Reviewerly/exHarmony huggingface dataset:
New Teaching Method of Body Training Course for Flight Attendants Based on Computer Multimedia Technology

Physical training is a physical and mental training method with certain scientific basis and evaluation standards, and it is also one of the ways to cultivate comprehensive professionals for civil aviation. This paper attempts to incorporate multimedia computing systems into the teaching and learning of flight attendants from training classes, with the aim of tackling the issues that exist at this stage of the educational programme, promoting the standard and effectiveness of teaching and learning. Firstly, the paper explains the stewarding staff form training concept, including the professional requirements of flight attendants, the concept and characteristics of physical training, and the problems existing in the teaching process and then introduces the instruction system according to the multimedia computing system. Finally, this paper compares it using the classical teaching mode. The outcome of the investigation indicated that about 70% of the students achieved good grades and above with the assistance of multimedia computing devices, which verifies its effectiveness.
A Robust Information Security Model to Preserve Data Integrity in Cloud Computing Environment

With the fast growth of cloud computing, more and more clients want to store their data on PCS (public cloud servers). More clients will benefit from using the public cloud if new security issues can be addressed. PCS's proxy will process the client's data and upload it if he is unable to access PCS. Data storage on remote servers must be protected, and cloud computing is a major factors in determining how this might be done. The data owners and users may rest certain that their data is safe and secure thanks to the usage of authentication and authorization mechanisms. A New proof assisted trustworthy data management method, which enables owners of data to send the information into server using Modified Cryptographic Standards MCS, RSA and others. These systems allow the data owner and the data users to keep and retrieve the data to and from the server successfully. Secure cloud computing methods preserve data on a distant server with suitable permission and authentication systems, as well as providing trust-based services to its customers with the highest level of security.
Analysis on Content Based Image Retrieval - A logical survey

In present time, the introduction of free internet and database has made use data on the real time application and web media. The prominence of visual proficiency in human visual attention was remarkable. This paper is proposed to identify the prominent effect of visual saliency in Content based image retrieval (CBIR) through to other quality experiments. Over a period, the major images have been kept in in huge data bases. Any single method not been utilised to help in retrieval of any images from the database and it became a tough task. In this paper a technique is used to make process simpler, in which the provided query image in input is utilised To retrieve image of set of images having parallel attributes. The major aim of the image retrieving technique is to get at most clarity to the input query image on the basis of standard features. In the area of image processing, the state of the art technologies has been discussed in this paper.
Comparison of Image-Based and Skeleton-Based ML Methods in the Task of Alphabetical Sign Language Recognition

Hand sign language has played a significant role in bridging connections between different people with different cultures and backgrounds. A current problem is that hand sign languages are not highly accessible to many people. This paper proposes a skeleton-based machine learning (ML) system that provides very accurate alphabetical sign language recognition in real time.
Classifying Source Code Using Boosting Algorithms

Hundreds of lines of code make up large software systems. During maintenance and reuse, reading through the full source code will be quite tough. Many programmers stick to reading and navigating code than they develop it. Most experienced developers reuse existing code functionality. The cost of searching for and integrating reusable code determines the success of source code reuse. One method for identifying source code functionality names is to use a topic model. While numerous studies have focused on using topic models to analyze software systems, none have retrieved topics from actual source code syntax. By considering the optimal number of conditional statements, expressions, arrays, Boolean operators, and other unique programming keywords utilized is critical for any source code functionality name prediction. The dataset for the research model was collected from a variety of software developers' source code throughout the world. In software development, there are many different types of source code repositories, including source code algorithms. These repositories will be unstructured and unusable, requiring a technique to transform them into something useable. The source code data will be decomposed into smaller bits of programming language phrases using the Java parser library. Boosting algorithms are still more advantageous under the regime of limited training data and low knowledge for parameter modification. This study looked at supervised learning boosting algorithms such as XGBoost, CatBoost, LightGBM, and AdaBoost. It was discovered that the aforementioned algorithms performed equally, despite the fact that CatBoost surpassed all other methods based on accuracy with a value of 98%.
Shape and Intensity Combined Statistical Atlas Registration for Torso Organ Segmentation from Mouse Mirco-CT Images

Segmentation of soft organs from mouse micro-CT images is a prerequisite step of preclinical image analysis in drug development and cancer research. However, due to the high variation in image contrast, noise level, mouse body posture and body weight, mouse micro-CT images present inconsistent grayscale appearances and organ shapes. State-of-the-arts (SOTA) methods used digital atlas registration strategy to compensate for the inter-subject shape variations. However, due to the lack of grayscale appearance modeling, the current methods are not generalizable enough to varied image contrasts or noise levels. In this paper, we propose to use voxel grid representation to jointly model the anatomical and appearance variability of the mouse micro-CT images. We construct a statistical atlas of mouse torso with parametrized shape and appearance features. In this atlas, the appearance parameter affects the grayscale intensity of the atlas, manifesting the global or local contrast variation of the image. The shape parameter drives each voxel of the atlas to shift in the form of a deformation field. We also developed a method to anatomically register the atlas to mouse micro-CT images. Experimental results based on a comprehensive testing dataset show that our method is robust to different types of micro-CT images, covering a variety of body shapes, organ contrast and noise levels. Our method yields more accurate organ segmentation than the standard digital mouse atlas registration approach and the SOTA statistical shape model approach.
Design and Build a Student Value Processing Information System (eRapor)

SMK Negeri 1 Kecamatan Luak is a government agency that organizes vocational education. Every semester SMK Negeri 1 Luak Regency does data processing of student scores, but in processing student grades they still use the manual system, namely by collecting assignment scores, test scores, mid-semester test scores and end-semester test scores. The exam is then processed by the subject teacher, then the score is submitted to the administrative staff, after that the administrative staff submits all student scores to the homeroom teacher to be entered into leger paper which is then processed into report cards. This is considered less effective, causing a lot of time and energy needed in the process of processing student grades. Facilitate the process of processing student scores to become e-reports at SMK Negeri 1 Luak District. With the information system for processing student grades at SMK Negeri 1 Luak District, it helps process student grades into report cards and ledgers easily, quickly and precisely. In addition, in a pandemic situation, the information system processing student grades data is very helpful for submitting semester report cards to students and their parents in order to avoid crowds to prevent transmission of the Covid-19 virus. This application is designed using the System Development Life Cycle (SDLC) method, in developing the SDLC the author uses the waterfall method in designing information systems.
Surface traffic monitoring using OpenCV for various weather conditions through enhanced spatial correlation method

It is a big challenge in the present urban traffic management system to develop a vehicle detection and traffic surveillance monitoring system through video capture using real time data capture model whereby machine learning may be used to recognise and categorise things. The idea is to analyse the motion and approach of cars acquired from CCTV cameras footage installed on the roads in different weather conditions and create an interactive software which should be able to gather and update important statistics like vehicle type classification and counts, lane usage details, for better regulation and control with ease. The core of this working model is to enhance vehicle detection and counting module which is implemented by analysing consecutive frames of video using frame differencing technique. The government has tried the best to ensure swift movement by implementing the broad usage of intelligent traffic controller system design. According to the WHO, around 2 million road traffic accidents result in deaths, with some nations devoting 2% of their GDP to road traffic accidents. Opportunities in the intelligent transport system market is growing at a global compound annual growth rate (CAGR) from 2019 to 2025 as an effective tool for road safety.
Blending Weighted TF-IDF &amp; BERT for Improving Semantic Search

Based on a weighted TF-IDF algorithm and the BERT natural language model, this research study provides an enhanced semantic search methodology. With the ongoing BERT improvements and to justify the use of a weighted TF-IDF technique, the research is based on the fact that the questionable spans in a document encapsulates the document&#x2019;s topics and, as a result, should be given more weight when computing the TF-IDF score. The BERT natural language model is used to compensate for the TF-IDF framework&#x2019;s shortcomings in interpreting a document&#x2019;s underlying semantic meaning. As a result, the developed model encodes the questional ranges and true semantics of a report. Scalability is key in today&#x2019;s growing data size and is taken into consideration as part of the study. This first of its kind study demonstrates that proposed methodology outperforms existing similar techniques by a significant margin in a variety of semantic pursuit probes question-answering datasets.
Review of various Image Mining Techniques

One of the easiest ways of communication is possible through interpreting wide range of images on various applications. It is the non-structural data that does not have clear semantics. Image mining techniques implemented on the images are used to extract features/train the real time systems. This can be achieved using combination of various algorithms of image processing, machine learning and artificial intelligence. This paper discusses about those image mining techniques and the challenges faced by various researchers.
Knowledge Graph Entity Typing with Contrastive Learning

Knowledge graph entity typing is an important way to complete knowledge graphs (KGs), aims at predicting the associating types of certain given entities. However, previous methods suppose that many (entity, entity type) pairs can be obtained for each entity type, performing poorly on entity types that only have a few associative entities. Besides, these methods cannot fully exploit the inherent correlation and complementarity information across different entities sharing the same entity type. To this end, we propose a novel model named Contrastive Entity Typing (CET) for KG entity tying. CET can better learn the mutual interactions among the entities with the same entity type and can fully utilize the hierarchical information in entity type trees by two contrastive learning modules. The main benefit of the proposed contrastive learning modules is that they can effectively encourage the consistency of the entity representations with the same type while improving the discriminability of the entity type classifiers. Empirically, our model achieves state-of-the-art results on KG entity typing benchmarks.
An Improved Self-Training Model with Fine-Tuning Teacher/Student Exchange Strategy to Detect Computer-Generated Images

Computer-generated (CG) images have become indistinguishable from natural images due to powerful image rendering technology. Fake CG images have brought huge troubles to news media, judicial forensics, and other fields. How to detect CG image has become a key point to solve the problems mentioned above. The image classification method based on deep learning, due to its strong self-learning ability, can automatically determine the differences in the image features between CG images and natural images and can be used to detect CG images. However, deep learning often requires a large amount of labeled data, which is usually a tedious and complex task. This paper proposes an improved self-training strategy with fine-tuning teacher/student exchange (FTTSE) to solve the problem of missing labeled datasets. Our method is actually a strategy based on semisupervised learning to train the teacher model through labeled data and to predict the unlabeled data by the teacher model to generate pseudo labels. The student model is obtained by continuous training on the mixed dataset composed of labeled and pseudo-labeled data. A teacher/student exchange strategy is designed for iterative training; i.e., the identities of the teacher model and the student model are exchanged at the beginning of each round of iteration. And then the new teacher model is used to predict pseudo labels, and the new student model exchanged from teacher model in the previous round of iteration is fine-tuned and retrained by the mixed dataset with new pseudo labels. Furthermore, we introduced malicious image attacks to perturb the mixed dataset to improve the robustness of the student model. The experimental results show that the improved self-training model we proposed can stably maintain the image classification ability even if the testing images are maliciously attacked. After iterative training, the CG image detection accuracy of the final model increases by 5.18%. The robustness against 100% malicious attacks is also improved, where the final trained model has an accuracy improvement of 7.63% higher than the initial model. The self-training model with FTTSE strategy proposed in this paper can effectively enhance the detection ability of the existing model and can greatly improve the robustness of the model with iterative training.
An efficient scene text detection neural network

Abstract: We introduce a new type of text detection neural network, which can accurately locate the position of the text in a variety of complex environments and give the best rectangle containing them. It is composed of three parts, the first part is the backbone composed of residual network, which is responsible for refining the feature map. the second part is the sequence module composed of transformer, which processes the feature map as a linear behavioral unit, so as to deeply mine the context of characters in the image, and the last part is the multi-scale detection module, which is based on different sizes of feature maps The best target box is detected as the result. The residual backbone ensures that there will be no gradient explosion in the process of back propagation.as information between grid cells in the same line is consistent, the transformer module pay more attention to the text line. The detection module uses multiple anchors in the vertical direction at the same time, which achieves good results in speed and accuracy. Based on the data set icdar2015, which is commonly used in the field of text detection, we do experiments and achieve a f score of 0.7.
Subjective Prediction of Questions in Q &amp; A System based on the Open Domain of Daily Life

People and computers have different understandings of questions, and people have different needs for answers. For some questions, people may not need objective answers, but developmental opinions. This paper analyzes long and difficult questions in an open domain question answering system and provides effective information to the system with subjective predictions. It uses pseudo-label technology and the blending of multiple pre-trained language models to improve the understanding of long and difficult text question sentences. In addition, by designing a variety of subjective labels, the model's prediction of the subjectivity and objectivity of questions can provide effective information for the question-and-answer system. Since there are currently no standard definitions or standards for subjective labels and long and difficult text question sentences, we have conducted a subjective analysis of long text questions based on 30 question sentence subjective labels and long text question longer than 512 characters, using Spearman's relative coefficient as the evaluation standard for model prediction. This work is the first to implement subjective prediction of long and difficult text in the open domain area by designing 30 subjective labels.
Development and Realization of the Psychological Counseling Platform for Colleges in the Cloud Environment

This article first introduces the theoretical knowledge of psychology in more detail, analyzes the psychological characteristics of college students, introduces some basic knowledge of psychological evaluation and mental health, and fully analyzes the necessity of developing this platform and description. This platform uses object-oriented technology to develop the entire system, mainly using ASP.NET and AJAX technology. The core technology used is the clustering algorithm of data mining&#x2014;k-means algorithm, which is used to distinguish groups of students with different types of psychological problems. The results show that psychological recovery has increased by 7.3%.
Leakage Detection and Identification of Power Plant Pipelines Based on Improved Faster RCNN

For steam and oil leak detection problems in power plant pipelines, the technology and system implementation based on CBAM-Faster RCNN are proposed. First, VGG16 is replaced by the ResNet101 structure in order to obtain rich semantic information of feature graph, which is the feature extraction network of CBAM-Faster RCNN. A lightweight attention mechanism module CBAM is added to the feature extraction network and the full connection layer is replaced by one-dimensional convolution. These are able to solve the problem that the feature information of small leakage objects is tend to lose. Next, the new anchor scale in RPN network is obtained based on Kernel K-means clustering method, which is suitable for corresponding to the scale of the leakage object. Then, the ROI Pooling structure in CBAM-Faster RCNN is replaced by ROI Align to improve the accuracy of leakage detection algorithm. The mAP value of CBAM-Faster RCNN network reaches 96.5 percent, which proves accurate detection of steam and oil leakage is realized. Finally, the pipeline leakage detection system based on CBAM-Faster RCNN is embedded into the platform of intelligent inspection robot in key areas of power plant and the practicability of this method is proved.
Management Challenges in Bimodal IT Organizations

Most information technology (IT) organizations are experimenting on adoption of the dual mode of operation, involving traditional and agile methods of work. These two incompatible development worlds need to be in sync for IT to operate effectively. However, both types of operation map onto generic project management principles allowing the manageability of IT projects. Management issues and challenges are presented as points for paying particular attention when managing bimodal (both waterfall and agile practices) IT organizations in two stages: the transition from the traditional to the bimodal and after the adoption of bimodal. These issues and challenges belong to the management and organizational, people, process and technology categories. The management challenges presented in this study are important for the decisions to be made regarding the management and governance of projects into bimodal IT organizations.
Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction

The damage of road auxiliary facilities poses a major hidden danger to driving safety. It is urgent to study a method that can automatically detect the damage of the road auxiliary facilities and provide help for the maintenance of traffic safety auxiliary facilities. In the method for identifying the absence of road auxiliary facilities based on deep convolutional network for image segmentation and image region correction, the PointRend model based on the deep convolutional networks (CNN) is first used to achieve the pixel-level fine segmentation of the auxiliary facilities area, and then, the multiple images in the same image are segmented. In anti-glare panel area, on the largest outer polygon estimated by the convex hull algorithm, the optimal outer quadrilateral is determined according to the distance between the vertices, and then, the anti-glare panel area correction is completed by affine transformation and finally through the image one-dimensional projection mapping and adjacent shading. The distance correlation between the boards realizes the identification and positioning of the missing light-shielding board. The highway anti-glare panel missing recognition method based on deep convolution image segmentation and correction uses the vertex distance to quickly determine the external quadrilateral, which is suitable for estimating the shape of the area in a dynamic scene. After actual testing and verification, it can accurately and efficiently identify the disease of the anti-glare plate. Compared with traditional image segmentation methods, the method using the PointRend target segmentation model has better segmentation quality for target details, and it is more robust when dealing with background interference.