Reviewerly/exHarmony huggingface dataset:
A Framework for Patient-Centric Medical Image Management using Blockchain Technology

In smart systems context, the storage and distribution of health-critical data – medical images, test reports, clinical information etc. that is processed and transmitted via web portal and pervasive devices which requires a secure and efficient management of patients’ medical records. The reliance on centralized data centers in the cloud to process, store, and transmit patients’ medical records poses some critical challenges including but not limited to operational costs, storage space requirements, and importantly threats and vulnerabilities to the security and privacy of health-critical data. To address these issues, this research proposes a framework and provides a proof-of-the-concept named Patient-Centric Medical Image Management System (PCMIMS). The proposed solution PCMIMS utilizes the Ethereum blockchain and Inter-Planetary File System (IPFS) to enable secure and decentralized storage capabilities that lack in existing solution for patients’ medical image management. The PCMIMS design facilitates secure access to Patient-Centric information for health units, patients, medics, and third-party requestors by incorporating the Patient-Centric access control protocol, ensuring privacy and control over medical data. The proposed framework is validated through the deployment of a prototype based on smart contract executed on Ethereum TESTNET blockchain that demonstrates efficiency and feasibility of the solution. Validation results highlight a correlation between (i) number of transactions (i.e., data storage and retrieval), (ii) gas consumption (i.e., energy efficiency), and (iii) data size (volume of Patient-Centric medical images) via repeated trials in Microsoft Windows environment. Validation results also indicate computational efficiency of the solution in terms of processing three most common types of Patient-Centric medical images namely (a) Magnetic resonance imaging (MRI) (b) X-radiation (X-Rays), (c) Computed tomography (CT) scan. This research primarily contributes by designing, implementing, and validating a blockchain based practical solution for efficient and secure management of Patient-Centric medical image management in the context of smart healthcare systems.
Virtual Route Guide Chatbot Based on Random Forest Classifier

Improvements in the quality of tourism services and the number of human resources will affect the quality of social services and information services provided to foreign tourists, thereby enhancing the quality of services offered regarding tourist destination information in the Malang Raya area.Considering the urgency of foreign tourists in obtaining information related to directions, routes, and access roads to their desired tourist destinations, especially in East Java, due to limited data from the government agencies handling the tourism sector, as well as the difficulty in communication with residents who may not understand what is being communicated by foreign tourists.Therefore, the need for an interactive chatbot to assist in obtaining routes and access information to the desired tourist destinations will facilitate foreign tourists.To improve the accuracy of the chatbot's ability to answer sentence selection, the use of artificial intelligence, specifically the Random Forest Classifier, is necessary.This study obtained the highest accuracy value using a tree quantity of 200, a maximum tree depth of 20, and a minimum sample split of 5. Using these quantities resulted in an accuracy of 95.88%, precision of 96.29%, recall of 96.03%, and f-measure of 96.16%.
Genetic Approach for Improved Prediction of Adaptive Learning Activities in Intelligent Tutoring System

The intelligent tutoring system registers the reference data of the learners in a database. This data is stored for later use in the instructional module. Designing a student model is not an easy task. It is first necessary to identify the knowledge acquired by the learner, then identify the learner's level of understanding of the functionality and finally identify the pedagogical strategies used by the learner to solve a problem. These elements must be taken into account in the development of the learner model. Learner characteristics must be considered in several forms. To build an effective learner model, the system must take into consideration both static (Learner preferences) and dynamic (Compartmental action) student characteristics. The objective of the article is to work out the learner model of the intelligent tutoring system by suggesting a new learning path. This proposal is based on the constructivist approach and the activist style (based on experimentation). According to the KOLB model, the authors propose a list of pedagogical activities depending on the learners' profile. Based on the learners' actions, the system reduces the list of activities based on two criteria: the learner's preference and the presence of one or more activities based on the activist style using genetic algorithm as an evolutionary algorithm. The results obtained led us to improve the learning process through a new conception of the ITS learner model.
Secure Data Sharing in Smart Homes: An Efficient Approach Based on Local Differential Privacy and Randomized Responses

Smart homes are smart spaces that contain devices that are connected to each other, collecting information and facilitating users’ comfortable living, safety, and energy management features. To improve the quality of individuals’ life, smart device companies and service providers are collecting data about user activities, user needs, power consumption, etc.; these data need to be shared with companies with privacy-preserving practices. In this paper, an effective approach of securing data transmission to the service provider is based on local differential privacy (LDP), which enables residents of smart homes to provide statistics on their power usage as disturbances bloom filters. Randomized Aggregatable Privacy-Preserving Ordinal (RAPPOR) is a privacy technique that allows sharing of data and statistics while pre-serving the privacy of individual users. The proposed approach applies two randomized responses: permanent random response (PRR) and instantaneous random response (IRR), then applies machine learning algorithms for decoding the perturbation bloom filters on the service provider side. The simulation results show that the proposed approach achieves good performance in terms of privacy-preserving, accuracy, recall, and f-measure metrics. The results indicate that, the proposed LDP for smart homes achieved good utility privacy when the value of LDP ϵ = 0.95. The classification accuracy is between 95.4% and 98% for the utilized classification techniques.
Hospital services Management system using Internet of Things (IoT) Techniques

A hospital patient tracking system using RFID technology is a web application developed for medical personnel (doctors and nurses) to monitor the movement of hospitalized patients. A RFID reader placed in each room will detect a patient wearing a registered tag when entering and leaving the room. It is designed to address the problems of long queues, overcrowding, delayed treatments and insufficient beds for patients. The tracking process is used to track all registered patients. At the same time, the duration of the patient process in each room will be recorded and calculated to obtain the range processing times in each room. It can be used to refer to and address the bottlenecks they face at the hospital. All medical personnel must register and be approved by the system administrator before access the system.
IoT Network Traffic Classification Using Machine Learning Algorithms: An Experimental Analysis

In this project, classification of IoT network traffic using random forest classifier is proposed. More-dataset is utilized to separate the IoT traffic classification. Data preprocessing is the concept of changing the raw data into a clean data set. The dataset is preprocessed in order to check missing values, noisy data, and other inconsistencies before executing it to the algorithm. Extraction takes place using Chi-square based feature extraction. To improve classification, the extraction regions are processed using the Chi- Square technique to extract several features and select the necessary features. This project uses the effective Chi-square technique to find feature extraction and feature selection. Finally, the selected features are fed into an extra tree and random forest classifier for accurate classification. This project is implemented using Python software.
Application of Improved Ant Colony Algorithm Integrating Adaptive Parameter Configuration in Robot Mobile Path Design

Under the background of the continuous progress of Industry 4.0 reform, the market demand for mobile robots in major world economies is gradually increasing. In order to improve the mobile robot's movement path planning quality and obstacle avoidance ability, this research adjusted the node selection method, pheromone update mechanism, transition probability and volatility coefficient calculation method of the ant colony algorithm, and improved the search direction setting and cost estimation calculation method of the A* algorithm. Thus, a robot movement path planning model can be designed with respect to the improved ant colony algorithm and A* algorithm. The simulation experiment results on grid maps show that the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the Tianniu whisker search algorithm, and the particle swarm algorithm designed in this study converged after 8, 37, 23, and 26 iterations, respectively. The minimum path lengths after convergence were 13.24m, 17.82m, 16.24m, and 17.05m, respectively. When the edge length of the grid map is 100m, the minimum planning length and total moving time of the planning model constructed in view of the improved algorithm, the traditional ant colony algorithm, the longicorn whisker search algorithm, and the particle swarm algorithm designed in this study are 49m, 104m, 75m, 93m and 49s, 142s, 93s, and 127s, respectively. This indicates that the model designed in this study can effectively shorten the mobile path and training time while completing mobile tasks. The results of this study have a certain reference value for optimizing the robot's movement mode and obstacle avoidance ability.
Symbol Detection in a Multi-class Dataset Based on Single Line Diagrams using Deep Learning Models

Single Line Diagrams (SLDs) are used in electrical power distribution systems. These diagrams are crucial to engineers during the installation, maintenance, and inspection phases. For the digital interpretation of these documents, deep learning-based object detection methods can be utilized. However, there is a lack of efforts made to digitize the SLDs using deep learning methods, which is due to the class-imbalance problem of these technical drawings. In this paper, a method to address this challenge is proposed. First, we use the latest variant of You Look Only Once (YOLO), YOLO v8 to localize and detect the symbols present in the single-line diagrams. Our experiments determine that the accuracy of symbol detection based on YOLO v8 is almost 95%, which is more satisfactory than its previous versions. Secondly, we use a synthetic dataset generated using multi-fake class generative adversarial network (MFCGAN) and create fake classes to cope with the class imbalance problem. The images generated using the GAN are then combined with the original images to create an augmented dataset, and YOLO v5 is used for the classification of the augmented dataset. The experiments reveal that the GAN model had the capability to learn properly from a small number of complex diagrams. The detection results show that the accuracy of YOLO v5 is more than 96.3%, which is higher than the YOLO v8 accuracy. After analyzing the experiment results, we might deduce that creating multiple fake classes improved the classification of engineering symbols in SLDs.
Impact of the Use of the Video Game SimCity on the Development of Critical Thinking in Students: A Quantitative Experimental Approach

The objective of the research is to determine to what extent the use of the SimCity video game allows the development of critical thinking in the teaching-learning processes of students. The methodology applied consisted of a research with a quantitative approach of experimental type, working with a sample of 25 students selected through a simple random sampling of a population of 100 students, 10 sessions were developed using the SimCity video game, a pretest and posttest of skills and abilities required to develop critical thinking of Watson Glaser were applied, whose dimensions measured were: Inferences, assumptions, deductive reasoning, logical interpretation and evaluation of arguments. The results show that with adequate stimulation through the use of the SimCity video game, critical thinking can have a moderate but effective development in the students, from the comparison of the data obtained in the pretest and posttest a significant progress in terms of scores is observed; likewise, the effectiveness of the use of the SimCity video game is reflected to a greater extent in inferences and evaluations of arguments, since during the posttest evaluations greater progress was observed in comparison to other skills; while the interpretation of information obtained less progress in comparison to the other skills, the use of skills such as deductive reasoning, inferences and evaluation of arguments were moderately developed. In conclusion, the use of the SimCity video game allows the development of skills and abilities to develop critical thinking according to various factors, such as the way in which it is incorporated into the curriculum, the orientation and guidance of teachers, and the way in which reflection and analysis is carried out after the game experience.
The GOAT of GOATS

&lt;strong&gt;A large chunk of Twitter is furious.&lt;/strong&gt; In the wake of Tom Brady winning his 7th Super Bowl Ring, the consensus NFL GOAT (Greatest of All Time) has started to be called the "GOAT of GOATS." Brady's Super Bowl win this last week was with a new team and a shortened offseason due to Covid-19. This seemed to solidify to most doubters that the fabled "Patriot Way" may have been the "Tom Brady Way" all along.
An Improved Honey Badger Algorithm through Fusing Multi-Strategies

The Honey Badger Algorithm (HBA) is a novel meta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers. The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA, which has been applied in photovoltaic systems and optimization problems effectively. However, HBA tends to suffer from the local optimum and low convergence. To alleviate these challenges, an improved HBA (IHBA) through fusing multi-strategies is presented in the paper. It introduces Tent chaotic mapping and composite mutation factors to HBA, meanwhile, the random control parameter is improved, moreover, a diversified updating strategy of position is put forward to enhance the advantage between exploration and exploitation. IHBA is compared with 7 meta-heuristic algorithms in 10 benchmark functions and 5 engineering problems. The Wilcoxon Rank-sum Test, Friedman Test and Mann-Whitney U Test are conducted after emulation. The results indicate the competitiveness and merits of the IHBA, which has better solution quality and convergence traits. The source code is currently available from: .
A Novel S-Box Generation Methodology Based on the Optimized GAN Model

S-boxes can be the core component of block ciphers, and how to efficiently generate S-boxes with strong cryptographic properties appears to be an important task in the design of block ciphers. In this work, an optimized model based on the generative adversarial network (GAN) is proposed to generate 8-bit S-boxes. The central idea of this optimized model is to use loss function constraints for GAN. More specially, the Advanced Encryption Standard (AES) S-box is used to construct the sample dataset via the affine equivalence property. Then, three models are respectively built and cross-trained to generate 8-bit S-boxes based on three extended frameworks of GAN, i.e., Deep Convolution Generative Adversarial Networks (DCGAN), Wasserstein Generative Adversarial Networks (WGAN), and Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Besides, an optimized model based on WGAN-GP referred to as WGP-IM is also proposed, which adds the loss function constraints to the generator network of the WGAN-GP model, including bijection loss, differential uniformity loss, and nonlinearity loss. In this case, 8-bit S-boxes can be generated with cross-training. Experimental results illustrate that the WGP-IM model can generate S-boxes with excellent cryptographic properties. In particular, the optimal differential uniformity of the generated S-boxes can be reduced to 8, and the nonlinearity can be up to 104. Compared with previous S-box generation methods, this new method is simpler and it can generate S-boxes with excellent cryptographic properties.
Modified Dragonfly Optimization with Machine Learning Based Arabic Text Recognition

Text classification or categorization is the procedure of automatically tagging a textual document with most related labels or classes. When the number of labels is limited to one, the task becomes single-label text categorization. The Arabic texts include unstructured information also like English texts, and that is understandable for machine learning (ML) techniques, the text is changed and demonstrated by numerical value. In recent times, the dominant method for natural language processing (NLP) tasks is recurrent neural network (RNN), in general, long short term memory (LSTM) and convolutional neural network (CNN). Deep learning (DL) models are currently presented for deriving a massive amount of text deep features to an optimum performance from distinct domains such as text detection, medical image analysis, and so on. This paper introduces a Modified Dragonfly Optimization with Extreme Learning Machine for Text Representation and Recognition (MDFO-EMTRR) model on Arabic Corpus. The presented MDFO-EMTRR technique mainly concentrates on the recognition and classification of the Arabic text. To achieve this, the MDFO-EMTRR technique encompasses data pre-processing to transform the input data into compatible format. Next, the ELM model is utilized for the representation and recognition of the Arabic text. At last, the MDFO algorithm was exploited for optimal tuning of the parameters related to the ELM method and thereby accomplish enhanced classifier results. The experimental result analysis of the MDFO-EMTRR system was performed on benchmark datasets and attained maximum accuracy of 99.74%.
Pendampingan Sistem Informasi Akuntansi Pengadaan Barang Pada PT Jetcoms Netindo

Procurement of goods (purchase) is the main thing in carrying out the company's operational activities. Due to the complexity of purchasing procedures, companies should have interrelated functions to realize good and well-controlled purchasing activities. Purchasing accounting information systems are needed to avoid errors and fraud that can occur in carrying out purchasing activities.&#x0D; In the procurement accounting information system (purchase) it must have advantages and disadvantages, therefore it is necessary to have a review and evaluation as a reference for improvement so that it becomes a good and perfect purchasing accounting information system.&#x0D; This study aims to find out how the procedures for procurement activities (purchases) which include the documents used, related functions, and accounting records used in the procurement accounting information system applied to PT Jetcoms Netindo.&#x0D; In my opinion, the procurement (purchase) procedure at PT Jetcoms Netindo is good enough, but still has some weaknesses. The weaknesses include the existence of tasks and responsibilities that are too piled up in one section, and the lack of accounting records used. It would be better if the separation of functional duties could be more clearly separated.&#x0D; Keywords: purchase, purchasing accounting information system
Automatic RSS Feed for Each Tag in a Hugo Blog

This is a test to see if tag-specific RSS feeds are automatically generated for each tag in a Hugo blog. The front matter for this post is: --- title: "Automatic RSS Feed for Each Tag in Hugo" date: 2021-09-03T09:33:37-04:00 draft: false tags: - test --- Woohoo! /tags/test/index.xml was automatically created on deploy!
Open, Reproducible, Sustainable

Irving et al.&lt;sup&gt;1&lt;/sup&gt; approach digital research infrastructure through three related but distinct concepts: openness, reproducibility, and sustainability. Openness is about making digital resources generally accessible for reuse, sharing, and adaptation through an open license.
A Delimiters-in-Front Coding Style

When writing a multiline data structure literal, the usual convention is to put delimiters like commas at the end of each line.
Analytical Hierarchy Process Algorithm for Traffic Sign Improvement Priority

Traffic signs are part of road equipment that is very important for motorists because they can provide direction while on the highway, and if there is damage, repair or replacement must be carried out immediately because it can cause traffic accidents. Data collection for damaged traffic signs is still done using the manual method, so it takes a long time. Therefore, a web- and android-based application was designed that implements the Analytical Hierarchy Process (AHP) algorithm in determining the priority of repair or replacement of traffic signs on the route of South Sulawesi Province. As a result of this research, the public can report the type of damage and its location via Android, and then the officer processes the data so that it displays the type of damage that is a priority for repair or replacement. Implement the Analytical Hierarchy Process algorithm into the application for prioritization of traffic sign improvement using two (two) web-based and Android platforms. System design using UML produces use cases (2 actors, admin, and user) and class diagrams (15 admin classes and 4 user classes). The black box used as a test produced 40 modules, of which all were in line with expectations
ANALISIS KESULITAN SISWA DALAM MEMBACA LANCAR DI SD NEGERI 5 SABU BARAT

This study aims to describe the characteristics of fluency reading difficulties and the factors influencing students' reading fluency in grade III. This research was conducted at SD Negeri 5 West Sabu. The type of research used is descriptive qualitative. Data collection techniques used by researchers in this study were oral tests, interviews, observation, and documentation. The focus of the research in this study was the teacher and students while the research subjects were all 30 class III students at SD Negeri 5 West Sabu, with 18 female students and 12 male students. The results showed that the characteristics of fluency reading difficulties in class III were pronouncing words and sentences incorrectly, spelling, omitting letters and words, adding words, flat intonation and not paying attention to punctuation marks, unable to string letters together, reading haltingly and not understanding reading content. The factors that cause difficulty reading fluently are internal and external factors. Internal factors include (difficulty recognizing letters, eliminating letters and words, lack of interest in reading, and physical factors). While external factors include (how to teach teachers and family environment factors).&#x0D; &#x0D;