Reviewerly/exHarmony huggingface dataset:
Literature Study on Predicting Gold Prices using Machine Learning

Gold is a metal that is believed to be able maintain prices and can be a medium of exchange that has different forms and functions. In addition, gold can be used as a long-term investment tool at various prices. Price variations can be influenced by many factors, so predicting gold prices can minimize risk. Gold price predictions are not only interesting for collectors but also interesting for analysts to discuss. Machine learning is one method that is often used to predict gold prices. In this study will discuss the study of literature based on the methods and results of previous literature. The purpose of this study is to determine the best performance of the methods that have been used and can be used as a reference in predicting gold prices. The purpose of writing this journal is to provide an overview as well the benefits of applying data mining techniques to machine learning. These benefits include development better understanding of machine learning, as well as improved decision making and technological innovation.
Utilization of Desktop (Mobile) Applications Borrowing Laboratory Equipment at the IKesT Muhammadiyah Palembang Integrated Laboratory

Utilization of information technology in all fields is a must to assist in operational activities in processing data quickly and precisely. Each IKesT Muhammadiyah Palembang health laboratory has assets in the form of goods or equipment to support practicum activities and the learning process of the laboratory system. In every semester of lectures there will be practicum activities for several subjects and will always involve laboratory assistants to prepare and teach practicum. where the borrowing process is still manual. Application is a subclass of computer software that directly utilizes computer capabilities to perform a task desired by the user. the author will build a system for borrowing goods and equipment at the Muhammadiyah Palembang IKesT Laboratory Management Unit using a Desktop application using Laravel 8
Federated Data Quality Assessment Approach: Robust Learning With Mixed Label Noise

Federated learning (FL) has been an effective way to train a machine learning model distributedly, holding local data without exchanging them. However, due to the inaccessibility of local data, FL with label noise would be more challenging. Most existing methods assume only open-set or closed-set noise and correspondingly propose filtering or correction solutions, ignoring that label noise can be mixed in real-world scenarios. In this article, we propose a novel FL method to discriminate the type of noise and make the FL mixed noise-robust, named FedMIN. FedMIN employs a composite framework that captures local-global differences in multiparticipant distributions to model generalized noise patterns. By determining adaptive thresholds for identifying mixed label noise in each client and assigning appropriate weights during model aggregation, FedMIN enhances the performance of the global model. Furthermore, FedMIN incorporates a loss alignment mechanism using local and global Gaussian mixture models (GMMs) to mitigate the risk of revealing samplewise loss. Extensive experiments are conducted on several public datasets, which include the simulated FL testbeds, i.e., CIFAR-10, CIFAR-100, and SVHN, and the real-world ones, i.e., Camelyon17 and multiorgan nuclei challenge (MoNuSAC). Compared to FL benchmarks, FedMIN improves model accuracy by up to 9.9% due to its superior noise estimation capabilities.
Collision Avoidance for AGV Navigation Using the Time Elastic Band Algorithm

This paper presents a design and implementation of an automated guided vehicle (AGV) that addresses the dynamic obstacle avoidance problem in navigation. We employ the TEB path planner, which is based on laser range sensors, as the algorithm for dynamic obstacle avoidance. In the development process, the Stage simulation is previously conducted to obtain suitable parameters. The environment used in the Stage simulation is a flat map of the real vehicle verification site, and the robot specifications and sensors used are the same as those of the actual vehicle. Finally, we apply the parameters obtained from the simulation to the real AGV for validation.
Adaptive Wireless Streaming for Tile-based 360-degree Video

Wireless 360-degree video is an important application in 5G and beyond. However, 360-degree video consumes huge bandwidth for video transmission so as to deliver the contents of all angles to the user. To reduce bandwidth consumption, tile-based 360-degree video streaming is a promising technique. In this paper, we address two key challenges when designing a tile-based 360-degree video streaming system: (1) How to determine which tiles to transmit and their corresponding quality levels under various bandwidth conditions? (2) How to evaluate the user experience of watching a 360-degree video? We develop a tile selection algorithm for 360-degree video applications which can adapt to bandwidth condition while maintaining low Motion-to-Photon (MTP) latency to reduce cybersickness. Simulation results show the effectiveness of the proposed algorithm.
Implementation of Extreme Learning Machine Algorithm for Age-related Macular Degeneration Detection on OCT volumes

This paper presents the synthesis result of extreme learning machine (ELM), a machine learning technique, to detect age-related macular degeneration (AMD), an eye disease prevalent in elderly people. The model is trained with optical coherence tomography (OCT) images and a desirable clock period, area, and power is obtained. This is the first of its kind synthesis result of ELM implementation for AMD detection on OCT images.
Waiter’s Communication Abilities at Some Cafes on Jimbaran Beach

This study aims to determine the ability to communicate in English for waiters in cafes in the tourist area of Jimbaran beach. The English language is expressions of politeness in welcoming guests, in offering food and drinks, handling payments and in providing other services. The research data is in the form of primary data taken directly from English speech by waiters in interacting with foreign consumers, and collected by observation method. All data regarding grammatical errors were identified, classified, and analyzed using Lennon's theory. The method used in this research is descriptive qualitative. The results of the study indicate that there are several errors that are often found, in the form of grammatical errors in the form of addition errors and omission errors. To overcome this problem it is very important to hold training so that waiters are able to serve customers in an excellent manner. This will give a good impression to foreign consumers. So this will have a very good effect on increasing the arrival of foreign guests to Jimbaran in particular and Bali in general.
Enhancing Gaming Performance: A Recommender System for Selecting Optimal Gaming Headsets Based on SAW Method

Aims: The objective of this research is to design and develop a gaming headset recommendation system using the Simple Additive Weighting (SAW) algorithm based on a website. Additionally, the goal is to obtain user satisfaction ratings for the gaming headset recommendation system using the SAW algorithm, based on the End-User Computing Satisfaction (EUCS) model&#x0D; Study Design: This study was designed with Simple Additive Weighting method to build a gaming headsets website&#x0D; Place and Duration of Study: This study's respondents were recruited from the Edu Computer Store on Jl Citra Raya Boulevard, Cikupa, Tangerang, Indonesia. From January to June of 2023, and this study was conducted at Universitas Multimedia Nusantara.&#x0D; Methodology: In this research, the method designed is the Simple Additive Weighting (SAW) method, which aims to facilitate users in making the right decisions regarding gaming headsets. The SAW method provides a straightforward approach for evaluating and comparing multiple criteria to assist users in their decision-making process.&#x0D; Results: Based on the conducted test results, there is a user satisfaction rate of 83.35% based on the End User Computing Satisfaction (EUCS) model, indicating that users strongly agree with the system.&#x0D; Conclusion: The gaming headset recommendation system has been successfully designed and built using the Simple Additive Weighting (SAW) method. This system functions to recommend gaming headsets based on the comparison of six criteria: weight, price, driver size, frequency response, impedance, and sensitivity, according to user preferences. The system is developed on a web platform using the PHP Laravel framework.
Loop Aborts Strike Back: Defeating Fault Countermeasures in Lattice Signatures with ILP

At SAC 2016, Espitau et al. presented a loop-abort fault attack against lattice-based signature schemes following the Fiat–Shamir with aborts paradigm. Their attack recovered the signing key by injecting faults in the sampling of the commitment vector (also called masking vector) y, leaving its coefficients at their initial zero value. As possible countermeasures, they proposed to carry out the sampling of the coefficients of y in shuffled order, or to ensure that the masking polynomials in y are not of low degree. In this paper, we show that both of these countermeasures are insufficient. We demonstrate a new loop-abort fault injection attack against Fiat–Shamir with aborts lattice-based signatures that can recover the secret key from faulty signatures even when the proposed countermeasures are implemented. The key idea of our attack is that faulted signatures give rise to a noisy linear system of equations, which can be solved using integer linear programming. We present an integer linear program that recovers the secret key efficiently in practice, and validate the efficacy of our attack by conducting a practical end-to-end attack against a shuffled version of the Dilithium reference implementation, mounted on an ARM Cortex M4. We achieve a full (equivalent) key recovery in under 3 minutes total execution time (including signature generation), using only 5 faulted signatures. In addition, we conduct extensive theoretical simulations of the attack against Dilithium. We find that our method can achieve key recovery in under 5 minutes given a (sufficiently large) set of signatures where just one of the coefficients of y is zeroed out (or left at its initial value of zero). Furthermore, we find that our attack works against all security levels of Dilithium. Our attack shows that protecting Fiat–Shamir with aborts lattice-based signatures against fault injection attacks cannot be achieved using the simple countermeasures proposed by Espitau et al. and likely requires significantly more expensive countermeasures.
Effect of Environmental Disclosure on Shareholders’ Value Maximization: Evidence from Non-Financial firms in Nigeria

This study examined the effect of environmental disclosure on shareholders’ value maximization. The population of the study is all quoted non financial firms listed in Nigerian Stock Exchange. Sample of 60 companies from different sectors were used for the period of ten years spanning from 2011 to 2020. The study employed ex-post facto and cross sectional research design. The secondary sources of data were collected from annual reports and account of the selected non financial firms quoted in Nigeria stock exchange and three (3) specific objectives and hypotheses were tested and analyzed. The panel data were subjected to preliminary data tests such as descriptive analysis, correlation analysis and Hausman effects tests for the period of ten years. Multiple panel least regression analysis was employed via E-Views 10.Using a sample of 600 firm-year observations, the result of the tested hypotheses revealed that employee health and safety disclosure, and environmental remediation disclosure have positive but insignificant effect on shareholders’ value maximization while environmental waste management disclosure has positive and significant effect on shareholders’ value maximization which was statistically significant at 5% level of significance. The study recommends among others, that managers of non-financial firms should pay more attention to environmental waste management disclosure in their host communities to boost their performance and hence add value to their shareholders’ wealth creation. Moreover, due attention should be paid to environmental remediation disclosure by non financial firms in Nigeria since such disclosure influence strategic decision such as shareholders’ value maximization.
Enhancing Campus Communication and Collaboration: Design and Development of a Social Community Media Website for Universitas Multimedia Nusantara, Indonesia

Problem: The Multimedia Nusantara University (UMN) is one of Tangerang's universities with hundreds to thousands of students and academics. Currently, the UMN campus uses bulletin boards, student e-mails, social media accounts of a campus organization, and various other media to disseminate information to a large number of students and the UMN academic community; however, these media have deficiencies, such as a lack of exposure to this information or information that is not relevant to the recipient's specialization or study program.&#x0D; Aim: The primary aim of this research was to create and implement a social community media website system at Universitas Multimedia Nusantara (UMN) using the Rapid Application Development technique. Furthermore, the objective of the study was to evaluate and determine the level of satisfaction about the user interface of the social community media website system implemented by UMN.&#x0D; Study Design: This study was designed with Rapid Application Development to build a social media UMN community website.&#x0D; Place and Duration of Study: Department of Informatic Universitas Multimedia Nusantara, between September 2022 to July 2023.&#x0D; Methodology: Designing and developing a social media community website system using the Rapid Application Development methodology, which consists of three stages: requirement planning, design workshop, and implementation. Rapid Application Development is a method that allows designing and developing a system quickly and effectively, with good results.&#x0D; Results: The social community media website system has been effectively developed and constructed, and the results of User Acceptance Testing calculations have been obtained. Specifically, the Perceive Usefulness question yielded a score of 88.08%, while the Perceive Satisfaction question yielded a score of 88.67%.&#x0D; Conclusion: Based on the findings derived from the user satisfaction questionnaire, it can be inferred that the users exhibit a high level of agreement with the functionality and efficacy of the Sircle social media platform.
Deep Learning Model for Gender and Age Detection

Age and gender, two significant facial attributes, wield considerable influence within society. The automation of age and gender recognition holds substantial promise across a spectrum of real-world applications, including customer service, priority voting systems, medical diagnosis, and human-computer interaction. Leveraging deep learning techniques has emerged as a common thread in many research endeavours, yielding noteworthy performance enhancements. The integration of diverse deep learning models, coupled with an assessment of accuracy improvements, paves the way for further exploration. The central objective of this paper is to meticulously scrutinize age and gender recognition across various datasets and deep learning models. The paper expounds upon the advancements achieved in this domain, accentuating the contributions made. It outlines the models and datasets employed and rigorously evaluates
A Deep Learning Approach for ASL Recognition and Text-to-Speech Synthesis using CNN

Abstract: Sign language is a visual language that is used by the deaf and hard-of-hearing community to communicate. However, sign language is not universally understood by non-signers, which can create communication barriers for the deaf and hard-ofhearing individuals. In this paper, we present a novel application for American Sign Language (ASL) to text to speech conversion using deep learning techniques. Our app aims to bridge the communication gap between hearing-impaired individuals who use ASL as their primary mode of communication and individuals who do not understand ASL. The app comprises three main components: a hand gesture recognition module that detects and recognizes ASL signs, a text-tospeech synthesis module that converts recognized ASL signs to text, and a speech synthesis module that converts the text to speech
Evaluation of trace elements in the sediments of intertidal zone around Mahin-Ugbo Area, southwestern Nigeria

This study assessed the spatial distribution of Trace Elements (TE) in the muddy sediments of the Mahin-Ugbo coastal area of Southwestern Nigeria, using Inductively Coupled Plasma-mass spectrometry, (ICP-MS). The physicochemical parameters (pH, EC and TDS) of the sediments were also determined. Results showed that the mean values for pH, E.C and TDS were 5.09, 153.6 μs/cm and 76.8 mg/L, respectively. Further, there was a varying distribution pattern of TE concentrations: Mn&gt; Zn &gt; Ba &gt; Cr &gt; V &gt; Sr &gt; Pb &gt; La &gt; Cu &gt; Ni &gt; Co &gt; As &gt; Th &gt; Sc &gt; Ga. The As, Pb, and Zn concentrations in the sediment with mean values of 11.2, 40.4 and 122.1 ppm, respectively, were significantly higher than the average concentrations of As (10 ppm), Pb (20 ppm) and Zn (90 ppm) in natural shale, suggesting anthropogenic TE enrichment in the study area. To corroborate this, the TE source apportionments in the sediments using correlation coefficient, bi-variant plots and principal component analyses showed that the TE enrichment may have been anthropogenically induced. Pollution status assessments using I-geo, contamination factors and pollution load index indicated that the study area's sediments is slightly to moderately polluted. These findings suggest that although the area is not heavily polluted, increasing human activities, from inland rivers to the coastal area, in the study area will in no time be a cause for concern.
Exploring Economic Growth Determinants in Developing Countries of Asia and Africa

Asian and African regions have experienced periods of economic growth, yet they continue to grapple with distinct challenges and issues that hinder their ability to sustain and distribute that growth effectively. In addressing this concern, our research investigates the impact of exports, exchange rates, foreign direct investment, bank credit, government spending, and the Human Development Index on the economic growth of developing nations in Asia and Africa. The employed methodology is the panel least square method, which utilizes secondary data. The Asian region comprises 28 countries, and Africa encompasses 34 countries, from 1946 to 2018. The outcomes of the study reveal that within developing Asian countries, variables such as exports, exchange rates, foreign direct investment, bank credit, government spending, and the Human Development Index have positive and noteworthy effects on economic growth. In the context of African nations, the research finds that exports, exchange rates, and government spending have favorable and significant impacts. However, the Human Development Index displays a detrimental and significant influence. On the other hand, foreign direct investment and bank credit exhibit positive yet inconsequential effects on economic growth in developing African countries. Consequently, these findings underscore the need for robust macroeconomic foundations in developing economies to counteract global economic uncertainties. This strategy is imperative to facilitate swift economic expansion that aligns with each nation's distinctive economic development plans.
IMPROVING YOUNG LEARNERS SPEAKING SKILL THROUGH ROLE-PLAY

In improving young learners speaking skill, role-play is one of the techniques that can stimulate students to communicate with fun. The activity will stimulate students to speak English and make learning alive. Role-Play also increases motivations and develops fluency in speaking. This study aims at finding out young learners’s improvement in speaking skill through role-play. The research method was quasi-experimental using non-randomized pre-test and post-test group as her design. The writer gave two types of techniques in two different groups—Role Play in the experimental group and Presentation in the controlled one.The pretest and post-test was analyzed using t-test. The data shows a significant difference between pre-test and post-test scores among two groups.
Divide-and-Conquer Text Simplification by Scalable Data Enhancement

Text simplification is a task to reduce the complexity of a text while retain its original meaning. It can facilitate people with low-literacy skills or language impairments, such as children and individuals with dyslexia and aphasia, to read and understand complicated materials. Normally, substitution, deletion, reordering, and splitting are considered as four core operations for performing text simplification. Thus an ideal model should be capable of executing these operations appropriately to simplify a text. However, by examining the degree that each operation is exerted in different datasets, we observe that there is a salient discrepancy between the human annotation and existing training data that is widely used for training simplification models. To alleviate this discrepancy, we propose an unsupervised data construction method that distills each simplifying operation into data via different automatic data enhancement measures. The empirical results demonstrate that the resulting dataset SimSim can support models to achieve better performance by performing all operations properly.
Multilabel Classification for Keyword Determination of Scientific Articles

In writing scientific articles, there are provisions regarding the structure or parts of writing that must be fulfilled. One part of the scientific article that must be included is keywords. The process of determining keywords manually can cause discrepancies with the specific themes discussed in the article. Thus, causing readers to be unable to reach the scientific article. The process of determining the keywords of scientific articles is determined automatically by the classification method. The classification process is carried out by determining the set of keywords owned by each scientific article data based on the abstract and title. Therefore, the classification process applied is multi-feature and multi-label. Classification is done by applying the Contextualized Word Embedding Method. The implementation of Contextualized Word Embedding Method is done by applying BERT Model. By applying the BERT Model, it is expected to provide good performance in determining the keywords of scientific articles. The evaluation results by applying the BERT Model to the case of multi-label classification on abstract data for keyword determination resulted in a loss value of Training Data is 0.514, loss value of Validation Data is 0.511, and an accuracy value of 0.71, a precision value of 0.71, a recall value of 0.71, an error value of 0.29 and f-1 score of 0.83. Based on the results of the evaluation, it shows that the BERT Classification Model can carry out a classification process to determine a set of keywords from each abstract data in scientific articles.&#x0D;