Reviewerly/exHarmony huggingface dataset:
PERCEPTUAL STUDY OF PLASTIC WASTE BRICKS FOR SHARIA APARTMENT IN INDONESIA

Bricks from plastic waste have the potential to become the wall material for apartment units because they are lightweight and have an interlock system that makes it easy to dismantle and reuse. Sharia Apartments in Indonesia exist because of the market potential for the majority of Muslims who are interested in a halal lifestyle. This article identifies Muslim perceptions of the application of bricks from plastic waste to sharia apartment residential units. The perception is carried out by comparing three plastic waste brick product companies. The method used is through questionnaires and interviews based on regional demographics. It was found that brick of plastic waste can be received positively by Muslim respondents, but its use is limited to particular spaces to avoid negative perceptions.
Customer Relationship Management to Retain Customers with the Topsis Method

In the era of globalization, the increasingly rapid development of technological sophistication is an aspect that can be utilized to achieve convenience, including the flow of information. Each company has its own way of retaining customers. At the Metacom store, efforts to retain customers have not been carried out in an updated and online manner. Customer Relationship Management (CRM) is one way of retaining customers to fulfill this goal by combining CRM and TOPSIS methods. For this reason, customer data analysis was carried out at the Metacom store using the Topsis method, from 10 transaction data, 2 were recommended to be given a discount on the goods. The results of applying TOPSIS use ranking results from goods transaction results. This Topsis analysis can be used to change a CRM application, where this application is equipped with a sales information system as well as goods data collection (Cashier) and a sales application using the Topsis method with a combination of these two methods in the Metacom Store. From the processed data
A systematic review A Conversational interface agent for the export business acceleration

Conversational agents, which understand, respond to, and learn from each interaction using Automatic Speech Recognition (ASR), Natural Language Processing (NLP), Advanced Dialog Management, and Machine Learning (ML), have become more common in recent years. Conversational agents, also referred to as chatbots, are used to have real-time conversations with individuals. As a result, conversational agents are now being used in a variety of sectors, including those in education, healthcare, marketing, customer assistance, and entertainment. Conversational agents, which are frequently used as chatbots and virtual or AI helpers, show how computational linguistics is used in everyday life. It can be challenging to pinpoint the variables that affect the use of conversational agents for business acceleration and to defend their utility in order to enhance export company. This paper provides a summary of the evolution of conversational agents from a straightforward model to a sophisticated intelligent system, as well as how they are applied in various practical contexts. This study contributes to the body of literature on information systems by contrasting the different conversational agent types based on the export business acceleration interface. This paper also identifies the challenges conversational applications experience today and makes recommendations for further research.
AES based security scheme to remove man-in-the middle attack in online banking

For illuminating the man middle attack during authentication process of public key certification process, we have proposed an encryption scheme based on Advance Encryption Standard. This method is an efficient and secure form of encryption which keep users valuable information safe. Public- key cryptography which is based on the concept of a key compare, comprises of an accessible key and a exclusive key. Information that has been scrambled with an open key can be decoded as it were with the comparing private key. Index Terms: AES, authentication, Public key certificate, Certification Authority, security, Man in the middle attack, online bank in.
Speaking Activities in English Second Language Classroom and the Oral Proficiency of the Grade 11 Students

The study was conducted in Our Lady of Mount Carmel Seminary in Sariaya, Quezon. The respondents were composed thirty (30) Grade 11 senior high school students. It sought to determine the oral communication proficiency of the students based on articulation, modulation, stage presence, facial expression, and rapport with the audience before and after being exposed to the teaching strategies in speaking story completion and picture description in terms of pretest and post-test. Moreover, the significant difference between pretest and post-test of students was also explored. This study used experimental research design with the use of speaking activities, and a statistical tool employing frequency and percentage and Test of Difference. Random sampling was used to select the sample respondents. The findings of the study revealed that there is a significant difference between the pretest and post-test result of the student respondents. Based on the results, since the interpreted results on all variables under pretest and post-test conform to the significant difference of 0.00 level, the overall result states that there is a significant difference between the pretest and post-test of the grade 11 students when exposed to the speaking activities, story completion and picture description. The researcher recommends educators may apply the same speaking strategies in learning environment. Furthermore, language educators must consider that the speaking strategies must be appropriate, relevant, and useful. Likewise, the speaking strategies must have the ability to attract learners and sustain their interest and be helpful in terms of attainment of essential learning outcomes. They may also explore and endeavor other speaking strategies that may help improve the oral communication proficiency needs of the students.
GTCAlign: Global Topology Consistency-based Graph Alignment

Graph alignment aims to find correspondent nodes between two graphs. Most existing algorithms assume that correspondent nodes in different graphs have similar local structures. However, this principle may not apply to some real-world application scenarios when two graphs have different densities. Some correspondent node pairs may have very different local structures in these cases. Nevertheless, correspondent nodes are expected to have similar importance, inspiring us to exploit global topology consistency for graph alignment. This paper presents GTCAlign, an unsupervised graph alignment framework based on global topology consistency. An indicating matrix is calculated to show node pairs with consistent global topology based on a comprehensive centrality metric. A graph convolutional network (GCN) encodes local structural and attributive information into low-dimensional node embeddings. Then, node similarities are computed based on the obtained node embeddings under the guidance of the indicating matrix. Moreover, a pair of nodes are more likely to be aligned if most of their neighbors are aligned, motivating us to develop an iterative algorithm to refine the alignment results recursively. We conduct extensive experiments on real-world and synthetic datasets to evaluate the effectiveness of GTCAlign. The experimental results show that GTCAlign outperforms state-of-the-art graph alignment approaches.
ANCIENT PRACTICE TO MODERN TECHNOLOGY: RIVERBANK FILTRATION (RBF) FOR DRINKING WATER

ABSTRACT This study focuses on applying riverbank filtration (RBF) in Malaysia, aiming to address water quality challenges caused by river water pollution, particularly the presence of harmful pathogens that can lead to waterborne diseases. Malaysia heavily relies on surface water as its primary source of potable water, making it crucial to explore practical and sustainable technologies for water treatment. Riverbank filtration is a promising low-cost, environmentally friendly technology that has shown significant potential in reducing the concentration of harmful bacteria and contaminants in water supplies. However, research into RBF's effectiveness in Malaysia has been limited, warranting further investigation and understanding of its application. The study emphasizes the importance of protecting surface water from contamination and conducting risk assessments of microorganisms in groundwater, particularly in the context of RBF implementation. A thorough understanding of the movement and retention of pathogenic bacteria in the aquifer is vital for optimizing RBF's efficiency and ensuring drinking water safety. Moreover, the study highlights the significance of considering soil properties and characteristics to determine suitable sites for RBF application. By providing valuable insights into RBF's potential benefits and capacity to address water quality issues, this research contributes to the advancement of sustainable water resource management in Malaysia, offering a promising solution for safeguarding public health and water security in the country. KEYWORDS: Riverbank filtration (RBF), drinking water, pathogenic pollutants
Flood Disaster Response Cadre Training in Keliling Benteng Ulu Village, Martapura Barat Sub-District

A flood is an event where land is submerged due to an increase in water volume. The flood that occurred in RT.05 Keliling Benteng Ulu Village was the worst compared to other RTs, because they live on the edge of the Martapura river and swamp environment. This service activity educates cadres to fulfill basic needs (clean water and sanitation needs, food clothing, temporary shelter, and health services) and provides leaflets/booklets on the stages of flood disaster management in fulfilling the basic needs of the community. The results of the pre and post test evaluation, cadres' knowledge has increased ≥ 80% regarding flood disaster response and about fulfilling the basic needs of flood disaster management independently (making freshwater fish jerky and instant ginger).
ONLINE TEACHING METHOD OF BASIC ENGLISH STRUCTURE VIA MOODLE SYSTEM

This research focuses on teaching methods used by three teachers of Basic English Structure. This study aims to answering the following research questions: (i) What are the methods of teachers in teaching Basic English Structure using Moodle system; (ii) How do the methods facilitate the teachers to teaching Basic English Structure using the Moodle. This study adopts qualitative research with descriptive approach. Three teachers and three students are interviewed whereas their working on online teaching is observed and then other supporting data are documented. The results of this study identifies that (1) teachers deliver material by three methods intensively (The most frequently-used method is Video Based Learning in speech delivery and Assignment Method for learning evaluation); (2) The role of the teaching methods involves ease of accessing material, long-term storage, time efficiency, and ease of presence list.
Potential and challenges of applying artificial intelligence (AI) in geosciences to the search for a high-level waste repository in Germany

Abstract. In a science-based site selection procedure (StandAV), the Federal Republic of Germany is searching for a site with the best possible safety for a repository for high-level waste (HLW) over a period of 1 million years. This is a challenging task, given the size of Germany, its geological variability and the verification period required to ensure the best possible long-term safety. In addition, an enormous amount of heterogeneous geodata has to be processed. The application of artificial-intelligence-based methods in geosciences has great potential for dealing with large heterogeneous data sets. In the German Federal Office for the Safety of Nuclear Waste Management (BASE)-funded project “Application of artificial intelligence (AI) in the site selection process for a deep geological repository (FKZ 4721E03210)”, an interdisciplinary assessment tool is being developed to evaluate the applicability of AI methods in the geosciences. This contribution focuses on the potential and challenges of applying AI in geosciences with respect to key geological activities in the StandAV. Limitations that may arise from the use of AI with respect to key activities in the StandAV are highlighted. The necessary conditions for its future applicability are proposed. The results show that AI methods offer clear advantages over conventional methods for data management and handling large geological datasets and for modelling complex long-term and coupled geological processes. However, AI methods are generally only transferable to the geoscientific questions of the StandAV with methodological and subject-specific adaptations. Furthermore, the use of AI requires sufficient data, both in terms of quality and quantity. The study also shows that AI should only be used in a supportive way to address geological issues in key activities and should not have any decision-making power when used in StandAV. For example, AI can carry the risk of data and developer bias, which in turn can have serious consequences for the correct interpretation of results. High demands must be placed on the traceability of the AI methods used. AI methods that do not meet the transparency requirements of the StandAV carry a significant risk of jeopardising public confidence in the participation process. This could increase the general mistrust and scepticism towards AI in the public perception (Krob et al., 2023). It is strongly recommended that all methods should be evaluated and validated iteratively and that the results should be made available to the public when applied to the key activities of the StandAV.
Earthquake Forecasting Using Optimized Levenberg–Marquardt Back-Propagation Neural Network

In this study, an effective earthquake forecasting model is introduced using a hybrid metaheuristic machine learning (ML) algorithm with CUDA-enabled parallel processing. To increase the performance and accuracy of the model, a novel hybrid ML model is developed that utilizes parallel processing. The model consists of a Chaotic Chimp based African Vulture Optimization Algorithm (CCAVO) for feature selection and a optimized Levenberg-Marquardt Back-Propagation Neural Network (OLMt-BPNN) for prediction. The proposed model follows a four-step process: preprocessing the raw data to identify seismic indications, extracting features from the preprocessed data, using optimized ML algorithms to forecast the earthquake and its expected time, epicenter, and magnitude, and implementing the model using the Python platform. The model’s performance is evaluated using various criteria, including accuracy, precision, recall, F-measure, specificity, false negative ratio, false positive ratio, negative prediction value, Matthew’s correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error. The proposed model achieved an accuracy of 98%, which is higher than the accuracy of existing earthquake prediction methods.
Grouping Building Material Sales for Management Optimization with the K-Means Algorithm

Sales transactions at the Pedi Building Store are recorded in the daily logbook provided by the store, so that it cannot be immediately known which types of products are selling well or which are not selling well. To overcome this problem, an analysis of sales data is carried out using the K-Means Algorithm. The K-Means algorithm is an algorithm that requires as many input parameters and divides a set of objects into clusters so that the level of similarity between members in one cluster is high, while the level of similarity with members in other clusters is very low. This study aims to determine the types of products that sell well and don't sell well. The data used in this analysis are 100 data samples with 2 clusters formed, namely unsold goods (Cluster 1) and salable goods (Cluster 2). With the analysis of the K-Means Algorithm, it produces 4 data items that are not selling well (Cluster 1), while 96 data items that sell well (Cluster 2). With the results of the analysis of the K-Means Algorithm, a sales application was developed for the Pedi Building Store which is managed by the analysis of the K-Means Algorithm. The application makes it easy for sellers to manage data to form sales processes of salable and less salable goods
Development of Plumbing Work Practice Learning Videos In the Department of Civil Engineering Education and Planning UNY

&lt;em&gt;This research contributes to (1) developing a learning video of Plumbing and Sanitation Practice Practice, (2) knowing the feasibility of the Plumbing and Sanitation Practice Practice video learning media, (3) knowing user perceptions regarding the Plumbing and Sanitation Practice Practice learning video applied to the Department of Education UNY Civil Engineering and Planning.&lt;strong&gt; &lt;/strong&gt;This research is an R&amp;amp;D research with a 4D model approach (Define, Design, Development, Disseminate). Data collection techniques are based on research subjects in making video products for Plumbing practice learning videos, namely experts related to plumbing materials, media experts. The instrument used uses a Likert scale to measure the feasibility level of the product being developed. The results obtained are (1) the define stage, namely analyzing the initial conditions, analyzing the character of students, analyzing concepts and learning objectives, the design stage, namely designing a script for the delivery of information consisting of opening, content, closing, the development stage, namely the process of recording making a video learning until the completion of editing the resulting video and then testing the eligibility validation of the product by experts, the disseminate stage, namely the limited dissemination of the product produced in this case the learning video of plumbing work practices to samples to assess and find out the level of feasibility and usefulness of the product; (2) the level of product feasibility based on the material content made is worth 4.58 belonging to the "very feasible" category, while the assessment based on the media made is worth 4.91 belonging to the "very feasible" category; (3) The results of the disseminate that has been carried out obtain the value of the user's perception of the product which is included in the very high category, the percentage of product feasibility is 91.52% which is included in the "Very Eligible" category.&lt;/em&gt;
FUTURISTIC HEADBAND: MIND READING CONCEPT USING AI

Drawing encouragement from psychology, computer vision and machine learning, the team in the Computer Laboratory at the University of Cambridge has developed mind-reading machines - computers that contrivance a computational model of mind-reading to conclude mental states of people from their facial signals.Prior knowledge of how specific mental states are expressed in the face with analysis of facial expressions and head gestures going on in real time.
Design of Memory System for Recursive Neural Network Hardware Accelerator

With the remarkable effectiveness of recurrent neural network (RNN) in speech recognition, machine translation and other fields, more and more scholars at home and abroad have begun to pay attention to the research of cyclic neural network acceleration. In recent years, due to the increase of the scale of the recurrent neural network, the software can speed up the network through the weight pruning network model compression technology. The acceleration of the cyclic neural network does not only stay in the aspect of software acceleration, but also in the aspect of hardware, the acceleration strategy includes the design of RNN accelerator based on GPU, FPGA and special ASIC circuit. The storage system almost determines the upper limit of the working efficiency of the accelerator. When the input data cannot be provided to the computing unit in time, the computing unit has to enter the idle state frequently, resulting in low working efficiency. Therefore, storage systems with continuous data feeds are very important for accelerators. This paper proposes a mapping mechanism of MVM operations on hardware operation units, and proposes a storage system with continuous data feeds.
Pemetaan Tingkat Kerawanan Bencana Banjir di Kecamatan Hantakan Kabupaten Hulu Sungai Tengah

Hantakan Subdistrict is one of the sub-districts in Hulu Sungai Tengah Regency that experienced flash floods in 2021. The flood event repeats it self every rainy season and result in floofing. The purpose of this study is to analyze the level of flood vulnerability in Hantakan District, Hulu Sungai Tengah Regency. The study was carried out in Hantakan Subdistrict as a population and using a presumption map modeling flood vulnerability as a sample. The analysis method used is by scoring on each flood vulnerability parameter and testing the accuracy of flood vulnerability presumption modeling data for making flood vulnerability maps. With the flood vulnerability map, people can fin out places that are prone to flooding and can be alert if there is a flood
Power of Public Investment in Curated Big Health Data

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