Reviewerly/exHarmony huggingface dataset:
Immunohistochemical evaluation of the expression of specific membrane antigens in patients with pancreatic ductal adenocarcinoma undergoing surgery

The morphological similarity of organs is studied with feature vectors based on geometric and Zernike 3D moments. It is particularly investigated if outliers and average models can be identified. For this purpose, the relative proximity to the mean feature vector is defined, principal coordinate and clustering analyses are also performed. To study the consistency and usefulness of this approach, 17 livers and 76 hearts voxel models from several sources are considered.In the liver case, models with similar morphological feature are identified. For the limited amount of studied cases, the liver of the ICRP male voxel model is identified as a better surrogate than the female one. For hearts, the clustering analysis shows that three heart shapes represent about 80% of the morphological variations. The relative proximity and clustering analysis rather consistently identify outliers and average models.For the two cases, identification of outliers and surrogate of average models is rather robust. However, deeper classification of morphological feature is subject to caution and can only be performed after cross analysis of at least two kinds of feature vectors. Finally, the Zernike moments contain all the information needed to re-construct the studied objects and thus appear as a promising tool to derive statistical organ shapes.
Design of an Early Warning Intelligent Model for Enterprise Industrial and Commercial Crisis based on Multi-Dimensional Computing Data Outlier Analysis

This paper analyzes and studies the effect of the selection of the number of real clusters on the outlier detection in the cluster-based outlier detection algorithm and proposes an outlier detection algorithm based on the automatic clustering method. Firstly, factor analysis, mean test and correlation analysis are used to screen financial indicators and corporate governance variables, respectively, to obtain representative indicator variables, and then use support vector machine method to conduct empirical analysis. The research results show that the support vector machine model has a strong predictive ability for enterprise bankruptcy risk. Using the distance relationship between each candidate outlier and its data block and adjacent data blocks, it is determined which processor each candidate outlier needs to carry out network communication. Metrics, Alerts, Diagnoses and Gaining Experience.
Application of Expression Capture based on Image Perspective Analysis in Animation Production

With the rise of big data and multimedia technology, animation production and animation production have entered the bottleneck period of the new era. This paper studies the production process and possibilities of 3D and 2D-live action animation based on image perspective analysis and facial expression capture and recognition; image-based the accuracy of the expression capture technology of perspective analysis has increased by 9%; then it is analyzed that the expression capture technology has increased the accuracy of animation production and the smoothness of the picture by 10%.
PHP Implementation of a Comprehensive Evaluation Platform for Innovative Talent Training Quality based on Internet Data Clustering and Integration

PHP implementation of the comprehensive evaluation platform for innovative talent training quality based on Internet data clustering and integration is studied in the paper. The feature space reorganization technology is combined to reorganize the industrial Internet abnormal dataset structure and obtain the active defense control and detection of industrial Internet abnormal data through fuzzy feature matching and neural network control. The unified data platform adopts a distributed, multi-layer architecture, which is divided into application layer, platform layer, infrastructure layer, access layer and data source layer from top to bottom. Hence, with this feature, this paper then studies the implementation of a comprehensive evaluation platform. The performance is then tested under different aspects.
Comparison of Performative Analysis based on High-Resolution Camera Image Difference Modeling Algorithm

Based on the difference modeling algorithm of high-resolution camera images, this paper explores and compares various aspects of performance aerobics and dance cheerleading. To enhance the idea of the inverse thermal diffusion equation in the algorithm, and based on the anisotropic diffusion function in the Perona-Malik model, a sharpening function that can combine the original diffusion function is studied. Physical quality, dance body vocabulary, expressiveness, mental quality training content and methods, aesthetic value, fitness value and social value are similar.
Application of Hybrid Parallel Cloud Architecture in Java+Oracle Implementation of Enterprise Management Innovation

This article takes the application of multi-management system integration and operation in enterprises as the research object, hoping to solve the “run-in” pain caused by the parallel operation of multiple management systems at this stage. Therefore, the article proposes to improve the organizational structure, and at the same time analyzes and discusses the performance and optimization technology of Oracle database, and expounds the architecture of the enterprise management information system. And learn the basic knowledge of related financial accounting used in the enterprise. In terms of development technology, the author mainly studied core technologies such as C/S architecture, Java programming language, and Oracle database system.
Verification of the Effectiveness of Interactive Teaching Evaluation based on Visual Computer Modeling Algorithm Platform

This study uses systematic evaluation method and meta-analysis method to compare and analyze the effect of gamification teaching and traditional teaching in order to obtain more scientific conclusions. This paper expounds the design of the algorithm-based animation-assisted teaching platform “Xiaohuaya”, describes its teaching practice process, and verifies whether this method can stimulate students’ interest in exploring and learning, and reduce teachers’ teaching workload. The research explores the formal modeling and verification of service combination. and model-driven visualization techniques. Using the modeling method based on behavior description language, the test results show that the 16 classic experimental algorithms of artificial intelligence courses included in the platform can be clearly and dynamically demonstrated
Centralized Analysis Algorithm of Multi-Dimensional Traditional Data for Smart New Computer Network Course Platform

This paper proposes a visual analysis method of parallel coordinate axis arrangement based on clustering. Hierarchical clustering algorithm is used to perform cluster analysis on multi-dimensional attributes of statistical data, and information entropy is used to measure the correlation between attributes based on clustering. A frequent sequence mining algorithm and cluster analysis method for smart classroom teachers’ teaching mode are proposed., Through the analysis and verification of teaching video cases, it is found that the cluster of excellent teachers obtained by cluster analysis for a specific subject has a strong consistency with the excellent teachers in actual teaching.
The C# Implementation of the Management System for the Evaluation of Leading Cadres based on Hybrid Cloud

This project is aimed at the cumbersome management of cadre growth information, and makes full use of advanced computer technologies such as hybrid cloud, MIS, J2EE related technologies, MySQL database technology, XML, Web Service, component technology, etc., to build a system that can integrate information management for cadres established in various places. “An intelligent, unified and powerful cadre growth information management system” based on system data can manage and mine the growth information of all cadres, and provide effective information support and powerful technology for the party and government when promoting and using cadres. Means, the test results show that the efficiency is increased by 7.8%.
FED6G: Federated Chameleon Learning for Network Slice Management in Beyond 5G Systems

A cornerstone of wireless connectivity involves trust and privacy in the data shared between users and network elements as wireless connectivity becomes an integrated, fundamental element of society. With a large influx of data in Beyond 5G (B5G) systems from end-users and network elements, it is imperative to understand how data is collected and used for real-time data processing operations. The current wireless network learning involves centralizing the training data, which is inefficient as it continuously requires end devices to send their collected data to a central server. Federated Learning (FL) effectively allows end-devices to train ground-truth data on-device, and only model update parameters are sent back to the federated server. This work proposes a Chameleon FL model, FED6G, for network slicing in 5G and Beyond systems to solve complex resource optimization problems without collecting sensitive, confidential information from end devices. The evaluation results reflect more than 39% improvement in Mean Squared Error (MSE), 46% better model accuracy, and more than 23% reduced energy cost for training the proposed FED6G against the traditional deep learning neural network model.
System to Optimize the Process of Medical Consultations Using QR Codes in the Hospitals of Peru

Poor hospital care in Peru is responsible for the constant dissatisfaction and time delays in care in a large number of patients who go to public hospitals. In this work, the development of a system was carried out to optimize the process of medical consultations using QR codes in the Hospitals of Peru that would allow the reduction of time in care and the increase in patient satisfaction. The implementation of the proposed system was supported by a new medical care process diagram, as well as the development of a mobile application for scheduling and managing appointments, complemented by the use of QR codes to display information. The validation process was carried out among a selected group of patients, determining that the time spent in medical attention decreased compared to that of a current medical appointment, in the same sense, satisfaction was increased.
Innovative elements in distance learning in a multicultural environment

Scientific and technological innovations in education actually complete the process of establishing distance learning as an independent educational format. Information and digital cluster forms specific educational components in different socio-cultural dimensions, one of which is multiculturalism. Scientific exploration aims to analyze the dynamics of the implementation of scientific and technological innovations in the multicultural educational environment through the actualization of distance learning. The objectives are to investigate the transformation of educational strategies of multicultural space, which are updated online thanks to the ICT-potential. The article uses general scientific, pedagogical, culturological, and philosophical methods of research. We see a characteristic feature of the study as methodological activity in the dynamics of the rapid socio-cultural development of modern education. Consequently, modern sociocultural space is characterized by diversity, which dictates education its development prospects, which are provided by scientific innovations and advanced technologies. Prospective areas of research are the direction of innovative dimensions to overcome the negative manifestations of distance learning and taking into account the multicultural nature of the educational environment.
Breast Cancer Detection Using Concatenated Deep Learning Model

The research on cancer is taken a superior space for medical and technological professionals. Since a few years its research grows for both where maximum involvement of technological research is clearly visible. In the article, we analyze the earlier works for detection. Along with it, a model is proposed that performs well and will take the readers to next level of work. A concatenated model containing convolutional layers and long short term memory layers is proposed for cancer detection from the histopathological images. The Adam optimization algorithm is used for minimizing the error and to train the network that is one of the supervised learning methods. To check the practicability of the proposed method publicly available breast cancer dataset is taken to train, validate, and test the network. The proposed method resulted in 95.32% testing accuracy.
Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner.
Sentiment Analysis of Stress Among the Students Amidst the Covid Pandemic Using Global Tweets

Covid-19 pandemic has affected the lives of people across the globe. People belonging to all the sectors of the society have faced a lot of challenges. Strict measures like lockdown and social distancing have been imposed several times by governments throughout the world. Universities had to incorporate the online method of teaching instead of the regular offline classes to implement social distancing. Online classes were beneficial to most of the students; at the same time, there were many difficulties faced by the students due to lack of facilities to attend classes online. Students faced a lot of challenges, and a sense of anxiety was prevalent during the uncertain times of the pandemic. This research article analyzes the stress among students considering the tweets across the globe related to students stress. The algorithms considered for classification of tweets as positive or negative are support vector machine (SVM), bidirectional encoder representation from transformers (BERT), and long short-term memory (LSTM). The accuracy of the abovementioned algorithms is compared.
Topic Model—Machine Learning Classifier Integrations on Geocoded Twitter Data

Topic Models are unsupervised probabilistic models that are used to explore the hidden semantic structure of a corpus of documents by generating latent topics. Latent topics are discrete distribution over words that need to be interpreted and labeled by humans. The latent topics can be also considered as a summary of the various topics that are discussed in the corpus of documents, so that the topic model can also serve as a dimension reduction method. Latent topics generated by topic models are difficult to measure in their quality [4] and their outputs are commonly used in a descriptive manner in order to gain topical insights into the topic distributions of chosen documents. A so far unexplored way, is the use of labeled data to assess the informational content of latent topics. In this paper, topic models are examined in terms of their informative value regarding classification problems for labeled text data. We use geo-coded social media posts from Twitter, but our approach can be expanded to other labeled documents. The output of Latent Dirichlet Allocation (LDA) [3] models and Structural Topic Models (STM) [29] are used as an input for machine learning classifiers, after pooling tweets based on the hashtag pooling algorithm of [24]. Their predictive power is compared with the performance of state-of-the-art Artificial Neural Networks (ANN) that are trained on a specifically optimized word-embedding and all the available metadata of the tweets. We find that the machine learning classifiers that are trained on topics can compete with the predictive performance of the ANNs, even for out-of-sample predicted topic distributions.
A new secure outsourcing ridge regression protocol with matrix encryption

Ridge regression is an important algorithm in machine learning, which has a wide of practical applications such as recommendation systems. Therefore, ridge regression outsourcing scheme has been widely studied in recent years. However, it would be difficult to outsource ridge regression to an untrusted cloud server without giving away information. In this work, we propose a new secure and effective outsourcing ridge regression scheme. We use a series of disguise techniques with permutation matrices and unimodular matrices for encryption. Through theoretical and experimental analysis, our protocol has two advantages: 1) The protocol guarantees against malicious cloud server attack and the client can verify the results returned by the cloud server; 2) There is only one round of communication between the client and the cloud server. When the dimensionality increases, the computational cost of our protocol approaches the computational cost of the original problem.
An improved turning-angle considered time-optimal ant colony optimization

Aiming at the problems encountered by the traditional ant colony optimization in the time optimal path planning, the method of transforming the ant-cycle system model is studied. In the optimization process, it is coupled with the algorithm model by quantifying the node turning time, the pheromone update rule is adjusted accordingly, and the computational efficiency is taken into account. The improved mathematical model is simulated by MATLAB software, and the effectiveness of the improved ACO model is verified by comparing the optimization results of the standard map traveling salesman problem with the classical ant colony. The main variable parameters are selected, and the optimal combination is preliminarily screened out through the simulation analysis of their sensitivity to the results.
Efficient Data Hiding Scheme Using Steganography and Cryptography Technique

Transportation of information across the internet necessitates security concerns. Internally, transmission of data in organisation must also be secured, according to security standards. In a typical distribution, all traffic transferred via the public network (Internet) is secured. However, sensitive information must not be easily accessible in the event that an attacker compromises any services on the hosts and gains access to their resources. The advancement of information technology has enabled large amounts of digital data to be reliably transported through unrestrained communication channels. Today’s information world is a digital world. Data transmission over an unsecure channel is becoming a major issue of concern nowadays. And at the same time intruders are spreading over the internet and being very active. So to protect the secret data from theft, some security measures need to be taken. In order to keep the data secret various techniques have been implemented to encrypt and decrypt the secret data. Findings of this study show how steganography and cryptography techniques are used for efficient data hiding to prevent unauthorised access to confidential and sensitive information. The study present recommendation for policy and practice. Keywords: Efficiency, Data Hiding Scheme, Steganography, Cryptography, Security
Effects of VR on Learning Experience and Success

Virtual Reality (VR) and Serious Games have proven to be effective in employee training, especially when teaching practice-oriented tasks. This is of great value for intralogistics and its practical and complex processes. The VR-based learning game InGo simulates a receiving goods process for intralogistics. A study carried out in this work explores the effects on the learning success as well as other factors when learning with the VR-based game. It compares the effects with a control group that learns the same process with traditional text-based learning. Although the results of the study show that there is no significant difference between the two groups in terms of learning success itself, they confirm the positive influence on other factors that reinforce learning, such as intrinsic motivation and flow. This supports previous research that indicates the high potential of VR-based learning and educational games. The different preconditions of the VR group compared to the control group emphasize the positive effect of VR.
Materials Science and Engineering Education Based on Reality-Virtuality Technologies

The use of reality-virtuality technologies (RVTs) in the field of engineering education has shown a growing trend for more than a decade. The teaching of Materials Science and Engineering (MSE) is no stranger to this phenomenon, and there are numerous examples of it. In order to better understand how RVTs are implemented in MSE teaching, this review presents an analysis that has been carried out after a systematic search in three different databases. This systematic search aimed to find academic works describing tools based on RVTs to improve the teaching-learning process of MSE. The results obtained provide an overall picture of how RVTs have been used since 2010 in teaching MSE: (i) virtual reality (immersive and non-immersive) is the most widely used technology, followed at a considerable distance by augmented reality, while mixed reality is a technology that is rarely used currently; (ii) the field of MSE on which most papers have focused is Materials structure, processing, and properties. Based on this information, it is possible to open up lines of research that have not been explored until now.
Evaluation of the Bibliographical Importance of Digital Educational Disruption Related to Social Networks. The Case of LinkedIn Learning

The social importance of the virtual world is growing. The different social habits have been transferred to digital reality, but issues are as crucial as social relations or education. Concerning the latter, we developed the current research, worrying about how the scientific community can transfer this reality to the different studies that are being developed. In addition to observing the direction of the numerous studies that relate social networks to education, we aim to investigate the evolution of publications associated with a social networking tool such as the one developed by LinkedIn to offer education. As shown, the interest in the possibilities offered by social networks in e-Learning is growing, and the scientific production increases every year, but this does not translate into a significant volume of specific publications on LinkedIn Learning. On the other hand, it is remarkable to observe, in a concrete way, that within the little developed by the scientific community around LinkedIn Learning, 40.91% of these studies relate this social network to the concept of e-Learning.