Reviewerly/exHarmony huggingface dataset:
Dialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue Context

Authors:
raw_author_name: Xiao Wei
raw_author_name: Yidian Lin

Abstract: International Journal of Software Engineering and Knowledge EngineeringAccepted Papers No AccessDialogue Generation Model with Hierarchical Encoding and Semantic Segmentation of Dialogue ContextXiao Wei, Yidian Lin, and Qitao HuXiao Wei, Yidian Lin Search for more papers by this author , and Qitao Huhttps://orcid.org/0009-0009-8190-9796 Search for more papers by this author https://doi.org/10.1142/S0218194024400011Cited by:0 (Source: Crossref) Next AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles in C++ Programming! FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 30 July 2023 Accepted 5 February 2024 PDF download
Enhancing Multi-Layer Perceptron Performance with K-Means Clustering

Authors:
raw_author_name: Doughlas Pardede
raw_affiliation_string: Universitas Deli Sumatera
raw_author_name: Sugeng Riyadi
raw_affiliation_string: Universitas Deli Sumatera

Abstract: Machine learning plays a crucial role in identifying patterns within data, with classification being a prominent application. This study investigates the use of Multilayer Perceptron (MLP) classification models and explores preprocessing techniques, particularly K-Means clustering, to enhance model performance. Overfitting, a common challenge in MLP models, is addressed through the application of K-Means clustering to streamline data preparation and improve classification accuracy. The study begins with an overview of overfitting in MLP models, highlighting the significance of mitigating this issue. Various techniques for addressing overfitting are reviewed, including regularization, dropout, early stopping, data augmentation, and ensemble methods. Additionally, the complementary role of K-Means clustering in enhancing model performance is emphasized. Preprocessing using K-Means clustering aims to reduce data complexity and prevent overfitting in MLP models. Three datasets - Iris, Wine, and Breast Cancer Wisconsin - are employed to evaluate the performance of K-Means as a preprocessing technique. Results from cross-validation demonstrate significant improvements in accuracy, precision, recall, and F1 scores when employing K-Means clustering compared to models without preprocessing. The findings highlight the efficacy of K-Means clustering in enhancing the discriminative power of MLP classification models by organizing data into clusters based on similarity. These results have practical implications, underlining the importance of appropriate preprocessing techniques in improving classification performance. Future research could explore additional preprocessing methods and their impact on classification accuracy across diverse datasets, advancing the field of machine learning and its applications
Communities of Quantum Technologies: Stakeholder Identification, Legitimation, and Interaction

Authors:
raw_author_name: Steven Umbrello
raw_author_name: Zeki C. Seskir
raw_author_name: Pieter E. Vermaas

Abstract: International Journal of Quantum InformationAccepted Papers No AccessCommunities of Quantum Technologies: Stakeholder Identification, Legitimation, and InteractionSteven Umbrello, Zeki C. Seskir, and Pieter E. VermaasSteven Umbrello, Zeki C. Seskir Search for more papers by this author , and Pieter E. Vermaashttps://orcid.org/0000-0002-8175-9179 Search for more papers by this author https://doi.org/10.1142/S0219749924500126Cited by:0 (Source: Crossref) PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out Annual Physics Catalogue 2019 and recommend us to your library! FiguresReferencesRelatedDetails Recommended Accepted Papers Metrics History Received 25 October 2023 Accepted 8 February 2024 PDF download
Research on Cloud Computing

Authors:
raw_author_name: Dhyan Patel

Abstract: Abstract: Cloud computing represents a pivotal advancement in the IT sector, offering efficient management and distribution of vast amounts of data and resources across the internet. Essentially, it enables accessing IT infrastructure over a network without local installations. Businesses benefit from the flexibility to adjust resource levels according to operational demands, leading to reduced infrastructure costs. Additionally, cloud computing facilitates faster application testing, streamlined management, and adaptable resource allocation. Its widespread application across various contexts underscores its significance in daily life. This paper comprehensively examines cloud computing, encompassing its architecture, characteristics, varieties, service models, advantages, and challenges.
The Backpack Quotient Filter: a dynamic and space-efficient data structure for querying<i>k</i>-mers with abundance.

Authors:
raw_author_name: Bertrand Le Gal
raw_affiliation_string: Univ. Rennes, Inria, CNRS, IRISA - Taran team, ENSSAT engineering school, Lannion, France
raw_author_name: Pierre Peterlongo
raw_affiliation_string: Univ. Rennes, Inria, CNRS, IRISA - UMR 6074, Rennes, F-35000 France

Abstract: Genomic data sequencing has become indispensable for elucidating the complexities of biological systems. As databases storing genomic information, such as the European Nucleotide Archive, continue to grow exponentially, efficient solutions for data manipulation are imperative. One fundamental operation that remains challenging is querying these databases to determine the presence or absence of specific sequences and their abundance within datasets. This paper introduces a novel data structure indexing k -mers (substrings of length k ), the Backpack Quotient Filter (BQF), which serves as an alternative to the Counting Quotient Filter (CQF). The BQF offers enhanced space efficiency compared to the CQF while retaining key properties, including abundance information and dynamicity, with a negligible false positive rate, below 10 -5 %. The approach involves a redefinition of how abundance information is handled within the structure, alongside with an independent strategy for space efficiency. We show that the BQF uses 4x less space than the CQF on some of the most complex data to index: sea-water metagenomics sequences. Furthermore, we show that space efficiency increases as the amount of data to be indexed increases, which is in line with the original objective of scaling to ever-larger datasets.
WEBINARS &amp; WORKSHOPS INTRODUCTION TO COMPUTER VISION: UNRAVELING THE WONDERS OF COMPUTER VISION

Authors:
raw_author_name: None Muhammad Arif

Abstract: The computer vision workshop is a crucial platform addressing the escalating demands of this rapidly evolving field within computer science. In today's era, visual data processing is integral across diverse sectors such as healthcare, security, and industry, making the workshop a guiding beacon for participants navigating the complex terrain of computer vision. Central to its agenda is the dissemination of practical insights into training data for object recognition, a cornerstone for unleashing the full potential of computer vision applications. Participants delve into fundamental concepts, encompassing nuances of deep learning alongside the intricacies of data training and object detection. Employing Google Colab and Python programming language, the workshop emphasizes practical application, equipping participants with cutting-edge tools. Expected outcomes encompass participants fostering a profound grasp of underlying principles, refining proficiency in data training for nuanced object detection, and bolstering confidence through hands-on exercises. Designed holistically, the workshop integrates elements such as deep learning, computer vision, interactive Q&amp;A sessions, and practical exercises, ensuring participants acquire a robust skill set ready for real-world challenges in the dynamic realm of computer vision.
Segmented Acoustic Displays

Authors:
raw_author_name: Sriram Subramanian
raw_affiliation_string: Department of Computer Science University College London London WC1E 6EA United Kingdom
raw_author_name: Diego Martinez Plasencia
raw_affiliation_string: Department of Computer Science University College London London WC1E 6EA United Kingdom

Abstract: For more than a century, segmented displays, such as the seven‐segment display, have been a popular and cost‐effective option. They can display various commonly used characters by controlling a few custom‐shaped binary light emitters. This paper introduces the acoustic equivalent of segmented displays, which uses heterogeneous sound modulators to generate a limited set of acoustic holographic images. Designing segmented acoustic displays is more challenging than optical ones due to the complex relationship between emitted sound and generated holographic images. To address this challenge, we propose a design methodology based on unsupervised learning techniques. Our approach balances the cost of acoustic displays and the quality of the images they generate, resulting in segmented displays that outperform existing general‐purpose ones when generating a finite set of acoustic images. Using simulations and physical fabrication of metamaterial‐based acoustic displays, we have proven that our approach can create segmented acoustic displays that produce high‐quality images at a lower cost. Additionally, we have applied our methodology to multi‐frequency acoustic displays and assessed its scalability as the number of images increases. The cost‐reduction through segmentation presented in this work is expected to democratise sound manipulators for acoustic displays and other applications like acoustic levitation and noise cancellation. This article is protected by copyright. All rights reserved.
Pengenalan Literasi Finansial Awal Pada Anak Usia Dini Di TK Aqidah Palangka Raya

Authors:
raw_author_name: Aghnaita Aghnaita
raw_author_name: Annisa Rahmasari
raw_author_name: Ervina Ervina

Abstract: This community service is motivated by the importance of introducing learning to children, especially in introducing edupreneur activities. Based on this, it is necessary to provide an introduction to edupreneur activities in terms of introduction to make children have an edupreneur spirit through activities to introduce children to early financial literacy. The aim of this community service is so that children can learn about educational activities that have been carried out, namely activities to introduce early financial literacy, and through these activities it can also teach children about transactions when selling, namely the existence of sellers and buyers using money. The target participants for this community service are children aged 5-6 years who are at the Palangka Raya Aqidah Kindergarten and students of the Early Childhood Islamic Education Study Program, Faculty of Tarbiyah and Teacher Training, IAIN Palangka Raya who are also actively involved in this activity. The results of this community service show that the children are very enthusiastic about participating in the initial financial literacy introduction activities that have been carried out at Bank Indonesia (BI) and the children look enthusiastic. The hope is that through the introduction of edupreneur activities carried out in early childhood, it will be able to stimulate children's cognitive abilities in recognizing early financial literacy, introduce children to the importance of edupreneurship for children, and increase children's experience because they have carried out edupreneur activities, namely through the introduction of early financial literacy. So that the aim of the introduction will be achieved optimally.
Penguatan Literasi Digital Dalam Pemanfaatan Website Pada Badan Usaha Milik Desa Cibogo

Authors:
raw_author_name: Indah Umiyati
raw_author_name: Bambang Sugiharto

Abstract: This community service activity aims to improve digital literacy in website utilisation at BUMDes in Subang Regency, namely BUMDes Cibogo. The scope of digital literacy in this activity is Digital Skill, which is the ability of individuals to know, understand, and use ICT hardware and software as well as digital operating systems in everyday life. The methodology used in this activity is Rapid Rural Appraisal (RRA), a strong approach to rural community development. RRA includes aspects of respecting the community, encouraging the community to express and share ideas or opinions, asking questions, listening attentively, and taking notes. After the community service activities, the management of BUMDes Cibogo understands digital literacy at the digital skill level and a perception that digital literacy is very important as an effort to optimise BUMDes performance.
Likelihood-Free Estimation of IRT Models in Small Samples: A Neural Networks Approach

Authors:
raw_author_name: Dmitry I Belov
raw_author_name: Oliver Lüdtke
raw_author_name: Esther Ulitzsch

Abstract: Existing estimators of parameters of item response theory (IRT) models exploit the likelihood function. In small samples, however, the IRT likelihood oftentimes contains little informative value, potentially resulting in biased and/or unstable parameter estimates and large standard errors. To facilitate small-sample IRT estimation, we introduce a novel approach for small-sample IRT estimation that does not rely on the likelihood. Our estimation approach capitalizes on item pool information and trains a neural network (NN) to interpolate the relationship between response patterns and item parameters. We describe and evaluate our approach for the three-parameter logistic (3PL) model; however, it is applicable to any model with an item characteristic curve (ICC). Three types of NNs are developed, supporting to obtain both point estimates and confidence intervals for IRT model parameters. The results of a simulation study demonstrated that for sample sizes of 300 and below, these NNs can perform at the level of Bayesian estimation using Markov chain Monte Carlo (MCMC) methods or even better in terms of quality of the point estimates and confidence intervals, but much faster. Among others, these properties facilitate (1) to pretest items in a real-time testing environment (e.g., CAT), (2) to pretest more items and, as a consequence, to replenish operational pools quicker and assemble more tests as well as (3) to pretest items only in a secured environment (e.g., in strictly proctored test centers) to eradicate possible compromise of new items in online testing.
A Web-Based Platform (AgileNudge+) to Facilitate Positive Behavioral Change Through Nudge Strategies: A Mixed Methods Feasibility Study (Preprint)

Authors:
raw_author_name: Malaz Boustani
raw_author_name: Richard J. Holden

Abstract: <sec> <title>BACKGROUND</title> In today’s digital age, web-based applications have become integral to daily life, driving transformative shifts in human behavior. "AgileNudge+” is a web-based solution to catalyze positive behavior change. By leveraging knowledge in behavioral economics, design, and technology, AgileNudge+ utilizes nudging interventions to facilitate long-lasting behavioral changes. </sec> <sec> <title>OBJECTIVE</title> This paper describes the design process, methodologies, and feasibility of "AgileNudge+," evaluated by testing usability, user-friendliness, and usefulness. </sec> <sec> <title>METHODS</title> Iterative processes incorporating principles from behavioral economics (nudges) and user experience design were used to design AgileNudge+. Usability testing was used to assess user experience, ease of use, and the overall usefulness of the web-based software. </sec> <sec> <title>RESULTS</title> Qualitative findings offer a multifaceted evaluation of AgileNudge+'s impact on facilitating positive behavior change. The combination of user feedback coupled with a uniquely Agile design process was able to streamline the development of various nudges to create demand for a new healthcare model within the healthcare delivery system. </sec> <sec> <title>CONCLUSIONS</title> This paper highlights the significance of web-based solutions in fostering positive behavior change. The usability testing validates the software's user-friendliness and usefulness, shedding light on its potential impact. </sec>
Topicality Boosts Popularity: A Comparative Analysis of NYT Articles and Reddit Memes

Authors:
raw_author_name: Marcell Nagy
raw_affiliation_string: Budapest University of Technology and Economics
raw_author_name: Roland Molontay
raw_affiliation_string: Budapest University of Technology and Economics

Abstract: Abstract This study sheds light on interconnected topic dynamics across traditional news sources and social media platforms, emphasizing the influential role of topicality in shaping content popularity in social media. Using the Latent Dirichlet Allocation and BERTopic models, we define sets of 120 New York Times (NYT) topics to compare with 899,766 image-with-text memes from Reddit, showing that social media content aligns with many of the same topical patterns observed in news outlets. Topicality is formalized based on the temporal distributions of the topics over the past 5 years. Using these topicality features, the investigation reveals significant correlations between the rising popularity of NYT topics and increased average upvotes on Reddit, particularly evident in "innovator" memes posted during the early stages of a topic's prevalence in the NYT. Furthermore, topicality features show significant predictive power over other content-based control features in a CatBoost classifier prediction of viral Reddit memes.
Inverse Firefly-Based Search Algorithms for Multi-Target Search Problem

Authors:
raw_author_name: Ouarda Zedadra
raw_affiliation_string: LabSTIC Laboratory, Department of Computer Science, 8 Mai 1945 University, P.O. Box 401, Guelma 24000, Algeria
raw_author_name: Antonio Guerrieri
raw_affiliation_string: CNR, National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR), Via P. Bucci 8/9C, 87036 Rende, Italy
raw_author_name: Hamid Seridi
raw_affiliation_string: LabSTIC Laboratory, Department of Computer Science, 8 Mai 1945 University, P.O. Box 401, Guelma 24000, Algeria
raw_author_name: Giancarlo Fortino
raw_affiliation_string: CNR, National Research Council of Italy, Institute for High Performance Computing and Networking (ICAR), Via P. Bucci 8/9C, 87036 Rende, Italy; Department of Informatics, Modeling, Electronics, and Systems, University of Calabria, 87036 Rende, Italy

Abstract: Efficiently searching for multiple targets in complex environments with limited perception and computational capabilities is challenging for multiple robots, which can coordinate their actions indirectly through their environment. In this context, swarm intelligence has been a source of inspiration for addressing multi-target search problems in the literature. So far, several algorithms have been proposed for solving such a problem, and in this study, we propose two novel multi-target search algorithms inspired by the Firefly algorithm. Unlike the conventional Firefly algorithm, where light is an attractor, light represents a negative effect in our proposed algorithms. Upon discovering targets, robots emit light to repel other robots from that region. This repulsive behavior is intended to achieve several objectives: (1) partitioning the search space among different robots, (2) expanding the search region by avoiding areas already explored, and (3) preventing congestion among robots. The proposed algorithms, named Global Lawnmower Firefly Algorithm (GLFA) and Random Bounce Firefly Algorithm (RBFA), integrate inverse light-based behavior with two random walks: random bounce and global lawnmower. These algorithms were implemented and evaluated using the ArGOS simulator, demonstrating promising performance compared to existing approaches.
Cancer Diseases Prediction Using Multiple Transfer Learning based on CNN Algorithm

Authors:
raw_author_name: None Maysaa H. Abdulameer
raw_author_name: None Ruwaida M. Yas
raw_author_name: None Huda H. Ali

Abstract: For many years, the most common types of cancers that causes death has been lung and brain cancer. In addition, they are very hard to treat once they have been spread in the late stages. Early cancer detection and diagnosis have become crucial for providing immediate and efficient care to those who are afflicted with the disease. To predict cancer, this process needs cancer image classification which is a difficult task in the recent improvement of computer-based diagnostics. The main objective of this research is to classify Computed-Tomography (CT) images of the lungs and brain as cancerous or not. Therefore, this research shows the possibility of using a convolutional-neural-network (CNN) for lung and brain cancer detection through transfer learning (TL) without the difficulty of specifying features for image-classification. The proposed TCNN is trained and tested from scratch using two public and one private dataset using the TL technique. The suggested transfer learning-based model attained an accuracy of 92.81%, 97.21%, and 99.40% for Scratch TCNN Model, Pre-Trained TCNN Model, and Transfer Pre-Pre-Trained Model in that order. Hence, the proposed method gives high accuracy and decreases the time of diagnosis and treatment.
Uncertainty Quantification of Neural Reflectance Fields for Underwater Scenes

Authors:
raw_author_name: Xinhao Li
raw_affiliation_string: Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
raw_author_name: Xin Wen
raw_affiliation_string: School of Software, Taiyuan University of Technology, Jinzhong 030600, China
raw_author_name: Mengxi Zhang
raw_affiliation_string: State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin 300350, China
raw_author_name: Jieyuan Zhang
raw_affiliation_string: Academy of Military Science, Beijing 100091, China
raw_author_name: Yilin Qu
raw_affiliation_string: Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology, Northwestern Polytechnical University, Xi’an 710072, China; School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China; Unmanned Vehicle Innovation Center, Ningbo Institute of Northwestern Polytechnical University, Ningbo 315103, China

Abstract: Neural radiance fields and neural reflectance fields are novel deep learning methods for generating novel views of 3D scenes from 2D images. To extend the neural scene representation techniques to complex underwater environments, beyond neural reflectance fields underwater (BNU) was proposed, which considers the relighting conditions of on-aboard light sources by using neural reflectance fields, and approximates the attenuation and backscatter effects of water with an additional constant. Because the quality of the neural representation of underwater scenes is critical to downstream tasks such as marine surveying and mapping, the model reliability should be considered and evaluated. However, current neural reflectance models lack the ability of quantifying the uncertainty of underwater scenes that are not directly observed during training, which hinders their widespread use in the field of underwater unmanned autonomous navigation. To address this issue, we introduce an ensemble strategy to BNU that quantifies cognitive uncertainty in color space and unobserved regions with the expectation and variance of RGB values and termination probabilities along the ray. We also employ a regularization method to smooth the density of the underwater neural reflectance model. The effectiveness of the present method is demonstrated in numerical experiments.