Reviewerly/exHarmony huggingface dataset:
Dual-mutation mechanism-driven snake optimizer for scheduling multiple budget constrained workflows in the cloud

Scheduling workflows in cloud computing is to find the most appropriate mapping from a series of dependent tasks to a set of available virtual resources so as to minimize or maximize some user concerned objectives, which is very significant to a sustainable and high-efficient cloud data center. However, it still faces challenges since its NP-hardness and the diversified requirements of both the cloud service consumers and cloud service providers need to be satisfied at the same time, especially in addressing multiple applications requested simultaneously. This work proposes a dual-mutation mechanism-driven snake optimizer for scheduling multiple workflows in the cloud for minimizing the makespan of each workflow under user pre-defined budget constraints. Firstly, a continuous optimization algorithm, namely Snake Optimizer (SO) is adopted into discrete optimization, i.e., workflow scheduling. Secondly, a task execution order aware fitness function is designed to reduce the gaps or waiting time between parent and child tasks within a workflow and thus reduce the total execution time of the workflow. Besides, we analyze the existing snake optimizer and adjust the parameters corresponding to different evolutionary stages to adapt to our considered problem. Finally, a dual-mutation mechanism is developed by introducing a non-improvement iteration number for each snake and applying a standard bit mutation operation to prematurely converging snakes and the individuals randomly selected from the remaining snakes so that the population diversity can be enhanced and more potential solutions can be explored. Experimental results on a set of real-world scientific workflows show that our proposed algorithm is of great superiority in constraint satisfiability, meaning that compared with its peers, it is always the first to find feasible solutions.
Quality guided reversible data hiding with contrast enhancement

Reversible data hiding with contrast enhancement (RDH-CE) enables contrast enhancement of the image and lossless recovery of carrier image after embedded data extraction. Although the current RDH-CE method can be effectively enhanced and ensures reversibility, image quality control is still lacking, which may lead to poor image visualization. In more serious situations, the image detail information may be lost. The peak pair selection mechanism plays an extremely crucial role in image quality enhancement; however, existing work often neglects this. In this paper, we propose an RDH-CE algorithm that pursues high image quality maintenance, aiming to maintain image quality by guiding and controlling the selection of peaks and the direction of histogram shift through quality metrics. Specifically, instead of simply selecting only one pair of peaks at each peak selection stage, our algorithm selects several pairs of peaks that meet the requirements as candidate peak pairs. It then first performs simulated histogram equalization on these candidate peaks, followed by analyzing the situation of the quality metrics generated by each pair of peaks accordingly, and then selects the optimal peaks for the current iteration based on the performance of each pair of candidate peaks to be used in the equalization process. Our selection mechanism also dynamically changes the peak selection strategy based on before-and-after changes in the actual metrics, which can better ensure image quality stability over the iterations. Under the evaluations of a series of image quality metrics, the proposed method can still maintain high quality and visual effect under high iteration and has strong stability for image quality control, thus demonstrating the effectiveness and superiority of the proposed method.
LegalHTML: Semantic mark-up of legal acts using web technologies

We introduce here LegalHTML, an extension of the HTML language thought for representing legal acts. LegalHTML has been conceived in the context of an exploratory study conducted for the Publications Office of the European Union, with the objective of overcoming the proliferation of formats for the electronic redaction of legal acts, dedicated to different steps of the editorial process (e.g. first draft, content editing, proof reading, introducing semantics, publishing) and of realizing a model and a language that could bind all processes and exigencies under a common umbrella. LegalHTML satisfies these requirements by providing an explicit domain language addressing all structural aspects of an act, such as articles, paragraphs, items, references and an associated ontology (foreseeing both inline annotations through RDFa and explicit RDF code within script elements) providing rich semantics to describe the editorial and jurisdictional history of the act and to insert references to entities of the domain. Being based on HTML, presentation is also offered by the same language, an aspect missing from all most notable standards for the legal domain. Furthermore, LegalHTML addresses consolidation of an act and its subsequent modifications into a single document using a tree-based representation of the original content and of its modified versions. Finally, alongside the language & ontology, we implemented a CSS stylesheet for the default rendering of LegalHTML documents and a JavaScript file imbuing documents with an API supporting TOC generation, footnote cross-references and the said point-in-time visualization of consolidated legal acts.
The Darkside of Blockchain Technology: A Case of Cryptocurrency Mining and Environmental Sustainability

Sustainability and Climate ChangeVol. 16, No. 5 ViewpointThe Darkside of Blockchain Technology: A Case of Cryptocurrency Mining and Environmental SustainabilityBasit Ali Bhat, Manpreet Kaur Makkar, and Nitin GuptaBasit Ali BhatAddress correspondence to: Basit Ali Bhat, Mittal School of Business, Lovely Professional University, Punjab 144402, India E-mail Address: [email protected]Mittal School of Business, Lovely Professional University, Punjab, India.Search for more papers by this author, Manpreet Kaur MakkarMittal School of Business, Lovely Professional University, Punjab, India.Search for more papers by this author, and Nitin GuptaMittal School of Business, Lovely Professional University, Punjab, India.Search for more papers by this authorPublished Online:19 Oct 2023https://doi.org/10.1089/scc.2022.0106AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 16Issue 5Oct 2023 InformationCopyright 2023, Mary Ann Liebert, Inc., publishersTo cite this article:Basit Ali Bhat, Manpreet Kaur Makkar, and Nitin Gupta.The Darkside of Blockchain Technology: A Case of Cryptocurrency Mining and Environmental Sustainability.Sustainability and Climate Change.Oct 2023.328-337.http://doi.org/10.1089/scc.2022.0106Published in Volume: 16 Issue 5: October 19, 2023PDF download
Analisis Cluster Fuzzy C-Means dan Diskriminan untuk Pengelompokan Data Kesejahteraan Rakyat

Cluster analysis is a multivariate method that aims to classify objects into a cluster based on different characteristics. The statistical technique that can be used for clustering is Fuzzy C-means (FCM). The FCM uses membership degree to determine the existence of each data point in a cluster. To ensure accurate clustering and that the criteria are met, cluster validation must be done to produce good data clustering. Moreover, this study uses a discriminant analysis test to validate the results of cluster solutions. The study aims to classify 34 provinces in Indonesia based on the level of people's welfare. The data used in this study is data on indicators of people's welfare in Indonesia in 2021. The variables used in this study were age/life expectancy, the average length of schooling, the 16-18 years of enrollment, and households with proper access to water. Based on the study results, two clusters were formed: cluster 1, areas with low people’s welfare indicator values in 16 provinces, and Cluster 2, regions with high people's welfare indicator values in 18 provinces.
Extract, Transform, Load Monitoring sales Multi Marketplace

This research is made in the design of a database system and online sales files for sales on a medium business scale, Era 5.0 Society many marketplaces make it easy for medium businesses to sell products, So business actors need to see the development of digital business and forecast their merchandise that is sold out online. As for the implementation of 1) database from RDBMS into MYSQL. The research uses an open source database, namely MYSQL, 2) The research method uses Microsoft Access program tools with the link to the Windows 10 operating system odbc in Extract Transform Load (ETL) which is carried out automatically with button 3 process Extract, Transform, Load from the download folder into the relational database management system (RDMS) in ms access and stored in the MySQL database. 3) This research forms raw data facts from several marketplaces and is processed into normalized table dimensions in the data warehouse. This research aims to design a combination of open-source systems and Microsoft access licenses, for business actors who need sales analysis that is sold multichannel (online or offline), to optimize stock and provide products consistently in each sales channel.
Designing and Developing of Learning Class Grouping Applications Base on Genetic Algorithms

The student learning class grouping application is a crucial service within the academic lecture system to facilitate learning and make it easier for educators to choose strategies and teaching methods to optimize academic achievement. In this grouping application, a genetic algorithm is implemented to optimize the distribution of learning classes, adopting the concept of biological evolution where the initial population of learning groups is considered as "individuals" with information about different grouping criteria. Through the process of selection, crossover, and mutation, these individuals undergo evolution from generation to generation, where those with the highest fitness value (according to the specified criteria) are passed on to the next generation, while those with lower fitness values may be eliminated. This evolutionary process continues until an optimal learning group is obtained, with a combination of suitable and best criteria to achieve the desired intra-heterogeneous and interhomogeneous characteristics in learning.
Analysis of User Satisfaction Virtual Reality Tourism Kemenparekraf Using the End User Computing Satisfaction Method

Virtual Reality (VR) has developed on the concept of tourism where people can enjoy virtual tourist destinations called VR Tourism. The tourism industry, one of which is Indonesia, is utilizing this technology development as a marketing strategy for tourist objects, one of which is VR Tourism Kemenparekraf. Previous research on how VR affects marketing has been carried out a lot, but this research will focus on user responses to VR Tourism services which are still little done. From this, by utilizing the End User Computing Satisfaction (EUCS) method, it is used to measure the satisfaction of VR Tourism Kemenparekraf users, as well as to see whether this method is still relevant to use. Respondents were determined as many as 75 people who had used VR Tourism Kemenparekraf at least once. The data was processed using SPSS software, where the research results found that all variables contained in the EUCS method had a positive effect on user satisfaction. Users are satisfied with VR Tourism Kemenparekraf. The EUCS method is still relevant for use in testing user satisfaction in VR Tourism Ministry of Tourism and Creative Economy.
Bentuk Ekspresi Emosional Tokoh Utama dalam Kumpulan Cerpen Lelaki-Lelaki Tanpa Perempuan Karya Haruki Murakami: Kajian Psikologi Sastra

Literary works are imaginative creations of authors reflecting upon the social environment of life. Literary works arise from contemplating the meanings and essence of life experienced, felt, witnessed, and expressed. This study examines the forms of emotional expression of the main characters in the "Lelaki-Lelaki Tanpa Perempuan" short story collection by Haruki Murakami. This study aims to describe the forms of emotional expression experienced by the main characters in the "Lelaki-Lelaki Tanpa Perempuan" short story collection by Haruki Murakami. The analysis of the forms of emotional expression of the main characters in the short story collection of "Lelaki-Lelaki Tanpa Perempuan" used a literary psychology approach. Emotional expression is rooted in inner conflict as it pertains to the psyche of an individual. The data for this study consisted of quoted sentences that represent the emotional expression of the main characters in the "Lelaki-Lelaki Tanpa Perempuan" short story collection by Haruki Murakami. A qualitative descriptive method was used to analyze the forms of emotional expression of the main characters. Data collection techniques involved reading and note-taking. Data analysis techniques included data reduction, data presentation, and drawing conclusions. The validity of the data was employed through theoretical triangulation. This study reveals various forms of emotional expression of the main characters in the "Lelaki-Lelaki Tanpa Perempuan" short story collection by Haruki Murakami. These forms of emotional expression include disappointment, resignation, curiosity, worry, anxiety, confusion, fear, and sadness. Keywords: Short Story Collection of "Lelaki-Lelaki Tanpa Perempuan", Emotional Expression, Inner Conflict, Literary Psycholog
Research on Dual Channel Medical Short Text Intention Recognition Algorithm

Common medical short text intention recognition When extracting local information, Convolutional neural network is better than other neural networks, but Convolutional neural network can't consider the whole situation of short text. To utilize whole sentence information, we propose a dual channel short text intention recognition model combining TextCNN and BiGRU-Att, which uses local features and global features to recognize short text intention, Optimized the use of intent recognition models for global text features. The AB-CNN-BGRU-att algorithm first utilizes the ALBERT multi-layer bidirectional Transformer structure to vectorize the input text, and then sends the vectors into the TextCNN and BiGRU network models to obtain local and global features. Fuse these two features and obtain the intention through softmax. The experimental data shows that in THUCNews_Title dataset, the AB-CNN-BGRU-att algorithm proposed in this article achieved an Acc of 96.68%, an F1 value of 96.67%, and an accuracy of 86.02% on the KUAKE-QIC dataset. Compared to other intention recognition models, the performance has been improved.
Research on Super-resolution Image Processing Based on Deep Learning

When the existing super-resolution model preprocesses low-resolution images, the efficiency of the feature extraction module is low and the details cannot be accurately captured. In this paper, SRGAN is selected as the basic method of the topic, and it is used as a reference to obtain the existing lightweight multilevel generated Adversarial network. In this paper, SRGAN is selected as the basic method of the topic, and it is used as a reference to obtain the existing lightweight multilevel generated aversion network. The network model of this method adopts the core idea of generating antagonistic network, and makes progress from the mutual game between two network modules to achieve better results. By taking advantage of the advantages of SRGAN network, the residual channel block and skip connection are mainly added to overcome the low operating efficiency of SRGAN network, and the PNSR improves by 1.01 and SSIM improves by 0.053. It is also more stable during the operation of image super resolution.
Research on Sentiment Analysis Fusion Based on Neural Network

With the aim of overcoming the limitations in feature extraction capability, OOV (out-of-vacabulary) and polysemous word in Chinese text sentiment analysis, dual attention gated recurrent neural network (AW-Att-BiGRU) with word and character information fusion was proposed. Firstly, character and word level vector are taken as input to the model to obtain semantic representation to the maximum extent. Then, characters and words with obvious emotional characteristics are given higher weights through the attention mechanism, to achieve a more comprehensive understanding of the emotional tendencies within the text and enhance the accuracy of sentiment analysis. Based on the experimental results using the Tan Songbo Hotel review dataset, the proposed model achieved an accuracy rate of 94.56%, and the accuracy rate on Weibo review dataset is 93.87%, which improves the performance compared with a single model and has certain application value.
A Spatial-temporal Detecting Method for Low-altitude Slow and Small Target

UAV, as a rapidly developing and applied aircraft, poses a great threat to low altitude safety while bringing various changes. The complexity of low altitude environments and the small size, low speed, and flexibility of UAVs pose significant challenges to the detection of low altitude UAVs. Visual methods have the characteristics of real-time and flexibility, making them suitable for fast detection in close range. But the problems of detecting features of UAV from low contrast and low-resolution images by applying vision method are needed to be solved. A detecting method which combines morphology filtering in spatial domain and correlation in temporal domain is presented in this paper. The contrast of images is enhanced by extract background of images using morphology filtering. This improves the efficiency and reliability of detection. Because positions of object in the images are continuous, the incorrect results are rectified by calculating the correlation of positions in temporal domain. The theoretical analysis and experiment show that this method can detect the UAV successfully from low contrast images and is feasible.
Research on UAV Route Planning Based on Global Improved Ant Colony Algorithm

With the development of UAV technology, the route planning, obstacle avoidance and optimal flight path selection of UAV in the course of flight have become crucial. Ant colony algorithm, as an intelligent optimization algorithm, has a very powerful search ability and has been widely used in the field of route optimization. However, ant colony algorithm has some problems, such as slow convergence speed, local optimization and contradiction between population diversity and convergence speed. In this paper, the global improved ant colony algorithm is studied from the aspects of space modeling, obstacle shielding, guided path, adaptive random step algorithm, pheromone update and optimized route. From the three aspects of theoretical introduction, simulation experiment and experimental comparison, it is concluded that the improved ant colony algorithm has been significantly improved in terms of search performance and search efficiency.
Sensing Image Super-resolution Reconstruction Based on the UNet Decoding Network

To enhance the super-resolution reconstruction quality of remote sensing images, this paper fully consider the multi-scale nature of internal features and proposes a multi-scale multi-level fusion network. This network is based on the UNet encoding and decoding network, introducing multi-scale feature extraction branches to further obtain rich scale features. Simultaneously focusing on features at all levels, a fusion module is proposed using the global attention mechanism (GAM) to improve the model's feature extraction ability. The image reconstruction task is completed using sub-pixel convolution. In this paper, the UC Merced dataset is selected and reconstructed with 2x, 3x and 4x reconstruction, in which the peak signal to noise ratio (PSNR) values are 34.07 dB, 29.65 dB, and 27.63 dB, respectively, which are improved compared with some algorithms. These results further demonstrate the network presented in this paper is helpful in increasing the reconstruction quality of remote sensing images.
Integrating Star Graph and Dynamic Intent Awareness for Session-based Recommendation

A session-based recommendation model combining star graph and dynamic perception is proposed to solve the problem of remote information, ignoring the dynamic change of users' interests and the inaccurate expression of short-term preferences. Construct star graph and obtain remote node information by using Graph neural network. Then, the dual gating mechanism is used to integrate the information of item, time and user, and establish the dynamic interest model of user. Improved adaptive fusion mechanism, combining remote node information with users' dynamic interests to get global preferences. Improve short-term graphs and enhance recent interactive representations with global information for more accurate short-term interest. Compared with the optimal benchmark model, HR@20 reaches 54.29% at the highest, an increase of more than 2.1 percentage points, and MRR@20 reaches 18.84% at the highest, an increase of more than 0.9 percentage points, which verifies that this method can better match the real intention of users.
Real-Time Voice Command Recognition System Based on DTW and MFCC

Voice command recognition is an important research direction in the field of intelligent speech. Its task is to detect and recognize a given set of command words in continuous speech streams. For command recognition tasks with a voice command vocabulary, this article establishes a real-time voice command recognition system based on DTW and MFCC, using Python tools, to meet the requirements of real-time command recognition tasks. Firstly, the system processes the real-time collected voice based on dynamic time warping (DTW), and then uses the Deep Neural Network (DNN) as a feature extractor to extract the MFCC feature vector of the voice, and calculate the Euclidean distance between the feature vectors of the test voice and the template. The experimental results demonstrate that the real-time speech command recognition system based on DTW and MFCC has comprehensive performance advantages, which can meet the application scenarios of real-time control of speech commands and free users' hands.
Research on Multilevel Degradater Model under Unsupervised Learning

In most super-resolution experiments, because the data set in the real scene cannot meet the quantity requirements, it is necessary to downsample the high-resolution images using the degradation method to obtain the same proportion of low-resolution images. However, because the same degradation method is used to process images, the processing process of low-resolution images is relatively simple, which is easy to cause model solidification in the subsequent training process. After the training is completed, the generalization ability will be generally low due to the single underlying degradation method. This paper uses the SRMD method to build an unsupervised multilevel degradation, and takes advantage of the adduction network generated in unsupervised learning. After extracting image features, it adopts anisotropic Gaussian fuzzy kernel, bi-cubic subsampling and additive Gaussian noise to compose the degradation training data set. Compared with the traditional bi-cubic PNSR, the performance is improved by 5%. The SSIM value is increased by 3%, and the running speed is increased by 7% on average.