Reviewerly/exHarmony huggingface dataset:
CParts Platform Analisis Harga Komponen Komputer pada Marketplace

Amidst the fluctuations in computercomponent prices brought about by the Covid-19pandemic, a comprehensive analysis platform hasbeen developed to address this challenge. Thisinnovative platform harnesses historical datasourced from prominent e-commerce platforms suchas Shopee, Blibli, and Tokopedia, presenting userswith insightful average price graphs categorized bycomponent series and types. The researchmethodology adopted follows a prototype approach,encompassing meticulous phases ranging fromneeds analysis, application design, prototyping,testing, evaluation, prototype refinement, through toimplementation and ongoing maintenance. Thesuccessful implementation of this system, powered bythe Python programming language, features robustfunctionalities including product search anddynamic graphical representations derived fromhistorical data. The data retrieval process, utilizingthe Scraping method, occurs at regular intervals ona weekly basis. Upon meticulous analysis ofhistorical data spanning from January to July 2022,a noteworthy trend emerged, highlighting thatShopee and Tokopedia consistently offer computercomponents at relatively more affordable pricescompared to Blibli. The conclusive findings of thisresearch underscore the platform's efficacy inproviding an essential tool for users navigating thecomplex landscape of computer component pricedynamics, particularly in the unprecedented contextof the ongoing pandemic. This platform not onlyfacilitates monitoring but also empowers users withvaluable insights crucial for informed purchasingdecisions based on stable and budget-friendly pricingstructures.
Exploring High-Order Features in Multimodal Recommendations using Graph Convolutional Neural Networks

Abstract Following the introduction of graph neural networks, researchers have discovered the potential of using these networks to extract interconnections between users and items, which are often challenging for traditional recommendation algorithms to capture. The use of graph neural networks in constructing recommendation algorithms has gradually become a current trend in recommendation algorithm research. Currently, most recommendation algorithms based on graph neural networks extract user and item features using ID information, neglecting the implicit user and item features present in other data such as textual reviews. Alternatively, some algorithms incorporate auxiliary information, but this often complicates network training. To address this, our paper proposes a recommendation algorithm based on a graph neural network that integrates traditional deep learning, combining different user and item features. We introduce an attention mechanism to capture higher-order characteristics between users and items as part of the user and item feature representation. Simultaneously, we employ a traditional deep learning network to extract user and item features from textual data. The final user and item feature representation is obtained by merging these two feature representations, leading to improved recommendations. Experimental results demonstrate that this method effectively integrates high-order features and general characteristics of users and items, enhancing the representation of user and item features and consequently improving recommendation performance.
Evaluation of EnMAP hyperspectral satellite data for epithermal alteration mapping at Cuprite-Goldfield, southwestern Nevada, USA

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission launched into orbit on April 1, 2022. The EnMAP records 246 hyperspectral channels in the 0.42-2.45 μm wavelength range, with a spatial resolution of 30 m. This wavelength range is useful for environmental and geological mapping. This study evaluates the application of the EnMAP data to the mapping of epithermal alteration systems at Cuprite and Goldfield in southwestern Nevada, USA. The Cuprite has served since 1980s as a test site of the U.S. National Aeronautics and Space Administration (NASA) for the evaluation of airborne and satellite remote sensing sensors. The surface reflectance Cuprite-Goldfield EnMAP scene analyzed in this study was recorded on April 9, 2023. The EnMAP data were analyzed using the spectral mixture analysis technique. The results obtained from the EnMAP moderate spatial resolution satellite hyperspectral data show accurate detection of the alteration zones in the Cuprite-Goldfield study area. The study indicates the potential of the EnMAP data for various geological and environmental applications.
Using Large Language Models to generate socio-economic datasets

Abstract We report the results of an experiment in which we use a Large Language Model (LLM) to generate a socio-economic dataset from unstructured online data. Specifically, we design a query to obtain the geolocation of the production facilities of a firm and loop this query over the set of top 2000 global firms. Such a dataset is not publicly available and is required to perform economic assessment of climate impacts. The LLM provided meaningful results for 75% of the firms, recovering a total of 35809 production sites. The rate of correct answers among those is of the order of 70%.
Object Recognition in AI

Object Recognition Using AI is a pioneering concept at the crossroads of artificial intelligence and computer vision, devoted to automating the identification and localization of objects within images and video streams. This paradigm harnesses machine learning algorithms to emulate human visual cognition, empowering systems to discern and precisely pinpoint diverse objects, transcending traditional image analysis. The essence of Object Recognition Using AI lies in its transformative impact across a multitude of applications. In retail, it revolutionizes inventory management by autonomously recognizing products on shelves. Within the security domain, it enhances surveillance systems, enabling real-time tracking of individuals and objects. In the automotive landscape, it empowers self-driving vehicles to interpret their surroundings, safeguarding passengers and pedestrians alike. Our research endeavors to advance the field of object recognition through the implementation of Mask R-CNN, a state-of-the-art deep learning architecture. We aim to achieve highly accurate object detection and segmentation by leveraging the power of convolutional neural networks. In pursuit of this, we conducted comprehensive experiments and evaluations. Our results showcase the effectiveness of Mask R-CNN in various real-world scenarios, with impressive precision and recall rates. In conclusion, this study contributes valuable insights and tools to the realm of computer vision, enabling enhanced object recognition applications. Keywords:Object Recognition, Mask R-CNN, Deep Learning, Convolutional Neural Networks, Computer Vision.
Nanomaterial Application for Healthy Environment

Nanotechnology is very effective at removing pollutants in the field of air, water, and wastewater. The techniques for treating pollution detection that have been created using nanotechnology include nanoadsorbents, nanofiltration, nanocatalysts, nanoparticles, and nanosensors. Nanoparticles can be used to synthesize several types of nanomaterials with a range of benefits. Nanomaterials applications have advanced significantly as a result of the disciplines of materials in explosive expansion. Novel nanomaterials can be designed and synthesized to provide improved performance in applications related to the environment. The intensified human activities are shaking the environment balance and human health by nourishing with large quantities of anthropogenic unsafe toxic elements which pollute water, atmosphere, and soil and subsequently intimidate public health of human beings. Therefore, nanostructured materials can be used to handle any environmental issue, such as extremes in temperature or high wear, severe corrosive conditions, and environmental pollution. In order to handle the most recent developments in nanomaterials and their applications, knowledge distribution is crucial.
Portal Masjid “Mosque Wanted” Solusi Pencarian Lokasi Masjid, Info Kajian &amp; Berita Seputar Masjid di Yogyakarta

The dissemination of information and news about mosques in the Yogyakarta area has predominantly relied on social media and the Internet. Most mosques, especially the larger ones, have established their own social media accounts to showcase their activities. However, the information shared is typically limited to text and graphics, such as WhatsApp broadcasts, Instagram posts, Facebook updates, Twitter tweets, or posters displayed within the mosque premises. This text and graphic-based approach restricts accessibility for individuals who are unable to attend mosque activities due to various reasons, including difficulties in navigating to the mosque. This research aims to develop a Web-based Geographical Information System (GIS) that provides a mosque search solution, study information, and news about mosques in Yogyakarta. The system utilizes a responsive web design to enhance the dissemination of information, facilitate navigation, and stimulate public interest in studying Islamic sciences in Yogyakarta. The outcomes of this study offer valuable support to mosque administrators in providing comprehensive information about the mosque, particularly for Islamic da'wah activities. By incorporating detailed information and location data into the system, the dissemination of da'wah information can be improved. Moreover, the system enables the community to easily locate nearby mosques that offer Islamic da'wah activities and access real-time information about these activities.
A SEMIOTIC ANALYSIS FOUND ON A POSTER IN KITKAT INSTAGRAM ACCOUNT

This study discusses the importance of effective communication in exchanging information and ideas between individuals through verbal and visual signs. A semiotic approach is used to analyze the meaning contained in the Instagram KitKat poster. The theory of Saussure (2011) and Barthes (1977) was used to analyze the verbal and visual signs on the posters. The aim of this analysis is to understand what verbal and visual signs are and to uncover the complex messages behind their surface appearances, highlighting how signs are used to convey each meaning. Three KitKat posters from the @kitkat account were reviewed, revealing a dynamic brand communication strategy. This analysis shows how verbal signs such as slogans and brand names relate to visual signs such as images and symbols to create subtle narratives about KitKat posters that imply the meaning of always enjoying KitKat during break time. This research increases the understanding of how brands use semiotics to build strong messages across various dimensions of communication.
Interpretable Mortality Prediction Model for ICU Patients with Pneumonia: Using Shapley Additive Explanation Method

Abstract Background Pneumonia, a leading cause of morbidity and mortality worldwide, often necessitates Intensive Care Unit (ICU) admission. Accurate prediction of pneumonia mortality is crucial for tailored prevention and treatment plans. However, existing mortality prediction models face limited adoption in clinical practice due to their lack of interpretability. Objective This study aimed to develop an interpretable model for predicting pneumonia mortality in ICUs. Leveraging the Shapley Additive Explanation (SHAP) method, we sought to elucidate the Extreme Gradient Boosting (XGBoost) model and identify prognostic factors for pneumonia. Methods Conducted as a retrospective cohort study, we utilized electronic health records from the eICU-CRD (2014–2015) for all adult pneumonia patients. The first 24 hours of each ICU admission records were considered, with 70% of the dataset allocated for model training and 30% for validation. The XGBoost model was employed, and performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHAP method provided insights into the XGBoost model. Results Among 10,962 pneumonia patients, in-hospital mortality was 16.33%. The XGBoost model demonstrated superior predictive performance (AUC: 0.732 ± 0.0065) compared to traditional scoring systems and other machine learning method, which achieved an improvement of 8% points. SHAP analysis identified Aspartate Aminotransferase (AST) as the most crucial predictor. Conclusions Interpretable predictive models enhance mortality risk assessment for pneumonia patients in the ICU, fostering transparency. AST emerged as the foremost predictor, followed by patient age, albumin, BMI et al. These insights, rooted in strong correlations with mortality, facilitate improved clinical decision-making and resource allocation.
Generative AI for Self-Healing Systems

In large-scale system production, the risk of component failures like hardware issues, software bugs, network disruptions, and memory errors is a concern. To mitigate this, human experts such as IT analysts, system engineers, and infrastructure architects use system monitoring to detect and respond to failures. This study aims to integrate generative AI technology into self-healing systems to enhance the operations of large-scale systems and facilitate automatic repairs. The focus is on optimizing system functionality and efficiency at scale while reducing reactive tasks that require human intervention. Our proposed solutions involve leveraging generative AI for anomaly detection, code generation, debugging and auto generative report within self-healing systems. Furthermore, the automated response ansible scripts, generated by generative AI such as GPT-4 to create a comprehensive and efficient python code completion solution that enhances backend system functionality and repairs failing components.
A Character-Level Restoration of Sukhothai Inscriptions Using The Masked Language Model

The stone inscription is one type of written literature that recorded the history story and the manifestation of cultural identity in that era through a character engraving method on the stone with sharp metal material for each character until a sentence formed. To convey the message for the readers to understand the meaning. Therefore, the completeness of that sentence is of great importance natural language processing tasks. In particular, when transcription stone inscriptions, it is found that inscriptions’ parts cannot interpret. As a result of the period that elapsed, those inscriptions may have suffered deterioration from various causes, resulting in scratches over the text or faded markings, destroyed from natural disasters that making it impossible to analyze which specific characters were damaged. To address enhance the completeness of the missing sentence, this research employs a method of generating predictive models for the missing characters from the text. It utilizes the technique of incorporating a masked language model to assist in processing the experimental data, utilizing 3 types of multilingual pre-trained models as following models are used: (1) XLM-RoBERTa, (2) Bert-base-multilingual-cased, and (3) DistilBERT-base-multilingual-cased. In each training round, random characters are masked using the token “<mask>“ or “[MASK]” to prompt the model to predict the missing words at the masked positions. From the experimental results, it was found that the accuracy of prediction from the three types of pre-trained models is as follows: (1) 42, (2) 53, and (3) 50 percent respectively.
MENINGKATKAN KEMAMPUAN SISWA DALAM MENULIS TEKS DESKRIPSI DENGAN PENERAPAN MODEL PEMBELAJARAN TEAM GROUP TOURNAMENT

The use of Team Game Tournament learning model in the classroom is still rarely used in classes in Indonesia. However, this Classroom Action Research shows an increase in each cycle that occurs when conducting research. The purpose of this study was to determine the impact of using the TGT (Team Group Tournament) learning model to improve students' writing learning outcomes in English lessons.
USING JOURNALING PROMPTS ON EMMO APPLICATION TO ENHANCE STUDENTS’ DESCRIPTIVE TEXT WRITING

The objective of this study is to know the influence using journaling on EMMO application to enhance students’ descriptive text writing. This research was conducted in SMP Arraisiyah Pamulang, South Tangerang. The population of this research was 8th grade students and the sample was grade 8.2 consist of 37 students. The method of this study was quantitative method using pre-experimental design. To collect the data, the researcher used pre-test and post-test as an instrument that given to the students. The result score of the data concluded that the total score of students in pre-test were 2.352 with the average score was 63.57. The lowest score in pre-test were 36 and the highest score is 85. The total score of students in post-test were 2.963 with the average 80.08. The lowest score of post-test were 60 and the highest score were 95. The result showed that there was significant different between students’ pre-test and post-test scores. Also from the results of the t-test, the tcalculation obtained is higher than the ttable (17.198 &gt; 2.028). It means that there is a significant change and effect in students' pre-test to students' post-test scores and using journaling prompts on the EMMO application can enhance students' descriptive text writing.
30 Communicating to Mitigate Behavioral Cyber Risks: The Case of Employee Vulnerability

Cyber-attacks are an increasingly significant safety and risk issue for companies of any size and many organizations have already been attacked. Preparing for such an event is imperative for every organization - besides up-to-date technological infrastructure, employee awareness, and alert online behavior are critical components of any defense mechanism. Although the importance of and urgency for employee preparedness and the role of cyber-risk communication in that process have been stressed in academic papers and business practice, there remains a dire need for empirical research. Thus, we investigate the hidden mental models driving employee behavior by applying structured brainstorming to distill common themes and define communication goals. We then translate these insights into instructional risk messages and actions. Our study contributes to the field of internal risk and safety communication in numerous ways: First, by focusing on the employee risk perceptions of cyber risk we unpack the internalization triggering the effect of learning and behavioral change in a pre-crisis phase. Second, we develop risk communication messages targeting these perceptions and internalization goals. Lastly, we develop a diagnostic questionnaire to evaluate the cyber-risk readiness and maturity of the employees in order to customize the risk communication effort.
11 Embracing Dialogue While Cultivating Convergence: Organizational Challenges in Responsibly Communicating Risk

Organizations have an essential duty to manage and communicate about risks, which includes engaging publics in a continuous exchange to enable understanding and sound decision-making while fostering dialogue and permitting different perspectives to interact. Because risk issues are perceived and understood differently by individuals, this multivocal approach may cultivate inaccurate or inconsistent messaging, prohibiting individuals from receiving crucial information or adopting the recommended protective behaviors. The message convergence framework (MCF) provides an approach for reconciling diverse risk perceptions and highlights how individuals engage in decision-making based on arguments and information from various sources on the same topic. Using the MCF, this chapter examines the role of corporate social responsibility (CSR) by exploring the challenges an organization may face as it navigates the tension of embracing its obligation to facilitate risk-related dialogue while effectively and ethically fostering message convergence to guide risk message interpretation and decision-making.
A Statistical Survey on the Socioeconomic and Demographic Livelihood of Brick Kiln Workers: A Case Study of Bahawalpur District, Punjab, Pakistan

The working population at the brick kilns is considered as the people living under poverty line. This study aims to investigate the socioeconomic and demographic characteristics and housing and living conditions of the brick kiln workers in Bahawalpur district. Total 20 kilns were visited in 5 tehsils of the district as sample sites. A questionnaire was designed and a field survey was conducted by using simple random sampling techniques to collect the data of 400 workers from 20 brick kilns. The quantitative analysis was performed in Statistical Package for Social Sciences (SPSS) v. 21 by applying descriptive and inferential statistics. Findings showed that majority of the workers and heads of working households were male (85.75% and 97.5% in rural and urban kilns respectively) and Saraiki speaking (70%) belonged to Muhajir, Ranagr and Khokhar castes. The early age (14-18 years) marriages were common among the workers especially among the females. Dependency ratio of the kiln workers was 37.6% including mostly the children and aged. Dominant share of the kiln workers (70-80%) were illiterate and very few were literate from primary to intermediate level. The workers’ income varied between PKR 11,000 to 15,000 which was lower than the average wage (PKR 25,000) as per the government rules. Brick moulders and soil suppliers were the main occupants at kilns. Most of the workers were resided in their own houses (1 or 2 rooms) made with kacha (mud made) material. Although various facilities were available to the workers at brick kilns but they have no proper access to safe drinking water (80%), accident risk facility (0%) and first aid (0%) crucial for their health. The chi-square results also verify the miserable social and economic life of the kiln workers. Thus, these facts demonstrated that kiln workers led a meagre life style. This work will serve as a reminder to authorities, planners and Non-Governmental Organizations (NGOs) to take action to improve the living conditions of kiln workers. Lastly, few suggestions were proposed to uplift the lives of the kiln workers.
Preface WI-IAT 2023

The 2023 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT'23) serves as a premier international forum that brings together researchers and practitioners from diverse fields to present original research and to exchange innovative and practical experiences on web intelligence and intelligent agent technology.
Absorbing Markov Chain Saliency Driven Active Contour Model for Digital Image Boundary Extraction

The image segmentation approach using the active contour model (ACM) has achieved notable success in digital image analysis for extracting image boundaries. The region-based ACM can be divided into two classes: global segmentation and selective segmentation. Global segmentation, in which all desired boundaries of targeted objects are extracted from an input image, is preferable to the selective model due to its high utility, particularly in shape analysis. However, when segmenting a digital image with inhomogeneous intensity, the global ACM appears to produce unsatisfactory results. Thus, this research provides a new global ACM for segmenting digital images with an inhomogeneous intensity incorporating an absorption Markov chain (AMC) saliency image map and local image fitting principles. In addition, the Euler-Lagrange (EL) Partial Differential equation for solving the proposed model was provided. Thirty sets of digital images were used to validate the model. The accuracy of the proposed model, as indicated by Dice and Jaccard values, is approximately 3.81% and 10.63% higher, respectively, than that of the competing model, as determined by numerical analysis. In addition, the segmentation process taken by the proposed model is faster than the existing model. The proposed model has high potential to be extended into colour and multiphase formulations in future research.