Reviewerly/exHarmony huggingface dataset:
Phonetically rich corpus construction for a low-resourced language

Authors:
raw_author_name: Amadeus, Marcellus
raw_author_name: Castañeda, William Alberto Cruz
raw_author_name: Lobato, Wilmer

Abstract: Speech technologies rely on capturing a speaker's voice variability while obtaining comprehensive language information. Textual prompts and sentence selection methods have been proposed in the literature to comprise such adequate phonetic data, referred to as a phonetically rich \textit{corpus}. However, they are still insufficient for acoustic modeling, especially critical for languages with limited resources. Hence, this paper proposes a novel approach and outlines the methodological aspects required to create a \textit{corpus} with broad phonetic coverage for a low-resourced language, Brazilian Portuguese. Our methodology includes text dataset collection up to a sentence selection algorithm based on triphone distribution. Furthermore, we propose a new phonemic classification according to acoustic-articulatory speech features since the absolute number of distinct triphones, or low-probability triphones, does not guarantee an adequate representation of every possible combination. Using our algorithm, we achieve a 55.8\% higher percentage of distinct triphones -- for samples of similar size -- while the currently available phonetic-rich corpus, CETUC and TTS-Portuguese, 12.6\% and 12.3\% in comparison to a non-phonetically rich dataset.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network  Wrappers

Authors:
raw_author_name: Guleryuz, Onur G.
raw_author_name: Chou, Philip A.
raw_author_name: Isik, Berivan
raw_author_name: Hoppe, Hugues
raw_author_name: Tang, Danhang
raw_author_name: Du, Ruofei
raw_author_name: Taylor, Jonathan
raw_author_name: Davidson, Philip
raw_author_name: Fanello, Sean

Abstract: We propose sandwiching standard image and video codecs between pre- and post-processing neural networks. The networks are jointly trained through a differentiable codec proxy to minimize a given rate-distortion loss. This sandwich architecture not only improves the standard codec's performance on its intended content, it can effectively adapt the codec to other types of image/video content and to other distortion measures. Essentially, the sandwich learns to transmit ``neural code images'' that optimize overall rate-distortion performance even when the overall problem is well outside the scope of the codec's design. Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures. The results demonstrate substantial improvements (up to 9 dB gains or up to 30\% bitrate reductions) compared to alternative adaptations. We derive VQ equivalents for the sandwich, establish optimality properties, and design differentiable codec proxies approximating current standard codecs. We further analyze model complexity, visual quality under perceptual metrics, as well as sandwich configurations that offer interesting potentials in image/video compression and streaming.
Menumbuhkan Minat Baca Anak melalui Penyediaan Pojok Baca dan Metode Reading Aloud di Desa Kutalanggeng Karawang

Authors:
raw_author_name: Ajat Rukajat
raw_affiliation_string: Universitas Singaperbangsa Karawang

Abstract: Reading is one of the types of writing language skills, and the interest and habit of reading among Indonesian society are relatively low. The goal of this Community Service (PkM) is to understand the process, inhibiting factors, driving factors, and teachers' efforts in fostering children's reading interest through the provision of reading corners. The theories underlying this PkM include: non-formal education, the nature of early childhood, reading interest, storytelling methods, storytelling techniques, and storytelling and reading interest. The PkM method used is descriptive method, and the techniques used are observation, documentation study, questionnaire, interview, with a total sampling of 52 children. Based on the PkM findings, it is found that: 1) There is a reading corner and sufficient supporting books, 2) storytelling activities with reading aloud techniques, 3) difficulties in using storytelling methods in the process of fostering reading interest, 4) efforts in fostering children's reading interest carried out by teachers. It can be concluded that within a week, teachers tell stories less than 3 times with a duration of 10-15 minutes. Supporting factors include the availability of storybooks in the reading corner, inhibiting factors include choosing interesting storybooks, and conditioning children during storytelling. Teachers' efforts in fostering children's reading interest are through storytelling with reading aloud techniques and communicating with parents.
Cloud-based disaster management architecture using hybrid machine learning approach in IoT

Authors:
raw_author_name: Figen Özen
raw_affiliation_string: Department of Electrical and Electronics Engineering, Haliç University, Istanbul, 34060, Turkey
raw_author_name: Alireza Souri
raw_affiliation_string: Department of Software Engineering, Haliç University, Istanbul, 34060, Turkey

Abstract: Abstract Natural disasters are becoming more frequent and more severe as a result of global warming. It is critical to take precautions before disasters, to gather and analyze information simultaneously while they are happening, and to make accurate assessments after them given that the deaths and injuries brought on by such disasters both leave lasting traumas in the life of society and damage the economy. Internet of Things (IoT) technology, is a young field that can assist intelligent safety-critical systems with data collection, processing in cloud edge data centers, and application of prediction methodologies for discovering key points and unexpected patterns using 5G technology. With the use of a cloud-based prediction algorithm for disaster management in the IoT environment, this study seeks to quickly process the data that is gathered during disasters and to speed up the analysis that will be done both during and after the disasters. An Optimized Ensemble Bagged Tree (OEBT) algorithm with ANOVA-based feature selection is developed for this aim. The experimental results show that accuracy, F1-Score, precision, and recall of the proposed OEBT algorithm utilizing the US Natural Disasters Dataset are 97.9%, 78.3%, 98.7%, and 78.9%, respectively. Comparisons with decision tree, logistic regression, and the traditional ensemble techniques are made. The suggested algorithm outperforms them all in terms of success rates.
A Comprehensive Review of Text Mining Approaches for Predicting Human Behavior using Deep Learning Method

Authors:
raw_author_name: None Md Tuhin Mia
raw_affiliation_string: School of Business, International American University Los Angeles, CA, USA

Abstract: This article presents a systematic review of research on predicting human behavior through unstructured textual data, employing a comprehensive selection process illustrated in a flow diagram. The review categorizes 82 selected papers into three primary behavioral domains: emotional, social, and cognitive. Each paper undergoes meticulous examination, identifying objectives, algorithms, computational models, and applications. Natural language processing (NLP) emerges as a dominant text mining approach, utilized in over half of the literature, followed by data extraction, report arrangement, and clusterization. The study further employs VOSviewer to visualize the co-occurrence of the term "text mining," revealing prevalent associations and emphasizing the challenges in analyzing unstructured data efficiently. The article contributes to understanding the evolving landscape of behavior analysis through text mining, addressing the need for automated methods in evaluating individuals' attitudes, emotions, or performance.
Validation Using Structural Equations of the “Cursa-T” Scale to Measure Research and Digital Competencies in Undergraduate Students

Authors:
raw_author_name: Antonio Palacios-Rodríguez
raw_affiliation_string: Faculty of Educational Sciencies, Universidad de Sevilla, 41013 Seville, Spain
raw_author_name: Leticia Rodríguez Segura
raw_affiliation_string: Institutional Directorate of Educational Innovation and Research, University of the Valley of Mexico (UVM), Mexico City 06060, Mexico

Abstract: Research competencies are considered essential in fields such as science, academia, and technology, and this research seeks to provide a reliable tool to evaluate them. Therefore, the main objective of this study is to validate the “Cursa-T” scale through an exploratory and confirmatory factor analysis, as well as through structural equations, to ensure that the data collected fit the proposed theoretical model. The study sample consists of 1104 university students, mostly female, and a questionnaire based on previous studies is used. The most important results of the research include the validation of the “Cursa-T” scale through advanced statistical methods, such as exploratory and confirmatory factor analysis. The scale is found to be reliable and valid for measuring undergraduate research and digital competencies, and the data collected fit the proposed theoretical model. The discussion of the research highlights the importance of technology, devices, software, and the use of platforms in the development of research and digital competencies in Health Sciences students. It also reflects on the role of social networks in these competencies, as they can facilitate participation in academic communities. Ultimately, the research underlines the relevance of preparing undergraduate students in health areas.
An Algorithm with Improved Complexity for Pebble Motion/Multi-Agent Path Finding on Trees

Authors:
raw_author_name: Stefano Ardizzoni
raw_affiliation_string: a:1:{s:5:"en_US"; s:20:"Università di Parma"; }
raw_author_name: Irene Saccani
raw_author_name: Luca Consolini
raw_author_name: Marco Locatelli
raw_author_name: Bernhard Nebel

Abstract: The pebble motion on trees (PMT) problem consists in finding a feasible sequence of moves that repositions a set of pebbles to assigned target vertices. This problem has been widely studied because, in many cases, the more general Multi-Agent path finding (MAPF) problem on graphs can be reduced to PMT. We propose a simple and easy to implement procedure, which finds solutions of length O(|P|nc + n2), where n is the number of nodes, P is the set of pebbles, and c the maximum length of corridors in the tree. This complexity result is more detailed than the current best known result O(n3), which is equal to our result in the worst case, but does not capture the dependency on c and |P|.
Domain Generalization with Small Data

Authors:
raw_author_name: Chen, Kecheng
raw_author_name: Li, Haoliang

Abstract: In this work, we propose to tackle the problem of domain generalization in the context of \textit{insufficient samples}. Instead of extracting latent feature embeddings based on deterministic models, we propose to learn a domain-invariant representation based on the probabilistic framework by mapping each data point into probabilistic embeddings. Specifically, we first extend empirical maximum mean discrepancy (MMD) to a novel probabilistic MMD that can measure the discrepancy between mixture distributions (i.e., source domains) consisting of a series of latent distributions rather than latent points. Moreover, instead of imposing the contrastive semantic alignment (CSA) loss based on pairs of latent points, a novel probabilistic CSA loss encourages positive probabilistic embedding pairs to be closer while pulling other negative ones apart. Benefiting from the learned representation captured by probabilistic models, our proposed method can marriage the measurement on the \textit{distribution over distributions} (i.e., the global perspective alignment) and the distribution-based contrastive semantic alignment (i.e., the local perspective alignment). Extensive experimental results on three challenging medical datasets show the effectiveness of our proposed method in the context of insufficient data compared with state-of-the-art methods.
Zero Trust Architecture in Cloud Networks: Application, Challenges and Future Opportunities

Authors:
raw_author_name: Sina Ahmadi
raw_affiliation_string: National Coalition of Independent Scholars (NCIS), Seattle, WA, USA.

Abstract: Cloud computing has become essential in this digital world as it provides opportunities and challenges for organizations. This research explores the implementation and effectiveness of Zero Trust Architecture (ZTA) in addressing security challenges within cloud networks. Utilizing qualitative research methods, including a systematic literature review from 2020 to 2024, the study investigates insights from diverse sources such as journal articles, academic literature, and case studies. Thematic analysis organizes findings into critical themes, revealing ZTA's impact on mitigating lateral movement, reducing insider threat probability, enhancing network micro-segmentation, and improving identity and access management. The comparative analysis demonstrates significant improvements in security incidents post-ZTA implementation. Moreover, the study highlights best practices for ZTA adoption and outlines future advancements, including integration with emerging technologies like machine learning and artificial intelligence. This research underscores ZTA's pivotal role in fortifying cloud network security and offers valuable insights for practitioners and researchers.
\texttt{qec\_code\_sim}: An open-source Python framework for estimating the effectiveness of quantum-error correcting codes on superconducting qubits

Authors:
raw_author_name: Perdue, Gabriel N.
raw_affiliation_string: Fermilab

Abstract: Quantum computers are highly susceptible to errors due to unintended interactions with their environment. It is crucial to correct these errors without gaining information about the quantum state, which would result in its destruction through back-action. Quantum Error Correction (QEC) provides information about occurred errors without compromising the quantum state of the system. However, the implementation of QEC has proven to be challenging due to the current performance levels of qubits -- break-even requires fabrication and operation quality that is beyond the state-of-the-art. Understanding how qubit performance factors into the success of a QEC code is a valuable exercise for tracking progress towards fault-tolerant quantum computing. Here we present \texttt{qec\_code\_sim}, an open-source, lightweight Python framework for studying the performance of small quantum error correcting codes under the influence of a realistic error model appropriate for superconducting transmon qubits, with the goal of enabling useful hardware studies and experiments. \texttt{qec\_code\_sim} requires minimal software dependencies and prioritizes ease of use, ease of change, and pedagogy over execution speed. As such, it is a tool well-suited to small teams studying systems on the order of one dozen qubits.
Evaluation Metrics for Text Data Augmentation in NLP

Authors:
raw_author_name: Amadeus, Marcellus
raw_author_name: Castañeda, William Alberto Cruz

Abstract: Recent surveys on data augmentation for natural language processing have reported different techniques and advancements in the field. Several frameworks, tools, and repositories promote the implementation of text data augmentation pipelines. However, a lack of evaluation criteria and standards for method comparison due to different tasks, metrics, datasets, architectures, and experimental settings makes comparisons meaningless. Also, a lack of methods unification exists and text data augmentation research would benefit from unified metrics to compare different augmentation methods. Thus, academics and the industry endeavor relevant evaluation metrics for text data augmentation techniques. The contribution of this work is to provide a taxonomy of evaluation metrics for text augmentation methods and serve as a direction for a unified benchmark. The proposed taxonomy organizes categories that include tools for implementation and metrics calculation. Finally, with this study, we intend to present opportunities to explore the unification and standardization of text data augmentation metrics.
Engineering Weighted Connectivity Augmentation Algorithms

Authors:
raw_author_name: Faraj, Marcelo Fonseca
raw_author_name: Schulz, Christian

Abstract: Increasing the connectivity of a graph is a pivotal challenge in robust network design. The weighted connectivity augmentation problem is a common version of the problem that takes link costs into consideration. The problem is then to find a minimum cost subset of a given set of weighted links that increases the connectivity of a graph by one when the links are added to the edge set of the input instance. In this work, we give a first implementation of recently discovered better-than-2 approximations. Furthermore, we propose three new heuristic and one exact approach. These include a greedy algorithm considering link costs and the number of unique cuts covered, an approach based on minimum spanning trees and a local search algorithm that may improve a given solution by swapping links of paths. Our exact approach uses an ILP formulation with efficient cut enumeration as well as a fast initialization routine. We then perform an extensive experimental evaluation which shows that our algorithms are faster and yield the best solutions compared to the current state-of-the-art as well as the recently discovered better-than-2 approximation algorithms. Our novel local search algorithm can improve solution quality even further.
Flux qubit-based detector of microwave photons

Authors:
raw_author_name: Shevchenko, S. N.

Abstract: A theory of detection of microwave photons with a flux qubit-based detector is presented. We consider semiclassical approximation with the electromagnetic field being in a coherent state. Flux qubit is considered as a multilevel quantum system (qudit). By solving the Lindblad equation, we describe the time evolution of occupations of the qudit's levels for readout and reset stages of detection. When considering the reset stage, the time evolution is described by multiple avoided-level crossings, thus providing a multilevel Landau-Zener-Stuckelberg-Majorana (LZSM) problem. In addition to numerical calculations, we present an approximate analytical solution for the description of the reset stage dynamics based on the adiabatic-impulse approximation and rate equation approach. Our theory may be useful for the theoretical description of driven-dissipative dynamics of qudits, including applications such as single-photon detection.
Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes

Authors:
raw_author_name: Ernesto Alonso Paiva-Peredo
raw_affiliation_string: University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland

Abstract: Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.
Diagnosis of Diseases of the Nose Using the Certainty Factor Method

Authors:
raw_author_name: I Gusti Prahmana
raw_affiliation_string: STMIK KAPUTAMA

Abstract: The nose is the human respiratory organ and is a very important sense of smell for patients. Nasal polyps and sinus infections are several diseases that can affect the health of the nose. The nasal cavity reduces inflammation and protects the nasal passages. Many people think these symptoms are flu symptoms and do not immediately seek medical help. So many people find out too late that they have a nasal disease. Public understanding of nasal diseases is still lacking, so many people do not know clearly what disease they suffer from. The certainty factor is to measure something certain and uncertain when making a decision.
Application for Recommending Tourist Attractions on The Island of Java with Content Based Filtering Using Cosine Similarity

Authors:
raw_author_name: Mutiara Sovina
raw_author_name: Yusfrizal Yusfrizal
raw_author_name: Faisal Amir Harahap
raw_author_name: Ivi Lazuly

Abstract: Indonesia is a country with a high level of tourism, with natural beauty and historical places and other tourist destinations that continue to develop from year to year. Java Island is one of the islands visited by many tourists with various tourist attractions. Currently, many tourists like to travel, but during holidays tourists are confused about which tourist destination to visit. With advances in internet technology and the abundance of information in online media, it can make it easier for tourists to find information, but because there is so much information provided, it will make tourists confused about deciding and choosing a place. A tourist recommendation application is very necessary to provide good recommendation accuracy to make it easier for tourists to find tourist destinations according to the desired category. To get the best results, the Content Based Filtering method using Cosine Similarity used in this research will provide several tourist recommendations according to the level of similarity of various cities on the island of Java.
Classification of Public Sentiment Toward 2024 Presidential Candidates on Social Media Platform X Using Naïve Bayes Algorithm

Authors:
raw_author_name: Agung Pambudi
raw_affiliation_string: Universitas Muhammadiyah Sukabumi
raw_author_name: None Asriyanik
raw_affiliation_string: Universitas Muhammadiyah Sukabumi

Abstract: This research examines the use of Naïve Bayes algorithm to classify public sentiment on social media X towards Indonesia's 2024 presidential candidates. Against the backdrop of the importance of presidential elections in a democracy, this research focuses on analyzing public sentiment from June to August 2023. The Naïve Bayes method was chosen to process review data about the three main candidates. The classification results provide insight into the positive and negative sentiments of the public, providing benefits for political parties and researchers in understanding public opinion. This research also enhances the understanding of sentiment classification in a political context and provides readers with a useful reference on the Naïve Bayes approach to sentiment classification. In terms of accuracy, the developed naïve bayes model shows a success rate with an accuracy of 74% for Anies Baswedan, 74% for Ganjar, and 88% for Prabowo.