Reviewerly/exHarmony huggingface dataset:
Evolutionary Computation Visualization: ECvis

This paper presents a visualisation tool (ECvis) that aids the development of population based numerical optimisation algorithms such as genetic algorithms and differential evolution. The tool provides a simple interface with three modes: A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Density</i> mode that allows the user to quickly view the distribution and density of the population throughout the fitness and search space for high dimensional problems. This provides the ability to quickly establish where the population is clustering, which can indicate potential local and global minima; A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Statistical</i> mode that allows the user to visualise the individuals that have statistical significance in the population and their location in the search space; A <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ranges</i> mode that provides the user with a windowed average of the minimum, maximum and median value of each parameter of the population and whether the range of each parameter has changed since the previous window. This allows the user to see whether the population is exhibiting exploration or exploitation properties, as well as convergence properties such as the variance of each parameter. As examples of the usefulness of ECvis, two well known, high dimensional functions are optimised using differential evolution with ECvis being used to provide information on the performance of the optimisation.
Multi-platform symbolize software development of aerogeophysical data

In order to improve the reusability of aero-geophysical data in multi-platforms, we take in depth studies of data storage and symbolize principle in aero-geophysical database. In this paper we carry out symbol management and symbolize method in different working platforms. Then, we build the combination between different platforms by parameter configuration and using C# development language to carry out a new software in .NetFramework 4.0, and make it a plug-in for aero-geophysical data management software Geoprobe Mager. The aero-geophysical data-symbolize software is designed to complete data symbolization according to user requirements. In practice, we also achieve the aims that to simplify the workflow and data reusement.
Epidemiological profile of acute myocardial infarction in Brazil between 2009 and 2019

Acute myocardial infarction (AMI) is the syndrome with classical clinical manifestations, laboratory evidence of necrosis by ischemia of the heart muscle, and characteristic changes in electrocardiogram (ECG). AMI can be classified into several types according to electrocardiographic, pathophysiological, clinical, prognostic and etiological criteria, which is valid because it allows specific and more assertive interventions to be performed
Discover, Explanation, Improvement: An Automatic Slice Detection Framework for Natural Language Processing

Pretrained natural language processing (NLP) models have achieved high overall performance, but they still make systematic errors. Instead of manual error analysis, research on slice detection models (SDM), which automatically identify underperforming groups of datapoints, has caught escalated attention in Computer Vision for both understanding model behaviors and providing insights for future model training and designing. However, little research on SDM and quantitative evaluation of their effectiveness have been conducted on NLP tasks. Our paper fills the gap by proposing a benchmark named "Discover, Explain, Improve (DEIM)" for classification NLP tasks along with a new SDM Edisa. Edisa discovers coherent and underperforming groups of datapoints; DEIM then unites them under human-understandable concepts and provides comprehensive evaluation tasks and corresponding quantitative metrics. The evaluation in DEIM shows that Edisa can accurately select error-prone datapoints with informative semantic features that summarize error patterns. Detecting difficult datapoints directly boosts model performance without tuning any original model parameters, showing that discovered slices are actionable for users.
­Fully Automatic Initialization and Segmentation of Left and Right Ventricles for Large-Scale Cardiac MRI Using a Deeply Supervised Network and 3D-ASM

Background and objective: The sheer volume of data generated by population imaging studies is unparalleled by current capabilities to extract objective and quantitative cardiac phenotypes; subjective and time-consuming manual image analysis remains the gold standard. Automated image analytics to compute quantitative imaging biomarkers of cardiac function are desperately needed. Data volumes and their variability pose a challenge to most state-of-the-art methods for endo and epicardial contours, which lack robustness when applied to very large datasets. Our aim is to develop an analysis pipeline for the automatic quantification of cardiac function from cine magnetic resonance imaging data.Method: This work adopt 4,638 cardiac MRI cases coming from UK Biobank with ground truth available for left and RV contours. A hybrid and robust algorithm is proposed to improve the accuracy of automatic left and right ventricle segmentation by harnessing the localization accuracy of deep learning and the morphological accuracy of 3D-ASM (three-dimensional active shape models). The contributions of this paper are three-fold. First, a fully automatic method is proposed for left and right ventricle initialization and cardiac MRI segmentation by taking full advantage of spatiotemporal constraint. Second, a deeply supervised network is introduced to train and segment the heart. Third, the 3D-ASM image search procedure is improved by combining image intensity models with convolutional neural network (CNN) derived distance maps improving endo- and epicardial edge localization.Results: The proposed architecture outperformed the state of the art for cardiac MRI segmentation from UK Biobank. The statistics of RV landmarks detection errors for Triscuspid valve and RV apex are 4.17 mm and 5.58 mm separately. The overlap metric, mean contour distance, Hausdorff distance and cardiac functional parameters are calculated for the LV (Left Ventricle) and RV (Right Ventricle) contour segmentations. Bland–Altman analysis for clinical parameters shows that the results from our automated image analysis pipelines are in good agreement with results from expert manual analysis.Conclusions: Our hybrid scheme combines deep learning and statistical shape modeling for automatic segmentation of the LV/RV from cardiac MRI datasets is effective and robust and can compute cardiac functional indexes from population imaging.
Monitoring of crop water consumption changing based on remotely sensed data and techniques in North Sinai, Egypt

This paper aims to approximate and verify crop water use based on satellite results. Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) were used as the critical parameters derived from NOAA/AVHRR and landsat8 satellite data. Reference evapotranspiration (ETo) was determined using FAO-Penman-Monteith (FPM) agrometeorological data as a standard process. Based on data from remote sensing, the ETo was calculated based on the Hargreaves (Har) process. ETo-FPM has been used to calibrate ETo-Har under the same conditions for five years (2002-2006). Landsat8 data was obtained on 25 June 2013 and 28 June 2014 and used to estimate the crop coefficient (Kc) based on satellite data (Kc-Sat). The LST was used to predict the maximum, minimum, and mean Tair (oC) levels in June 2013 and 2014. ETo was calculated using the expected maximum, minimum, and mean Tair according to the Har method and was used with Kc-Sat to estimate ETc-Har. ETo-FPM is used to measure ETc-FPM with Kc-Sat. LST and NDVI have been used to measure the Water Deficiency Index (WDI). WDI incorporated ETc to measure the actual evapotranspiration of the crop (ETa). ETa-FPM was used for the evaluation of ETa-Har. The relationship between ETa-FPM and ETa-Har was high, where R2 was 0.99 in 2013 and 2014. ETa determined by Hargreaves based on remotely sensed data was overestimated at about 0.8 (mm/day) compared to the FPM process.
Blockchain-based Patient-driven Inter-operable Model for Healthcare 5.0 System

With the rise of powerful computational technologies, healthcare systems are going through a paradigm shift to the era of healthcare 5.0, also known as" smart healthcare". This new-age healthcare system not only enhances the life of patients but also aims to reduce healthcare costs significantly. People's health data collected through various means, including various Internet of Things (IoT) devices, is too sensitive to be regulated by particular organisations owing to increasing privacy and security threats. The sheer size of data makes it difficult for centralised bodies to regulate, due to which third-party involvement is introduced, which again poses threats to the privacy of the data holder. Motivated by the abovementioned gaps, we propose a decentralised approach using Ethereum blockchain technology and Oasis protocol. The patient-driven solution approach effectively solves the privacy problem by providing complete control of data to the owner. Along with this, byproducts such as fine-grained access control and on-chain data processing capabilities are also obtained.
ANALISIS SENTIMEN REVIEW WISATAWAN PADA OBJEK WISATA UBUD MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE

Since the world was hit by the Covid-19 pandemic, it had an impact on activities in Ubud. According to the Central Bureau of Statistics from Bali, the number of foreign tourists visiting from January to October 2021 has decreased by 99.996 percent. On October 14 2021 tourist attractions in Ubud began to reopen. Based on these problems, this research will carry out sentiment analysis from reviews on the TripAdvisor site on tourist attractions in Ubud using the Support Vector Machine algorithm with the Knowledge Discovery in Database method. The results obtained in this study resulted in 551 positive sentiment and 118 negative sentiment based on 669 data test, these results resulted in a positive value for tourist attractions in Ubud. Testing will be carried out on the Support Vector Machine algorithm using the Confusion Matrix which gets good results in conducting sentiment analysis with an accuracy of 84.01%, a recall of 89.83%, a precision of 90.40% and an F1-Score of 90. 11%.
Binary text classification using genetic programming with crossover-based oversampling for imbalanced datasets

Binary text classification using genetic programming with Binary text classification using genetic programming with crossover
Regularizing End-to-End Speech Translation with Triangular Decomposition Agreement

End-to-end speech-to-text translation (E2E-ST) is becoming increasingly popular due to the potential of its less error propagation, lower latency, and fewer parameters. Given the triplet training corpus $\langle speech, transcription, translation\rangle$, the conventional high-quality E2E-ST system leverages the $\langle speech, transcription\rangle$ pair to pre-train the model and then utilizes the $\langle speech, translation\rangle$ pair to optimize it further. However, this process only involves two-tuple data at each stage, and this loose coupling fails to fully exploit the association between triplet data. In this paper, we attempt to model the joint probability of transcription and translation based on the speech input to directly leverage such triplet data. Based on that, we propose a novel regularization method for model training to improve the agreement of dual-path decomposition within triplet data, which should be equal in theory. To achieve this goal, we introduce two Kullback-Leibler divergence regularization terms into the model training objective to reduce the mismatch between output probabilities of dual-path. Then the well-trained model can be naturally transformed as the E2E-ST models by the pre-defined early stop tag. Experiments on the MuST-C benchmark demonstrate that our proposed approach significantly outperforms state-of-the-art E2E-ST baselines on all 8 language pairs, while achieving better performance in the automatic speech recognition task. Our code is open-sourced at https://github.com/duyichao/E2E-ST-TDA.
Implementation of multi-source and multi-agent and fuzzy logic optimized QOS-based dynamic resource allocation

The cloud computing system that is based on the Internet of Things is faced with new challenges on a regular basis. This is due to the sophisticated structure of system clusters as well as the vast quantity of data that is handled by the systems. The ability to obtain resources in an elastic manner is seen as the most compelling rationale for the implementation of an architecture for cloud computing that is based on the Internet of Things (IoT). The capacity to dynamically scale up or down the amount of virtual resources in response to the requirements of Internet of Things (IoT) cloud users is the primary focus of the concept of elasticity. Elasticity refers to the ability to accomplish this. The purpose of this article is to propose a multi
Cosine Similarity Based Hierarchical Skeleton and Cross Indexing for Large Scale Image Retrieval Using Mapreduce Framework

The imaging data in various fields like industries, institutions, medical, and so on has grown exponentially in recent years.An innovative software solution is required for the efficient management of image data.The MapReduce framework is used for large-scale image data processing.Various cross-indexing techniques are developed to transform the image into binary sequences but retrieving the image from the reducer on the feature vector results in a major challenge.Image retrieval using large-scale image databases attained major attention, where cross-indexing plays a key role in the research community.Therefore, in this research, a new method for image retrieval, named Cosine Similarity-based hierarchical skeleton and cross-indexing, is proposed to perform the retrieval process in the MapReduce framework effectively.The feature vector of the images is converted to binary sequences.The Most Significant Bit (MSB) of the binary code is used to store the images in the mapper using the cross-indexing model.The image retrieval process is achieved through the reducer based on the tanimoto similarity measure.The binary sequence for the query image is calculated based on the feature vector.The MSB bit of the binary code is matched with the MSB code of the images in the mapper to achieve the retrieval process.The proposed method effectively achieved better performance through the cross-indexing model with the usage of the feature vector.The performance of the proposed method is compared with the existing techniques using the UK bench dataset.The proposed method attains the values of 0.784, 0.729, 0.75, 31.23,17.84secfor F1-score, precision, recall, computational cost, and computational time with the query set-1 by considering four mappers.
Proof-of-Work for Merkle based Access Tree in Patient Centric Data

With the advent of wearable devices and smart health care, the wearable health care technology for obtaining Patient Centric Data (PCD) has gained popularity in recent years.To establish access control over encrypted data in health records, Ciphertext Policy-Attribute Based Encryption (CP-ABE), is used.The most critical element is granting secure access to the generated information.However, with growing complexity of access policy, computational overhead of encryption and decryption process also increases.As a result, ensuring data access control as well as efficiency in PCD collected by wearables is crucial and challenging.This paper proposes and demonstrates a proof-of-work for the Merkle-based access tree using notion of hiding the sensitive access policy attributes.
Lip Print Recognition Algorithm Based on Convolutional Network

Identity information security is faced with various challenges, and the traditional identification technology cannot meet the needs of public security. Therefore, it is necessary to further explore and study new identification technologies. In order to solve the complex image preprocessing problems, difficult feature extraction by artificial design algorithm, and low accuracy of lip print recognition, a method based on the convolutional neural network is proposed, by building a convolutional neural network called LPRNet (Lip Print Recognition Network). The obtained lip print image is inputted into the training recognition model of the network to simplify the lip print image preprocessing. By extracting feature information and sampling operation, the model training parameters are reduced, which overcomes the difficulty of designing a complex algorithm to extract features. By analyzing and comparing the experimental results, a higher recognition rate is obtained, and the validity of the method is verified.
Correction to: Fast Parallel Bellman-Ford-Moore Algorithm Implementation for Small Graphs

In an older version of this paper, the presentation of Sergey Komech’s affiliation was misleading. This has been corrected.
Chess cheats in check?

Abstract As the chess world reels from a cheating scandal, Jordi Prats looks at the problem of computer- assisted cheating and efforts to combat it
WEB BASED TENDER MANAGEMENT SYSTEM USING BLOCKCHAIN

The traditional tender and contract management process is often time-consuming and prone to errors and fraud.In order to improve the efficiency and transparency of the process, a web-based smart tender and contract management system using blockchain technology is proposed.The system utilizes the features of blockchain such as immutability, transparency, and security to ensure a fair and transparent evaluation process.By eliminating intermediaries and manual processes, the system streamlines the tender management process and provides a secure and tamper-proof record of all transactions.Additionally, the use of smart contracts automates the evaluation process, reducing the risk of human error and ensuring a consistent and objective outcome.This paper outlines the potential benefits of implementing a blockchain-based smart tender and contract management system and its potential impact on the procurement industry.Improved stakeholder engagement: The web-based system allows for real-time collaboration and communication between all stakeholders, resulting in improved engagement and decision-making.Increased accountability: The decentralized nature of blockchain allows for greater accountability and reduces the risk of fraud or manipulation.Traceability: The complete and transparent record of procurement activities provided by the system enables easy tracking of the flow of goods, works and services.Cost savings: By streamlining the tender management process and reducing the need for intermediaries, the system has the potential to result in significant cost savings for both the procuring organization and the bidders.Future developments: The proposed system can be further developed and integrated with other existing systems to provide a comprehensive procurement solution.Overall, the implementation of a web-based smart tender and contract management system using blockchain has the potential to significantly improve the procurement process and bring numerous benefits to the industry.
Barcode-Based Light Fire Extinguisher (APAR) Monitoring with the Waterfall Case Study Method of PT. PLN (Persero) UPK Nagan Raya

Light Fire Extinguishers (APAR) play a very important role in a company, this aims to prevent and overcome in the event of a fire. The development of technology for equipment inspection has now begun to use digital methods including using android applications and barcodes to facilitate the fire extinguisher inspection process. In this study, it uses the waterfall method, which is a technical model in software development, where a project will be detailed sequentially. Based on the results of research, a method of monitoring the fire extinguisher using androite-based barcodes that can be accessed through the internet network anytime and anywhere. This barcode-based monitoring method can minimize officer errors in writing data on the results of checking the fire extinguisher, searching for data on the results of the fire extinguisher check and can be viewed and accessed easily. Barcodes have been affixed to each fire extinguisher and the barcode has been designed complete with a description of the state of the fire extinguisher in it, including the type of fire extinguisher, weight, name of the officer, expiret, type. It can be concluded by using this method the quality of the resulting system will be better because its implementation is carried out gradually or sequentially.
An Enhanced Expectation Maximization Text Document Clustering Algorithm for E-Content Analysis

Nowadays, there are many types of digital materials that can be used in the classroom. Students and scholars are migrating from textbooks to digital study materials because textbooks are too large and expensive. Teachers and college students can use and modify the materials that are freely available or with some constraints for their learning and teaching. E-content can be designed, evolved, utilized, re-used, and distributed electronically from anywhere at anytime. Because of the flexibility of time, place, and speed of learning, e-content is becoming extremely popular. It can be readily and instantly shared and communicated with an infinite number of clients all across the globe. Document clustering is most commonly used to group documents that are related to a specific topic. Text document clustering can be used to group a collection of documents regarding the information they include and to deliver search results when a user searches the internet. In this paper mainly focuses on text document clustering to cope with massive collection of E-Content documents. Enhanced Expectation Maximization Text Document Clustering (EEMTDC) clustering algorithm was proposed and compared with Expectation Maximization (EM) clustering, K-Means clustering, and Hierarchical clustering (HC) algorithms. The experiment shows that the performance of proposed EEMTDC algorithm produces greater clustering accuracy than existing clustering algorithms.
Designing an Android-based Nursing Theory Reminder of Educational Game Application (GEMPITA)

Nursing theory is an important thing to learn for nursing students. Nursing theory can be studied by students in philosophy and nursing theory courses. It can be seen from the results of the final evaluation that students obtained sufficient scores of 49%. This has an impact on the student achievement index and student professionalism in carrying out nursing practice, both in clinical practice and later when they have worked in nursing services. The learning method used by lecturers is to form groups and give assignments to students in the form of papers and presentation in the form of powerpoints in front of the class. Therefore, researchers try to make a learning innovation in the form of an android-based educational game. This educational game is called the nursing theory reminder education game (GEmPITA). This study aimed to design an android-based GEmPITA as an innovation for students’ learning media in philosophy and nursing theory courses. Mobile application development lifecycle model (MADLC) with 7 stages including the rapid application development method, with 3 stages, namely requirement planning, design system, and implementation. The results of this study are in the form of a prototype of an android-based game application that will be used by nursing students. By making the GEmPITA, an interactive educational game, students can learn nursing knowledge in a more fun way and make learning nursing science easier.&#x0D; Keywords: gaming apps, education, android, nursing theory