Reviewerly/exHarmony huggingface dataset:
DATA_SPHERE

This paper presents a comprehensive overview of Database Management Systems (DBMS) and their significance in modern information management. DBMS technology plays a crucial role in the storage, organisation, retrieval, and manipulation of vast amounts of data in various domains, ranging from business operations to scientific research. This abstract highlights the key aspects covered in the paper, including the evolution of DBMS, its architectural components, and the challenges and advancements in the field. The paper begins by discussing the historical development of DBMS, tracing its origins from file-based systems to the emergence of relational databases and the subsequent rise of object-oriented and NoSQL databases. We explore the motivations behind these advancements and their impact on data management. Next, we delve into the fundamental architectural components of a DBMS. We examine the storage layer, which encompasses data structures and access methods, and discuss different indexing techniques for efficient data retrieval. The query processing and optimization module are explored, focusing on query execution plans and cost-based optimization strategies. Additionally, we analyse the transaction management component, highlighting concepts such as ACID properties, concurrency control, and recovery mechanisms. The abstract also highlights the challenges faced by modern DBMS. With the proliferation of big data and the advent of cloud computing, scalability, availability, and performance have become critical concerns. We examine techniques such as parallel and distributed databases, replication, and sharding to address these challenges. Furthermore, we discuss the integration of DBMS with emerging technologies like machine learning and blockchain to leverage their capabilities in data analytics and secure data transactions. Lastly, the abstract touches upon recent advancements in DBMS, including the rise of graph databases for managing interconnected data, the adoption of in-memory databases for high-performance applications, and the exploration of new database models to handle unstructured and semi-structured data. In conclusion, this paper provides a comprehensive overview of DBMS, covering its historical evolution, architectural components, challenges, and recent advancements. By understanding the principles and advancements in DBMS, researchers and practitioners can effectively harness the power of data management systems to tackle the complexities of modern data-driven applications.
Evaluation of Agility in Innovative Projects in the Manufacturing Industry

Studies confirm that companies in sectors that produce physical products when developing innovative projects, seek agility by adopting methods and practices from agile project management (APM) theory. The APM, designed by the software industry, recognized the power to bring rapid results to projects performed in environments with high volatility, uncertainty, complexity, and ambiguity, typical of innovative challenge tasks. In this context, studies carried out in the region with a diversified manufacturing park would benefit from understanding how to make its creative projects agile. Besides, this region is a representative sample to confirm the assumption that the combination of administrative aspects and practices can lead to greater agility. The research aims to evaluate the agile practices, the organizational factors, and their contribution to agility performance in the innovation projects of the manufacturing industry. Developed several methods to evaluate the agility in project management, so performed a systematic bibliometrics review was performed. The results analysis involved the use of the quantitative approach. Expect to have results highlighting the favorable aspects and barriers to implementing agility in this sector, besides reinforcing the assumption of the interdependence mentioned above between practices, context, and project performance.
Impact of Audio-Visual Resources on Student Performance in Higher Education in Support of SDG 4

The Sustainable Development Goal (SDG) 4 emphasizes the importance of quality education for all, aiming to provide inclusive, equitable, and lifelong learning opportunities. In line with this goal, higher institutions have embraced technological advancements, particularly the integration of audiovisual resources, to enhance the learning experience for students. This research explores the impact of audiovisual resources on student achievement in higher institutions and its contribution to advancing SDG 4. Students in higher institutions have widely adopted mobile technologies in their academic pursuits, presenting an opportunity to leverage these technologies for educational purposes. Specifically, the study focuses on the use of audiovisual resources to teach Arabic language skills to non-native speakers at Universiti Brunei Darussalam. A semi-experimental approach was employed, and a randomly selected sample of Arabic language students participated in the study.
Big Data-Driven Active Optimization Algorithm for Grid Fault Disposal Plan Assisted Decision Logic

In the current active optimisation of the grid fault disposal plan auxiliary decision logic, the logical relationships are more ambiguous, resulting in a low accuracy of the active optimisation results. To this end, a big data-driven active optimisation algorithm is proposed for the auxiliary decision logic of grid fault handling plans. Deep level access to grid fault information. Integration of fault type disposal plans. Develop auxiliary decision logic based on big data, extract keywords and correspond to fault states logically. Generate an active optimisation algorithm for the auxiliary decision logic. The experiments show that the average detection rate of the method is 95.14%, which is a substantial improvement and has high application value.
Data Association for Multiple Radar Targets Using Graph Neural Network

In the complex target tracking scenarios, Bayesianbased method and the random finite set-based method cannot solve the data association problem. This paper introduces an end-to-end data association method based on graph neural networks. As far as we know, this is the first time in the field of radar that a graph neural network method is used to solve the data association problem. Different from the traditional data association method, our method can automatically learn the association criterion from the labeled samples, and the algorithm is more adaptable to complex scenarios. The simulation and experiments on real data can verify the effectiveness of our method.
Design and Use of an Educative Blog in a 4th CSE Spanish Classroom

Since the middle of the 20th century, science and technology have unleashed profound changes in all areas of society. Social, economic, and political changes spread at great speed, thanks, among other factors, to the birth of the Internet in 1966 and its rapid development to become a space to host information. Blogs are easy-to-create tools that do not require very specific knowledge to use; however, like any other tool, the result of its use depends, to a great extent, on the methodology and the objectives with which it is introduced into the blog, the educational program, and therefore, the original activity that is proposed will be accompanied by the appropriate framework for its introduction into the classroom resources. The choice of the students of the last year of compulsory secondary in Spain for the field study has not been something casual. Creating and maintaining a blog requires text production and information processing skills that are not always available in the first years of CSE, especially if it has to be done in a foreign language.
Design Of Truck Ultra-High Warning System Based On Radar Detection Technology

With the rapid development of the transportation industry, the number of truck transportation under railway Bridges and culverts has increased sharply, and some illegal ultra-high vehicles have also increased significantly. In order to ensure the safety and stability of railway operation, an all-round, high-reliability ultra-high warning system for truck is proposed based on image processing, vibration monitoring and radar technology. Firstly, the point cloud data of vehicle within the monitoring range of laser radar is constructed, and the vehicle is analyzed comprehensively according to the data, and the image processing technology is used to identify whether the vehicle is exceeds the height limit. Then, a three-axis acceleration sensor network with low power sleep technology is designed to sense the collision process between the vehicle and the height limit protection frame, which can provide data support for the rapid evaluation of collision accidents and improve the efficiency of accident handling. The final experimental results show that the system can detect whether the vehicle is exceeds the height limit at 25 meters away in real time, effectively solve the problem of vehicle escape and difficult to determine the damage, and has high accuracy and reliability, and has wide application value.
Y-Net: Convolutional Networks for Multi-Domain Image Segmentation

In order to solve the drawback that most existing SOD networks cannot extract local details and global contrast information well, and often have insufficient detail on the edges, we design an accurate and compact saliency multi-domain image segmentation algorithm, Y-Net for short. This network combines the new U-shaped network U2NetP and RAS Net segmentation network in the field of deep learning, and well combines the characteristics of each of the two segmentation networks through the self-made module and residual mechanism. It is outstanding in segmentation of different types of images. Y-Net has been tested to show stronger performance than the original two base networks in five major public datasets.
A Novel Grid Track Fast Matching Method

Aiming at the deep radar detection and perception demand, a fast and practical grid track matching method is proposed for the classical track matching problem of air targets. By mining the target motion law implied in the accumulated radar historical tracks, combining with the target real-time track characteristics, and based on the idea of weighted matching of typical track segments, the grid track distribution density calculation matching method is used to realize the fast matching of real-time tracks and classical tracks. The experimental results based on the measured radar track data show that the proposed method can efficiently complete the task of matching the target real-time tracks with short computation time and high matching accuracy, which has strong engineering practicality and provides strong support for the situation analysis.
A Security Posture Prediction Method for Power Networks Based on Extreme Learning Machine

To address the problems of single information source and lack of real-time of the existing network security posture prediction, an extreme learning machine based power network security posture prediction model is proposed by examining the characteristics of network security posture change in order to accurately grasp the network security development posture. First, the security posture value data set is constructed using the sliding window method, and the training sample set is used to train the limit learning machine neural network to portray the backward and forward dependencies of the security posture at different moments and predict the network security posture at the next moment. The effectiveness of the prediction model is verified by the dataset based on the on-site security monitoring data of power enterprises. The simulation experiments show that the method can predict the network security posture more accurately, which verifies the feasibility and effectiveness of the method proposed in the paper in network security posture prediction.
Research on Video Surveillance Security System Based on Domestic Password

Video surveillance system has become the most important technical means to maintain public security. With the increasingly wide application of video surveillance system, it has also become an important target of network attacks. Video surveillance system has different kinds of security risks in front-end equipment and background system. It is becoming a consensus in the international security field to build a video surveillance security technology system to effectively resist network attacks against video surveillance system and ensure the security of video data. In this paper, we take the monitoring system of session initiation Protocol (SIP) as an example, use domestic cryptographic technology to build video surveillance security protection framework, and comprehensively use bidirectional authentication, video stream encryption, video stream and signaling integrity validate and other technologies to improve the comprehensive security protection level of video surveillance.
Optimization Scheduling Method for Remote Upgrade of Massive Power Terminals Based on Automation

In order to solve the problem of secure and reliable upgrade of massive terminals in the new power system, this paper proposes an automated remote upgrade optimization scheduling algorithm for massive terminals. By using this method, the optimal upgrade package distribution time and distribution object can be calculated, thereby ensuring the efficiency and stability of terminal upgrade data packet transmission, and thereby improving the efficiency and success rate of massive terminal program upgrades in the new power system. Compared with the original terminal program upgrade algorithm, the method proposed in this article can significantly shorten the upgrade time of massive terminals and reduce the upgrade failure rate, thus having high promotion prospects.
Disentangling Ensemble Models on Adversarial Generalization in Image Classification

Convolutional neural networks are widely used in computer vision and image processing. However, when the original input is added with manually imperceptible perturbations, these deep network models mostly tend to output incorrect predictions. The vulnerability of these models poses great threat to intelligent applications, and these manually imperceptible perturbations are called adversarial examples. Current baseline methods have achieved considerable white-box attack success rate, but black-box rate remains to be improved. To boost the adversarial generalization, ensemble models method is introduced to the process of generating adversarial examples. This paper proposes multiple ensemble strategies with baseline attack methods based on existing ensemble strategy used by former methods. Experiment on ImageNet dataset empirically verifies the optimal ensemble strategy on boosting adversarial generalization.
Hardware Design and Optimization of Polynomial Multiplication for Post-Quantum Cryptography Algorithm Based on NTT

The Number Theoretical Transform (NTT) plays a crucial role in post-quantum cryptography algorithms, with its computational performance directly impacting system operating speed. This article proposes a high-performance NTT hardware architecture based on a pipeline architecture to address the challenges of lengthy computing processes and complex control logic in NTT hardware implementation. Firstly, a recursive NTT is advanced to simplify the calculation process and facilitate hardware implementation. Next, effective pipeline segmentation is applied to the computing process to reduce hardware architecture complexity. Finally, a two-stage butterfly operation is employed to implement butterfly elements, and the reduction calculation process is optimized using shift and addition, resulting in reduced hardware resource costs. The proposed NTT hardware architecture is implemented on Quartus II (EP2AGZ225FF35C3) taking the CRYSTALS-Kyber anti-quantum cryptography scheme as an example, since the selected prime number can meet requirements. Experimental results demonstrate that the proposed design outperforms other related designs in terms of computational performance and hardware overhead.
Application of VR Technology in Interactive Virtual Clothing Feature Extraction

In order to solve the problem that virtual clothing templates and real clothing templates do not fit well, and to enable the design host to accurately capture the clothing features, the application of VR technology in interactive virtual clothing feature extraction is investigated. Using VR modelling ideas, the initial processing of garment images is carried out to improve the principles of interactive processing based on VR technology. In the given metric space, colour features, texture features and shape features of the designed garment are determined separately to extract the virtual garment features. In comparison with the experimental results, the fit error between the virtual and real garment templates remains within one metric unit, which is more in line with the need for accurate capture of garment features than fine-grained feature extraction methods.
Wireless Channel Scenario Classification Based on ISODATA

In recent years, the research of using machine learning for channel scenario classification has received increasing attention. However, when wireless channel is continuously measured and the receiver is moving, the number of channel scenarios is difficult to be decided artificially. A novel scenario classification method based on Iterative Self-organizing Data (ISODATA) algorithm is proposed. The proposed scheme first adaptively adjusts the optimal number of scenarios based on ISODATA, then use the number of scenarios for clustering with the Gaussian Mixture Model (GMM) algorithm, and its superior performance is demonstrated.
An Image Processing-Based Approach for Fuzzy Image Recognition

Developing in parallel with image processing technology, image recognition technology becomes an important sector in the artificial intelligence domain and is widely adopted in different fields. However, the existence of various fuzzy images in real life brings new challenges to image recognition. To effectively improve the feature matching rate between motion-blurred and original images, this paper first carries out a blurring simulation on the image with Gaussian noise, then performs a recovery operation on the re-blurred image by inverse filtering and Wiener filtering (WF), and later uses the ORB algorithm to perform feature point matching between the recovered image and the original image. The experimental results verify that the WF-based approach achieves satisfactory results in processing the recovery of simulated motion blur images with added noise. It is clear that the WF-based approach can contribute to the higher recognition rates motion blur image recognition.
Improvement of Threshold Function Based on Wavelet Transform for Denoising ECG Signals

Cardiovascular disease is a major threat to human health, and the study of electrocardiogram signals is crucial for analyzing and exploring this disease. Based on a summary and analysis of previous work, this paper collects electrocardiogram data from the MIT-BIH database and uses a combination of wavelet transform algorithm and Mallet algorithm to analyze and study the classic soft and hard threshold functions. A new threshold function is proposed to address the shortcomings of these functions in denoising electrocardiogram signals. Finally, experimental results in MATLAB demonstrate that the new threshold function can better denoise and obtain pure electrocardiogram signals.
A Service Component Migration Method to maintain VM Performance

In component-oriented cloud service systems, service components share virtual machines (VMs) and their resource utilization ratio is influenced by multiple factors. This paper introduces a method for ensuring virtual machine performance through component migration. The method consists of three phases: migration assembly triggering, target migration component selection, and target virtual machine selection. These phases address challenges related to VM migration and service migration at a large granularity. The approach applies resource dynamic adjustment by analyzing the load characteristics and resource correlation of each component, enabling more precise resource allocation at the component level. This enhances the applicability of resource adjustment and reduces resource costs for cloud service providers while meeting service level agreements (SLAs). The method also considers the reconfigurability and scalability of cloud computing, effectively meeting SLA constraints, and optimizing cloud application performance by adjusting resources at the component level. Experimental verification demonstrates the effectiveness of the proposed method in ensuring virtual machine performance.
A Minireview of the Challenges and Opportunities of Virtual Learning Post-COVID-19 Era in Developing Countries

The rapid spread of COVID-19 globally transformed the educational sector from the conventional face-to-face teaching to virtual learning to control the transmission of the virus. This study explored on the transition to online learning, the opportunities and challenges post-COVID-19 with specific focus on developing countries. It was found that virtual learning was delivered using a variety of apps in synchronous and asynchronous modes. Irrespective of the model, teachers and students enjoyed time and location flexibility, convenience and comfort while providing and accessing the learning materials. However, limited technical skills to ran online classes, technical issues such as instabilities in internet connection, software limitations and demotivation due to unattendance by learners were raised by teachers. Moving forward post-COVID-19, this study provided recommendations to enhance virtual learning since it is a necessity rather than a choice.
BRS-based Model for the Specification of Multi-view Point Ontology

Abstract In this paper, we propose a new approach, based on bigraphic reactive systems (BRS), to provide a formal modelling of the architectural elements of a Multi-Viewpoints ontology (MVp ontology). We introduce a formal model in which the main elements of MVp ontology find their definition in terms of bigraphic concepts by preserving their semantics. Besides, we enrich the proposed model with reaction rules in order to handle the dynamic reactions of MVp ontology. In order to confirm the applicability of our approach, we have carried out a case study using the proposed model.
PhoBERT: Application in Disease Classification based on Vietnamese Symptom Analysis

Abstract Besides the successful use of support software in cutting-edge medical procedures, the significance of determining a disease early signs and symptoms before its detection is a growing pressing requirement to raise the standard of medical examination and treatment. This creates favourable conditions, reduces patient inconvenience and hospital overcrowding. Before transferring patients to an appropriate doctor, healthcare staff must have the patient’s symptoms. This study leverages the PhoBERT model to assist in classifying patients with text classification tasks based on symptoms they provided in the first stages of Vietnamese hospital admission. The outcomes of PhoBERT on more than 200 000 text-based symptoms collected from Vietnamese hospitals can improve the classification performance compared to Bag of Words (BOW) with classic machine learning algorithms, and some considered deep learning architectures such as 1D-Convolutional Neural Networks and Long Short-Term Memory. The proposed method can achieve promising results to be deployed in automatic hospital admission procedures in Vietnam.