Reviewerly/exHarmony huggingface dataset:
Sandwiched Compression: Repurposing Standard Codecs with Neural Network  Wrappers

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

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

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.
Analysis on Two Fake “Topology Diagram of Mineral Paragenesis Combination in Multi-Component Open Systems” Published by Jingwen Mao, Academician of The Chinese Academy of Engineering

The making principles and methods of "multi-component open system mineral Paragenesis combination topology diagram" are one of the most basic cutting-edge research topics in geoscience. So far, only a few professors among Chinese geologists can do this kind of work. Researcher Mao Jingwen of the Institute of Mineral Resources of the Chinese Academy of Geological Sciences published an academic paper and a doctoral thesis in 1986 and 1988 respectively, which contained two "topological diagrams of multi-component open system mineral Paragenesis combinations". We have proved through detailed research that the two diagrams produced by Mao Jingwen have the following series of fundamental errors: ①He doesn’t comply with the principle of mineral phase law (P≦C), ② He doesn’t comply with the Gibbs phase law (F=Ci+2-φ=0), ③ He doesn’t know how to determine the effective inert components and excess components (as shown in Figure 1 of the text, he replaced the effective inert components with excess components in the composition ⊿ diagram), ④ He doesn’t know how to determine the relationship between symbiotic minerals (For example, the four univariable reaction equations in Figure 2 of this article are wrong), ⑤He doesn’t know how to determine the stoichiometry of univariable reaction equations, ⑥It violates the principle of "mass conservation" before and after the univariate reaction. This fully shows that Mao Jingwen himself does not understand the principles and methods of drawing topological diagrams of mineral symbiosis combinations, that is, both diagrams are fake, and Mao Jingwen deliberately falsified them.
Eigenface and Wavelet Feature-based Face Recognition for the Development of Security System

The security system is very crucial in our daily sphere of life. With the rapid advancement of technology and the increasing demand for secure access control systems, developing efficient security systems has become a critical area of research. This paper represents the two significant techniques Eigenface and Wavelet feature. Eigenface utilizes Principal Component Analysis (PCA) to extract the most significant features from any facial image. However, the performance of the eigenface can be affected by various factors such as lighting, pose, and expression. To overcome this inability, we have adopted the wavelet technique. The wavelet feature extraction technique helps to extract detailed information about the image by decomposing it into different sub-bands. A comprehensive facial image of a dataset is initially collected and then the Eigenface technique is applied to extract features. The wavelet technique is also performed to recognize images and identify the image having distortion to it. By imposing Wavelet features, higher accuracy, and reliability of the classifier are obtained. We hope the concept of security system development will benefit by implementing this technique.&nbsp;&nbsp
ANALYSIS OF FUZZY IMPLICATION

Fuzzy logic (fuzzy sets) possesses some widespread application in decision-making due to its ability to handle uncertainties in the implemented data and information. The application of fuzzy logic in decision-making usually involves the design of fuzzy rule-based systems. The functioning of the designed fuzzy systems can be compactly defined as the fuzzy inference process. One of the main steps of fuzzy inference is fuzzy implication, and, therefore, the selection of proper implication function possesses the great impact on the corresponding decision-making process. The currently reported research provides a comparative analysis on two fuzzy implications: Ali-2 and Zadeh. The reported research involves the application of an efficiency index and is performed on a real-world example. Keywords: Fuzzy implication, If-Then rules, membership function, fuzzy set, efficiency index
A Comprehensive Review of Text Mining Approaches for Predicting Human Behavior using Deep Learning Method

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

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

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

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.
Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning

The principal objective of this research was to examine strategies for detecting and mitigating cyber threats in the next generation, by underscoring Artificial Intelligence (AI) and Machine Learning (ML). This study provides a comprehensive overview of the role of AI, ML, and deep learning (DL) in the domain of cybersecurity. Furthermore, this study highlights the benefits of integrating deep learning into cybersecurity practices. The researcher explored the effectiveness of consolidating AI and ML techniques into the Feedzai security system to reinforce the detection of fraudulent activities. To validate the methodology, the investigator experimented by employing the supervised machine learning random forest algorithm on a dataset comprising historical transaction records in CSV format. The results of the research ascertained that by employing Feedzai's AI-based software combined with the random forest algorithms, future financial institutions can achieve real-time fraud detection and accurate identification of legitimate transactions. The Random Forest framework had the highest accuracy rate, at 83.94%. By contrast, the Naïve Bayes framework had an accuracy rate of 79.23%, and the KNN model had the lowest accuracy rate, of 78.74%. These results ascertained that the Random Forest system was the most effective for pinpointing cyber-attacks.
The Role of Artificial Intelligence in Shaping the Future of Education: Opportunities and Challenges

Artificial intelligence has become a booming technology whereas it brings numerous positive changes within the educational process. The aim of the research is to describe the role of artificial intelligence in education through the analysis of its opportunities and challenges. The study involved the integration of qualitative (interviews, focus groups, and classroom observations) and quantitative methods (survey and statistical analysis). All the research procedures were organized according to the ethical standards for data collection and analysis. Over 50 recent scientific works were selected to analyze the research problem from different perspectives and present its comprehensive overview. The study involved 56 participants representing instructors from different institutions of higher education in Ukraine. The inclusion criteria were based on subject specialization, institution type, curriculum accreditation, and experience with artificial intelligence technologies. It was found that the positive impacts of artificial intelligence include personalized and adaptive learning, automated administrative tasks, enhanced support, e-learning facilitation, inclusivity, data-driven decision making, gamification, increased engagement, behaviour and predictive analytics, improved assessment. The challenges concerned the data privacy, security, bias, lack of understanding, transparency, necessity for additional training. The findings showed that the implementation of artificial intelligence through personalized learning, predictive analytics, intelligent tutoring systems, content creation systems, Virtual Reality, automated administrative tasks, and chatbots can shape the educational process effectively in future and modernize the future specialists’ training. The research results can be used within the educational institutions to increase the awareness of using artificial intelligence tools.
Cloud Security System for ECG Transmission and Monitoring Based on Chaotic Logistic Maps

Biomedical data or information must be transmitted securely via the internet for smart healthcare. The Electrocardiogram (ECG) signal is amongst the most essential clinical signals which must be delivered to hospital facilities. Prime focus of this research is on the encryption of ECG for secure transmission. Chaos theory is used for the development of deterministic nonlinear systems, that can be used to create random numbers for the Chaotic Logistic Map (CLM) based encryption. This study describes a cryptographic algorithm for encrypting ECG signals that uses a mix of the CLM and fingerprint data. The common factor between the patient section and monitoring section is the operation on sample data points of ECG. The choice of proper encryption and decryption theme can save more amount of time and is invulnerable both to noise-based attacks and hacking instances. The proposed framework is implemented on Dropbox based cloud storage and access is possible from any given locations. Simulation tests are used to assess the system performance in terms of Structural Similarity Index Matrix (SSIM), Histogram, Spectral Distortion (SD), Correlation and Log-Likelihood Ratio (LLR). The incorporation of complex layers of CLM encryption increases security.
Zero Trust Architecture in Cloud Networks: Application, Challenges and Future Opportunities

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.
Replacing CAPTCHA with XNO micropayments

As technology and gadgets continue to evolve, the need for bot-friendly and user-friendly internet becomes increasingly critical. This work discusses a methodology for implementation and feasibility of replacing traditional CAPTCHA mechanisms with Nano(XNO) cryptocurrency micropayments as a win-win solution and leverages the decentralized and secure nature of cryptocurrencies to introduce a micropayment-based authentication system. This approach not only enhances security by adding a financial barrier for automated bots but also provides a more seamless and efficient user experience. The benefits of this approach include reducing the burden on users while creating a socio-economic model that incentivizes internet service providers and content creators, even when accessed by bots. Furthermore, the integration of XNO micropayments could potentially contribute to the broader adoption and acceptance of digital currencies in everyday online transactions.
\texttt{qec\_code\_sim}: An open-source Python framework for estimating the effectiveness of quantum-error correcting codes on superconducting qubits

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

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

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.