Reviewerly/exHarmony huggingface dataset:
A Survey on “Medicines at Your Fingertips”

Medicines at Your Fingertips is a website aimed to provide all the information about any medicine, it’s side effects and all the other health related information. All the data of the medicines is stored in our database and it is fetched during the execution of user’s request. We have created the frontend using HTML, CSS, JS, JQUERY, JQUERY UI and Bootstrap. The Backend is built using Django Framework of Python. Our website contains 6 main components which are: Chatbot: An intelligent chatbot which will give any information that the user has asked. The AI chatbot is created using the Pytorch library of python. It will also help the user to navigate from the whole website. Drugs a to Z, a drug dictionary to give all the information about the desired medicine. There are 2 ways to search the medicine. Pill Finder: To search the medicine alphabetically or using the search bar Phonetic Search: To search the medicine using voice command. Drugs By Condition: It contains the information about all types of health conditions, their causes and treatment along with some medicines which are used to treat them. Side Effects: It also has alphabetically sorted medicines which gives the information about the side effects of the particular medicine along with a search bar. First Aid: This part consists of 3 components which gives the information about first aid treatments and My Med List to set reminders for the doses of your medicine.
Performance Evaluation of Decentralized Social Media on Near Protocol Blockchain

Social media's decentralization gives users control of their data, contributing to privacy, ownership, and dissemination. The challenge is to develop a decentralized social media platform with the same features and performances as its centralized counterpart. This study aims to demonstrate and evaluate the performance of decentralized social media in terms of throughput and scalability. The prototype application is developed on NEAR (Near Protocol Blockchain), a non-EVM (Ethereum Virtual Machine) chain with sharding. The results show that the use of NEAR to store post content is not a scalable solution, and the workaround of using IndexedDB as a local database to store information reduces response time and increases throughput and scalability. Collaboration of on-chain and off-chain storage strategies performs better for decentralized social media applications.
Earthquake Aftershocks Pattern Prediction

Large earthquakes, especially those occurring in a city or population centers, create devastation and havoc, and often times kindle several deaths and injuries, and significant infrastructure damage that lead to several billions of dollars in losses.Marine earthquakes are the leading cause of large tsunamis which cause deaths, destruction, displacement of population, and a possible nuclear meltdown.Thus, prediction of earthquake or its aftershocks or earthquake early warning system has a great potential to mitigate the loss of life as well as different kinds of damage.Earthquake prediction would mean forecasting the occurrence of an earthquake by providing both its magnitude estimate and accurate location.Earthquake prediction has been an important area of seismology research for quite a while, and it looks like it will continue to be an important area of research.Recently, with the implementation of deep learning in seismology, scientists have been able to detect, predict, and model seismic waves and earthquake aftershocks.
Development of Quantum Circuit Architecture on Quantum Perceptron Algorithm for Classification of Marketing Bank Data

The creation of quantum circuit architecture based on the quantum perceptron algorithm to classify marketing bank data is proposed in this work. A quantum circuit is a quantum gate made up of two quantum gates. Quantum bits are used in this study's computation. The primary proposed learning method was not ideal, which is the context of this study. The percentage of qubits measurement value is still 90.7 percent. It is essential to raise the value of the qubit rate. Using the IBM Quantum Experience quantum computer, researchers measured, trained, and tested the quantum circuit architecture. Bank marketing data from the UCI Machine Learning Repository was used. A quantum circuit architecture model results from this research the quantum circuit measurement results.
Software as a Service-based Integrated Interactive Online Course System

Several online learning platforms use recorded videos as a medium for delivering their material. In addition, several applications, such as Zoom, Google Meet, and WhatsApp, can help communicate interactively during this learning process. However, because the application is still separate from the existing online learning platform, users must switch applications and make the necessary data settings. Another obstacle experienced is that not all online learning service providers can have the infrastructure to use online learning systems, especially if the providers are individuals. This research uses a simple sequential method and aims to build an integrated online course system equipped with an interactive learning management system that many online courses, in general, can use. The main features are submissions of new courses by teachers, ordering courses by students, classes can be held using live video streams, the interaction between teachers and students in real-time with live chat, and a learning management system that includes sending and receiving assignments and quizzes. The test results show that as many as 73.2% of respondents gave the highest score for the built system.&#x0D;
Klasifikasi Kedalaman Kejadian Gempa Menggunakan Algoritma K-Means Clustering: Studi Kasus Kejadian Gempa Di Sulawesi

Sulawesi region is one of the region that have complex geologic conditions so that disasters caused by large scale earthquake frequently occur in these region. Depth and magnitude attribute of the earthquake that cause the disasters are investigation using machine learning technique. Longitude, latitude, magnitude, depth attributes are used to depth cluster of the earthquake events in 1970-2022 period. The cluster number have been optimized by Elbow method, and validated by Davies-Bouldin index (DBI). The result is shown that the three cluster is the best cluster than the others, and its Davies-Boludin index is 0.397. Depth of the fist cluster is less than equal to 120 km (shallow earthquake), the second cluster is among 120 km and 350 km (intermediate earthquake), and the third cluster is greater than 350 km (deep earthquake). The cluster visualizations of the earthquakes are revealed that shallow earthquakes with above 5 SR are frequently occurred in shallow depth. Based on results, Sulawesi Region is vulnerable to earthquake hazard, and K-Mean clustering algorithm is successfully to cluster of earthquake depth.&#x0D;
Pengaruh Penerapan Quality Control Data Radar Terhadap Akurasi Estimasi Curah Hujan Di Wilayah Pontianak Dan Sekitarnya

Weather radar operational have a limitation that leads to a quality reduction of radar rainfall estimation, such as interference at Pontianak’s weather radar. The application of 3D Clutter Map and Data Processing (3DCDP), Brightband Echo Correction (BBC), and Z–Based Attenuation Correction (ZATC) which include in post–processing QC is the common procedure of radar limitation mitigation. This procedure applied to data of Pontianak weather radar over 1 January 2019 – 31 December 2019. The QC implemetation effect observed qualitatively and quantitatively by compare radar’s product before and after QC implementation. Data of radar rainfall estimation compared with rainfall observation data from AWS/ARG, and validated with RMSE and ME values. The comparison of the weather radar image shows that the interference and clutter around the weather radar is reduced. According to 19 validation value on the 10 cases of chosen rain, 18 value shows QC’s increased the accuracy of radar rainfall estimation, though insignificant and still underestimate.
Past Metal(loid) Pollution Records Inferred from the Sediments of Bukit Merah Reservoir Perak, Malaysia

Anthropogenic activities, especially due to agricultural practices, tourism, and other land-use forms, have resulted in serious pollution of Bukit Merah reservoir (BMR), the oldest artificial reservoir in Malaysia since the 1980s.This study assessed the past ecological conditions of the reservoir using 210 Pb, dating techniques, selected metals, and physicochemical analysis of the sediment core to better manage the reservoir. 210Pb results indicated 34 years of human impact on the reservoir, with arsenic recording the least concentrations in the 1980s and the highest concentrations in 2016 and 2018.The minimum and maximum concentrations of lead and cadmium were found in 1990 and 2009.Whereas Copper and Zinc, concentrations peaked in 2012.The mean EF values of metals in the sediment core were above 1 (EF>1), indicating anthropogenic sources.The I geo values of Arsenic and Cadmium ranged from uncontaminated to strongly contaminated, uncontaminated to moderately contaminated for Lead, Zinc, and Copper.Overall, the dated sediment of BMR revealed that human population growth and increasing economic and agricultural activities coupled with poor land-use forms at the catchment area are the main source of pollution to the reservoir.Therefore, regular monitoring and the enforcement of relevant environmental laws are needed to manage the reservoir.
Improved Harris Hawks Optimization Algorithm for Solving Real Engineering Optimization Problems

Abstract Harris Hawks Optimization (HHO) algorithm was proposed recently under the metaheuristic algorithms, which can fix many problems in various domains. However, it needs to improve in local search, which may lead to a loss of diversity, stuck in a local minimum, which procures premature convergence. Two steps have been introduced in this paper to avoid these issues. Firstly, integrating Opposition-based learning (OBL) with HHO accelerates the search process and enhances the choice of better solutions, namely OHHO. Then, using reinforcement learning to enhance the research technique of OHHO, called RLOHHO. CEC 2015 and CEC 2017 benchmark functions and real engineering problems are utilized to evaluate the efficiency. Finally, the proposed versions of HHO are compared with efficient optimization algorithms. The experiment results illustrate that the RLOHHO's version achieved better solutions than the original HHO, OHHO, and other similar published algorithms in the literature.
Analysis of the shortest vector problems with quantum annealing to search the excited states

Abstract The shortest vector problem (SVP) is one of the lattice problems and is the mathematical basis for lattice-based cryptography, which is expected to be post-quantum cryptography. The SVP can be mapped onto the Ising problem, which in principle can be solved by quantum annealing (QA). However, one issue in solving the SVP using QA is that the solution of the SVP corresponds to the first excited state of the problem Hamiltonian. Therefore, QA, which searches for ground states, cannot provide a solution with high probability. In this paper, we propose to adopt an excited-state search of the QA to solve the shortest vector problem. We numerically show that the excited-state search provides a solution with a higher probability than the ground-state search.
Following Hillary Clinton and Donald Trump: A dissection of their tweets in the 2016 U.S. presidential election

In this work, the tweets of Donald Trump and Hillary Clinton in the 2016 U.S. presidential campaign are studied and analyzed from a language-based perspective. The tweets are divided into two batches. The first is from the earliest announcement of candidacy until the last announcement of nomination of candidates. The second is between the end of the first interval and the inauguration of Trump. Readability statistics of tweets are computed and the Scholastic Aptitude Test (SAT) words — subject of much cramming by test takers — in the tweets are analyzed, as well as Ogden’s Simple English words. Some of the readability indexes exhibit minor differences, implying that Clinton’s tweets are more readable whereas the other readability indexes are proximate for the candidates. Clinton’s use of unique SAT words is found to be denser than Trump’s, indicating that employing such words less might be wiser for political campaigns. Simple English analysis does not tell of a noticeable difference. Syntactic Dependency Distance of tweets and Integrative Complexity of tweets were also analyzed but no significant difference for the two candidates was discerned.
A small comparison of various content based image retrieval approach

-Image retrieval is one of the most interesting and fastest growing research areas in the field of digital image processing as well as for the information retrieval from web contents. In most Content-Based Image Retrieval (CBIR) systems, an image is represented by a set of different level of visual features, by which can manage large databases.CBIR is used in many areas for retrieving images of simillar type from large database Most of the popular database removes the highlevel semantic information.
Multilayer-perceptron-based Slip Detection Algorithm Using Normal Force Sensor Arrays

Slip detection is an essential technology for robotic grippers to autonomously grasp unknown objects and can be achieved using a tactile sensor.In this paper, we propose a high-performance multilayer-perceptron-based slip detection algorithm that utilizes only normal force data obtained by frequency selective surface(FSS) sensor arrays.This is achieved in three stages in this study.First, slip and no-slip training data are aggregated such that the data closely resemble those of the real world.Second, the most suitable means of preprocessing the raw sensor output is identified.Third, the classification method with the highest performance is chosen on the basis of a performance comparison among various classification techniques.The online performance of the algorithm is evaluated by conducting two tasks: a simple pick and place task and a task of maintaining a stable grasp of an object whose weight is changing.
Platforms for Online Learning Process in Public Elementary Schools of Rural and Urban Areas

The utilization of online learning platforms in elementary schools is affected by several factors.One of the factors is the difference in the region, rural and urban.The objectives of this study are to investigate a) the platforms used to support online learning in public elementary schools based on regional differences, b) the online learning process through platforms for rural and urban areas, and c) the teacher's reason for selecting this platform.This study is a case study of online learning at a public elementary school in Bali, Indonesia.Mixed methods were used as the approach, with an explanatory sequential mixed methods design.A total of 179 public elementary school teachers were agreeable to being involved as respondents.They were spread out in both rural and urban areas in nine regencies and municipalities in Bali province.They filled in online questionnaires.Furthermore, interviews were conducted with 10 teachers who filled in the questionnaire.The data obtained were analyzed descriptively to determine the percentage.Thematic analysis was used in analyzing the interview data.The results showed that there are no significant differences in the platform used for online learning between rural and urban elementary school areas in Bali.There are similarities in the choice of the main platform between teachers in rural and urban areas in the implementation of online learning.The reasons for the similarity in choosing online learning platforms by teachers in the two regions were also revealed in the study.Thus, several recommendations are discussed in this paper.
Halal Cosmetics Awareness Through Augmented Reality Application: To Assist Consumer In Cosmetic Product Purchase Decision Making

Muslim populations are expanding over the world, and so is the need for Halal products. In recent years, the word of Halal cosmetics has become more common in the mainstream. Even so, there are Muslims in Muslim countries like Malaysia who have never heard of Halal cosmetics. They only know the Halal label on food and drink. As a result, people are spending more money on cosmetics, particularly multinational brands of makeup. Using augmented reality in the beauty industry could make things easier for both consumers and marketers. This project uses augmented reality (AR) to share information about halal cosmetic products so that it’s easier to meet consumers expectations. This will make consumers satisfied with their purchases and help them understand what they’re buying. This research targets on women who wear cosmetic products. Awareness about halal cosmetic products for Malaysian Muslim women is the aim of this study. The awareness will guide them in purchasing cosmetic products. The development of this project uses Vuforia SDK together with Unity, to be used in implementing three dimension (3D) AR modelling on Android smartphones. To produce cosmetic product models, it uses 3D modelling technology consisting of 3DS Max and Blender. Meanwhile, this project uses Adobe InDesign and Illustrator to design magazine layout templates. The ADDIE model was used for this study, which consist of Analysis, Design, Development, Implementation, and Evaluation phase. As a result, this augmented reality application will be able to increase consumer awareness on halal cosmetic products and assist them in cosmetic products purchase decision.
On the Problems and Countermeasures of Computer Aided Physical Education in Colleges and Universities

In recent years, China's scientific and technological level has been continuously improved. In this context, a variety of multimedia technology, multimedia equipment began to appear in the process of teaching in colleges and universities, they have become an important auxiliary tool to carry out teaching in colleges and universities. This computer-assisted teaching model promotes the reform of Chinese teaching concepts and teaching methods, and shows its strong vitality in our teaching process. It has not only enriched the teaching content and teaching tools, but also greatly improved the effect of college education and teaching, and it has been widely used in the teaching process of various subjects in colleges and universities. However, in the daily teaching process, it can be found that there are still some problems in the application of computer-assisted teaching in physical education and teaching effect, which also leads to problems in the efficiency of physical education in colleges and universities and students' understanding of sports knowledge. Based on this, this paper combines the concept of computer aided instruction and the significance of its application in physical education teaching, and makes a brief analysis of the problems and countermeasures in school computer aided physical education teaching. It is expected to further promote the reform of physical education in colleges and universities.
Dual Channel Hybrid Messaging-Passing Graph Convolutional Network for Recommendation

Graph convolution network (GCN) is widely used in recommendation. It has become the mainstream technology of collaborative filtering as its powerful graph representation learning ability. Since historical interactive data can be naturally modeled as a user-item bipartite graph, many excellent methods have been proposed for recommendation based on bipartite graphs. These methods have achieved impressive successes. However, most of these methods only utilize the intuitive topology information of bipartite graph and ignore semantic information. In this paper, we propose a new Dual Channel Hybrid Messaging-Passing Graph Convolutional Network(DCH-GCN) recommendation model. In our model, first the user-item bipartite graph and item-item co-occurrence graph is constructed based on historical interaction data. Then the explicit messaging passing channel is constructed for topology information by modeling user-item explicit interactions. In addition, the implicit message-passing channel is constructed for semantic information by modeling item-item implicit interactions. Finally the loss of the predictive user preference is co-trained and co-optimized. It is more interpretable that we add semantic information into the model in bipartite graph. We perform a comprehensive experiment on two public benchmark datasets to verify the effectiveness of our model. The experimental results show that our method can better learn user and item embedding by using dual channels, and achieve significant and consistent improvements over competitive baselines.
Rhythm-controllable Attention with High Robustness for Long Sentence Speech Synthesis

Regressive Text-to-Speech (TTS) system utilizes attention mechanism to generate alignment between text and acoustic feature sequence. Alignment determines synthesis robustness (e.g, the occurence of skipping, repeating, and collapse) and rhythm via duration control. However, current attention algorithms used in speech synthesis cannot control rhythm using external duration information to generate natural speech while ensuring robustness. In this study, we propose Rhythm-controllable Attention (RC-Attention) based on Tracotron2, which improves robustness and naturalness simultaneously. Proposed attention adopts a trainable scalar learned from four kinds of information to achieve rhythm control, which makes rhythm control more robust and natural, even when synthesized sentences are extremely longer than training corpus. We use word errors counting and AB preference test to measure robustness of proposed method and naturalness of synthesized speech, respectively. Results shows that RC-Attention has the lowest word error rate of nearly 0.6%, compared with 11.8% for baseline system. Moreover, nearly 60% subjects prefer to the speech synthesized with RC-Attention to that with Forward Attention, because the former has more natural rhythm.
A Machine Learning Approach to Identify the Feature Importance for Admission in the National Military High Schools

Abstract The article provides the impact of different averages (feature importance) within the admission exam for the national military high schools using and testing three supervised machine learning algorithms: logistic regression, K-Nearest Neighbors, and random forest. For this purpose, I have used the list with the results of candidates compounded by 430 rows, an unclassified document posted on the national military high school website, with details about: the final admission grade, the general grade for graduating of the secondary school, the general grade obtained at the national assessment, the mark obtained at admission test from Romanian language and mathematics items, etc. From the machine learning perspective, I have built a Jupyter notebook, a Python code using the specialized ML libraries (numpy, pandas, matplotlib, sklearn). In conclusion, the logistic regression algorithm identified the ‘feature importance’ (how each variable contributes to the predicted model) for admission in the national military high school: the mark obtained at admission test from Romanian language and Mathematics items - 4.821834, the general average obtained at the national assessment - 0.584434, the general average for graduating of the secondary school - 0.285446, etc. These are the expected results based on the admission methodology for the national military high schools.