Reviewerly/exHarmony huggingface dataset:
Usability Evaluation of Emergency Department Information System: A Case of Discharge and Admission Sections of an Educational Hospital

Introduction: The emergency department is amongst the most important parts of the hospital, which has a great impact on the performance of other departments. In this regard, Emergency Department Information System (EDIS) plays a vital role in providing effective and appropriate healthcare services.Material and Methods: To evaluate the user-perceived usability of admission and discharge sections of the Emergency Department Information System of Shohada-e-Tajrish Educational Hospital, the study is carried out utilizing SUS and PSSUQ scales. Research participants include all users in the admission and discharge sections of the emergency department of the Hospital.Results: The mean score of the system usability based on the results of the SUS scale was 49.62±23.23, which is lower than the acceptable level (score above 68). The results generated by the PSSUQ tool revealed that the information quality (4.79), interface quality (4.91), and system usefulness (4.42) of admission and discharge sections of the EDIS do not have appropriate scores and need to be improved in this regard. .The worst usability score was related to system usefulness. There was no statistically significant correlation between the age of participants and usability scores. Moreover, there was no statistically significant difference between the user-perceived usability and the education level of participants (p&gt;0.05).Conclusion: The study revealed that users are not satisfied with system usability. In the event that the usability of the system is suboptimal, the user fails to effectively utilize the system, thereby impeding the attainment of the intended ultimate objective for which the system has been designed. Regarding the importance of admission and discharge sections in the EDIS, strides need to be made to improve the usability of the system which is used at the emergency department of Shohada-e-Tajrish educational hospital.
IOTA TANGLE 2.0: AN OVERVIEW

The IOTA Tangle cryptocurrency represents the next evolution in distributed ledger technology, designed to facilitate transactions in the Internet of Things applications. Compared to traditional blockchain architectures, the IOTA presents attractive characteristics, including high scalability, zero-fee transactions, higher achievable transactions per second, and lower energy consumption. The consensus mechanism in IOTA 1.0 and 1.5 relied on a centralized coordinator solution for transaction validation. IOTA 2.0, designed in 2020, is the first fully decentralized network that supports smart contracts. This paper provides a comprehensive overview of the IOTA Tangle 2.0.
Face Recognition using Convolutional Neural Network Algorithms

Biometric applications have massive demand in today’s era. The areas of applications are mostly linked with the security of the system. Biometric features are regarded as the primary resource for security purposes due to their own distinctiveness and non-volatile essence. System authentication using biometrics is considered to be a sophisticated technology. Noise effect inducts variation in the biometric subject that causes an adverse impact on establishing the recognition. The proposed model supported the development of an effective method for performing facial biometric feature recognition. The model's goal is to reduce the number of false approvals and refusals. The proposed algorithm has been applied over a video dataset containing surveillance video frames that capture facial subjects dynamically. The first step is the pre-processing of the video frames that have been carried out in the proposed model. Then, the Viola-Jones algorithm was applied to detect the facial subjects in the video frames. Feature extraction from the facial subject has been accomplished by applying a deep reinforcement learning algorithm. Further, the proposed model applied a convolutional neural network (CNN) algorithm to perform feature recognition of facial identity accurately. The proposed technique aims to maintain a huge recognition rate of dynamic facial subjects under various unprecedented noise variations. In the classification algorithm, the recognition accuracy is found to be 98.85%&lt;br&gt;
Research on Key Technologies of Supply Chain Traceability Based on Blockchain

In the era of digital economy, the development of digital supply chain has brought many transaction disputes and transaction security problems. Establishing a traceability system has become an effective means to solve these problems. However, traditional traceability technology has problems such as opaque information and easy data tampering, which requires a more secure and reliable way to trace digital supply chain. Therefore, this paper proposes a digital supply chain traceability method based on blockchain technology and using Pailler algorithm, Merkle Tree and smart contract, aiming to improve the security and reliability of transactions. This method was developed on the FISCO BCOS platform and was evaluated for performance and visual effects, proving its effectiveness and feasibility.
Resources Optimization Research of Service Oriented Architecture Based on Hyper Converged Infrastructure Cloud Computing Platform

Nowadays Cloud Computing (CC) has been to a period of flourishing development in the worldwide. Hyper Converged Infrastructure (HCI) has been leading software defined datacenter of CC to a new stage. Service Oriented Architecture (SOA) in HCI CC platform provides the agile and abstract distributed IT architecture mode, and it will dramatically change the service style and efficient of software defined datacenter. The revised simplex method is used to solve the optimization question of resources schedule in SOA based HCI CC platform, and the result shows at least 25% profit promotion, while in medium or large-scale CC platform the promotion will be greater.
Research and Design of Online Education System Based on Blockchain Technology

This paper proposes a blockchain-based online education system for existing online education systems that are not transparent enough in calculating the benefits when resources are released, the centralized structure restriction, and the difficulty in confirming the rights of data resources. The system adopts ECC encryption algorithm to protect data security, uses smart contracts to control transaction operations, improves system reliability and execution efficiency through dual storage mechanism, uses blockchain’s characteristics such as tamper-evident and timestamp to achieve effective confirmation of rights in information traceability, and achieves accurate matching of transactions and shortens the cycle of resources on the chain with the trust endorsement of blockchain’s multiple participating subjects. It is proved by experiments that the system can effectively reduce the transaction cost of data resources, protect the rights and interests of resource publishers, and has good scalability and security.
A Quadratic-Spectrum-Based Radio Frequency Fingerprinting Method for Unmanned Aerial Vehicles

With the increase of unmanned aerial vehicle (UAV) application scenarios, the incidents of UAV interference and malicious harassment are increasing, which pose a threat to public privacy and security. Aiming at the problem that UAVs are difficult to control, this paper proposes a radio frequency fingerprinting (RFF) method based on the quadratic spectrum. Firstly, the small window power detection is used to detect the transmission signal of the image to be recognized and extract the effective signal. The quadratic spectrum preprocessing is performed on the extracted signal, and finally, the improved VGG-16 deep convolutional neural network in this paper is used to extract the RFF information of the signal. The softmax classifier is used to classify and recognize the signal. The experiment is carried out in three steps, and the results prove that the combination of quadratic spectrum and improved VGG-16 deep convolutional neural network for RFF can complete a series of identification of “whether – what-which”, and the time consumed is shorter.
Integrated Management and Control System of Infrastructure Construction Based on Adaptive Grouping Particle Swarm Optimization Algorithm

With the development of cloud computing technology, infrastructure resources are showing a trend of diversified and ubiquitous deployment. The integrated management and control system for infrastructure sites involves fixed cameras, edge-side smart devices, and cloud-based intelligent analysis centers. The important issues that need to be addressed by this system are how to coordinate resources in various aspects, achieve cloud-edge collaborative computing, minimize costs, and provide real-time warnings of hazards. This article proposes an adaptive grouping-based particle swarm optimization algorithm to address the problem of traditional particle swarm optimization easily falling into local optimal solutions. A cloud-edge multi-level resource scheduling model is designed to achieve integrated resource scheduling solutions for cloud-edge computing power. The model is applied in the comprehensive management and control system of infrastructure sites, and the results confirm that the system can achieve real-time warning and instant response for dangerous behaviors on site, while minimizing overall costs.
Information Injection by Modulating the Feedback Light of Reservoir Computing System Based on a Semiconductor Laser Subject to Optical Feedback

An information injection method for time-delay optical reservoir computing system is investigated, where the information is injected by modulating feedback light of the semiconductor laser-based reservoir. Compared with previous optical information injection methods, the system structure is simple and requires fewer parameters to be adjusted. The results show that under optimized parameters, the minimum errors of 0.0056 for Santa Fe task and 0.20 for NARMA10 task are obtained.
Construction and Application Research of Safe Building Information Sharing System Based on Blockchain Technology

In recent years, blockchain, as one of the emerging information technologies, has been increasingly integrated into the whole process of development in various economic and social fields and industries, playing an important role in global resource reorganization, global economic structure reshaping and global competition pattern change. This paper proposes a hybrid chain model combining DPoS-Quorum consensus algorithm for coalition chains and PANDA consensus algorithm for public chains in conjunction with the current situation of construction supply chain, and experiments prove that this paper’s method, while ensuring data security and privacy security, can accelerate the data circulation and sharing efficiency of construction supply chain.
Research on Crowd Location Algorithm Based on FIDTM

Crowd localization task is one of the core tasks in the field of computer vision, and it has become a research hotspot in crowd scene problems in recent years. The main task is to predict the specific position information of each head in images or videos. On the basis of existing crowd localization methods, this article conducts further research on crowd localization based on the Focal Inverse Distance Transform Maps (FIDTM). The CAI-CAN convolutional neural network is used as a regressor for the truth map of the focus inverse distance transform in the crowd localization network to predict the predicted results. The LMDS module and K-nearest neighbor algorithm are used to obtain the head localization point map and head localization block diagram, respectively. Experiments were conducted on two mainstream public datasets, and the experimental results demonstrated the effectiveness, feasibility, and robustness of the proposed method. And using the same network structure to regress the density map generated based on Gaussian kernel, the comparison results show that the proposed method can still clearly distinguish the head distribution in extremely crowded scenes. In addition, the method proposed in the text was applied in practical scenarios, and video was collected from dense crowds at Baotou Railway Station and Inner Mongolia University of Science and Technology campus. The data was applied to the method proposed in this article, and excellent prediction results were obtained.
Ontology Construction for Hazardous Chemical Fire Emergency Response

China’s chemical industry has developed rapidly, but chemical production safety is still in the primary stage, hazardous chemical fire occurs frequently. The ontology model Hazardous Chemicals Fire Emergency Reaction Ontology (HC-FERO) is constructed on the basis of the knowledge domain and the breadth and depth of ontology. First, ire emergency response upper ontology is constructed with the help of domain experts. Then, a quintuple is established from the aspects of Fire response sequence, response rules, fire extinguishing principle, etc, then we choose protege to generate the hazardous chemical fire emergency ontology. At last, we select the qualitative evaluation to evaluate HC-FERO.
Modelling of the Frame-Type ROV and Attitude Control Based on MATLAB Simulink

This paper realizes the modelling of a frame-based Remotely Operated Vehicle (ROV) and its attitude control. The modelling process primarily involves formulating momentum and angular momentum equations based on Newton’s second law. Regarding attitude control, the paper utilizes state-space control theory to linearize the nonlinear model, analyses system stability and controllability, designs a full-state feedback controller based on requirements, and conducts simulation and emulation of the results using Simulink.
A Deep Learning-Based Model for Relation Discovery in the Knowledge Graph of the Power Industry

In the field of the electric power industry, improving the entity relation model can increase the efficiency of knowledge graph completion, build a smart grid, and provide strong support for relevant regulatory decisions. Therefore, we propose a deep learning-based entity relation discovery model called BERT-SRR for the electric power domain. This model includes constructing an electric power domain knowledge base using professional domain knowledge and adding entities and their relationships to it. It utilizes a BERT model trained incrementally to extract Chinese text features and conducts sequence labeling learning with entity and relationship information from the knowledge graph. Furthermore, combining a stacked convolutional neural network and a student reordering network enables high-accuracy knowledge graph completion and prediction of entity relationships. The comparative experimental results demonstrate that the proposed model outperforms the benchmark model, showing a significant improvement in F1 score ranging from 3.66% to 9.78%.
Technological Integration in Pharmaceutical Logistics

This article explores the intricacies of the pharmaceutical logistics sector, encompassing its intricate challenges. The design and equipping of medical equipment warehouses and the distinctive hurdles associated with pharmaceutical product transportation are examined. Key technologies are integrated to enhance efficiency, reduce costs, and enhance patient safety. The integration of these technologies, including Big Data, Artificial Intelligence (AI), Internet of Things (IoT), Autonomous Robots (AR), Mobility (Mob), Cybersecurity, Horizontal and Vertical Integration (HVI), Simulation (simul), Cloud, New Human Interfaces (NHI), Additive Manufacturing (AM), Blockchain, and Robotic Process Automation (RPA), is dissected to showcase their contributions to different aspects of pharmaceutical logistics. This integration, achieved through equations and descriptive insights, unveils how each technology contributes to crucial parameters. The findings reveal notable enhancements in efficiency (up to 15%), cost reduction (up to 18%), and improved patient safety (up to 25%). This paper highlights the transformative potential of these advancements when meticulously implemented, enabling the pharmaceutical industry supply chain to thrive in a technologically enriched landscape.
Constructing an Earthquake Catalog Based on Deep Learning

In this study, we employed deep learning-based seismic signal detection method (PhaseNet) to detect the seismic sequences of the Weishan region in Shandong, China. By combining seismic association techniques and NLLoc seismic location technology, we constructed a seismic catalog. The deep learning-based catalog was compared with the manual catalog, and its performance, completeness, and accuracy were tested and evaluated for medium and small seismic sequences. The results demonstrated that the deep learning-based seismic catalog exhibited higher efficiency. Compared to the manual catalog, it included 1.44 times more seismic events. Additionally, the deep learning-based catalog showed better seismic association, with the majority of missed events concentrated in cases of smaller magnitudes, while capturing fewer phases. In summary, the deep learning-based seismic catalog demonstrated advantages in terms of efficiency, seismic quantity, seismic association, and the number of picked phases. This study provides a more accurate and comprehensive seismic catalog for the detection and location of medium and small seismic sequences, offering valuable support to seismic monitoring and related research fields.
A Multimodal Named Entity Recognition Model for Power Equipment Based on Deep Neural Network

Digital empowerment of China’s power energy sector is a key factor in increasing its economic and social benefits, and named entity recognition technology is the most fundamental and core task of information extraction technology in the digital empowerment process. Therefore, we propose a multimodal named entity recognition model PE-MNER for power equipment based on deep neural networks. Compared to text multimodality, text and image multimodality can use image information to supplement missing information in the text, thus enabling more accurate entity extraction. The model first obtains a BERT neural network through incremental training, and then extracts Chinese character features through the network. Then, a hierarchical visual prefix fusion network is used to fuse image information. From the comparative experimental results, it can be seen that the proposed model has the best performance compared to the benchmark model, with an improvement of 4.08%∼7.20% in the F1 score compared to the benchmark model.
Detection of GAN Generated Fake Satellite Images Using Deep Learning

Fake Images generation has become a common practice nowadays and this process has become elementary with the advent of Generative Adversarial Networks. Although though fake images are entertaining and have their benefits, they also have a few negative effects. Modern satellite images offer crucial data that aids in tracking various packages, including spatial decision, spectral properties, sensor sensitivity, and many others. Critical details needed for a few applications may be massively obtained from these photos. Yet, altering these images by removing, incorporating, or replicating objects leads to false perceptions of reality. It is our intention to come across those kind of fake satellite images. We use in-depth learning architectures to identify fake Satellite photos in order to distinguish between authentic and fake photos.
Visual Feature Extraction Based on Region of Interest and FCNN

At present, image and video has become more and more the main form of multimedia expression, how to effectively locate the user really need from the large-scale image video data image block, has become a more popular problem in the field of image and video processing, extraction of interest area is the key technology to solve this problem. Based on this, many visual attention models have been proposed, the most representative of which is the extraction algorithm proposed by and. The algorithm first extracts the features of the image such as brightness, color and direction into the feature map, and then uses the “center surround” mechanism similar to the field of perception for each feature map. In recent years, with the rapid development of three-dimensional display technology, more and more elements are integrated into the daily life. Compared with the previous flat display, three-dimensional display technology can often bring the viewer a higher quality of visual experience and lifelike visual feelings, so it is very popular. Due to the addition of depth information, the traditional image-based region of interest extraction method can not predict the salient region in stereo video very well. In this paper, we study the mechanism of human visual attention, adopt the bottom-up method, combine the traditional and video sequence motion features, comprehensively consider the depth information of stereoscopic video, introduce the perception characteristics of depth of human visual system into the region of interest extraction, and propose a visual attention model of stereoscopic video. Experiments show the effectiveness of our model.
Multi-Strategy Equilibrium Optimizer Based on Comprehensive Improvement

Improvement on the updating equation of an algorithm is among the most improving techniques. Aimed at the defects, such as insufficient search ability, high computational complexity and poor operability, of equilibrium optimizer (EO) in solving complex optimization problems, this paper proposes an improved EO, namely Multi-strategy on updating Synthetized EO (MS-EO). Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages. Lots of experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability.
Blockchain and Illicit Trafficking in Cultural Goods

Blockchain could be used as a mechanism to certify the provenance and movements of cultural goods and contribute to the fight against illicit trafficking of cultural property. By strengthening the traceability of cultural goods, blockchain can simplify due diligence requirements and facilitate prosecution and convictions for all the relevant offences of illicit trafficking in cultural goods. However, such an impact is necessarily tempered due to the specificity of this kind of illicit trafficking, as well as the technical and conceptual constraints of the blockchain itself. Even though blockchain in its current form cannot overcome the legal obstacles which hamper the efficacy of the fight against illicit trafficking, such as the lack of harmonization of legal systems, its value in this area should not be dismissed altogether. An overview of the relevant international and European norms, highlighted using national law examples, reveals the complementary role that blockchain can have in this field, not only in terms of best practices but also in terms of an increased visibility for cultural goods and their international movement.