Minds versus Machines: Rethinking Entailment Verification with Language  Models

Abstract: Humans make numerous inferences in text comprehension to understand discourse. This paper aims to understand the commonalities and disparities in the inference judgments between humans and state-of-the-art Large Language Models (LLMs). Leveraging a comprehensively curated entailment verification benchmark, we evaluate both human and LLM performance across various reasoning categories. Our benchmark includes datasets from three categories (NLI, contextual QA, and rationales) that include multi-sentence premises and different knowledge types, thereby evaluating the inference capabilities in complex reasoning instances. Notably, our findings reveal LLMs' superiority in multi-hop reasoning across extended contexts, while humans excel in tasks necessitating simple deductive reasoning. Leveraging these insights, we introduce a fine-tuned Flan-T5 model that outperforms GPT-3.5 and rivals with GPT-4, offering a robust open-source solution for entailment verification. As a practical application, we showcase the efficacy of our finetuned model in enhancing self-consistency in model-generated explanations, resulting in a 6% performance boost on average across three multiple-choice question-answering datasets.


Automated Description Generation for Software Patches

Abstract: Software patches are pivotal in refining and evolving codebases, addressing bugs, vulnerabilities, and optimizations. Patch descriptions provide detailed accounts of changes, aiding comprehension and collaboration among developers. However, manual description creation poses challenges in terms of time consumption and variations in quality and detail. In this paper, we propose PATCHEXPLAINER, an approach that addresses these challenges by framing patch description generation as a machine translation task. In PATCHEXPLAINER, we leverage explicit representations of critical elements, historical context, and syntactic conventions. Moreover, the translation model in PATCHEXPLAINER is designed with an awareness of description similarity. Particularly, the model is explicitly trained to recognize and incorporate similarities present in patch descriptions clustered into groups, improving its ability to generate accurate and consistent descriptions across similar patches. The dual objectives maximize similarity and accurately predict affiliating groups. Our experimental results on a large dataset of real-world software patches show that PATCHEXPLAINER consistently outperforms existing methods, with improvements up to 189% in BLEU, 5.7X in Exact Match rate, and 154% in Semantic Similarity, affirming its effectiveness in generating software patch descriptions.


Improving difficult inference tasks and likelihood evaluations with neural networks

Abstract: A number of simulation-based methods, such as Gillespie's algorithm or stochastic differential equations, provide a way to simulate data generated by a system following a model. By contrast to forward simulation-based strategies, to deduce dynamical laws or parameters from data requires inverse strategies such as Bayesian methods. Likelihoods are a central object of Bayesian modeling whose computation demand significant time and memory resources. To facilitate the task of evaluating likelihoods, these can be approximated using neural networks trained to emulate forward simulations. Here we use such methods for problems of relevance in biophysics from the protein to the cellular scale.


Optimization of a machine-learning-based approach to quantify and characterize microtubule dynamic instability

Abstract: Microtubules (MTs) are biological polymers that constitute an essential part of the cytoskeleton of eukaryotic cells and are found in all characterized eukaryotic organisms. MTs display “dynamic instability” (DI), a behavior traditionally characterized by stochastic switching between phases of growth and shrinkage separated by transitions called catastrophe and rescue. This behavior requires energy, is intrinsic to the MT subunits (it does not require additional proteins) and is fundamental to MT functions. To understand how MT dynamics are regulated, experimentalists must characterize and quantify DI behavior. Classical approaches assumed that DI has only two phases, with instantaneous transitions between them, but high-resolution experimental data shows that DI is more complicated. To address this problem, we developed the statistical tool for automated dynamic instability Analysis (STADIA), which uses unsupervised machine-learning approaches to characterize and quantify DI behaviors (including the “stutters” that frequently occur before catastrophe) using microtubule length-history data as input (Mahserejian et al., 2022). Our first release of the STADIA software serves as an improvement over traditional DI analysis methods and provides a foundation for further refinement. The purpose of this project is to improve STADIA by refining its methods for segmenting the inputted length-history data and classifying the line segments. We propose to use a change-point detection-based method to improve vertex selection in the segmentation stage and an advanced clustering algorithm to optimize the methodology used for classification. These changes will further refine the classification and quantification of DI, providing a more comprehensive and data-driven approach to analyzing MT dynamics compared to previously developed techniques.


Expanding tagging toolbox for visualizing translation live

Abstract: Proteins are essential molecules in living cells. Disrupted intracellular protein homeostasis could lead to many diseases, such as cancers and Alzheimer’s disease. Translation, the birth of proteins, is a critical step for maintaining protein homeostasis. Understanding the regulation mechanisms of translation helps us to explore novel therapeutics. Recently, the use of repeat short epitope tags combined with complementary fluorescent antibody fragments, such as the spaghetti monster/Fabs and the SunTag/scFv imaging systems, has made it possible to visualize translation dynamics live at single mRNA level. By applying these tagging systems, many novel mechanisms of translation have been uncovered. However, the lack of comparable and orthogonal genetically encodable tagging systems has made applications that require multicolor imaging challenging. To address this challenge, we developed a panel of orthogonal genetically encodable tagging systems with comparable lengths of epitope tags: the HA tag (9 aa), the FLAG tag (8 aa), and a novel UTag (11 aa). For this, we grafted complementary determining regions or loops of the antibodies to a stable single-chain variable fragment (scFv) scaffold that folds correctly in the reduced intracellular environment. After the loop grafting, the binding of the wild-type scFvs was rescued in living cells. Our FRAP data suggest the binding of these three tagging systems is comparably tight. We believe these favorable properties will make these tagging systems useful tools in multicolor imaging applications.


Are crypto and non-crypto investors alike? Evidence from a comprehensive survey in Brazil

Abstract: Cryptocurrencies and blockchain have become a global phenomenon transforming peo-ple's relationships with technology and offering innovative tools for businesses and individuals to strive in a digital age. However, little is still known about the main drivers of cryptocurrency ownership, especially in emerging markets. Based on a representative online survey among 573 Brazilian digital platform investors, we find that crypto investors tend to be young, male, more tol-erant to risk, less optimistic in their economic views, and consider themselves as ‘better’ investors compared to non-crypto online traders. While crypto and non-crypto investors have similar edu-cational backgrounds, our results show that cryptocurrency literacy positively and strongly relates to cryptocurrency ownership and intentions to invest in cryptocurrency. A gender gap among cryptocurrency investors has been confirmed. The findings further suggest that sophisticated in-vestors are more likely to hedge pessimistic economic expectations using cryptocurrency than their unsophisticated peers. We also find significant heterogeneity among cryptocurrency investors (e.g., early x late adopters) on attitudes and beliefs. The insights into digital investors' intentions to invest in cryptocurrency can be valuable for policymakers in designing strategies for the broader adoption of digital assets in the era of a decentralized economy, considering the planned adoption of CBDC in Brazil.


Application of Naive Bayes Classifier Method to Analyze Social Media User Sentiment Towards the Presidential Election Phase

Abstract: This research aims to analyze the sentiment of social media users towards the election. The author collected data in this research through a literature study and observation. The author uses a classification method with the Naïve Bayes Classifier Algorithm and Support Vector Machine to analyze sentiment results. Next, this research extracts word assessment features using TextBlob, which changes text into positive or negative classes. Based on the research results, after going through the text preprocessing stage of more than 15,000 tweets, 11,000 clean tweets were obtained, which were then labelled using the text blob library in Python. The labelling results show that 4,000 tweets are positive, and the rest are harmful, indicating that most social media users' sentiment towards the election is positive. Words that often appear in the positive class express support and confidence in implementing elections that are considered honest and fair. On the other hand, words in the negative class reflect negative sentiment towards implementing elections, which are considered unsuccessful and time-consuming. The Naïve Bayes method provides accuracy, precision, and recall values of 85%, 80%, and 75%. In the Support Vector Machine method, testing is carried out with three kernels (linear, RBF, and poly), where the poly kernel with the best parameter values C is ten and degree is 1 produces the highest accuracy, precision, and recall of 90%, 90%, and 85%, respectively.


Altruistic and egoistic motivations to engage with contact-tracing apps: Lessons learned from the Covid-19 pandemic

Abstract: During the Covid-19 pandemic, contact-tracing apps have offered effective help to bend the contagion curve. Thus, it is of critical importance to understand the factors that influence contact-tracing apps’ adoption among citizens. In particular, the successful adoption and usage of contact-tracing apps strongly relies on individual motives. Therefore, this study draws on the theory of altruistic and egoistic motivation for prosocial behaviours to analyse the underlying motives through which citizens engage in voluntary behaviours aimed at using and promoting the use of contact-tracing apps. The study also examines the mediating role of users’ trust in the app. Data from 221 users of Ireland’s Covid Tracker app was analysed. Structural equation modelling with PLS was used to test the research model. Findings show differences between egoistic and altruistic motivation in promoting app use and sharing. Egoistic motivation significantly promotes voluntary behaviours among citizens and users’ trust in the app mediates this influence. Yet, contrary to predictions, in the context of the pandemic, altruistic motivation does not play a significant role in engaging citizens in these voluntary behaviours, either directly or indirectly. The findings of this study are important for policy makers and may inform future policy decisions regarding the implementation of contact-tracing apps in the case of new pandemics or for other contexts requiring cooperative daily check-in.


Digital forensic analysis of attack detection and identification in private cloud environments for databases

Abstract: Today, a growing number of applications are transitioning to the cloud due to its numerous benefits and user-friendly nature. Data plays a pivotal role in the migration, and its security is of paramount importance. Database forensics is a field dedicated to investigating malicious activities carried out by attackers, whether they occur on the front-end or back-end. These activities can be traced through the analysis of various database logs, with network details aiding in the identification of the attacker. In this paper, the authors explore the activity indicators by examining and analysing activity logs, database logs and network packets. Cloud forensics presents the digital investigation for the detection of cyber crimes on cloud. This could include data breaches or identity thefts or modification of databases and attacks on the application server and webserver. URN:NBN:sciencein.jist.2024.v12.798


Exploring Data Agency and Autonomous Agents as Embodied Data  Visualizations

Abstract: In the light of recent advances in embodied data visualizations, we aim to shed light on agency in the context of data visualization. To do so, we introduce Data Agency and Data-Agent Interplay as potential terms and research focus. Furthermore, we exemplify the former in the context of human-robot interaction, and identify future challenges and research questions.


Data-efficient Large Vision Models through Sequential Autoregression

Abstract: Training general-purpose vision models on purely sequential visual data, eschewing linguistic inputs, has heralded a new frontier in visual understanding. These models are intended to not only comprehend but also seamlessly transit to out-of-domain tasks. However, current endeavors are hamstrung by an over-reliance on colossal models, exemplified by models with upwards of 3B parameters, and the necessity for an extensive corpus of visual data, often comprising a staggering 400B tokens. In this paper, we delve into the development of an efficient, autoregression-based vision model, innovatively architected to operate on a limited dataset. We meticulously demonstrate how this model achieves proficiency in a spectrum of visual tasks spanning both high-level and low-level semantic understanding during the testing phase. Our empirical evaluations underscore the model's agility in adapting to various tasks, heralding a significant reduction in the parameter footprint, and a marked decrease in training data requirements, thereby paving the way for more sustainable and accessible advancements in the field of generalist vision models. The code is available at https://github.com/ggjy/DeLVM.


Beyond interaction: Generative AI in conversational marketing - foundations, developments, and future directions

Abstract: This paper explores the integration of Generative Artificial Intelligence (AI) in conversational marketing, transitioning from traditional marketing to interactive, customer-centric strategies. It examines the shift from one-way communication to dynamic, AI-driven interactions that personalize customer experiences. Central to this study is how Generative AI facilitates real-time, tailored dialogues between brands and customers, enhancing customer engagement and satisfaction. The paper also addresses the challenges and ethical considerations of using anthropomorphic AI in marketing, balancing human-like AI traits with user expectations. Additionally, it presents a novel framework that conceptualizes the combination of Generative AI and anthropomorphism in conversational marketing into four distinct quadrants, providing a comprehensive analysis of their potential interplay. Conclusively, it offers strategic insights for leveraging AI in marketing while adhering to ethical practices, highlighting the potential of Generative AI to transform customer engagement in the digital age. This research has two important consequences. Practically, it offers valuable insights and strategic recommendations for businesses aiming to integrate Generative AI into their conversational marketing practices effectively. Theoretically, it contributes to the academic discourse by highlighting the transformative role of Generative AI in marketing, suggesting avenues for future research in this rapidly evolving field. This study provides a brief overview of the evolving role of AI in modern marketing strategies, emphasizing the future potential and implications of AI-driven conversational marketing.


Fault tree generation and analysis based on extended SysML model

Abstract: This paper proposes an extension of the SysML safety semantic approach to address the lack of safety and reliability semantics and the support for safety and reliability analysis of the model in SysML. On this basis, fault tree generation and analysis are performed. The method first adds semantic information about fault tree and redundant module to the model using the Stereotype extension mechanism, integrating design data and safety data through an extended configuration file in the SysML model. Secondly, sub-mode decomposition of Internal Block Diagram is conducted, and fault mode recognition is described in relation to fault tree mapping. Based on this, a search is conducted on the SysML model to obtain necessary information for fault tree generation, followed by an analysis of the generated fault tree.


Control of quantum coherence of photons exploiting quantum entanglement

Abstract: Accurately controlling the quantum coherence of photons is pivotal for their applications in quantum sensing and quantum imaging. Here, we propose the utilization of quantum entanglement and local phase manipulation techniques to control the higher-order quantum coherence of photons. By engineering the spatially varying phases in the transverse plane, we can precisely manipulate the spatial structure of the second-order coherence function of entangled photon pairs without changing the photon intensity distribution of each photon. Our approach can readily be extended to higher-order quantum coherence control. These results could potentially stimulate experimental research and applications of optical quantum coherence.


Application of Biofeedback Technology in Human-Computer Interaction in Video Games

Abstract: From the birth of video games until today, players have interacted with video games via controllers. However, with the maturation of biofeedback technology, players can now obtain brand-new interactive gaming experiences. The commands input to the game are no longer merely from the buttons on the game controller. Biofeedback technology uses sensors to read the physiological signals of the game player, enabling the game to directly perceive the player’s physiological state through data such as heart rate, eye movement, and facial emotions when they are playing the game and match the next step in the game progress. Compared to traditional games, video games based on biofeedback technology provide specific, real-time feedback to individual players, creating an immersive gaming experience through embodied interactions, guiding game designers better. When artificial intelligence technology combines with biofeedback technology, AI will learn and analyze a large amount of user physiological data collected by biofeedback technology, making the interaction between the game and the players more intelligent and personalized.


Exploring Digital Literacy Impact: Unveiling the Dynamics of Student Success in Primary School Environments

Abstract: The primary aim of this study was to evaluate the relationship between digital literacy and students' perceptions of success in primary school, with a specific focus on fourth and fifth-grade levels. The findings may provide crucial insights to inform educational policies and enhance students' perceptions of success. The research examined this relationship using the relational survey model. Using a population of 6733 students in primary schools in Northern Cyprus, a survey consisting of “Personal information form, Digital Literacy Scale for Primary School Students, and Perception of Success Scale (DLSPSS)” was applied to 400 students by applying a convenient sampling method. The obtained data showed that students' Digital Literacy Levels (DLL) and the academic success perceptions were high. The study revealed that 5th-grade students, aged eleven, exhibited a higher digital literacy level compared to 4th-grade students. However, interestingly, the 5th-grade students' perception of success was found to be lower in comparison. No significant relationships were found between DLL and academic success perceptions. These findings indicate that education policies should focus on improving digital literacy skills and developing individualised strategies to improve students' perceptions of success. The practical significance of this study is that it provides valuable insights for educators, policymakers, and parents in understanding the digital literacy level and academic success perceptions of primary school students. By identifying the higher academic success perceptions among female students, educators can develop strategies to promote similar perceptions among their male counterparts. Additionally, the findings suggest that efforts should be made to enhance the digital literacy level of younger students, particularly 10-year-olds, to improve their academic success perceptions.


Hubungan Antara Minat Belajar dengan Prestasi Belajar Siswa pada Mata Pelajaran PAI Kelas X di SMK IT Bina Adzkia Depok

Abstract: The purpose of this research namely: (1) to determine students' learning interest in class X PAI subjects at SMK IT Bina Adzkia Depok, (2) to determine student learning achievement in class X PAI subjects at SMK IT Bina Adzkia Depok, (3) to find out whether there is a relationship between learning interest and student learning achievement in class X PAI subjects at SMK IT Bina Adzkia Depok. The data collection technique used is a questionnaire by distributing data to 40 grade X students, for data analysis, namely scoring, normality test and homogeneity test and the method used is correlation. The results of this study show that: (1) student learning interest in PAI subjects is included in the medium category, (2) student learning achievement in PAI subjects is included in the medium category, (3) there is a positive and significant relationship between interest in learning and student learning achievement in PAI subjects which are included in the medium category.


Efektivitas Pembuatan Sistem Informasi Manajemen Perpustakaan SMPN 1 Pantai Labu Berbasis Digital oleh KKN 110 UINSU

Abstract: This application is built with a user-friendly user interface, so that users i.e. library employees can use it easily. The purpose of developing this system is to improve the operational efficiency of the library and support SMP N 1 Pantai Labu in becoming a public school that focuses on information technology in the future. The methods in this research are field research (field research) and literature research (library research). The results show that the implementation of the library management information system in the SMP N 1 Pantai Labu Library will already be carried out but still has constraints on infrastructure and human resources.


Implementasi Media Promosi Katalog Baju yang Lebih Interaktif Menggunakan Aplikasi Augmented Reality

Abstract: The current technological advancement are progressing rapidly in line with the changing times. With the current pace of development, many people, especially teenagers, are keen on following fashion trends. This has led to increased competition in the fashion catalog industry. Many industries require information technology innovation, including the products offered to attract customers' attention. Typically, catalog industries advertise their products using posters, photos, or videos. However, these methods are less interactive and engaging as promotional media ideas in the current digital era. One technology that can address this issue is Augmented Reality (AR), which combines virtual objects with the real-world environment captured by the camera, making virtual objects appear integrated into the real world. By using Mobile Augmented Reality (AR) applications and with the help of our smartphones, we can view virtual clothing catalog objects on our smartphones, making them appear as if they were real. Moreover, users can virtually try on clothing over their own images, providing them with a better understanding of how the clothing will look when worn. This can create a more interactive and engaging shopping experience


Analisis Perancangan Sistem Informasi CDC Berbasis Web untuk Meningkatkan Informasi Lowongan Pekerjaan Bagi Lulusan Politeknik Piksi Ganesha Bandung

Abstract: The Career Development Center (CDC) is a forum for providing preparation for entering the world of work for alumni and students who will graduate from the Piksi Ganesha Bandung Polytechnic. CDC is a strategic center that serves as a bridge between the worlds of education and industry, helping individuals design and implement successful career plans. This research uses descriptive qualitative methods. Research on the creation of the CDC (Career Development Center) can be accessed by alumni, students of the Piksi Ganesha Bandung Polytechnic and the general public who are looking for job vacancies or career paths on the website. Conclusion: The Piksi Ganesha Bandung Career Development Center website provides support and convenience for students and alumni in looking for work, news, and how to find work according to their respective expertise in their respective fields as well as to help individuals develop the skills, knowledge and resources needed to achieve career goals. The author suggests that this application can be developed by collaborating with labor providers and establishing cooperation based on the principle of benefit for each party.


Deep Spectral Meshes: Multi-Frequency Facial Mesh Processing with Graph Neural Networks

Abstract: With the rising popularity of virtual worlds, the importance of data-driven parametric models of 3D meshes has grown rapidly. Numerous applications, such as computer vision, procedural generation, and mesh editing, vastly rely on these models. However, current approaches do not allow for independent editing of deformations at different frequency levels. They also do not benefit from representing deformations at different frequencies with dedicated representations, which would better expose their properties and improve the generated meshes' geometric and perceptual quality. In this work, spectral meshes are introduced as a method to decompose mesh deformations into low-frequency and high-frequency deformations. These features of low- and high-frequency deformations are used for representation learning with graph convolutional networks. A parametric model for 3D facial mesh synthesis is built upon the proposed framework, exposing user parameters that control disentangled high- and low-frequency deformations. Independent control of deformations at different frequencies and generation of plausible synthetic examples are mutually exclusive objectives. A Conditioning Factor is introduced to leverage these objectives. Our model takes further advantage of spectral partitioning by representing different frequency levels with disparate, more suitable representations. Low frequencies are represented with standardised Euclidean coordinates, and high frequencies with a normalised deformation representation (DR). This paper investigates applications of our proposed approach in mesh reconstruction, mesh interpolation, and multi-frequency editing. It is demonstrated that our method improves the overall quality of generated meshes on most datasets when considering both the $L_1$ norm and perceptual Dihedral Angle Mesh Error (DAME) metrics.


Learning Families of Algebraic Structures from Text

Abstract: We adapt the classical notion of learning from text to computable structure theory. Our main result is a model-theoretic characterization of the learnability from text for classes of structures. We show that a family of structures is learnable from text if and only if the structures can be distinguished in terms of their theories restricted to positive infinitary $\Sigma_2$ sentences.


Limits of Transformer Language Models on Algorithmic Learning

Abstract: We analyze the capabilities of Transformer language models on learning discrete algorithms. To this end, we introduce two new tasks demanding the composition of several discrete sub-tasks. On both training LLaMA models from scratch and prompting on GPT-4 and Gemini we measure learning compositions of learned primitives. We observe that the compositional capabilities of state-of-the-art Transformer language models are very limited and sample-wise scale worse than relearning all sub-tasks for a new algorithmic composition. We also present a theorem in complexity theory, showing that gradient descent on memorizing feedforward models can be exponentially data inefficient.


Phonetically rich corpus construction for a low-resourced language

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

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

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

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.


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

Abstract: 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

Abstract: 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


ANALYSIS OF FUZZY IMPLICATION

Abstract: 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

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

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

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

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.


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

Abstract: 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

Abstract: 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

Abstract: 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

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.


Replacing CAPTCHA with XNO micropayments

Abstract: 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

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

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

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

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

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

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

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.


Analysis Of Poverty Level Mapping In Riau Province Using The K-Means Method

Abstract: This research aims to understand the pattern of poverty distribution in Riau Province, identify clusters that reflect similar characteristics and provide a basis for developing more targeted policies. This approach uses machine learning techniques, especially the K-Means algorithm, to form clusters based on poverty level data. The results of the analysis show cluster 0 (C0) with a high poverty level and cluster 1 (C1) with a low poverty level. K-Means proved effective in grouping areas with similar levels of poverty, and provided a strong foundation for further analysis. Evaluation results using the Adjusted Rand Index (ARI), Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index show that the quality of cluster formation is good. This analysis provides detailed insight into poverty patterns in Riau Province and provides an empirical basis for implementing more contextual policies.


Classification of Public Sentiment Toward 2024 Presidential Candidates on Social Media Platform X Using Naïve Bayes Algorithm

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.


Analysis Sentiment On Social Media Instagram Towards Metaverse Games Saindbox Aplha 2 With Support Vector Machine Algorithm

Abstract: Metaverse is part from development technology in the metaverse SandBox Alpha 2 Game world taking place worldwide , games in the virtual world like real very possible thing done . metaverse now Already in progress for can be implemented most affected technology to opinion from particular society _ enthusiasts game metaverse saydbox alpha 2. where game can create her world alone and various game For look for missions and coins can make money to sell _ in metaverse sandbox alpha. since emergence exists game that has been appeared on facebook that has been replaced be meta, create attention world public increasingly highlight technology this , someone _ welcome game the with good and some have _ worries to development technology the . So study This will dig analysis sentiment public Indonesia against development and use metaverse technology uses method algorithm Algorithm Support Vector Machine. analysis sentiment that will done on social media Facebook. Programming language used _ is Language Jupyter Notebook Python. Study This get results opinion public Indonesia to metaverse technology that shows behave neutral , negative and positive .


Augmented Reality-Based 3D Technology Learning Media for Human Respiratory Organs

Abstract: Augmented Reality (AR) technology is selected for seamlessly integrating virtual objects into the real world. Users can represent objects in three dimensions, offering an interactive, real-time experience. The educational sector widely embraces Augmented Reality due to its interactive and timely advantages. This study focuses on the human respiratory system, demonstrating Augmented Reality's efficacy as an interactive learning tool for understanding respiratory organs. The learning material utilizes three-dimensional visualizations with animations, sound, and vibrant colors, enhancing comprehension and preventing boredom. Employing the Multimedia Development Method and software tools like Unity 3D, Blender, CorelDraw, Vuforia, and Visual Studio Code, the research produces a learning media application facilitating the understanding of human respiratory organs. Evaluation through questionnaires, with a 93.16% approval rate from teachers and students, confirms the application's suitability as an effective learning tool.


The Alteration and Mineralization Characteristics of Miocene Porphyry Cu-Au Deposits of Chagai Magmatic Belt, District Chagai, Balochistan, Pakistan

Abstract: Subduction related Miocene porphyry type deposits are found in the east-west trending Chagai magmaticbelt (CMB) in Pakistan's western margin, Balochistan. This arc exists on the west segment of the Tethyan metallogenicbelt in the south-west of Pakistan. Tethyan metallogenic belt is widely spread over 12,000 km from east to westdirection from Indochina, Tibet, Pakistan, Iran, Turkey and Alpine mountain range in Europe. During the last thirty toforty years several porphyry deposits have been reported in the Chagai magmatic arc, including the very large RekoDiq H14-H15, large Saindak, Tanjeel, H35, H8 and medium Dasht-e-Kain porphyry deposits and many small porphyrycopper deposits. These porphyry deposits were developed within the phase of calc-alkaline type magmatism in theChagai arc. Tonalite, quartz diorite, and monzonite host the porphyry deposits within the adjacent sedimentary wallrock units of Sinjrani Volcanic Group, Juzzak, Saindak, and Amalaf Formations. The concentric zonal pattern ofhydrothermal alteration in these porphyry deposits of the Chagai magmatic arc follows the world's major porphyrydeposits' alteration pattern. Zones of hydrothermal alteration from distal to proximal part includes a potassic alteration,sericitic-clay-chlorite alteration, sericitic alteration, argillic alteration and propylitic alteration. Major ore mineralizationin these deposits is of copper, gold, silver, molybdenum, and minor constituents of other base metals that have beenreported to occur within hydrothermal alteration zones in the Miocene porphyry Chagai magmatic arc.


Regression Trees for Fast and Adaptive Prediction Intervals

Abstract: Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create statistically valid prediction regions around point predictions, but its naive application to regression problems yields non-adaptive regions. New conformal scores, often relying upon quantile regressors or conditional density estimators, aim to address this limitation. Although they are useful for creating prediction bands, these scores are detached from the original goal of quantifying the uncertainty around an arbitrary predictive model. This paper presents a new, model-agnostic family of methods to calibrate prediction intervals for regression problems with local coverage guarantees. Our approach is based on pursuing the coarsest partition of the feature space that approximates conditional coverage. We create this partition by training regression trees and Random Forests on conformity scores. Our proposal is versatile, as it applies to various conformity scores and prediction settings and demonstrates superior scalability and performance compared to established baselines in simulated and real-world datasets. We provide a Python package clover that implements our methods using the standard scikit-learn interface.


Quality Does Matter: A Detailed Look at the Quality and Utility of  Web-Mined Parallel Corpora

Abstract: We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are significant quality differences between different portions of web-mined corpora and that the quality varies across languages and datasets. We also show that, for some web-mined datasets, Neural Machine Translation (NMT) models trained with their highest-ranked 25k portion can be on par with human-curated datasets.


LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied  Agents

Abstract: Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction. To address this, we propose a benchmark system for automatically quantifying performance of task planning for home-service embodied agents. Task planners are tested on two pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. We expect that the proposed benchmark tool would accelerate the development of language-oriented task planners.


FLASH: Federated Learning Across Simultaneous Heterogeneities

Abstract: The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in data distribution, but also in data quality, as well as compute/communication latency. An integrated view of these diverse and concurrent sources of heterogeneity is critical; for instance, low-latency clients may have poor data quality, and vice versa. In this work, we propose FLASH(Federated Learning Across Simultaneous Heterogeneities), a lightweight and flexible client selection algorithm that outperforms state-of-the-art FL frameworks under extensive sources of heterogeneity, by trading-off the statistical information associated with the client's data quality, data distribution, and latency. FLASH is the first method, to our knowledge, for handling all these heterogeneities in a unified manner. To do so, FLASH models the learning dynamics through contextual multi-armed bandits (CMAB) and dynamically selects the most promising clients. Through extensive experiments, we demonstrate that FLASH achieves substantial and consistent improvements over state-of-the-art baselines -- as much as 10% in absolute accuracy -- thanks to its unified approach. Importantly, FLASH also outperforms federated aggregation methods that are designed to handle highly heterogeneous settings and even enjoys a performance boost when integrated with them.


Depth-aware Volume Attention for Texture-less Stereo Matching

Abstract: Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of rich geometric and semantic information. In this paper, we propose a lightweight volume refinement scheme to tackle the texture deterioration in practical outdoor scenarios. Specifically, we introduce a depth volume supervised by the ground-truth depth map, capturing the relative hierarchy of image texture. Subsequently, the disparity discrepancy volume undergoes hierarchical filtering through the incorporation of depth-aware hierarchy attention and target-aware disparity attention modules. Local fine structure and context are emphasized to mitigate ambiguity and redundancy during volume aggregation. Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models. We extensively validate the superiority of our proposed methods on public datasets. Results demonstrate that our model achieves state-of-the-art performance, particularly excelling in scenarios with texture-less images. The code is available at https://github.com/ztsrxh/DVANet.


Learning Interpretable Concepts: Unifying Causal Representation Learning  and Foundation Models

Abstract: To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate these two approaches and study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we formally define a notion of concepts and show that they can be provably recovered from diverse data. Experiments on synthetic data and large language models show the utility of our unified approach.


International exchanges and cooperation in art education in Ukraine: challenges and opportunities

Abstract: The purpose of the article is to study international exchanges and cooperation in art education in Ukraine, to identify challenges and opportunities in the development of these areas in the future. to achieve this goal, the article uses general scientific methods of analysis and synthesis. The methods of abstraction, comparison, content analysis, and the prognostic method were also used, which made it possible to formulate some comments for the future development of international cooperation in art education in Ukraine. The results of the study highlight the importance of international cooperation, which is becoming an increasingly important aspect of modern art education. The analysis of the legislative framework has shown that Ukraine has created favourable conditions for academic exchange, and joining the Bologna system contributes to the internationalisation of higher education in the arts. These trends are also reflected in the development strategies of art universities in different regions of Ukraine, such as Kharkiv, Lviv and Kyiv, where establishing international exchanges is considered an important priority for educational institutions. In addition, the Kyiv National University of Culture and Arts actively uses digital channels to transmit information, especially in remote locations. Although distance learning has its limitations in art education, the conditions of modern reality, such as Russian aggression, create an opportunity to organise international cooperation with foreign partners without physical risk. It is important to note that there are common challenges to the successful implementation of international exchanges and cooperation in art education, such as cultural and language barriers, financial support, visa and administrative restrictions, as well as academic requirements and potential global threats. However, a possible way to overcome these difficulties is to use the international experience of involving NGOs or special state structures that would coordinate the provision of grants for academic mobility of arts students. The conclusions emphasise that it is also worth considering the possibility of special digital platforms that provide opportunities for internships and distance learning, which can be an effective solution for organising international cooperation in the current situation.


Abstraction boundaries and spec driven development in pure mathematics

Abstract: In this article we discuss how <italic>abstraction boundaries</italic> can help tame complexity in mathematical research with the help of an interactive theorem prover. While many of the ideas we present here have been used implicitly by mathematicians for some time, we argue that the use of an interactive theorem prover introduces additional qualitative benefits in the implementation of these ideas.


Using the Backpropagation Algorithm to Distinguish Arabic Alphabet

Abstract: In this research, a study of the Arabic alphabet used a multi-layered neural network, which is the backpropagation error. Using the algorithm through the Losing activation function to train the network. The hidden numbers of nodes are 10, the number of cycles is 500, and the error is 0.001, using the Matlab R2013a program. The aim of the study It is the use of the network algorithm to recognize the characters, by training the network to recognize the characters in two cases. The first case is inputting the image of the letter into the grid and the second case is identifying the letter that represents the letter drawn in the image. And it was reached that the algorithm used for the network of nervousness to recognize the Arabic alphabet and then show it correctly.


Sequential composition of propositional logic programs

Abstract: Abstract This paper introduces and studies the sequential composition and decomposition of propositional logic programs. We show that acyclic programs can be decomposed into single-rule programs and provide a general decomposition result for arbitrary programs. We show that the immediate consequence operator of a program can be represented via composition which allows us to compute its least model without any explicit reference to operators. This bridges the conceptual gap between the syntax and semantics of a propositional logic program in a mathematically satisfactory way.


Language Teaching Materials for Writing Advertising Texts

Abstract: This study is based on extensive library research, utilizing a wide range of information and resources from various sources. Having the right instructional materials is crucial for the successful completion of any academic program, including Indonesian language classes. Junior high school Indonesian language classes in Class VIII explore a range of subjects, one of which is the art of crafting advertisement copy. To achieve optimal outcomes, it is crucial to utilize appropriate instructional resources while acquiring proficiency in the Indonesian language for advertising purposes. This research aimed to provide a comprehensive description of the different types of advertisements that can be utilized as teaching materials in Indonesian language courses, along with an exploration of the diverse concepts associated with these materials.


Learning program to develop digital skills in high school teachers

Abstract: The present study focused on evaluating the effectiveness of a learning program aimed at strengthening the digital skills of high school teachers, given the current lack of these skills in the educational environment. A quantitative approach was adopted through a quasi-experimental design, with a population of 73 teachers. A 21-item questionnaire was implemented, validated through expert judgment and the V-Aiken coefficient, with a reliability of 0.826. After the application of the designed program, significant improvements were observed in the experimental group compared to the control group, which shows the positive impact of the program on the development of digital skills. These findings highlight the need to integrate training and continuing education programs to enhance digital skills in the educational field.


Unveiling the Power of Deep Learning: Insights into Advanced Neural n Network Architectures

Abstract: Deep learning has emerged as a powerful paradigm within artificial intelligence, revolutionizing various fields with its ability to automatically discover intricate patterns and representations from vast amounts of data. In this article, we delve into the latest advancements in deep learning, focusing on the exploration of advanced neural network architectures. Through a comprehensive review and analysis, we uncover the underlying principles and methodologies driving the success of these architectures, elucidating their capabilities and limitations. From convolutional neural networks (CNNs) for image recognition to recurrent neural networks (RNNs) for sequential data processing, we highlight key innovations and breakthroughs that have propelled the field forward. Furthermore, we discuss recent trends such as attention mechanisms, transformer models, and graph neural networks, showcasing their potential applications and impact across diverse domains. By unraveling the power of deep learning and its advanced neural network architectures, this article aims to provide valuable insights into the state-of-the-art techniques driving AI research and development.


Scaling adaptive quantum simulation algorithms via operator pool tiling

Abstract: A systematic and easily generalizable method to perform adaptive variational quantum simulations on lattice models, termed operator tiling, is proposed, in which a small problem instance is first studied to learn the most relevant operators. State preparation circuits using operators identified from solving the small problem instance are then iteratively tiled to larger problem instances. The method is demonstrated on strongly correlated spin models in one and two dimensions and shown to yield effective ans\"atze.


A Sparse Self-Attention Enhanced Model for Aspect-Level Sentiment Classification

Abstract: Abstract Aspect based sentiment analysis (ABSA) manifests the well refined work as Aspect-level sentiment classification (ASC) due to recent high attention and profound outcomes. This paper reveals the exorbitant relation of concerned aspect in determining the sentiment polarity of a sentence in addition to their content. Considering an instance, “The food is tasty but restaurant is untidy”, aspect food reveals the polarity as positive, while in case of restaurant, the polarity seems negative. Hence to scout relation between an $$aspect$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>aspect</mml:mi> </mml:mrow> </mml:math> and $$context$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>context</mml:mi> </mml:mrow> </mml:math> words present in sentence is much vital. In spite of the exceptional progress, the ASC experiences certain pitfalls as (1) The current attention based methods makes the given aspect to falsely relate syntactically unrelated words as related ones. (2) Single context-independent representation is only achieved by traditional Word2Vec or GloVe based embedding vectors. (3) Sentiments for multiple words that are inconsecutive remain insufficient for CNN based models. A Sparse-Self-Attention-based Gated Recurrent Unit with Aspect Embedding (SSA-GRU-AE) implementing BERT for ASC is proposed to solve these issues. The proposed SSA-GRU-AE mechanism is centralized on various portions of sentence as multiple aspects are taken as input. The experimental analysis on ASC datasets has proved that proposed model enhanced performance on ASC.


GROWING STUDENT LITERACY ABILITY THROUGH THE SCHOOL LITERACY MOVEMENT

Abstract: In today's rapidly evolving world, literacy has become an essential skill for individuals to navigate and succeed in various aspects of life. Literacy is not limited to reading and writing but also encompasses critical thinking, comprehension, and communication skills. Recognizing the significance of literacy, many educational institutions and organizations have initiated programs and movements to enhance the literacy capabilities of students. One such movement is the School Literacy Movement. This study aims to explore gerakan literasi sekolah and its impact on the growth of students' literacy skills.


THE ROLE OF GOVERNMENT POLICY IN FOSTERING ENTREPRENEURSHIP: A USA AND AFRICA REVIEW

Abstract:
This Review explores the crucial role of government policies in fostering entrepreneurship by examining the experiences of the United States (USA) and various African nations. Entrepreneurship is a driving force for economic growth, job creation, and innovation, and government policies play a pivotal role in shaping the entrepreneurial landscape. In the USA, a robust history of entrepreneurial success is attributed in part to supportive government policies. The Review delves into specific policies that have contributed to fostering entrepreneurship, including tax incentives, access to funding through Small Business Administration (SBA) programs, and initiatives promoting innovation and research. The analysis extends to the regulatory environment, emphasizing the importance of flexible regulations that encourage innovation while ensuring consumer protection. Turning attention to Africa, the Review explores the diversity of government approaches across the continent. It considers initiatives implemented by African nations to promote entrepreneurship, such as the establishment of entrepreneurial ecosystems, funding mechanisms for startups, and efforts to streamline regulatory frameworks. The review acknowledges the unique challenges faced by African entrepreneurs, including limited access to capital and infrastructure constraints, and evaluates how government policies aim to address these issues. The comparative analysis highlights lessons that African nations can draw from the USA's successful policies, such as the importance of fostering a culture of innovation, providing targeted financial support, and creating an enabling regulatory environment. Moreover, it emphasizes the need for context-specific policies that consider the unique socio-economic conditions of each African nation. In conclusion, the Review underscores the critical role of government policy in nurturing entrepreneurship, drawing insights from the USA's experience and the varied approaches across African nations. It calls for a nuanced understanding of the diverse African entrepreneurial landscape and encourages policymakers to tailor initiatives that catalyze entrepreneurship, driving economic development across the continent.
 Keywords: Role, Government, Policy, Fostering, Entrepreneurship.


A CRITICAL REVIEW OF ERP SYSTEMS IMPLEMENTATION IN MULTINATIONAL CORPORATIONS: TRENDS, CHALLENGES, AND FUTURE DIRECTIONS

Abstract:
Enterprise Resource Planning (ERP) systems have become indispensable tools for managing complex business processes in multinational corporations (MNCs). This paper presents a critical review of the trends, challenges, and future directions associated with ERP systems implementation in the context of MNCs. The study synthesizes existing literature to offer a comprehensive understanding of the evolving landscape of ERP adoption in a globalized business environment. The trends in ERP implementation within MNCs underscore the increasing reliance on integrated software solutions to enhance operational efficiency and facilitate seamless communication across diverse geographical locations. Notably, the integration of emerging technologies such as artificial intelligence, machine learning, and blockchain into ERP systems has gained traction, presenting new opportunities and complexities for MNCs. However, the implementation of ERP systems in MNCs is not without challenges. Cultural diversity, varying regulatory frameworks, and differences in business practices across regions pose significant obstacles to successful deployment. Additionally, the sheer scale and complexity of MNCs' operations demand careful consideration of customization needs, scalability, and alignment with organizational goals. The critical review highlights the need for a strategic approach to ERP implementation in MNCs. Successful cases often involve top management commitment, extensive user training, and a phased implementation strategy that accommodates regional variations. Furthermore, the study emphasizes the importance of post-implementation evaluation and continuous improvement to address evolving business requirements. Looking ahead, future directions in ERP systems implementation within MNCs are shaped by the rapid evolution of technology and the dynamic nature of global business. The integration of cloud-based solutions, the advancement of Industry 4.0 technologies, and the increasing emphasis on sustainability are anticipated to influence the next wave of ERP adoption in MNCs. This critical review contributes valuable insights into the current state of ERP systems implementation in multinational corporations, addressing trends, challenges, and future directions. It provides a roadmap for practitioners, researchers, and policymakers to navigate the complexities of ERP adoption in the ever-evolving landscape of global business.
 Keywords: ERP, MNCs, Industry 4.0, Multinational, Corporation, Review.


A cross-talk robust multichannel VAD model for multiparty agent  interactions trained using synthetic re-recordings

Abstract: In this work, we propose a novel cross-talk rejection framework for a multi-channel multi-talker setup for a live multiparty interactive show. Our far-field audio setup is required to be hands-free during live interaction and comprises four adjacent talkers with directional microphones in the same space. Such setups often introduce heavy cross-talk between channels, resulting in reduced automatic speech recognition (ASR) and natural language understanding (NLU) performance. To address this problem, we propose voice activity detection (VAD) model for all talkers using multichannel information, which is then used to filter audio for downstream tasks. We adopt a synthetic training data generation approach through playback and re-recording for such scenarios, simulating challenging speech overlap conditions. We train our models on this synthetic data and demonstrate that our approach outperforms single-channel VAD models and energy-based multi-channel VAD algorithm in various acoustic environments. In addition to VAD results, we also present multiparty ASR evaluation results to highlight the impact of using our VAD model for filtering audio in downstream tasks by significantly reducing the insertion error.


How to validate average calibration for machine learning regression  tasks ?

Abstract: Average calibration of the uncertainties of machine learning regression tasks can be tested in two ways. One way is to estimate the calibration error (CE) as the difference between the mean absolute error (MSE) and the mean variance (MV) or mean squared uncertainty. The alternative is to compare the mean squared z-scores or scaled errors (ZMS) to 1. Both approaches might lead to different conclusion, as illustrated on an ensemble of datasets from the recent machine learning uncertainty quantification literature. It is shown here that the CE is very sensitive to the distribution of uncertainties, and notably to the presence of outlying uncertainties, and that it cannot be used reliably for calibration testing. By contrast, the ZMS statistic does not present this sensitivity issue and offers the most reliable approach in this context. Implications for the validation of conditional calibration are discussed.


Human Activity Recognition System with Smartphones Using Machine Learning

Abstract: Abstract: Human activity recognition (HAR) is a crucial task in the field of physical activity monitoring. It involves the identification of a person's activities and movements using sensors. The accuracy of HAR systems plays a significant role in enhancing physical health, preventing accidents and injuries, and improving security systems. One of the most common approaches to HAR is using smartphones as sensors. However, smartphones have limited processing power and battery life, which can impact the performance of HAR systems. To overcome these challenges, we can apply different machine learning models and compare their performances. We begin by selecting a standard dataset, the HAR dataset from the Machine Learning Repository. We then apply several Machine learning models, including Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN).To compare the performance of these models, we train and test each model on the HAR dataset. We also select the best set of parameters for each model using grid search. Our results show that the Support Vector Machine (SVM) performed the best (average accuracy 96.33%), significantly outperforming the other models. We can confirm the statistical significance of these results by employing statistical significance test methods.The SVM model demonstrated superior performance in recognizing human activities. This highlights the potential of machine learning models in revolutionizing the field of HAR. However, further research is needed to address the challenges of limited processing power and battery life in smartphones and to explore other potential applications of HAR systems.


Low-Rank Graph Contrastive Learning for Node Classification

Abstract: Graph Neural Networks (GNNs) have been widely used to learn node representations and with outstanding performance on various tasks such as node classification. However, noise, which inevitably exists in real-world graph data, would considerably degrade the performance of GNNs revealed by recent studies. In this work, we propose a novel and robust GNN encoder, Low-Rank Graph Contrastive Learning (LR-GCL). Our method performs transductive node classification in two steps. First, a low-rank GCL encoder named LR-GCL is trained by prototypical contrastive learning with low-rank regularization. Next, using the features produced by LR-GCL, a linear transductive classification algorithm is used to classify the unlabeled nodes in the graph. Our LR-GCL is inspired by the low frequency property of the graph data and its labels, and it is also theoretically motivated by our sharp generalization bound for transductive learning. To the best of our knowledge, our theoretical result is among the first to theoretically demonstrate the advantage of low-rank learning in graph contrastive learning supported by strong empirical performance. Extensive experiments on public benchmarks demonstrate the superior performance of LR-GCL and the robustness of the learned node representations. The code of LR-GCL is available at \url{https://anonymous.4open.science/r/Low-Rank_Graph_Contrastive_Learning-64A6/}.


WERank: Towards Rank Degradation Prevention for Self-Supervised Learning  Using Weight Regularization

Abstract: A common phenomena confining the representation quality in Self-Supervised Learning (SSL) is dimensional collapse (also known as rank degeneration), where the learned representations are mapped to a low dimensional subspace of the representation space. The State-of-the-Art SSL methods have shown to suffer from dimensional collapse and fall behind maintaining full rank. Recent approaches to prevent this problem have proposed using contrastive losses, regularization techniques, or architectural tricks. We propose WERank, a new regularizer on the weight parameters of the network to prevent rank degeneration at different layers of the network. We provide empirical evidence and mathematical justification to demonstrate the effectiveness of the proposed regularization method in preventing dimensional collapse. We verify the impact of WERank on graph SSL where dimensional collapse is more pronounced due to the lack of proper data augmentation. We empirically demonstrate that WERank is effective in helping BYOL to achieve higher rank during SSL pre-training and consequently downstream accuracy during evaluation probing. Ablation studies and experimental analysis shed lights on the underlying factors behind the performance gains of the proposed approach.


Probabilistic Reasoning in Generative Large Language Models

Abstract: This paper considers the challenges that Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values. This type of reasoning is relevant to a variety of contexts ranging from everyday conversations to medical decision-making. Despite improvements in the mathematical reasoning capabilities of LLMs, they still exhibit significant difficulties when it comes to probabilistic reasoning. To deal with this problem, we first introduce the Bayesian Linguistic Inference Dataset (BLInD), a new dataset specifically designed to test the probabilistic reasoning capabilities of LLMs. We then leverage this new dataset to thoroughly illustrate the specific limitations of LLMs for tasks involving probabilistic reasoning and present several strategies that map the problem to different formal representations, including Python code, probabilistic inference algorithms, and probabilistic logical programming. We conclude by providing an evaluation of our methods on BLInD and on an adaptation of a causal reasoning question-answering dataset, which further shows their practical effectiveness.


Scalable Graph Self-Supervised Learning

Abstract: In regularization Self-Supervised Learning (SSL) methods for graphs, computational complexity increases with the number of nodes in graphs and embedding dimensions. To mitigate the scalability of non-contrastive graph SSL, we propose a novel approach to reduce the cost of computing the covariance matrix for the pre-training loss function with volume-maximization terms. Our work focuses on reducing the cost associated with the loss computation via graph node or dimension sampling. We provide theoretical insight into why dimension sampling would result in accurate loss computations and support it with mathematical derivation of the novel approach. We develop our experimental setup on the node-level graph prediction tasks, where SSL pre-training has shown to be difficult due to the large size of real world graphs. Our experiments demonstrate that the cost associated with the loss computation can be reduced via node or dimension sampling without lowering the downstream performance. Our results demonstrate that sampling mostly results in improved downstream performance. Ablation studies and experimental analysis are provided to untangle the role of the different factors in the experimental setup.


Quantified Task Misalignment to Inform PEFT: An Exploration of Domain  Generalization and Catastrophic Forgetting in CLIP

Abstract: Foundations models are presented as generalists that often perform well over a myriad of tasks. Fine-tuning these models, even on limited data, provides an additional boost in task-specific performance but often at the cost of their wider generalization, an effect termed catastrophic forgetting. In this paper, we analyze the relation between task difficulty in the CLIP model and the performance of several simple parameter-efficient fine-tuning methods through the lens of domain generalization and catastrophic forgetting. We provide evidence that the silhouette score of the zero-shot image and text embeddings is a better measure of task difficulty than the average cosine similarity of correct image/label embeddings, and discuss observable relationships between task difficulty, fine-tuning method, domain generalization, and catastrophic forgetting. Additionally, the averaged results across tasks and performance measures demonstrate that a simplified method that trains only a subset of attention weights, which we call A-CLIP, yields a balance between domain generalization and catastrophic forgetting.