Reviewerly/exHarmony huggingface dataset:
A Crossover Optimization Method based on Correlation Coefficient in Differential Evolution

Optimiz ation is to carry out characteristic distribution according to the dependencies in complex variables, mainly in the form of thin ellipses. To achieve the best effect in the dependency relationship, it is necessary to slide the long axis of the ellipse and change the variables at the same time. In addition, under the same distribution state, the optimal solution will also appear when the set of search points is far away in the variable separation problem. In differential evolution, it is difficult to cross a specific genetic gene at the same time because it determines whether their variables cross with the same probability. In this paper, to test this shape, a method of the searching point correlation coefficient is proposed The correlation matrix is obtained according to the distribution of search points, and the genes with a strong correlation coefficient are grouped into groups, which are crossed or not crossed at the same time. This method is introduced into the representative method of differential evolution JADE to optimize the basic problems. The experimental results of this method are verified by comparing the performance.
Feature Adaptive Developmental Mechanisms for Mobile Apps Recommendations System using the Nearest Centroid Classification Algorithm

Reviews and ratings play an important part in modern technology since they provide insights that may be used to enhance the functionality and performance of apps. Some customers give app reviews that don't provide developers with useful feedback for improving the app. Developers can't fix bugs or release new versions of their software if they don't catch them as soon as possible. With this method, we extract the most helpful user comments from existing app evaluations and compare them to the apps' overall star ratings. We present a Nearest Centroid Classification (NCC) approach based on supervised machine learning with the goal of spotting new app problems via review analysis. To successfully illustrate resilience to the extreme values that are beyond the range, the suggested framework contains mean vectors of k to evaluate both distance closeness and geographical extent of k-neighbors in each class. After a long time of waiting, developers may at last quickly correct bugs and fine-tune the app's features to enhance the user experience.
Analyses of Face Mask Detection Using Deep Neural Network

Corona virus started and spreading throughout the world and many peoples are affected from 2019 to 2021. To prevent the spreading government ask all the citizens to wear the mask without fail in all public places. However, some people are not wearing masks as the spread of corona virus increasing day by day. To avoid these problems, it is necessary to look for people who are not wearing masks in public places, but there is a problem that those who check may be infected with coronavirus. In order to solve this problem, we need to implement an automatic facemask detector by using deep neural network algorithm for monitor the proper wearing of mask in all places and detect the improper wearing in accurate manner. We yield 97% detection accuracy whereas KNN yields 95% and BPNN yields 96%.
Data analytics to reflect the current politics on twitter by using Machine Learning

Many unstructured text messages, chats, postings, and blogs have been produced as social media users have skyrocketed. In addition to serving as a platform for information dissemination, social media platforms provide a natural forum for the wide dissemination of ideas and viewpoints that acquire traction when supported by a sizable audience. The positive or negative feelings individuals have about a person, group, or location might be reflected in how well they are known. Vast quantities of material on social media platforms like Twitter provide political insights that may mine to understand public sentiment and foretell election outcomes. An effort is made in this paper to mine tweets for political sentiment and represent this extraction process as a supervised learning problem. Twitter users’ attitudes toward the main national political parties campaigning in the 2019 Indian election are analyzed, and tweets related to the upcoming election are extracted. The sentiment analysis categorization algorithm is thus ready to forecast the tweets’ propensity to infer election outcomes. The classification model is built using Long Short Term Memory (LSTM) and compared to traditional ML approaches.
Development Of Text Characterization Using Natural Language Processing Techniques

Purpose:Text summarization means it reduces the text without losing the content. By this, we can reduce the text size. Method:In this work, we attempt to use tokenization and stop word removal to extract the useful and main content of the text. The model read the given text and performs tokenization, it considers the punctuations, and words as the token and it will eliminate the white spaces, then it removes the stop words. We calculate the cost of the sentence in the authentic corpus. By this, we can obtain our minimized text. Result:The proposed system can work with an accuracy of 96%, which means the summarized text is accurate to the given text.
of Multimodal English Corpus Based on Functional Software

Multimode nursing English database is established based on audio-visual data, which can reflect the comprehensive application of nursing English. It has great potential in promoting English audio-visual education and research. Compared with text-based nursing English corpus, multi-mode nursing English corpus has advantages in context performance, authenticity and rapidity. Starting from the construction and application theory of multimode nursing English corpus, this paper studies the collection, display content and construction tools of multimode nursing English corpus, analyzes the application value of multimode nursing English corpus, and puts forward application countermeasures combined with educational practice.
Deep Data Mining Method of Multimedia Film and Television Based on Neural Network

The deep data mining method of multimedia film and television based on neural network is a new technology for extracting useful information from multimedia film and television. Deep learning neural network technology has been used to extract useful information from multimedia movies or TV programs (such as audio, video, images, etc.). It can be applied to image recognition, speech recognition, text analysis and other fields. The deep data mining method of multimedia film and television based on neural network is artificial intelligence. Multimedia film and television deep data mining is a process of extracting meaningful information from a large number of unstructured data (such as images, audio and video clips, text documents, etc.). Deep learning technology is used to extract patterns from these large amounts of data. The extracted patterns can be used for a variety of purposes, including: Classification - classifying objects according to their features (e.g., face) or attributes (e.g., gender).
An Improved Method for Chinese Relationship Extraction

In the field of information extraction, Chinese relationship extraction has become a problem which deserves the attention in both academia and industry. Unlike English, the semantic relations in Chinese are more complex with ambiguity. In order to perform more accurate Chinese relationship extraction, this paper discards the traditional pipeline-level extraction method and adopts a joint-level extraction method, proposing an RSO model. On the whole, the RSO model is divided into an encoding module, a subject entity extraction module and an object entity extraction module, which solve some problems in Chinese relationship extraction. The encoding module incorporates the four layers of the RoBERTa model that work best for textual tasks. The subject entity extraction module and the object entity extraction module use the concept of pointer annotation. Finally, the performance of RSO is compared with related work on the Chinese relationship extraction dataset, proving the effectiveness and feasibility of the proposed model.
Research and Implementation of Film Recommendation System Based on Deep Learning

This paper studies the research and implementation of film recommendation system based on deep learning. Deep learning is a technology that uses artificial neural network to learn and classify data patterns. It has been widely used in image recognition, speech recognition, natural language processing, machine translation and so on. The deep learning model uses back propagation algorithm to learn from a large amount of data. As more and more training data are available for model learning, this technology will continue to improve over time. It is widely used in many industries, such as finance, transportation and health care. In this blog post, we will study how to apply deep learning to film recommendation system by considering various factors (such as user demographics, type preference).
Cloud Data Integrity Verification Algorithm of Accounting Informatization in Sharing Mode

In order to ensure the security of accounting information in general financial model, a data integrity verification algorithm based on cloud computing is proposed. Firstly, the exchange coefficient data information system based on cloud computing is analyzed, which provides the overall system structure and network topology for cloud data storage. In addition, the existing single user data verification algorithm is improved, and the multi-user parallel verification algorithm is applied to realize accounting computerization, so as to reduce the verification times. The experimental results show that the parallel verification algorithm used by many users ensures the integrity and consistency of data files.
Regional Sharing Model of Information Education Resources in Rural Schools Based on Cloud Computing

As an integral part of educational rectification, plays an important role in the all-round development of quality education and the construction of educational modernization. Improving rural education informatization is the basis for improving the level of education informatization, an important symbol of the comprehensive modernization of education, and an important means to promote the balanced development of compulsory education. Its essence is to make education information, and the most important thing is to make education become human information. At the same time, there are many difficulties in the development of rural education and traditional forms of education. Cloud computing has not only changed people’s online lifestyle, but also had a profound impact on the basic education of the whole region. Change the current situation of uneven distribution of basic education resources in this region, and promote the transformation of basic education services and the sharing of basic education resources in this region.
Hand Gesture Control of Video Player

The rise of ubiquitous computing has expanded the role of the computer in our daily lives. Though computers have been with us for several decades, still we follow the same, old, primitive methods such as a mouse, keyboard, etc. to interact with them. In addition, a variety of health issues are brought on by a person's continual computer use. In the study of language, hand gestures are a crucial part of body language. The usage of a hand-held device makes human-computer interaction simple. The proposed work aims to create a gesture-controlled media player wherein we can use our hands and control the video played on the computer.
GEAR: Gaze-enabled augmented reality for human activity recognition

Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they allow novel interaction methods and provide insights into user attention, intentions, and activities. However, only few studies have used gaze-enabled AR displays for human activity recognition (HAR). In an experimental study, we collected gaze data from 10 users on a HoloLens 2 (HL2) while they performed three activities (i.e., read, inspect, search). We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved an up to 98.7% activity-recognition accuracy. On the HL2, we provided users with an AR feedback that is relevant to their current activity. We present the components of our system (GEAR) including a novel solution to enable the controlled sharing of collected data. We provide the scripts and anonymized datasets which can be used as teaching material in graduate courses or for reproducing our findings.
GANDER: a Platform for Exploration of Gaze-driven Assistance in Code Review

Gaze-control and gaze-assistance in software development tools have so far been explored in the setting of code editing, but other developer activities like code review could also benefit from this kind of tool support. In this paper, we present GANDER, a platform for user studies on gaze-assisted code review. As a proof of concept, we extend the platform with an assistant that highlights name relationships in the code under review based on gaze behavior, and we perform a user study with 7 participants. While the participants experience the interaction as overwhelming and lacking explicit actions (seen in other similar user studies), the study demonstrates the platform’s capability for mobility, real-time gaze interaction, data logging, replay and analysis.
GAN and MIRNet-Assisted Image Enhancement: A Promising Future in Visual Computing

Abstract: We present an extensive strategy for improving images using deep learning-based techniques, concentrating onsuperresolution (SR) and low-light image improvement utilizing generative adversarial networks (GANs) and misalignment-robust networks (MIRNet), respectively. During the SGAN training phase, a deep convolution neural network learns a complete link between low- and high-resolution pictures. Our approach simultaneously optimises all layers to provide cutting-edge restoration quality and quick speed for practical application. To achieve performance and speed trade-offs, we also investigate alternative network setups andparameter settings.Additionally, we upgrade the overall reconstruction quality by expanding our network to handlethree color channels concurrently. In contrast, MIRNet is utilized for low light image improvement, which has grown in significance as a result of the increased need for trustworthy picture enhancement systems in real-time applicationsincluding autonomous driving, surveillance footage, and crime scene investigations. MIRNet, which may be used for a range of image improving applications.
"Development of Topographic-Geological Model for Iron Ore Deposits in The Kursk Magnetic Anomaly"

The results of the course work of SOF-MGRI students in the discipline "Information technologies in mining" on the topic "Development of a cartographic model of a given iron ore deposit of KMA based on the given geological map" are considered.Each student completed work for only one deposit and one geological map from the set of six its types.Generalization and analysis of the results of more than 20 student works with a unique combination of deposits and geological maps made it possible to draw meaningful conclusions about the presence of interrelations in the distribution of deposits over the territory of the KMA and also to make an archaeological discovery thru identification the habitat of the ancient Kursk miners.
Implementation of Executive Information Systems in Decision Making in the Development of Branch Leadership Organizations of IPNU-IPPNU Pretek Central Java

Executive information system (EIS) is defined as a computer-based information system designed to provide executives with internal and external information that is easily accessible and relevant to management activities. The application of EIS in companies or organizations is expected to be able to help the work of leaders in making the right decisions. One of them in the development of branch leadership organizations of IPNU-IPPNU Pretek. This organization is a community organization under the auspices of Nahdlatul Ulama. Members of the student age where the state of environmental conditions and human resources that are still not considered important to technological developments. In order to maximize the role of the management information system. Members must jointly learn about the management information system. So that future activities facilitate work and can support the taking of executive holders.
Distributed Algorithms for Resource Allocation and Load Balancing

Modern computing is increasingly using distributed systems, and improving their load distribution and resource allocation is essential for obtaining optimal performance.Distributed algorithms for resource allocation and load balancing employ a variety of techniques, including heuristic-based, optimization-based, and machine learning-based ones.In this work, we present a review of distributed load-balancing and resource-allocation approaches.We explore the difficulties in developing efficient algorithms and emphasise the need of meticulously analysing and contrasting various algorithms in light of the requirements of a certain system and workload.Additionally, based on the concept of particle swarm optimisation, we present a distributed method for load balancing and resource allocation in cloud computing environments.Our suggested method tries to reduce the average task waiting time while simultaneously maintaining some semblance of resource parity among nodes.By putting our technique through its paces in a simulated cloud computing environment and examining the outcomes, we compare it against cutting-edge algorithms.Our research demonstrates that our suggested technique has the potential to greatly improve system performance by reducing the typical amount of task waiting time and ensuring that the load is distributed evenly among nodes.This shows how particle swarm optimisation may be used to create efficient distributed load-balancing and resource-allocation algorithms.
A multi-geophysical data integration framework guided by petrophysics to improve bedrock map of Limerick Basin, Ireland

Summary Much of Irish Midland’s bedrock geology is overlain by thick glacial overburden which hinders the precise mapping of the geological units in this region. Recent acquisition of high resolution geophysical data provides a new perspective for geological mapping in the areas under cover. Geophysical data acquired using various prospecting methods like gravity, magnetic ,and electromagnetic, are capable of highlighting the variable response originating due to different physical properties of rocks. However, an integrated geophysical data analysis framework guided by petrophysics can be more efficient in mapping the sources of multiple geophysical responses in better interpretable manner. So, we formulated a multi-geophysical data integration framework guided by petrophysics that can aid in classifying the formations and probable rock types, thus providing new insights about the bedrock and shallow subsurface geology in Limerick Basin, Ireland. An iterative framework including semi-supervised UMAP dimensionality reduction incorporated with ground truth data, followed by HDBSCAN clustering technique has been applied to derive cluster maps. The cluster maps when thoroughly examined and interpreted in the light of petrophysical data reveal new insights about the geology of the study area.
Human Activity Recognition in Video Sequences Based on the Integration of Optical Flow and Appearance of Human Objects

Recognition of human activities has emerged as a critical research area due to its potential applications in many automated monitoring applications. However, it is still a challenging problem due to inter- and intra-class variations in human activities, varying illumination conditions, viewpoint changes, etc. This work presents human activity recognition framework for motion activities recorded in realistic and multi-view environments. To represent complex motion activities, we designed a novel feature descriptor based on the integration of motion information and the appearance of moving human objects. For this purpose, first, we employed an object segmentation technique capable of dealing with camera motion, varying lighting conditions, and scale of the human object. Then we used the optical flow technique to compute each moving pixel’s velocity vector and orientation by avoiding background noise. Then histogram of oriented gradients of velocity and orientation information is computed to get the relative distribution of motion information for samples of each activity category. Finally, a feature fusion strategy integrates a local-oriented histogram of velocity and orientation information to construct the final feature vector. Support vector machine is used to compute the class score of activity categories. In order to give an empirical justification, we conducted several extensive experiments on three publically available datasets, namely IXMAS, UT Interaction, and CASIA. To demonstrate the effectiveness of the proposed methods, we compared their results with several state-of-the-art methods. The recognition results demonstrate the supremacy of the proposed method over the other state-of-the-art methods considered for comparison.
Examination of the Strategic Vision of Banks in Digitalization and the Effects of Innovation on Performance and Artificial Intelligence Perception

This study examines the effects of strategic vision and digital innovation on the performance and perceptionsof digital natives regarding artificial intelligence (AI) in banks. This is due to the increased use of mobile banking applications with the proliferation of the internet. An online questionnaire was administered to 603 experts working in bank headquarters. The collected data were analyzed using theSmartPLS 3.3.3 software. Banks that adopt digital business strategies can gain a significant competitiveadvantage. To successfully transform and compete, banks need to address customer demands in the areasof digitalization, innovation, and mobile banking. This research is original in its evaluation of the strategic visions and digital innovations of banks, which play a crucial role in the service sector, with implications for finance, innovation, and mobile applications. The results demonstrate the positive effects of strategic vision and digital innovation on performance and digital natives' perceptions of AI. The hypotheses support the importance of digitalization and innovation for banks.
Parameter estimation of Fh data link signal based on time-frequency ridge

To solve the problem of low estimation accuracy of FH data link signal parameters based on traditional time-frequency ridge method under low SNR, a time-frequency clustering estimation method based on GA optimization is proposed. Firstly, genetic algorithm is used to extract the time-frequency interval of STFT time-frequency diagram of data link signal; then the time-frequency ridge is extracted; finally, K-means clustering algorithm is used to estimate the frequency hopping frequency by classifying the time-frequency ridge with a cluster number of 6. Experimental results show that this method can accurately estimate the frequency hopping frequency under SNR =−18dB, and effectively improve the accuracy of parameter estimation under low signal-to-noise ratio compared with the traditional time-frequency ridge parameter estimation method.