Reviewerly/exHarmony huggingface dataset:
Applications of Artificial Intelligence in Education

Authors:
raw_author_name: Zh. Turdubaeva

Abstract: The article examines current trends and prospects for the implementation of artificial intelligence (AI) technologies in educational processes. The authors analyze various aspects of the use of AI, such as individualized learning, automated assessment of knowledge, the creation of intelligent educational platforms and personalized educational plans. The article also analyzes the ethical and social issues associated with the use of artificial intelligence in education and offers recommendations for the development of effective strategies for integrating technology into educational systems. Overall, the article seeks to shed light on the role of artificial intelligence in modern education and its potential in improving the quality of learning and developing educational practices.
Assessing Factors Influencing Customers’ Adoption of AI-Based Voice Assistants

Authors:
raw_author_name: Neeraj Kaushik
raw_affiliation_string: National Institute of Technology, Kurukshetra, India
raw_author_name: Brijesh Sivathanu
raw_affiliation_string: COEP Technological University (Formerly College of Engineering Pune), Pune, India
raw_author_name: Nripendra P. Rana
raw_affiliation_string: Qatar University, Doha, Qatar

Abstract: The study aims to assess the factors that engage and accentuate usage pertaining to consumers with artificial intelligence-based voice assistants. Drawing on the Behavioral Reasoning Theory (BRT), the paper establishes relationships between the enablers and inhibitors on the adoption intention of voice assistants. To examine the hypothesized relationship, a quantitative approach employing PLS-SEM was utilized to evaluate a representative sample of 1189 Indian consumers and examining the psychological and functional constructs leading to adoption intention for voice assistants. The study reveals that the “value of openness to change” poses a significant effect toward “reasons for” adoption, among which “performance expectancy” and “hedonic motivation” play the most significant role in establishing a favorable attitude toward voice assistants’ adoption. Among the “reasons against”, the “value barrier” and “image barrier” come out to be major determinants leading to inhibitions regarding the usage of voice assistants. Besides this, the findings reclaim that “reasons” (for/against) shape to form the attitude of consumers toward voice assistants. The current research offers preliminary evidence with regard to the investigation of voice assistant adoption using the BRT that jointly examines enabling and inhibiting elements leading to consumers’ attitude formation. The study findings contribute to Human-Computer interaction literature by utilizing BRT framework which provides incremental evidence demonstrating a much broader and organic explanation of consumers’ cognition for the adoption of voice assistant technology. It also provides implications for tech-managers and algorithm designers to build effective voice technology for superior user experience.
Pengaruh Komunikasi Interpersonal Pustakawan terhadap Kepuasan Pemustaka di Perpustakaan Era Digital (Studi Kasus di Poltekkes Kemenkes Padang)

Authors:
raw_author_name: Jeihan Nabila
raw_affiliation_string: Universitas Negeri Padang

Abstract: The purpose of this study was to determine (1) describe the interpersonal communication of librarians in the Library of Poltekkes Kemenkes Padang. (2) describe the level of user satisfaction at the Poltekkes Kemenkes Padang Library. (3) describe the effect of librarian interpersonal communication on the level of library satisfaction at the Poltekkes Kemenkes Padang Library in the digital era. This research is a type of quantitative research with descriptive methods, data obtained through distributing questionnaires at the Poltekkes Kemenkes Padang Library. The population in this study was visitor data from January to December 2022 with a sample size of 99 respondents. Sampling is determined based on the Simple Random Sampling Technique which is a sampling technique that is carried out randomly without regard to the strata in the population. The results of this study are (1) interpersonal communication of librarians at the Poltekkes Kemenkes Padang Library is classified as high with an average value of 3.07 on a good interval scale. (2) user satisfaction at the Poltekkes Kemenkes Padang Library is high with an average of 3.19 on a good interval scale. (3) Librarian interpersonal communication affects library satisfaction at the Poltekkes Kemenkes Padang Library, where the Fhitung value (39.747)&gt; Ftabel (3.94) at a significant level of 5% which states that there is a significant influence between librarian interpersonal communication on library satisfaction at the Poltekkes Kemenkes Padang Library.
Trimodal extension based on the flexible generalized skew-normal distribution

Authors:
raw_author_name: Michele Bufalo

Abstract: We propose a novel class of generalized skew-normal densities that improvesthe flexibility of empirical distributions and can systematically capture skewness,heavy tails, and multimodality. We extend the so-called flexible generalized skewnormal(FGSN) density developed by Y. Ma and M.G. Genton in 2004. The mainnovelty is the existence of a fifth-order degree term in the polynomial that appearsin the cumulative distribution function of such a density. In this case, we provethat our density has at most three modes under certain conditions for the parameters.Leveraging this new approach eases the modeling of data consisting of threesubpopulations. For validation, we present examples of both univariate and multivariatecases using demographic data from the Human Mortality Data Base (HMD).
Score: Mechanical Asynchronicity – audio album

Authors:
raw_author_name: None Martin Scheuregger

Abstract: This exposition is the online, open-access audio album of 'Score: Mechanical Asynchronicity' by Danica Maier and Martin Scheuregger. The album allows you to play full ensemble versions of the project's two pieces, and also mix your own versions of each piece by combining and balancing the individual instruments. It accompanies the publication of a related 40-page book on the project from Beam Editions.
Generative AI: An AI paradigm shift in the making?

Authors:
raw_author_name: Risto Miikkulainen
raw_affiliation_string: Cognizant AI Labs San Francisco California USA; The University of Texas at Austin Austin Texas USA

Abstract: Abstract It is sometimes difficult to evaluate progress in Generative AI, that is, image generation and large language models. This may be because they represent a paradigm shift in AI, and the traditional ways of developing, evaluating, understanding, and deploying AI systems no longer apply. Instead, we need to develop new such approaches, possibly by extending those currently in use in cognitive neuroscience and psychology. In this manner, a new AI paradigm can be created, providing a significant leap in AI research and practice.
REVITALIZING THE SCHOOL LIBRARY: EMBRACING TECHNOLOGY AND INNOVATION

Authors:
raw_author_name: None Mariel Kristine M. Cortez
raw_affiliation_string: Don Manuel Rivera Memorial Integrated National High School, Laguna, Philippines

Abstract: This innovation revolves in the relaunching of the new library with the aid of modern technology. Installation of new library facilities such as the multimedia corner, educational board games corner and printing corner motivated learners to visit the school library that made them more excited in learning, reading and discovering things around them. This innovation was made possible through the unwavering support from various stakeholders extending their assistance by providing the financial needs of this project. KEYWORDS: Library, Innovation,Technology
MENINGKATKAN KETERAMPILAN MENULIS DIALOG PADA MATA PELAJARAN BAHASA INDONESIA MELALUI PENDEKATAN COOPERATIVE LEARNING SISTEM STAD PADA SISWA KELAS III MI PULOERANG LAKBOK KABUPATEN CIAMIS

Authors:
raw_author_name: Yuyun Yuningsih

Abstract: One of the problems faced by the teacher is the passivity of students in class, in group study in class to express opinions, it can be said that the students' abilities are not yet fluent. not optimal, Indonesian learning achievement is still low, especially in formative tests, summative students are not yet brave enough to answer questions or express opinions, the use of models or approaches in learning is not maximized. The research model is a procedure that describes how this research is carried out. In this class action research, the researcher used the action research model developed by Kemmis and McTaggart (1990:11). Based on the success indicator, namely 75% of the class population scored above the KKM, the action in cycle II was successful. As many as 16 students (88.89%) scored above the KKM. The learning process is further improved, namely students have done peer tutoring and students are more active in learning activities with the teacher. Giving material that has been repeated makes students understand more about the material presented by the teacher. Students who better understand the meaning of the activities carried out by the teacher help friends who do not understand. Thus cooperative learning is achieved. In the pre-action, the dialogue writing skills of class III MI Puloerang Lakbok students were still low. Students who can achieve KKM only 3 children (16.67%). After carrying out the STAD type cooperative learning actions in the first cycle, it produced 8 students (44.44%). Furthermore, in cycle II, there were 16 students (88.89%) who succeeded in achieving the KKM. BIBLIOGRAPHY Anita Lie. 2002. Cooperative Learning. Jakarta: Gramedia. Dimyati dan Mudjiono.2009. Belajar dan pembelajaran, Jakarta Rineka Cipta Haryadi dan Zamzani. 1996/ 1997. Peningkatan Keterampilan Berbahasa Indonesia, Jakarta. DepDikBud Dirjen Dikti PPTK Isjoni. 2009. Cooperative Learning. Bandung : Alfabeta Kemmis, San Mc. Taggart, R.1990. The Action Reseach Planner. Deakin University Larry King Bill GILBERT. 2007 Seni berbicara, Jakarta : PT Gramedia Pustaka Utama Slamet, 2008. Keterampilan-Keterampilan Berbahasa Indonesia. Surakarta: UNS Press. Suharjono, 1995. Pedoman Karya Tulis Ilmiah, Jakarta : Dep. P &amp; K Suharjono. 2007. PTK sebagai Kegiatan Pengembangan Profesi Guru, Jakarta : Bumi Aksara Suharsimi Arikunto. 2006 Penelitian Tindakan Kelas, Jakarta : PT Bumi Aksara. UU RI No. 20 tahun 2003 tentang SISDIKNAS. Bandung : Fokus Media UU RI No 14 tahun 2005 tentang guru dan dosen. Jakarta : Novindo Pustaka Mandiri
Exchangeability, prediction and predictive modeling in Bayesian  statistics

Authors:
raw_author_name: Fortini, Sandra
raw_author_name: Petrone, Sonia

Abstract: Prediction is a central problem in Statistics, and there is currently a renewed interest for the so-called predictive approach in Bayesian statistics. What is the latter about? One has to return on foundational concepts, which we do in this paper, moving from the role of exchangeability and reviewing forms of partial exchangeability for more structured data, with the aim of discussing their use and implications in Bayesian statistics. There we show the underlying concept that, in Bayesian statistics, a predictive rule is meant as a learning rule - how one conveys past information to information on future events. This concept has implications on the use of exchangeability and generally invests all statistical problems, also in inference. It applies to classic contexts and to less explored situations, such as the use of predictive algorithms that can be read as Bayesian learning rules. The paper offers a historical overview, but also includes a few new results, presents some recent developments and poses some open questions.
Hidden Traveling Waves bind Working Memory Variables in Recurrent Neural  Networks

Authors:
raw_author_name: Sejnowski, Terrence J.
raw_author_name: Siegelmann, Hava T.

Abstract: Traveling waves are a fundamental phenomenon in the brain, playing a crucial role in short-term information storage. In this study, we leverage the concept of traveling wave dynamics within a neural lattice to formulate a theoretical model of neural working memory, study its properties, and its real world implications in AI. The proposed model diverges from traditional approaches, which assume information storage in static, register-like locations updated by interference. Instead, the model stores data as waves that is updated by the wave's boundary conditions. We rigorously examine the model's capabilities in representing and learning state histories, which are vital for learning history-dependent dynamical systems. The findings reveal that the model reliably stores external information and enhances the learning process by addressing the diminishing gradient problem. To understand the model's real-world applicability, we explore two cases: linear boundary condition and non-linear, self-attention-driven boundary condition. The experiments reveal that the linear scenario is effectively learned by Recurrent Neural Networks (RNNs) through backpropagation when modeling history-dependent dynamical systems. Conversely, the non-linear scenario parallels the autoregressive loop of an attention-only transformer. Collectively, our findings suggest the broader relevance of traveling waves in AI and its potential in advancing neural network architectures.
Align before Attend: Aligning Visual and Textual Features for Multimodal  Hateful Content Detection

Authors:
raw_author_name: Hossain, Eftekhar
raw_author_name: Sharif, Omar
raw_author_name: Hoque, Mohammed Moshiul
raw_author_name: Preum, Sarah M.

Abstract: Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach's effectiveness with F1-scores of $69.7$% and $70.3$% for the MUTE and MultiOFF datasets. The scores show approximately $2.5$% and $3.2$% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://github.com/eftekhar-hossain/Bengali-Hateful-Memes.
BLOCKCHAIN IN U.S. ACCOUNTING: A REVIEW: ASSESSING ITS TRANSFORMATIVE POTENTIAL FOR ENHANCING TRANSPARENCY AND INTEGRITY

Authors:
raw_author_name: None Andrew Ifesinachi Daraojimba

Abstract: This study critically examines the transformative potential of blockchain technology in enhancing transparency ansd integrity within U.S. accounting practices. Employing a systematic literature review and content analysis methodology, the research draws on a range of peer-reviewed articles, industry reports, and authoritative databases. The study's objectives include understanding the evolution of blockchain in accounting, assessing its impact on transparency and integrity, identifying challenges in adoption, and exploring future trends and stakeholder implications. Key findings reveal that blockchain acts as a catalyst for transparency and integrity in accounting, offering a decentralized and immutable record-keeping system that significantly reduces the likelihood of fraud and errors. The technology's integration into accounting practices is evolving, indicating a future of automated, real-time auditing processes and enhanced financial reporting. However, challenges such as regulatory ambiguity, technical complexities, and scalability issues persist, necessitating strategic solutions. The study recommends that industry leaders and policymakers proactively embrace blockchain technology through education, regulatory framework development, and collaboration between technology developers and the accounting industry. Standard-setting bodies are also encouraged to update accounting standards in light of blockchain's implications. Future research directions include exploring the long-term impact of blockchain on various accounting aspects, empirical assessments of its real-world impact, and strategies to overcome adoption barriers. In conclusion, the study highlights blockchain's significant role in revolutionizing accounting practices, emphasizing the need for continued exploration and strategic integration of this technology in the accounting sector.&#x0D; Keywords: Blockchain Technology, Accounting Transparency, Regulatory Frameworks, Financial Integrity.
Struggles and Strategies in Understanding Information Visualizations

Authors:
raw_author_name: Melanie Tory
raw_author_name: Sheelagh Carpendale

Abstract: While the visualization community is increasingly aware that people often find visualizations difficult to understand, there is less information about what we need to do to create comprehensible visualizations. To help visualization creators and designers improve their visualizations, we need to better understand what kind of support people are looking for in their sensemaking process. Empirical studies are needed to tease apart the details of what makes the process of understanding difficult for visualization viewers. We conducted a qualitative study with 14 participants, observing them as they described how they were trying to make sense of 20 information visualizations. We identified the challenges participants faced throughout their sensemaking process and the strategies they employed to help themselves in overcoming the challenges. Our findings show how details and nuances within visualizations can impact comprehensibility and offer research suggestions to help us move toward more understandable visualizations.
Knowledge Sharing Behavior Shaped by Organizational Climate, Social Network, Perception, and Achievement Motivation

Authors:
raw_author_name: Hamidah .
raw_affiliation_string: Management Postgraduate, Jakarta State University, Indonesia

Abstract: To date, knowledge sharing commonly discussed just as a part of knowledge management. However, knowledge sharing as a behavior is imperative to be investigated and managed carefully at a firm level. This study was aimed to examine employee knowledge sharing behavior could be shaped by organizational climate, social network, individual perception on knowledge itself, and achievement motivation. Data collected from questionnaires filled by 121 respondents who work at a multinational Japanese company operating in Indonesia (PT MII). Path analysis was used to evaluate causal model among variables. Result indicates that achievement motivation acts well as an intervening variable influences knowledge sharing behavior. Organization climate has a strongest path among variables with good practices performed by a supervisor; corporate policy to deploy knowledge; a knowledgeable feedback and corrective actions; and an assignment with a considerable time to deadline. Employees perceived that sharing behavior encouraged first in the network, promoted and managed by the organization, and lastly as a personal initiative. Motivated employees sharing their knowledge for self’s empowered, a sense of self merit, and share first at the situation of immediate and necessity. Idea for a better work more often shared than to find solutions for instant actions, tacit knowledge tends to be shared when requested, self’s refined knowledge shared individually rather than at a workgroup, and still there are some reluctances of senior owners to contribute their precious tacit knowledge at once. Keywords: Knowledge Sharing Behavior, Organizational Climate, Social Network, Individual Perception, Achievement Motivation
Wasserstein GAN-based architecture to generate collaborative filtering synthetic datasets

Authors:
raw_author_name: Jesús Bobadilla
raw_affiliation_string: Universidad Politécnica de Madrid, ETSISI, Ctra. de Valencia Km. 7, Madrid, Spain
raw_author_name: Abraham Gutiérrez
raw_affiliation_string: Universidad Politécnica de Madrid, ETSISI, Ctra. de Valencia Km. 7, Madrid, Spain

Abstract: Abstract Currently, generative applications are reshaping different fields, such as art, computer vision, speech processing, and natural language. The computer science personalization area is increasingly relevant since large companies such as Spotify, Netflix, TripAdvisor, Amazon, and Google use recommender systems. Then, it is rational to expect that generative learning will increasingly be used to improve current recommender systems. In this paper, a method is proposed to generate synthetic recommender system datasets that can be used to test the recommendation performance and accuracy of a company on different simulated scenarios, such as large increases in their dataset sizes, number of users, or number of items. Specifically, an improvement in the state-of-the-art method is proposed by applying the Wasserstein concept to the generative adversarial network for recommender systems (GANRS) seminal method to generate synthetic datasets. The results show that our proposed method reduces the mode collapse, increases the sizes of the synthetic datasets, improves their ratings distributions, and maintains the potential to choose the desired number of users, number of items, and starting size of the dataset. Both the baseline GANRS and the proposed Wasserstein-based WGANRS deep learning architectures generate fake profiles from dense, short, and continuous embeddings in the latent space instead of the sparse, large, and discrete raw samples that previous GAN models used as a source. To enable reproducibility, the Python and Keras codes are provided in open repositories along with the synthetic datasets generated to test the proposed architecture ( https://github.com/jesusbobadilla/ganrs.git ). Graphical abstract