Reviewerly/exHarmony huggingface dataset:
Analysis of School Library Utilization in Developing a Literacy Culture for Elementary School Students at SDN Bendelan 1

Authors:
raw_author_name: Husnul Khotimah
raw_affiliation_string: Universitas Terbuka
raw_author_name: Yasir Riady
raw_affiliation_string: Universita Terbuka
raw_author_name: Eka Evriza
raw_affiliation_string: Universitas Terbuka

Abstract: Background: The cultural climate of literacy for elementary school students is intricately linked to the crucial role played by school libraries. Recognizing the significance of this connection, the research delves into understanding how school libraries contribute to the development of a robust literacy culture among elementary school students.&#x0D; Purpose: The primary aim of this research is to analyze the role of school libraries in fostering a literacy culture among elementary school students. Employing a descriptive qualitative research method, the study seeks to investigate the extent of library utilization, focusing on aspects such as book variety, literacy programs, and strategies designed to captivate students' interest in reading. Additionally, the research aims to explore collaborative efforts between librarians and teachers in enhancing students' literacy culture.&#x0D; Design and methods: The research employs a descriptive qualitative research method, utilizing interviews, observation, and documentation to collect data from school principals, teachers, and students. The data analysis focuses on evaluating the extent of library usage, the adequacy of book collections, and the effectiveness of literacy programs and strategies. The design also incorporates an examination of collaborative initiatives between librarians and teachers to attract students' interest in developing their literacy culture.&#x0D; Results: The findings of the research highlight the current state of the library collection at SDN Bendelan 1, indicating its adequacy but emphasizing the need for improvement in alignment with the Merdeka Curriculum. The results underscore the importance of ongoing development to ensure the relevance of library materials to the curriculum, ultimately meeting the evolving learning needs of elementary school students. This research provides valuable insights for educators, librarians, and policymakers to strategically enhance literacy initiatives in elementary schools through focused library collection development.
Library Promotion Strategy in the Digital Era

Authors:
raw_author_name: Yasir Riady
raw_affiliation_string: Universita Terbuka

Abstract: Background: The library is an institution that has an essential role in society. The efforts made by the library to carry out this role ensure that the public knows about the library's existence. The public can only use the library if they know its role. Thus, so that libraries can continue to carry out their role, libraries need to develop library promotion strategies. Promotion as a marketing activity carried out by libraries is essential so that the public knows about the products that the library can use. Society also uses digital technology to meet information needs in the digital era. However, society's tendency to use digital technology does not reduce the role of libraries as information institutions to meet society's information needs. The development of digital technology can be utilized to increase the existence of libraries through library promotion strategies.&#x0D; Purpose: This article reviews various promotional strategies libraries can implement using digital technology.&#x0D; Design and methods: The research method used is qualitative, with data collection techniques through document study and library observation via websites.&#x0D; Results: Promotional strategies that libraries can carry out are publications, advertising, sales promotions, personal sales, and digital marketing. Obstacles encountered in carrying out promotional activities are a consideration for libraries in determining promotional strategies that are appropriate to the conditions of the library.
SICURO+: A web platform to raise awareness on seismic risk in Italy

Authors:
raw_author_name: Antonella Di Meo
raw_affiliation_string: Eucentre, European Centre for training and research in Earthquake Engineering, Italy
raw_author_name: Barbara Borzi
raw_affiliation_string: Eucentre, European Centre for training and research in Earthquake Engineering, Italy

Abstract: Italy is a country with high seismic risk. In the past many catastrophic earthquakes have occurred causing severe damage to buildings and infrastructures, serious economic impacts, and the loss of thousands of human lives. To mitigate the impact of seismic risk, as well as of other risks, many efforts have been made, implementing preventive structural measures and acting to raise awareness in the community. In Italy, this need was strengthened with the emanation of the new Civil Protection Code (D.Lgs January 2, 2018 n.1), with the purpose of encouraging the participation of citizens and spreading risk knowledge and the culture of civil protection, thus contributing to “increase the resilience of communities” (art. 31). As part of the agreement between the Italian Civil Protection Department (ICPD) and EUCENTRE (European Centre for training and research in Earthquake Engineering), an interactive web-tool was developed to allow citizens to display the most updated seismic risk maps. These maps were produced in 2018 by the ICPD, with the relevant support of the scientific community operating in the field of seismic risk, in particular of ReLUIS (The Laboratories University Network of seismic engineering) and EUCENTRE. The web-tool, named SICURO+ (Information System for Risk Communication, www.sicuropiu.it), was created to raise awareness in citizens on seismic risk in the place where they live, work, or go on holiday. Communication strategies and behavioural techniques were designed and tested experimentally with the support of behavioural scientists, as part of an agreement between the National School of Administration and the Prime Minister's Office.
Sentiment Analysis on Roman Urdu Students’ Feedback Using Enhanced Word Embedding Technique

Authors:
raw_author_name: Sharin Hazlin Huspi Huspi
raw_affiliation_string: Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.
raw_author_name: Zafar Ali
raw_affiliation_string: Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia.&Department of Computer Science, Sukkur IBA University, Sukkur 65200, Pakistan.

Abstract: Students’ feedback is crucial for educational institutions to assess the performance of their teachers, most opinions are expressed in their native language, especially for people in south Asian regions. In Pakistan, people use Roman Urdu to express their reviews, and this applied in the education domain where students used Roman Urdu to express their feedback. It is very time-consuming and labor-intensive process to handle qualitative opinions manually. Additionally, it can be difficult to determine sentence semantics in a text that is written in a colloquial style like Roman Urdu. This study proposes an enhanced word embedding technique and investigates the neural word Embedding (Word2Vec and Glove) to determine which performs better for Roman Urdu Sentiment analysis. Our suggested model employs the BiLSTM network to maintain the context in both directions and eventually, results for ternary classification are obtained by using the final softmax output layer. A manually labeled data set was used to evaluate the model, data is collected from the HEIs of Pakistan. Model was empirically evaluated on two datasets of Roman Urdu, the newly developed student’s feedback dataset and RUSA-19 publically available data set of Roman Urdu. Our model performs effectively using the word embedding and BiLSTM layer. The proposed model is compared with the baseline models of CNN, RNN, GRU and classic LSTM. The experimental findings demonstrate the proposed model's efficacy with an F1score of 90%.
The Effect Of Optimizers On The Generalizability Additive Neural Attention For Customer Support Twitter Dataset In Chatbot Application

Authors:
raw_author_name: Sinarwati Mohamad Suhaili
raw_affiliation_string: Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia & Centre of Pre-University, Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia.
raw_author_name: Naomie Salim
raw_affiliation_string: Faculty of Computing, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia.
raw_author_name: Mohamad Nazim Jambli
raw_affiliation_string: Faculty of Computer Science and Information Technology, Universiti Malaysia Sarawak, Kota Samarahan, Sarawak,  Malaysia.

Abstract: When optimizing the performance of neural network-based chatbots, determining the optimizer is one of the most important aspects. Optimizers primarily control the adjustment of model parameters such as weight and bias to minimize a loss function during training. Adaptive optimizers such as ADAM have become a standard choice and are widely used for their invariant parameter updates' magnitudes concerning gradient scale variations, but often pose generalization problems. Alternatively, Stochastic Gradient Descent (SGD) with Momentum and the extension of ADAM, the ADAMW, offers several advantages. This study aims to compare and examine the effects of these optimizers on the chatbot CST dataset. The effectiveness of each optimizer is evaluated based on its sparse-categorical loss during training and BLEU in the inference phase, utilizing a neural generative attention-based additive scoring function. Despite memory constraints that limited ADAMW to ten epochs, this optimizer showed promising results compared to configurations using early stopping techniques. SGD provided higher BLEU scores for generalization but was very time-consuming. The results highlight the importance of finding a balance between optimization performance and computational efficiency, positioning ADAMW as a promising alternative when training efficiency and generalization are primary concerns.
Updated exploration of the Tor network: advertising, availability and protocols of onion services

Authors:
raw_author_name: Alejandro Buitrago López
raw_affiliation_string: Department of Information and Communications Engineering, University of Murcia, 30100, Murcia, Spain
raw_author_name: Javier Pastor-Galindo
raw_affiliation_string: Department of Information and Communications Engineering, University of Murcia, 30100, Murcia, Spain
raw_author_name: Félix Gómez Mármol
raw_affiliation_string: Department of Information and Communications Engineering, University of Murcia, 30100, Murcia, Spain

Abstract: Abstract The Tor network is known for its opaque characteristics and involvement in illicit activities, motivating to shed light on the exposure, lifetime, and functionalities of onion services. This study focuses on the appearance of Tor links in online advertising and monitors the connectivity status and protocols of the collected onion domains through the Tor network over 105 days. Out of 54,602 onion addresses gathered, it was found that 38% of Tor links were advertised only once, 43% between two and five times, and 19% more than five times. Furthermore, 50% of the addresses were exclusively advertised on the surface web, 6% on the dark web, and 44% on both portions. The temporal analysis revealed that 67% of the addresses were predominantly active, 7% were intermittent, and 26% were mostly inactive. The study examined fifteen protocols used by onion services, concluding that 94% employed a single protocol, while 6% utilized between two and eight protocols. Among active sites, HTTP was present in 99.75% of cases, followed by SSH (4.95%) and HTTPS (0.64%). Additionally, onion services without web services often deploy cryptocurrency or instant messaging servers. This study offers a comprehensive and current understanding of the dark web, surpassing previous research in its scope.
Visitor Experience Map and NFC-Based Scoring for Data-Driven Exhibition Enhancement

Authors:
raw_author_name: Siti Fatimah Abdul Razak
raw_affiliation_string: Faculty of Information Science and Technology, Multimedia University, Jln Ayer Keroh Lama, Melaka, 75450,
raw_author_name: Sumendra Yogarayan
raw_affiliation_string: Faculty of Information Science and Technology, Multimedia University, Jln Ayer Keroh Lama, Melaka, 75450,
raw_author_name: Md Shohel Sayeed
raw_affiliation_string: Faculty of Information Science and Technology, Multimedia University, Jln Ayer Keroh Lama, Melaka, 75450,

Abstract: In the current exhibition industry, it is crucial for organizers and exhibitors to comprehend and enhance visitor experiences. The objective of this study is to improve the exhibition setting by utilizing Near Field Communication (NFC) technology to capture, monitor, and analyze visitor behavior, engagement, and satisfaction. The main approach entails combining NFC technology with the Visitor Experience Map to fully understand the complexities of the visitor experience. NFC-enabled smartphones facilitate seamless interaction with the system, as users simply need to bring their smartphones close to NFC tags. This enables data collection and triggers the activation of a visitor scoring form for ratings and feedback. The study's findings indicate a mean system usability score of 81.4, which demonstrates successful implementation and great usability. This confirms the effective and easy-to-use nature of the strategy, guaranteeing that visitors can effortlessly provide their ratings and feedback. The originality and enhancement reside in the successful integration of NFC technology with the Visitor Experience Map, providing a strong and user-focused approach for organizers and exhibitors to enhance the exhibition experience. This study creates a favorable situation for both visitors and stakeholders, demonstrating the potential of technological advancements to greatly influence the exhibition industry. Doi: 10.28991/ESJ-2024-08-01-015 Full Text: PDF
Optimized RMDL with Transfer Learning for Sentiment Classification in the MapReduce Framework

Authors:
raw_author_name: Konda Adilakshmi
raw_affiliation_string: 1) Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India 2) Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Bachupally, Hyderabad - 500090, India
raw_author_name: Malladi Srinivas
raw_affiliation_string: Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
raw_author_name: Anuradha Kodali
raw_affiliation_string: Gokaraju Rangaraju Institute of Engineering and Technology (GRIET), Bachupally, Hyderabad - 500090, India

Abstract: A core task in sentiment analysis is sentiment categorization, and it is crucial to understand user feelings based on their remarks in social media or product evaluations. Due to ambiguous phrases, refusal words, and other factors, categorizing sentiment presents several challenging issues. The objective of this research is to develop a hybrid optimization-based deep learning model and MapReduce framework-based sentiment categorization approach. The review document is taken from a dataset and used in this case with the MapReduce methodology. MapReduce is a software framework and programming model for analyzing massive volumes of data that consists of two phases, mapper and reducer. BERT tokenization and aspect term extraction are executed in the mapper phase, whereas sentiment analysis is performed in the reducer stage utilizing random multimodal deep learning (RMDL) with transfer learning and AlexNet and ResNet 50 as pre-trained models. In addition, the exponential coot political algorithm (ECPA) is offered as an optimization algorithm for weight optimization in RMDL. The ECPA is obtained by combining the exponential weighted moving average model (EWMA) with the coot algorithm, as well as a political optimizer (PO). The proposed ECPA_RMDL model has acquired 90.9% precision, 89.7% recall, and 89.9% f-measure.
LUBM4OBDA: Benchmarking OBDA Systems with Inference and Meta Knowledge

Authors:
raw_author_name: Julián Arenas-Guerrero
raw_affiliation_string: Universidad Politécnica de Madrid, Spain
raw_author_name: María S. Pérez
raw_affiliation_string: Universidad Politécnica de Madrid, Spain
raw_author_name: Oscar Corcho
raw_affiliation_string: Universidad Politécnica de Madrid, Spain

Abstract: Ontology-based data access focuses on enabling query evaluation over heterogeneous relational databases according to the model represented by an ontology. The relationships between the ontology and the data sources are commonly defined with declarative mappings, which are used by systems to perform SPARQL-to-SQL query translation or to generate RDF dumps from the relational databases. Besides the potential homogenization of data because of using an ontology, some additional advantages of this paradigm are that it may allow applying reasoning thanks to the ontology, as well as querying for meta knowledge, which describes statements with information such as provenance or certainty. In this paper, (i) we adapt a widely used RDF graph store benchmark, namely LUBM, for ontology-based data access, (ii) extend the benchmark for the evaluation of queries that exploit meta knowledge, and (iii) apply it for performance evaluation of state-of-the-art declarative mapping systems. Our proposal, the LUBM4OBDA Benchmark, considers inference capabilities that are not covered by previous ontology-based data access benchmarks, and it is the first one for the evaluation of meta knowledge and the RDF-star data model. The experimental evaluation shows that current virtualization systems cannot handle some advanced inference tasks, and that optimizations are needed to scale RDF-star materialization.
A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Authors:
raw_author_name: Naira Elazab
raw_affiliation_string: Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
raw_author_name: Wael A. Gab-Allah
raw_affiliation_string: Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
raw_author_name: Mohammed Elmogy
raw_affiliation_string: Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt

Abstract: Gliomas are primary brain tumors caused by glial cells. These cancers' classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.
Unsupervised Machine Learning for Bot Detection on Twitter: Generating and Selecting Features for Accurate Clustering

Authors:
raw_author_name: Raad Al-azawi
raw_affiliation_string: University of Babylon
raw_author_name: Safaa O. AL-mamory
raw_affiliation_string: College of Business Informatics, University of Information Technology and Communications, Bagdad, Iraq

Abstract: Twitter is a popular social media platform that is widely used by individuals and businesses. However, it is vulnerable to bot attacks, which can have negative effects on society. Supervised machine learning techniques can detect bots but require labeled data to differentiate between human and bot users. Twitter generates a significant amount of unlabeled data, which can be expensive to label. Unsupervised machine learning techniques, specifically clustering algorithms, are crucial for managing this data and reducing computational complexity. Effective feature selection is necessary for clustering, as some features are more important than others. This study aims to enhance feature reliability, introduce new features, and reduce them to improve bot identification accuracy using clustering algorithms. The study achieved an accuracy rate of 0.99 in four clustering algorithms, including agglomerative hierarchy, k-medoids, DBSCAN, and K-means. This was accomplished by minimizing dataset dimensions and selecting essential features. By employing unsupervised machine learning techniques, Twitter can detect and mitigate bot attacks more efficiently, which can positively impact society&#x0D;
Monitoring Kesehatan Warga Desa Sidodadi Ramunia Melalui Pemberdayaan Kader Posyandu Berbasis Cloud Computing

Authors:
raw_author_name: Anita Sindar Sinaga
raw_author_name: Erwin Panggabean
raw_author_name: Sri Mulyani

Abstract: Empowering village residents requires support from network infrastructure and devices, software, digital literacy and assistance. Posyandu plays a role in helping the Village Government (Pemdes) improve basic health and social services. Problems that arise are that Posyandu cadres. IT skills need to be trained so that reporting from the field can be directly connected to village reports. The solutions provided through PKM activities are increasing the participation of mothers and toddlers, the elderly in posyandu and increasing the IT skills of Posyandu cadres. Community empowerment activities produce digital applications. during eight months service activity resulted in a health monitoring application system aimed at facilitating monitoring of posyandu activities in Ramunia Village.