Reviewerly/exHarmony huggingface dataset:
METODE ONE TIME PASSWORD MODIFIED SEBAGAI SISTEM KEAMANAN MONITORING IOT

Monitoring is an activity carried out to find out the process of running a program or system that has been designed, whether it runs well as expected or not, find out the obstacles that occur and how to overcome these obstacles. The cryptographic scheme to secure communication protocols is one of the most effective defenses against a variety of attacks, including eavesdropping and simple routing attacks, at the communication layer. One of the techniques studied in cryptography is encryption-description. This study aims to apply a one time password (OTP) modified algorithm into an IoT based temperature and humidity monitoring system using a Raspberry PI microcontroller device and a DHT 11 sensor. . The encryption process begins with hashing using the SHA method on the plaintext, followed by producing a private key and public key for RSA Signature encryption, the hashing results will be encrypted using the RSA Signature algorithm, the RSA Signature encryption results will be combined with the RSA Signature public key and plaintext, followed by producing an OTP that will be used as an AES key. Next the AES encryption process is executed, the OTP key will be encrypted RSA, the results of the RSA ciphertext and the AES ciphertext are combined and encoded with the Base64 method. The implementation of this security system gets the test results of the average duration for data encryption process is 435,163ms, and the data transmission with encryption process is 123,705ms
Jamstack Performance and Cost-Effective Analysis with Traditional CMS Websites

Website is a medium that cannot be separated from technological developments, websites are needed because of their practical and easy use, especially a landing page website that is used by business and non-profit people to convey information. The website is developed using a custom-built architecture so that the development and maintenance process runs well. The world is developing faster and faster so that the old website development architecture over time is increasingly difficult to adapt, so there is a new website development architecture, one of which is Jamstack, the Jamstack architecture is said to have advantages over the old architectures that are widely used until now, one of which is LAMP Stack, to ensure the correctness of these arguments, a detailed analysis is needed so as to get results that match the facts.
Observation on the Current Security Landscape of Intelligent Transportation

Location-primarily based totally services (LBS) allow cell customers to question points-ofinterest (e.g., restaurants, cafes) on numerous features (e.g., price, quality, variety). In addition, customers require correct question outcomes with updated journey instances. Lacking the tracking infrastructure for avenue traffic, the LBS may also acquire stay journey instances of pseudonym routes from on-line pseudonym direction APIs so as to provide correct outcomes. Our aim is to lessen the variety of requests issued with the aid of using the LBS extensively at the same time as maintaining correct question outcomes. Our proposed work, the consumer has an get admission to pseudonym router through an internet. Based on his gift area he has to pick the vacation spot point, after which LBS will speak with server and indicates you the closest locations of his choice. First, we advise K-NN Pseudonym direction evaluation to make the most latest pseudonym routes asked from pseudonym direction APIs to reply queries accurately. Then, we layout powerful lower/ top bounding strategies and ordering strategies to system queries efficiently. Also, we have a look at parallel pseudonym direction requests to similarly lessen the question reaction time. Our experimental assessment indicates that our answer is 3 instances greater green than a competitor, and but achieves excessive end result accuracy (above ninety nine percent). Combine records throughout a couple of pseudonym routes with inside the log to derive lower/top bounding journey instances, which help green and correct variety and KNN search. Develop heuristics to parallelize pseudonym direction requests for decreasing the question reaction time similarly. Evaluate our answers on a actual pseudonym direction API and additionally on a simulated pseudonym direction API for scalability tests.
Design Research for Healthcare

Importance of user-research for developing human centred solutions.In this article, Anmol talks about the role user research played in ensuring that their team's solution for rural healthcare was 'humans-first ' and not 'technology-first'.josha.org
ANALISIS KOMPONEN UTAMA UNTUK MENENTUKAN VARIABEL YANG BERPENGARUH TERHADAP CITRA PROGRAM STUDI ARSITEKTUR UNIVERSITAS SURAKARTA

Main component analysis can be used to facilitate agencies in making decisions in improving the services to be provided because with the main component analysis it can focus on only a few main components of the many variables that affect the level of public image. The research approach used is a quantitative approach with a descriptive method. This research was conducted at the Architecture Study Program at the University of Surakarta (UNSA), with primary data as a source of data which was conducted by distributing questionnaires to applicants in the Architecture Studies program at the University of Surakarta which contained 6 questions representing 6 specific variables. The data obtained from this study were analyzed using the principal component analysis method. With the principal component analysis method, two main components are formed, namely the first main component, the variable coefficient of X1 (relatively cheap tuition fees), X2 (There are afternoon classes or employee classes), X3 (Lecturers who are competent in their fields), X4 (Accreditation). ) respectively are 0.499, 0.456, 0.360 and 0.473 with variable scores/values X1, X2, X3, X4 are 0.702, 0.641, 0.506 and 0.665. These four variables are included in the internal components that form the image of the Surakarta University Architecture Study Program. The second main component, the coefficients of the variables X5 (complete supporting facilities) and X6 (strategic campus location) are -0.489 and 0.659 respectively and scores are -0.571 and 0.768, these two variables are included in the external components of the program image. Surakarta University Architecture Studies. The total diversity of image-forming of the Surakarta University Architecture Study Program on the internal component is 32.98% and the external component is 22.62%, so it is known that the internal component is the most dominant component and has more influence on the image-forming of the Surakarta University Architecture Study Program than the external component
Revisiting of AlphaStar

Research on StarCraft II (SC2) is considered important due to its similarity to real-life tasks and its potential to inspire game AI design. However, the complexity of SC2 presents considerable challenges. In 2019, DeepMind proposed AlphaStar (AS), an agent that achieved Grandmaster level in SC2. Nevertheless, the reasons for AS's success remain unclear. In this paper, we revisit AS by analyzing its technical details, implementation codes, and replays. We also propose the open-sourced mini-scaled AS's new versions to do ablation studies. We classify SC2 problems by difficulty level and suggest a research path for tackling them. We then identify several limitations of AS, such as its lack of strategic view, reasoning, scouting, changes in tactics, and planning. Our paper also presents the first analysis of AS's replays. In conclusion, we emphasize that there is still a long way to solve the final SC2 problem.
Using a Logistic Regression and K Nearest Neighbor Model to Accurately Diagnose Breast Cancer

Breast cancer is one of the most dangerous and rapidly growing diseases in the world. Diagnosing breast cancer is expensive, difficult, and time-consuming. However, artificial intelligence and machine learning algorithms can help physicians to diagnose people with breast cancer at an early stage which will help people to avoid exhaustive treatments. The objective of our research was to classify if someone has malignant or benign cancer. We used the Wisconsin Breast Cancer dataset which was obtained from the UCI repository to create models using supervised learning. We used K Nearest Neighbors, and Logistic Regression algorithms to obtain a model with high accuracy. Both the models had an accuracy of 97%. In the future, the model can be enhanced to be more accurate and accessible to people. This research can help others to create models to predict various other cancers. In the future, we would also like to improve the model by using other methods like image recognition and reducing the input from the user to make it more accurate and accessible.
Spatialization as a perceptual basis for information: How perception becomes a narrative

According to enaction theory, information perception results from a coupling between the individual and data in the perceptual space. Consequently, the minimal condition of achieving perception is that there be something to act upon, i.e., a salience or a hook. Data emerge in the background and become preponderant. But, being at first only a sensation, these data can only be constituted as information if they persist in the perceptive field. One should be able to leave them and return to them in the logic of topological continuity, for example, association, similarity/dissimilarity, divergence, etc. Our hypothesis is that there are logics that transform these data into information. We posit that, in addition to being spatializing, they embody above all the perceptual gestures that make these data legible. Such is, for example, the status of the various supports that serve as structures of data inscription available on the digital applications: lists, tables, diagrams, and cartographies. Starting from sensorimotor theories, in connection with the enactive approach, we intend to establish the semiotic conditions of these supports as a perceptive basis of information.
Improving The Reading Performance of Grade 3 Learners Using Multimodal Reading Instruction (School Level

This study aimed to develop multimodal reading instructions during COVID-19 pandemic for grade 3 learners. The subjects were the 33 Grade 3 pupils of Aplaya Elementary School, Lingayen III District, Schools Division Office I Pangasinan during the school year 2021-2022. The development of the instructional materials was based on the result of the teacher-made test in reading English. The researcher used a quasi-experiment. The respondents (19 or 57.6%) had their level of computational performance assessed as poor in the given multiple tests prior to the utilization of Multimodal Reading Instruction aimed at improving their reading performance and academic performance in English. There were 14 learners (42.4%) whose performance in the test was found satisfactory while none (0.0%) proved to be outstanding. On the other hand, a comparison of how they fared in the posttest after they were taught in English using MRI reveal a marked improvement in their level of performance. Notably, there were no longer learners whose performance was poor (0 or 0.0%) while there were just 10 (30.3%) learners whose performance was satisfactory and 23 or 69.7% were assessed as outstanding. This study looked into the influence of Multimodal Reading Instruction (MRI) as intervention in improving the reading fluency of the respondents. Results of the study reveal how the utilization of multiple modalities in reading instruction yielded positive results among the respondents. Teachers are then enjoined to explore multiple modalities or platforms in the development or of interventions aimed at addressing reading loss among elementary learners.
The Performance Comparison of Machine Learning Models for COVID-19 Classification Based on Chest X-ray

COVID-19 has become a pandemic spread to nearly all countries in the world. This virus has caused many deaths. Screening using a chest X-ray is an alternative to find out positive COVID-19 patients. Chest X-ray is advantageous because every hospital must have an X-ray device so that hospitals do not need additional equipment to detect COVID-19-positive patients. This study aims to compare the machine learning models of Naive Bayes, Decision Tree, K-Nearest Neighbor, and Logistic Regression to predict COVID-19 positive patients. The stages of the research carried out by this study are the Pre-process stage, feature extraction, and classification. The results showed that the Naïve Bayes classification method got the highest performance with an accuracy of 95.24%.
Psychological and Developmental Factors Related to the Choice of Avatars in a 3D Virtual World

Introduction: Virtual reality environments (VREs) are an emerging modality consisting of computer-generated worlds. Players in VREs can choose to create representative avatars. Such choices may reflect beliefs related to identity and psychological functioning. The aim of the present study was to examine patterns in how users who experienced early adversity and psychological distress select avatars, hypothesizing that individuals who choose to engage in VREs as children may have experienced a greater degree of childhood trauma and psychological distress as compared to other users.Methods: We recruited 436 participants in a VRE. Participants completed the Early Traumatic Inventory Self Report-Short Form and the Brief Symptom Inventory. We evaluated between-group differences (child avatar, parent avatar, and control) in psychological distress and trauma incidences.Results: Individuals who selected child avatars experienced a higher degree of psychological distress and childhood trauma, especially sexual abuse, as compared to individuals who selected other avatars. Individuals who selected parent avatars were more likely to have experienced psychological distress as compared to controls.Discussion: Avatar choice may reflect identity and psychological functioning. VREs may provide safe environments for a “do over” of adverse childhood experiences. Future research should explore VREs as venues to treat trauma and psychological distress.
A Novel Swarm Exploring Varying Parameter Recurrent Neural Network for Solving Non-Convex Nonlinear Programming

Aiming at solving non-convex nonlinear programming efficiently and accurately, a swarm exploring varying parameter recurrent neural network (SE-VPRNN) method is proposed in this article. First, the local optimal solutions are searched accurately by the proposed varying parameter recurrent neural network. After each network converges to the local optimal solutions, information is exchanged through a particle swarm optimization (PSO) framework to update the velocities and positions. The neural network searches for the local optimal solutions again from the updated position until all the neural networks are searched to the same local optimal solution. For improving the global searching ability, wavelet mutation is applied to increase the diversity of particles. Computer simulations show that the proposed method can solve the non-convex nonlinear programming effectively. Compared with three existing algorithms, the proposed method has advantages in accuracy and convergence time.
MULTI-OWNER DATA SHARING

In the case of Provable Multiple files Data that changes over time in cloud computing,possession refers to the dynamic transfer of stored data to a cloud server. Multiple files indicate that the data will be replicated on multiple servers. The project owner uploads the data to the cloud server using automatic data capture, and the files are then stored on multiple servers. Data loss due to hacking and server crashes can be avoided by uploading data to multiple servers. By distributing the secret key among the several users, multi-user searchable encryption directly adapts a single-user system to a multi-user environment. This is obviously not capable of achieving user accountability, and in the event of user revocation, it necessitates key renewal among non-revoked users, which then necessitates re-encrypting the external database. These issues might be resolved if the cloud implemented access control before responding to user search requests, but as noted in, access control deployment is prohibitively expensive in practise. To make matters worse, users must maintain a separate set of secrets for authentication and authorization. We introduced a new technique, fully homomorphic encryption (FHE), in this project for multiple data file acquisition, file security, and data corruption. For data protection, we must use the FHE algorithm in this project. There are three of them: keygen, copygen, and taggen.The process described above was carried out in the existing system. Keywords- Multiple file data; Cloud server; Fully homomorphic encryption ; FHE algorithm;
Improving Image Captioning via Dual-Stream Multi-Layer Dynamic Fusion

Abstract Recent researches in image captioning have focused on combining region features and grid features to enhance visual information. However, conventional fusion methods may introduce semantic noise that disturbs model prediction. To address the problem, we propose a simple and efficient dual-stream network, Multi-layer Dynamic Fusion Transformer (MDFT), which employs a multi-layer dynamic fusion method to simultaneously enhance region features and grid features. Specifically, we introduce Cross-layer Perceptual Self-Attention (CPSA) module that locally models visual features at multiple layers to refine visual features. Additionally, we design an Adaptive Selection Controller (ASC) to reduce the computational burden of the layered structure and dynamically select attention layers with similar semantic information for interaction. Through this fusion strategy, MDFT effectively reduces semantic noise and achieves complementary advantages of region features and grid features. The experimental results on the MS-COCO dataset indicate that the MDFT model achieved relatively advanced performance on both local and online test sets, with respective scores of 134.0% and 133.7%.
Chapter 4: Unsupervised Learning

PreviousNext No AccessMachine Learning for Science and Engineering Volume I: FundamentalsChapter 4: Unsupervised Learninghttps://doi.org/10.1190/1.9781560803898.ch4 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InReddit Abstract The supervised methods studied in the last chapter have a major drawback in that they need, as the name implies, to be “supervised.” In unsupervised learning, the algorithms work on their own to infer patterns or categories without being trained or guided by labels. This also means that the human interaction is minimized. The unsupervised algorithm may group unsorted objects according to patterns and data characteristics that may or may not be obvious. In this chapter we analyze the two popular clustering algorithms (k-means and HDBSCAN) that should be in every scientist’s and engineer’s arsenal of methods to uncover its less-known assumptions. Permalink: https://doi.org/10.1190/1.9781560803898.ch4FiguresReferencesRelatedDetails Mar 2023Pages: 408ISBN (print):978-1-56080-388-1ISBN (online):978-1-56080-389-8 publication data© 2023 Copyright © 2023 Society of Exploration Geophysicists. All rights reserved. This book or parts hereof may not be reproduced in any form without written permission from the publisher.Publisher:Society of Exploration Geophysicists History CITATION INFORMATION (2023), "Chapter 4: Unsupervised Learning," Course Notes Series : 297-346. https://doi.org/10.1190/1.9781560803898.ch4 Plain-Language Summary PDF DownloadLoading ...
Context-aware Multi-modal Recommendation Model based on Deep Auto-encoder

Abstract With the exponential growth of data in the era of big data, recommendation systems have become essential in e-commerce. Accurate prediction of users' ratings of products is the key problem for personalized recommendation services. The current research in recommendation systems is mainly divided into three types: content-based, collaborative filtering, and hybrid recommendation. However, the traditional matrix decomposition algorithm faces difficulties in adapting to the current complex environment. In recent years, deep learning techniques have made great breakthroughs in recommender systems. We propose a multi-modal recommendation architecture based on self-encoder to effectively fuse textual and visual modal data, contextual features, and textual information to assist recommendation and significantly improve video recommendation performance. The proposed video recommendation model can effectively avoid the situation that the recommendation results cannot be obtained due to the lack of unimodal data. Specifically, the architecture comprises three key components: (1) a contextual feature extraction module based on convolutional neural networks to extract contextual information from textual data; (2) a multi-modal fusion module that fuses the contextual features with visual features to effectively integrate the complementary information across modalities; and (3) a self-encoder-based recommendation module that incorporates the fused features to generate personalized recommendations for users. Experimental results on several real datasets with distinctive features demonstrate the effectiveness of the proposed architecture. The proposed model significantly outperforms state-of-the-art recommendation models on the video recommendation task.
Analysis of the Consumption of Selective Serotonin Reuptake Inhibitors (SSRIS) and Tricyclic Antidepressants (TCA), before and during the COVID-19 Pandemic, in Community Pharmacies in Brazil

Introduction: The COVID-19 pandemic has changed the way we live. It brought several effects to the lives of society as a whole and showed us an increase in the use of antidepressant medications.&#x0D; Objective: To evaluate the consumption of antidepressants in the period from 2015 to 2020 and the estimate for 2021 to 2025; as well as possible animated between consumption and the socioeconomic and demographic conditions of the different Brazilian regions.&#x0D; Methodology: The research was developed in three stages. 1st stage: search for data in the National Controlled Products Management System (SNGPC). 2nd stage: research on the platform of the Brazilian Institute of Geography and Statistics, IBGE for the collection of sociodemographic data and variables of interest. 3rd stage: descriptive statistical analysis, comparison of consumption rates and correlations to verify the association between numerical variables. Medians and interquartile ranges were left (median [Q1; Q3]) and the Kruskal-Wallis and Spearman non-parametric test was performed (ρ using the R 4.0.2 software (R Core Team, 2020).&#x0D; Results: If the increase in the consumption of antidepressant medications in the period from 2015 to 2021, and it is controlled that, in the year 2020, at the beginning of the pandemic by Covid-19, this increase of 15%, compared to the year 2019. However, the estimate from 2021 to 2022 is that there will be an increase of only 3% pre and pandemic caused by COVID-19 in Brazil Sociodemographic variables have a significant influence on the consumption of antidepressants.&#x0D; Conclusion: An observation from the analytical side found that, in this study, the increased consumption of antidepressants in Brazil during the pre- and pandemic period caused by COVID-19. The study shows the relationship between socioeconomic and demographic conditions.