Reviewerly/exHarmony huggingface dataset:
CORRELATION BETWEEN PLASMA FIBRINOGEN WITH DAS28 SCORE IN RHEUMATOID ARTHRITIS: A HOSPITAL BASED STUDY

Rheumatoid arthritis (RA) is a chronic systemic illness affecting joints and extra-articular structures and is the most prevalent kind of inammatory arthritis worldwide. Patients of rheumatoid arthritis have increase risk of cardiovascular disease as compared to general population. Local inammation of the affected joints, manifesting as joint pain and swelling, generally represents the clinical disease activity in RAand is measured by various joint indices like Disease Activity Score 28 (DAS 28) and Clinical Disease Activity Index (CDAI). Fibrinogen is the major determinant of plasma viscosity and it represents link between inammation, thrombosis and cardiovascular risk. The aim was to study the association of plasma brinogen with Disease activity score in rheumatoid arthritis and to compare levels of plasma brinogen in patients with rheumatoid arthritis with healthy population. Materials And Methods:This study was a cross sectional study done in 70 subjects ( 35 diagnosed RApatient by ACR 1987 criteria and 35 healthy subjects) in Assam Medical College and hospital from June 2021- May 2022.The quantitive determination of plasma brinogen was done using clot detection method. We also measured ESR using Westergren's method. Statistical analysis was done using SPSS software. Results: The mean value of plasma brinogen was 381.143±43.71 in rheumatoid arthritis versus 314.285±46.29 in controls. This difference was statistically signicant &lt;0.001.On evaluating brinogen level and DAS28 score, it was noted that patient with higher DAS28 had signicantly higher brinogen level (p&lt;0.01). Conclusion: This study concluded that plasma brinogen is signicantly elevated in patients with active rheumatoid arthritis and is signicant with increasing severity of the disease. As brinogen is the risk factor for cardiovascular disease it can also be added in routine monitoring of disease activity along with other parameters
Software Development Practice as Baradian Entanglement

Software development practice is a messy, complicated, and constantly shifting human endeavor. Barad’s concept of “entanglement” helps to theorize complex sociotechnical systems. We are testing the application of this theory to understand and explain software development practices, as our work appears to be the only ethnographic research using Barad in any technology industry. Our continual aim is to understand large-scale, collaborative software development more deeply in practice and to discover appropriate theories that describe our observations and insights. Both authors are experienced software engineers and researchers. Through an ongoing longitudinal ethnographic study at a large Australian software development company, we explore, support, and improve the lived experience and practice of the software developers that work there. Ethnographic insights and an appreciation of the mutual constitution of situated phenomena have expanded over several years into an elaboration of entanglement as a more insightful explanation of software development practice. This research is having a significant impact on the participant developers and organization, including changes in measurement practices, mentoring, knowledge management, and innovation.
Sample Efficient Reinforcement Learning Using Graph-Based Memory Reconstruction

Reinforcement learning (RL) algorithms typically require orders of magnitude more interactions than humans to learn effective policies. Research on memory in neuroscience suggests that humans' learning efficiency benefits from associating their experiences and reconstructing potential events. Inspired by this finding, we introduce a human brain-like memory structure for agents and build a general learning framework based on this structure to improve the RL sampling efficiency. Since this framework is similar to the memory reconstruction process in psychology, we name the newly proposed RL framework as Graph-Based Memory Reconstruction (GBMR). In particular, GBMR first maintains an attribute graph on the agent's memory and then retrieves its critical nodes to build and update potential paths among these nodes. This novel pipeline drives the RL agent to learn faster with its memory-enhanced value functions and reduces interactions with the environment by reconstructing its valuable paths. Extensive experimental analyses and evaluations in the Grid Maze and some challenging Atari environments demonstrate GBMR's superiority over traditional RL methods. We will release the source code and trained models to facilitate further studies in this research direction.
Research on enterprise financial crisis early warning management based on PLS-BP

Finding an efficient and accurate early warning method is of great significance to enterprises, investors and the development of China's national economy. The Partial Least Squares (PLS) method is used to screen the variables, the BP neural network is used to establish the model and the PLS-BP early warning model is constructed. In the simulation experiment, the prediction accuracy of PLS-BP early warning model in T-2 period is 100%, 93.75% in T-3 period and 87.5% in T-4 period, which are significantly higher than the traditional financial early warning methods. The above results prove that PLS-BP early warning model can accurately predict the financial crisis of enterprises and has certain practical value.
Understanding SyncMap’s Dynamics and Its Self-organization Properties: A Space-time Analysis

Human are shown able to rapidly recognize patterns in sequences by detecting and chunking together the patterns found, without supervised signals. Recently, inspired by how neuron groups act in quickly switching behaviors, SyncMap was proposed to solve chunking problems based solely on self-organization. The idea is to create dynamical equations that maintain an equilibrium state by dynamically updating with positive and negative feedback loops. When the underlying structure changes, the system can quickly adapt to the new structure. Although SyncMap can solve chunking problems effectively, the properties of its dynamics during training, is still underexplored. Here, we give a detailed investigation of SyncMap’s dynamics by using several experiments to demonstrate the behaviors of SyncMap from the perspectives of space and time, in which a problem that causes imprecise results in the original work was identified. We then propose a solution call SyncMap with moving average (i.e., SyncMap-MA), which surpasses the original work and the baselines in all experiments, suggesting that the modification here is effective and can be integrated in the future version of the algorithm.
Automatic Student Sign in Management System Based on Neural Network Algorithm

In the 21st century, with the improvement of scientific and technological level and people's quality of life, more and more people pay more attention to education, culture and other aspects. The sign in system is one of the technical means that can manage and record students' information through computer, and provide decision support function for schools. This paper designs a set of automatic student signing mechanism based on neural network algorithm, introduces the development of neural network technology, studies its application in sign in system, and analyzes and compares it. Finally, the student sign in management system is tested. The test results show that the system performs very well in automatic sign in management. The sign in rate basically reaches more than 90%, and the degree of automation is very high. It provides a good information feedback platform for the direct communication between students and teachers and managers.
Information Security Management System for Archives Management Based on Embedded Artificial Intelligence

Archival services are one of the main functions of an information security management system for archival management, and the conversion and updating of archival intelligence services is an important means to meet the increasing diversity and wisdom of the age of intelligence. The purpose of this paper is to study an information security management system for archival management based on embedded artificial intelligence. The implementation of an embedded control management system for intelligent filing cabinets is studied. Based on a configurable embedded system security model, the access control process and the functional modules of the system based on a secure call cache are analysed. Software for wireless RF communication was designed, and two remote control options were designed using CAN technology and wireless RF technology. Tests have shown that the system is easy to use, feature-rich and reliable, and can meet the needs of different users for regular control of file room management.
Informatization Growth Model of Artificial Intelligence Virtual Reality Technology Fusion Education

With the growth of computer technology, Artificial Intelligence virtual reality technology(AIVRT) is gradually applied to the field of education. The virtual reality system is widely used in the information age. This paper mainly expounds how to combine AI with relevant theories. First, it introduces the three-dimensional teaching model and web-based learning mode based on big data, then analyzes and summarizes its characteristics and future prospects, and discusses the use of VR platform to build an interactive intelligent online learning environment for independent training and personalized learning, so as to improve students' information literacy ability and promote students to actively participate in knowledge sharing for further exploration, providing new ideas and directions for virtual reality technology. Finally, this paper tests the performance of the model. The test results show that the model has excellent response speed in processing and analyzing data, and its compatibility rate is up to 90%. This shows that the model can meet the needs of users.
Construction of Cloud Computing Password Application System Based on Docker Technology

With the development of computer technology, people are increasingly aware of information security and pay more and more attention to information management. Cloud computing as a new service model appears in our life. By transforming traditional physical resources into virtualized data, it is widely used in various fields because of its large storage capacity and easy operation. This paper mainly studies the establishment of an office automation management system with automatic identification function and password protection function based on docker system. Firstly, this paper introduces the composition of cloud computing cryptographic application system, then deeply studies docker technology, designs cloud computing application system, and carries out simulation experiments. The experimental results show that under the optimization of docker technology, the error rate of the cloud computing password application system is very low, and the time to process password data is also less, which comprehensively shows that the security of the cloud computing password application system is very high.
Virtual reality (VR)

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Salient object detection via high-frequency edge detail enhancement

Although the detection accuracy of salient object detection (SOD) by deep learning has met most of the task requirements, how to learn the detailed information such as the edges of objects still has been challenging. Most of the existing SOD methods use interpolation operations in upsampling, which makes it difficult to capture the detailed information of salient objects. And this detailed information helps to improve the detection accuracy, so this paper proposes a high-frequency edge detail enhancement network of salient object detection to obtain the high-frequency detailed information of images and use this information to refine the result. To adaptively detect salient objects of different scales, the proposed method first uses a scale-varying detection module to spatially process the features of different sizes obtained by the backbone network. Then, the high-frequency residual block is used to obtain the high-frequency details lost due to interpolation operations. Finally, the high-frequency cascaded enhancement module utilizes the positional alignment mechanism to fuse high-frequency information into original salient objects features and obtains the final predicted results. Experimental results on five benchmark datasets demonstrate that the proposed method without any post-processing performs favorably outperform 12 state-of-the-art approaches.
Research on damage level evaluation model of military airport based on Dynamic Bayesian network

By applying the dynamic Bayesian network theory, a dynamic Bayesian network evaluation model is established according to the structure, damage level and functional characteristics of military airport buildings. Based on the construction of the airport model, a Bayesian network diagram for evaluating the damage effect of military airport buildings is established by using the powerful inference function of dynamic Bayesian network. On the basis of determining the damage evaluation network nodes of military airport, the network structure and network parameters are determined. The simulation experiment verifies the correctness and effectiveness of the calculation. This model can not only evaluate the damage of the whole airport building, but also evaluate the damage effect of the airport building structure and other facilities. It provides scientific assessment for the decision makers' flexible strike methods and strike tasks.
An adaptive density spatial clustering algorithm based on tuna swarm optimization for image segmentation

The density-based spatial clustering of applications with noise (DBSCAN) method is exceptionally sensitive to the selection of parameters, making it difficult to obtain more accurate clustering results. To solve the above problem, we propose an adaptive TSO-DBSCAN algorithm using the excellent global search performance of the Tuna Swarm Optimization (TSO) method. We take the clustering radius parameter as the search objective of the TSO method. Then, the obtained evaluation metrics are used as the fitness function of the TSO method to adaptively search for the best parameter value and the optimal clustering result. The experimental results show that the proposed method has better clustering accuracy and effect on the processing of image segmentation.
Development and construction of auxiliary tools for Chinese-Kazakh bilingual term translation in the IT field

This article introduces the construction of an auxiliary translation system for Chinese and Kazakh bilingual terminology in the field of information technology and the design of each functional module. And the overall frame structure of its design, the working principle of each functional module, the collection, preprocessing, classification and translation process of bilingual term corpus are explained.
Abstractive summarization based on pre-trained model and knowledge enhancement

Given the speedy development of Internet technology, the amount of information we get is increasing explosively, so the extraction and summary of information are extremely important. Text summarization technology, as one of the important research of natural language processing, can extract text expressing core ideas from massive data, to quickly obtain the required information. However, the traditional recurrent neural network has weak parallel computing ability, and there are cases of inconsistency with the original text content and fabrication of facts. To solve the above problems, an abstractive summary model combining a pre-trained language model and knowledge enhancement is proposed. The model incorporates the extracted factual knowledge into the summary generation process with the help of graph networks, to retain the original meaning to the greatest extent. Finally, the model is verified by standard text dataset CNN&Daily Mail and good experimental results were obtained.
Aggregated privacy preserving auditing scheme with a designated auditor in cloud storage

Integrity verification can effectively protect cloud data. Currently, users entrust third-party auditors to verify the integrity of their outsourced data. Unfortunately, multiple auditors participate in the integrity verification, which increases the risk of data privacy disclosure. Therefore, more users expect a specific verifier to check the files in cloud storage. We proposed a scheme with a designated verifier, which can effectively protect data privacy when auditing. In the process oey generate tags. In the evidence generation, bilinear results of data blocks and tags are directly calculated to realize privacyf tags generation, we utilize the designated auditor public k protection. Based on the computational Diffie– Hellman assumption in the Bilinear Maps, we prove the security for our scheme.
Development of data literacy class design strategies for middle school students to foster citizenship

Objectives The purpose of this study was to derive the design strategy and detailed guidelines of data literacy classes for middle school students to cultivate citizenship.&#x0D; Methods The first design guidelines were developed through a review of previous studies, and the design strategy and detailed guidelines were revised through expert review. After that, for on-site evaluation, it was put into the “Basic Competency Learning for Student Researchers” stage during the “learner-led project” course of the second semester of the first grade of C Girls' Middle School in Seoul. Three teachers in charge of the ‘learner-led project’ class at C Middle School and 61 first-year middle school students (4 classes) participated in the two-hour program.&#x0D; Results By collecting data on the responses of instructors and learners in various ways, 6 design strategies and 13 detailed guidelines were developed to design data literacy classes for middle school students to foster citizenship.&#x0D; Conclusions This study is expected to provide practical guidelines for middle school teachers to design data literacy classes to foster citizenship in a modular manner in connection with various curriculums.
Lightweight Siamese network-based single-target tracking algorithm for pedestrians

Aiming at the problem of slow running speed of pedestrian single-target tracking task using SiamBAN algorithm under limited hardware conditions, this paper proposes a lightweight pedestrian single-target tracking network GhostSiamBAN. Specifically, we use GhostModule to build a new lightweight backbone network to replace the original Resnet50. The evaluation on OTB2015 and VOT2018 shows that our method can well balance the tracking accuracy and speed. Compared with SiamBAN, the test results of pedestrian single target tracking sequence on Nvidia GTX 1050 show that the average running speed of our method is about 26fps with an accuracy loss of about 5.46%, and the speed is increased by nearly 271.4%
Compressed Video-Based Classification for Efficient Video Analytics

Videos have become a crucial part of human life nowadays and share a large proportion of internet traffic. Various video-based platforms govern the mass consumption of videos through analytics-based filtering and recommendations. Various video-based platforms govern their mass consumption by analytics-based filtering and recommendations. Video analytics is used to provide the most relevant responses to our searches, block inappropriate content, and disseminate videos to the relevant community. Traditionally, for video content-based analytics, a video is first decoded to a large raw format on the server and then fed to an analytics engine for metadata generation. These metadata are then stored and used for analytic purposes. This requires the analytics server to perform both decoding and analytics computation. Hence, analytics will be fast and efficient, if performed over the compressed format of the videos as it reduces the decoding stress over the analytics server. This field of object and action detection from compressed formats is still emerging and needs further exploration for its applications in various practical domains. Deep learning has already emerged as a de facto for compression, classification, detection, and analytics. The proposed model comprises a lightweight deep learning-based video compression-cum classification architecture, which classifies the objects from the compressed videos into 39 classes with an average accuracy of 0.67. The compression architecture comprises three sub-networks i.e. frame and flows autoencoders with motion extension network to reproduce the compressed frames. These compressed frames are then fed to the classification network. As the whole network is designed incrementally, the separate results of the compression network are also presented to illustrate the visual performance of the network as the classification results are directly dependent on the quality of frames reconstructed by the compression network. This model presents a potential network and results can be improved by the addition of various optimization networks.
Analisis Tingkat Kematangan E-Learning Pada Perguruan Tinggi

This study aims to assess the extent to which the e-learning maturity level of Metamedia University is related to data management. Through the COBIT 4.1 framework in the deliver and support domain, namely DS11 (managed data), it can be seen the maturity level of Metamedia University e-learning. This study resulted in a maturity value on the DS11 of 2.82 which means the maturity level is at level 3, namely defined process. In general, it can be explained that the data management process already has standard procedures that have been socialized, but the implementation of these steps is not controlled so that there are many deviations. One of the improvement effort made is the determination of performance indicators and targets that are socialized for each work unit as well as increased training and formal certification of data management personnel. In addition, management optimizes monitoring of data management and performance indicators as well as performance targets.