driven multi-pilot convolution neural network for MIMO-OFDM receiver (DMCNet). By incorporating complex convolution and complex fully connected structures, we design a receiver network to recover the transmitted signals from the received signals. We validate the accuracy and robustness of DMCNet under different channel conditions, comparing the bit error rates with different schemes. Additionally, we discuss the factors influencing various channel effects. Experimental results demonstrate that the DL-based reception scheme exhibits promising feasibility in terms of accuracy and interference resistance when compared to traditional approaches.
Data-driven multi period operation optimization of active distribution networks

Traditional physical models are difficult to model and analyze the uncertainty factors of the power grid, and have weak adaptability to changes in ADN structure and new elements. This paper proposes a data-driven active distribution network operation optimization strategy, which uses data-driven replacement process simulation to directly obtain control strategies from real-time environmental operation information. The implicit internal logic between the optimal operation of the operation scenario and the state of the control equipment can be explored through extreme learning machine, so the mapping relationship between the operation control strategy and distribution network operation scenario can be established. The data-driven reactive power optimization method proposed in this article does not rely on actual system power flow calculations, achieving optimized scheduling of active distribution networks under data-driven conditions, and providing a foundation for control strategies of active distribution networks.
An Improved Attention Transfer Teacher-Student Model for Edge Deployment

Large-scale deep learning models have done really well in things like understanding language, recognizing images, and other areas. However, these models also bring challenges, including computational complexity and storage requirements, which will induce the difficulty to implement large-scale deep learning models on edge computing devices. This paper proposes an improved attention transfer teacher-student model and related distillation loss function and training strategy, which is a teacher-student model knowledge distillation method for attention transfer through class activation maps. Not only does the teacher model change its predictions, but it also shares its attention with the student model, the goal is to make sure the student model works like the teacher model, so that the former can successfully learn the teacher model and be deployed at the edge of resource constraints device. The test results on open source datasets initially show that this method can further compress models and is suitable for deployment on resource-constrained devices.
Research on Artificial Intelligence Digital System Based on GPT Deep Learning Model

A GPT deep neural network algorithm with good function and strong practicability is proposed. First, complete the overall system framework and system database design. Through the design of data processing module and recommendation algorithm module, the main functional modules of the system are designed and implemented. Deep residual learning neural network is used to continuously train samples to improve the accuracy of product design judgment. Finally, the GPT based deep neural network intelligent product development decision method is tested and simulated to verify the correctness and feasibility of the method.
Exploration on Artificial Intelligence Technology in Power System State Estimation, Monitoring and Early Warning System

A modern dispatch center requires the ability to quickly, accurately, and comprehensively grasp the actual operation status of the power grid, predict and analyze the operating trend of the system, take corresponding measures to address problems that arise during operation, and provide decision-making for the next step of operation to ensure the safe and economic operation of the power grid. Therefore, developing new microcomputer monitoring and analysis methods has become an urgent task for the power grid dispatch department. The purpose of this article is to study the monitoring and warning system for power system state estimation based on artificial intelligence technology. In this design, the main technologies used include the Java platform, Document Object Model technology, Hibernate persistence technology, etc. On this basis, the data communication and analysis function module, state estimation and early warning monitoring module, and data viewing function were designed, and the entire monitoring system architecture was ultimately implemented. After the design was completed, the system testing was completed using black box as the main testing method. The test structure showed that the upgraded and optimized OPEN 3000 overcame the drawbacks of the original operation monitoring, and eliminated all functional bugs to 0, achieving its set concept.
Vision-Based Outdoor Garbage Collection Robot

Leaving garbage in the outdoor environment is harmful to nature, and traditionally, the collection process has been challenging, time-consuming, and labor-intensive. In order to automate the process of garbage detection and collection, I have designed this AI-powered garbage collection robot. The robot's chassis uses Mecanum wheels as the power source, enabling free rotation and translation on a flat surface. The detection method utilizes the YOLOv3 algorithm to identify the type, size, and distance of the garbage. The device has been tested in practical scenarios and can effectively recognize and clean small-sized garbage, reducing the burden on manual labor.
Improved Grey Wolf Optimization Algorithm Based on Secant Inertia Weight Strategy

Given the low solution accuracy, slow convergence speed, and easy entrapment in local optimal solution of Grey Wolf Optimization Algorithm (GWO) in solving function optimization problems, this paper introduces the secant function and proposes an improved Grey Wolf Optimization based on secant inertia weight strategy (SGWO). Compared with six other swarm intelligence optimization algorithms, the simulation experimental results on five standard test functions demonstrate that SGWO can effectively balance the global and local search ability, and significantly improve the convergence speed, accuracy, and stability.
Image Annotation Using VQGAN and Backtranslation

The paper presents an image annotation technique based on VQGAN and backtranslation. Using the VQGAN model, it learns a quantized codebook that expresses an image in block units, encodes the image using the codebook, and then trains a back-translation model which translate image to text using small amount of text/image pair data. Using the back-translation model, we can generate synthetic text data from image only dataset. Experimental result shows that we can generate synthetic text describing Korean face images and successfully train a text-to-image generation model.
An Neural Collaborative Filtering (NCF) based Recommender System for Personalized Rehabilitation Exercises

Rehabilitation exercises should be prescribed and performed by carefully selecting the type of exercise and intensity for each patient. If rehabilitation exercises are prescribed incorrectly, they may be ineffective or cause dangerous situations such as secondary injuries. In this study, we developed a personalized rehabilitation exercise recommender system based on data from a clinical trial of rehabilitation exercise intervention conducted at Chungnam National University Sejong Hospital. We generated a training dataset from the clinical trial data of rehabilitation exercise intervention for patients with shoulder adhesive capsulitis, rotator cuff injury, and back pain, and developed a rehabilitation exercise recommender system based on NCF's NeuMF model to verify its performance. The results show that the developed rehabilitation exercise recommender model outperforms the methods based on GMF and MLP models only. The personalized rehabilitation exercise recommender system in this study is expected to help rehabilitation patients recover their functions, improve their health, and return to society quickly and safely.
Unified Framework for End-User Authentication Protocol in Feature-as-a-Service Models

Feature-as-a-Service (FaaS) is a new cloud computing model that simplifies the implementation of new features in existing apps with minimal code modifications. One of the most important characteristics of FaaS is that the FaaS server manages its data independently of the service server. However, such a FaaS scheme has a potential issue. Specifically, in the FaaS server, authenticating end-users can be a problem because the service server usually manages user and authentication data. Although several FaaS products requiring end-user authentication have their own authentication methods to address this problem, there is no industry standard for a FaaS server to authenticate an end-user; i.e., commercial FaaS products have individualized end-user authentication methods. In this paper, we analyze existing FaaS products and propose an introductory guideline about end-user authentication methods for FaaS. This guideline aims to help FaaS customers understand the FaaS’s authentication and suggest implementation direction.
Intravascular Ultrasound image Registration based on Geometric Spatial Energy

This paper introduces a novel method for intravascular ultrasound (IVUS) image registration based on geometric spatial energy. By combining geometric vessel features with spatial energy computations, the proposed approach achieves accurate image alignment, even in the presence of vessel deformations. Key geometric features serve as reference points, guiding the optimization process to minimize spatial energy and achieve optimal image correspondence. Extensive experiments on diverse IVUS image pairs demonstrate the method's superior performance over traditional techniques, promising enhanced clinical applications in vessel analysis and treatment planning. The performance of the proposed method are 10.33 of MSE, 10.55 of HD, and 0.15 of TRE, respectively.
Improvement of a Secret Sharing Scheme to Reduce the Total Data Size

In recent years, a variety of information has been converted into electronic data including highly private information. To protect such secret data from loss or theft, secret sharing schemes have been proposed. In the schemes, for given integers n and k, n data blocks called shares are generated from the secret data so that following conditions (i) and (ii) are satisfied. (i) The secret data is constructed from k or more shares. (ii) We can not obtain any information regarding the secret data from k − 1 or less shares. We have proposed a secret sharing scheme to reduce the total data size of all shares. In this paper, we improve the scheme to further reduce the total data size using such a property of the EXOR operation that arbitrary logic variable a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> among n − 1 logic variables a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> , a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ⋯, a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n−2</inf> is obtained from a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> , a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , ⋯, a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i−1</inf> , a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i+1</inf> , ⋯, α <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n−2</inf> and the EXOR operation (a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> ⊕ a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ⊕ ⋯ ⊕ a <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n−2</inf> ) of the n−1 logic variables. The improved scheme can reduce the total data size by about 33% at most compared to our previous scheme.
Intelligent electronic monitoring supervision system based on multi-label classification

As the current electronic monitoring supervision system faces the problem of increasing work fatigue of probation officers and decreasing the efficiency of supervision work due to the rapid increase in the number of probationers, the Ministry of Justice is trying to solve the problem through an artificial intelligence-based electronic supervision service. To solve this problem, we propose an intelligent electronic monitoring supervision system that builds a daily life model using the location information of each probationer and applies it to real-time location information to detect individual deviations (abnormal situations) and provide the cause of deviation. Furthermore, we demonstrate the feasibility and efficiency of the proposed method through simulations that detect deviations from daily routines based on collected location information.
Efficient In-Orbit CNN Updates

Building Artificial Intelligence (AI) models for Earth Observation (EO) satellites can be a difficult task since there is often no, or very limited, real training data available for a particular sensor. Even if data exists from a previous mission, a change in the sensor may make the model unsuitable. Therefore, being able to update the trained model, using the actual data provided by the sensor after the satellite is in orbit, is highly important. Thus, the main purpose of this work is to investigate and develop how to update remotely a model placed in a satellite in orbit, and how to control or specify the update size using the training parameters. The goal is to improve the accuracy performance of the model, using new data acquired from the in-orbit satellite, and to achieve this performance improvement under the criterion of reducing the size of the model update that is uplinked to the satellite. The method proposed selects which Convolutional Neural Network (CNN) layer weights must be modified and which must be fixed during training in order to maximize the accuracy increase and minimize the update file size. For a sample network, and without the proposed method, results show an update size of 44.5MB for the retrained network (with an original network size of 48.9MB), with retraining using the new data (such as that acquired from a satellite post-launch) resulting in an accuracy improvement from 78.4% to 79.9%. With the proposed Efficient Network Update (ENU) method, the generated post-training network update is only 18MB in size, but still achieves an accuracy of 78.9%. This demonstrates the reduced data bandwidth requirement to update the model, while gaining accuracy on the original trained network.
CCSDS121-based High-Performance Hardware Architecture for Real-Time Data Compression

Imaging sensors embarked on Earth Observation satellites are increasing their acquisition capabilities in terms of resolution. The amount of data collected need to be reduced prior to be sent to ground, because of on-board storage limitations and the constrained downlink bandwidth. This results in the need of implementing compression techniques as part of the data handling system. This work presents a high-performance architecture based on the Consultative Committee for Space Data Systems (CCSDS) 121.0-B-3 data compression standard that is able to process collected data in real-time and with an associated low area footprint. Starting from the basis of the Hyperspectral Lossless Compressor for space applications (SHyLoC) 3.0 CCSDS-121 IP, a highly parallelized architecture, in which an operation-based control allows the effective coordination of the independent processing pipelines, has been designed and described in VHDL. Results demonstrate that this architecture is able to process data throughputs of up to 7.776 Gbps, consuming only the 11.68% of LUTs on a Xilinx Kintex UltraScale XCKU040 FPGA and without using internal memory resources.
BULLETIN WKU

Annotation.An increasing number of specialists are inclined to believe that the main cause of the ecological crisis lies not only in backward technologies, little effective environmental protection and imperfect legislation, but also in the extremely low ecological culture of people.The experience of the modern older generation is based on ecological values that do not correspond to the new time.Since most people of this generation are brought up in the spirit of consumption, in the spirit of conquerors of nature.Without changes in the culture of nature management, one cannot hope for positive changes in the environment.This article examines the experience of holding annual environmental actions, competitions and conferences among the students of the Aktau school, as a result of which there is an intensification of scientific and environmental activities, environmental education and involvement of children in the conservation of the biodiversity of nature and biological resources.