Reviewerly/exHarmony huggingface dataset:
The Data Availability Policies of Political Science Journals

This paper describes the data availability policies posted on the websites of a broad collection of political science journals. We code the policies of 221 political science journals, and we find that only 20% of these journals require researchers to share their data and/or code as a condition of publication. While requirements to share data and code are common among the most visible political science journals, these policies remain relatively rare in the discipline more broadly. In prioritizing reforms to improve openness and reproducibility in political science, we suggest remaining mindful of the relative rarity and simple effectiveness of requiring authors to share their data and code.
Generative AI: An AI paradigm shift in the making?

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

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

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

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

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

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.
Identification with Posterior-Separable Information Costs

I provide a model of rational inattention with heterogeneity and prove it is observationally equivalent to a state-dependent stochastic choice model subject to attention costs. I demonstrate that additive separability of unobservable heterogeneity, together with an independence assumption, suffice for the empirical model to admit a representative agent. Using conditional probabilities, I show how to identify: how covariates affect the desirability of goods, (a measure of) welfare, factual changes in welfare, and bounds on counterfactual market shares.
BLOCKCHAIN IN U.S. ACCOUNTING: A REVIEW: ASSESSING ITS TRANSFORMATIVE POTENTIAL FOR ENHANCING TRANSPARENCY AND INTEGRITY

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

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

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

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
Moving beyond the challenges of learning through technologies: The current status of ICT integration in South African schools

The purpose of this study was to understand the current status of ICT integration in South African schools. Yet, this study focused on the Tshwane West District schools in Gauteng Province, South Africa. This study employed a descriptive research design to understand the aforesaid phenomenon. Data was collected through interviews and open-ended questionnaires with 10 teachers and one curriculum specialist. This study employed thematic analysis as a qualitative research method. Furthermore, Roger's Diffusion of Innovations is used as a framework. Despite the existence of policies, this study revealed that the integration of ICT remained a significant challenge for several instructors in South Africa. The most interesting part was that schools had improved on most ICT tools to enable access to learning with technologies, but to a greater extent, insufficient internet coverage in classrooms made it impossible for most teachers to appreciate the benefits that come with using tools such as laptops, whiteboards, and online videos. This research proposes the installation of pocket Wi-Fi devices in every classroom in schools to provide comprehensive internet access. This will facilitate interaction between instructors and students through up-to-date learning management systems subscribed to by schools, as well as films. This will promote a digitally viable learning environment that promotes creative opportunities for learners.
Deep Learning Strategies for 5G and LTE Spectrum Sensing Communication

The idea of 5G innovations is a prevalent instrument for the pace of transmission and gathering of data and the accessibility of permitting all over the place. Notwithstanding that the fifth era convergences will embrace a keen procedure for the data transmission process. Sending and getting signals work in high coordination in 5G networks, since this innovation arranges flexible, geostationary earthbound correspondence with other medium and little circuit correspondences with short steering in straight correspondences, and the correspondence incorporates signal processing as well as way finding. In this study the responsiveness improvement of the correspondence range will be tested by applying blended deep learning methods, in which the data cross-over will be diminished with the upgraded smart control. Utilizing blended deep learning methods, this study exhibits the huge difficulties presented by 5G transmissions in keenly detecting the LTE signal range and different data in 5G remote sensor networks. Way obstructions are recognized as the essential hindrance. The states of the correspondence framework ought to be considered while plotting the network and sensors for the fifth era.
Building Trust Takes Time: Limits to Arbitrage for Blockchain-Based Assets

Abstract A blockchain replaces central counterparties with time-consuming consensus protocols to record the transfer of ownership. This settlement latency slows cross-exchange trading, exposing arbitrageurs to price risk. Off-chain settlement, instead, exposes arbitrageurs to costly default risk. We show with Bitcoin network and order book data that cross-exchange price differences coincide with periods of high settlement latency, asset flows chase arbitrage opportunities, and price differences across exchanges with low default risk are smaller. Blockchain-based trading thus faces a dilemma: Reliable consensus protocols require time-consuming settlement latency, leading to arbitrage limits. Circumventing such arbitrage costs is possible &amp;lt;italic&amp;gt;only&amp;lt;/italic&amp;gt; by reinstalling trusted intermediation, which mitigates default risk.
Examining Pathological Bias in a Generative Adversarial Network  Discriminator: A Case Study on a StyleGAN3 Model

Generative adversarial networks generate photorealistic faces that are often indistinguishable by humans from real faces. We find that the discriminator in the pre-trained StyleGAN3 model, a popular GAN network, systematically stratifies scores by both image- and face-level qualities and that this disproportionately affects images across gender, race, and other categories. We examine the discriminator's bias for color and luminance across axes perceived race and gender; we then examine axes common in research on stereotyping in social psychology.
NutePrune: Efficient Progressive Pruning with Numerous Teachers for  Large Language Models

The considerable size of Large Language Models (LLMs) presents notable deployment challenges, particularly on resource-constrained hardware. Structured pruning, offers an effective means to compress LLMs, thereby reducing storage costs and enhancing inference speed for more efficient utilization. In this work, we study data-efficient and resource-efficient structure pruning methods to obtain smaller yet still powerful models. Knowledge Distillation is well-suited for pruning, as the intact model can serve as an excellent teacher for pruned students. However, it becomes challenging in the context of LLMs due to memory constraints. To address this, we propose an efficient progressive Numerous-teacher pruning method (NutePrune). NutePrune mitigates excessive memory costs by loading only one intact model and integrating it with various masks and LoRA modules, enabling it to seamlessly switch between teacher and student roles. This approach allows us to leverage numerous teachers with varying capacities to progressively guide the pruned model, enhancing overall performance. Extensive experiments across various tasks demonstrate the effectiveness of NutePrune. In LLaMA-7B zero-shot experiments, NutePrune retains 97.17% of the performance of the original model at 20% sparsity and 95.07% at 25% sparsity.
Sequential Recommendation on Temporal Proximities with Contrastive  Learning and Self-Attention

Sequential recommender systems identify user preferences from their past interactions to predict subsequent items optimally. Although traditional deep-learning-based models and modern transformer-based models in previous studies capture unidirectional and bidirectional patterns within user-item interactions, the importance of temporal contexts, such as individual behavioral and societal trend patterns, remains underexplored. Notably, recent models often neglect similarities in users' actions that occur implicitly among users during analogous timeframes-a concept we term vertical temporal proximity. These models primarily adapt the self-attention mechanisms of the transformer to consider the temporal context in individual user actions. Meanwhile, this adaptation still remains limited in considering the horizontal temporal proximity within item interactions, like distinguishing between subsequent item purchases within a week versus a month. To address these gaps, we propose a sequential recommendation model called TemProxRec, which includes contrastive learning and self-attention methods to consider temporal proximities both across and within user-item interactions. The proposed contrastive learning method learns representations of items selected in close temporal periods across different users to be close. Simultaneously, the proposed self-attention mechanism encodes temporal and positional contexts in a user sequence using both absolute and relative embeddings. This way, our TemProxRec accurately predicts the relevant items based on the user-item interactions within a specific timeframe. We validate this work through comprehensive experiments on TemProxRec, consistently outperforming existing models on benchmark datasets as well as showing the significance of considering the vertical and horizontal temporal proximities into sequential recommendation.