Reviewerly/exHarmony huggingface dataset:
Are crypto and non-crypto investors alike? Evidence from a comprehensive survey in Brazil

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.