Trend Topic Analysis using Latent Dirichlet Allocation (LDA) (Study Case: Denpasar People’s Complaints Online Website)

According to the publication of the Central Bureau of Statistics 2017, the population of Denpasar people has increased to 914,300 people. The Increasing number of the population raises various problems that must be faced by the Denpasar’s Government. The variety of problems is in line with the increase in complaints data posted through Denpasar people’s complaints online website, which made it difficult to know the main topics of the problems. The purpose of this research is to find the main topics of complaints Denpasar residents quickly and efficiently. The method used to achieve the objective of the research is Latent Dirichlet Allocation topic models with Gibbs sampling parameter estimation. The number of topics obtained through the highest log-likelihood value -42,528.84, the value is in the number of topics 19. The trending topic was based on the highest topic probability, topic 4, with a topic probability value 0.055. Based on these results, the trend of a topic is on topic 4 which can be interpreted that many residents of Denpasar complained about damaged roads and requested to fix the roads

Robust Bayesian Tensor Factorization with Zero-Inflated Poisson Model and Consensus Aggregation

Tensor factorizations (TF) are powerful tools for the efficient representation and analysis of multidimensional data. However, classic TF methods based on maximum likelihood estimation underperform when applied to zero-inflated count data, such as single-cell RNA sequencing (scRNA-seq) data. Additionally, the stochasticity inherent in TFs results in factors that vary across repeated runs, making interpretation and reproducibility of the results challenging. In this paper, we introduce Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel approach for the factorization of high-dimensional count data with excess zeros. To address the challenge of stochasticity, we introduce Consensus Zero Inflated Poisson Tensor Factorization (C-ZIPTF), which combines ZIPTF with a consensus-based meta-analysis. We evaluate our proposed ZIPTF and C-ZIPTF on synthetic zero-inflated count data and synthetic and real scRNA-seq data. ZIPTF consistently outperforms baseline matrix and tensor factorization methods in terms of reconstruction accuracy for zero-inflated data. When the probability of excess zeros is high, ZIPTF achieves up to $2.4\times$ better accuracy. Additionally, C-ZIPTF significantly improves the consistency and accuracy of the factorization. When tested on both synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently recover known and biologically meaningful gene expression programs

Closing the gap between research and projects in climate change innovation in Europe

Innovation is a key component to equip our society with tools to adapt to new climatic conditions. The development of research-action interfaces shifts useful ideas into operationalized knowledge allowing innovation to flourish. In this paper we quantify the existing gap between climate research and innovation action in Europe using a novel framework that combines artificial intelligence (AI) methods and network science. We compute the distance between key topics of research interest from peer review publications and core issues tackled by innovation projects funded by the most recent European framework programmes. Our findings reveal significant differences exist between and within the two layers. Economic incentives, agricultural and industrial processes are differently connected to adaptation and mitigation priorities. We also find a loose research-action connection in bioproducts, biotechnologies and risk assessment practices, where applications are still too few compared to the research insights. Our analysis supports policy-makers to measure and track how research funding result in innovation action, and to adjust decisions if stated priorities are not achieved

Closing the gap between research and projects in climate change innovation in Europe

Innovation is a key component to equip our society with tools to adapt to new climatic conditions. The development of research-action interfaces shifts useful ideas into operationalized knowledge allowing innovation to flourish. In this paper we quantify the existing gap between climate research and innovation action in Europe using a novel framework that combines artificial intelligence (AI) methods and network science. We compute the distance between key topics of research interest from peer review publications and core issues tackled by innovation projects funded by the most recent European framework programmes. Our findings reveal significant differences exist between and within the two layers. Economic incentives, agricultural and industrial processes are differently connected to adaptation and mitigation priorities. We also find a loose research-action connection in bioproducts, biotechnologies and risk assessment practices, where applications are still too few compared to the research insights. Our analysis supports policy-makers to measure and track how research funding result in innovation action, and to adjust decisions if stated priorities are not achieved