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"Pictures Computer network resources."
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Accurate and versatile 3D segmentation of plant tissues at cellular resolution
Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on fixed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface.
Journal Article
Social media as a data resource for #monkseal conservation
The prevalence of social media platforms that share photos and videos could prove useful for wildlife research and conservation programs. When social media users post pictures and videos of animals, near real-time data like individual identification, sex, location, or other information are made accessible to scientists. These data can help inform researchers about animal occurrence, behavior, or threats to survival. The endangered Hawaiian monk seal (Neomonachus schauinslandi) population has only 1,400 seals remaining in the wild. A small but growing population of seals has recently reestablished itself in the human-populated main Hawaiian Islands. While this population growth raises concerns about human-seal interactions it also provides the opportunity to capitalize on human observations to enhance research and conservation activities. We measured the potential utility of non-traditional data sources, in this case Instagram, to supplement current population monitoring of monk seals in the main Hawaiian Islands. We tracked all Instagram posts with the identifier #monkseal for a one-year period and assessed the photos for biological and geographical information, behavioral concerns, human disturbance and public perceptions. Social media posts were less likely to provide images suitable for individual seal identification (16.5%) than traditional sighting reports (79.9%). However, social media enhanced the ability to detect human-seal interactions or animal disturbances: 22.1%, of the 2,392 Instagram posts examined showed people within 3 meters of a seal, and 17.8% indicated a disturbance to the animal, meanwhile only 0.64% of traditional reports noted a disturbance to the animal. This project demonstrated that data obtained through social media posts have value to monk seal research and management strategies beyond traditional data collection, and further development of social media platforms as data resources is warranted. Many conservation programs may benefit from similar work using social media to supplement the research and conservation activities they are undertaking.
Journal Article
Using gamification to discover cultural heritage locations from geo-tagged photos
Many enchanting cultural heritage locations are hidden from tourists, especially when considering countries full of historic attractions. Tourists tend to consider only mainstream monuments and towns, neglecting wonderful little jewels along their travel itinerary. However, this is generally not their fault, as travelers cannot be aware of all the surrounding beauties when visiting a new region. To this aim, we discuss and analyze here
PhotoTrip
, an interactive tool able to autonomously recommend charming, even if not mainstream, cultural heritage locations along travel itineraries.
PhotoTrip
is able to identify these points of interest by gathering pictures and related information from Flickr and Wikipedia and then provide the user with suggestions and recommendations. An important technical challenge for this kind of services is the ability to provide only the most relevant pictures among the many available for any considered itinerary. To this aim, we have exploited social networks, crowdsourcing and gamification to involve users in the process of improving the response quality of our system.
Journal Article
Quantification and measurement of relationship between movies and actors for production resources optimisation and box office business success: insights and reflections using network science approach
The entertainment industry deals with complex scenarios of managing and allocating resources and their optimisation for movies such as financial, creative and human resources. This is extremely important to have the best combination of such resources because huge financial, emotional, creative and human resources are involved in the movie making to make a profit and generate an impact in society. There is a need to understand objectively and get a visual insight into such complex scenarios to help filmmakers and stakeholders rationally. There is a lack of research work that assesses the impact of actors on movie success from several perspectives. A novel perspective from the network science approach which is an emerging domain can help to understand the social and economic phenomenon using nodes and links relationships. This paper attempts to explore, quantify and visualize the relationship between actors and movies’ success in terms of profit-making and impact in different categories and sections of society using advanced centrality measurements. Research work explores Indian movie data taken from IMDB. The proposed network models and quantified measurement of the relationship and link of actors in profit-making or hit movies to help the stakeholders. Applying the network science approach to explore the types of nodes and visualise them based on their types and strengths and visualising data in network graphs can show relationships more clearly. Research work is based on novel approaches to be useful for producers, viewers, directors and other stakeholders to make rational and qualified decisions for the selection of resources and movie outcomes success.
Journal Article
Mining the relationship between COVID-19 sentiment and market performance
In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy.
Journal Article
Gamifying cultural experiences across the urban environment
New media and devices are offering huge possibilities for the enhancement and the enrichment of heritage experiences, improving the users’ involvement. In particular, tourists equipped with their mobile devices are invading cultural attractions, sharing pictures and comments (together with hashtags and geo-localized positions) on social networks. These represent an unofficial source of data, which can be integrated with the official ones provided by GLAM (Galleries, Libraries, Archives, and Museums) and cultural heritage institutions, enriching them. At the same time, travel planners and mobile applications related to cultural heritage can play an interesting role in the development of smart cities, when they are integrated each other, engaging the user in touristic and entertainment activities, letting him/her be a source of cultural resources.This work focuses on equipping users (citizens and tourists) with a system providing support in computing personalized urban paths across cultural heritage places (monuments, palaces, museums, and other points of interest (POIs) related to cultural heritage in the urban environment) and in sharing multimedia resources about POIs, by exploiting gamification elements with the aim of engaging citizens and tourists. A mobile application prototype has been implemented, showing the feasibility of the proposed approach and exploiting crowdsourcing activities as a source of information for cultural places and works of art.
Journal Article
From intuition to intelligence: a text mining–based approach for movies' green-lighting process
PurposeThe purpose of this paper is to develop a predictive model for box office performance based on the textual information in movie scripts in the green-lighting process of movie production.Design/methodology/approachThe authors use Latent Dirichlet Allocation to determine the hidden textual structure in movie scripts by extracting topic probabilities as predictors for classification. The extracted topic probabilities are used as inputs for the predictive model for the box office performance. For the predictive model, the authors utilize a variety of classification algorithms such as logistic classification, decision trees, random forests, k-nearest neighbor algorithms, support vector machines and artificial neural networks, and compare their relative performances in predicting movies' market performance.FindingsThis approach for extracting textual information from movie scripts produces a valuable typology for movies. Moreover, our modeling approach has significant power to predict movie scripts' profitability. It provides a superior prediction performance compared to previous benchmarks, such as that of Eliashberg et al. (2007).Research limitations/implicationsThis work contributes to literature on predicting the box office performance in the green-lighting process and literature regarding suggesting models for the idea screening stage in the new product development process. Besides, this is one of the few studies that use movie script data to predict movies' financial performance by proposing an approach to integrate text mining models and machine learning algorithms with movie experts' intuition.Practical implicationsFirst, the authors’ approach can significantly reduce the financial risk associated with movie production decisions before the pre-production stage. Second, this paper proposes an approach that is applicable at a very early stage of new product development, such as the idea screening stage. The authors also introduce an online-based movie scenario database system that can help movie studios make more systematic and profitable decisions in the green-lighting process. Third, this approach can help movie studios estimate movie scripts' financial value.Originality/valueThis study is one of the few studies to forecast market performance in the green-lighting process.
Journal Article
Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach
Hyperspectral imaging (HSI) has been drastically applied in recent years to cultural heritage (CH) analysis, conservation, and also digital restoration. However, the efficient processing of the large datasets registered remains challenging and still in development. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative machine learning approach to the most common practices, such as principal component analysis(PCA). HCA has shown its potential in the past decades for spectral data classification and segmentation in many other fields, maximizing the information to be extracted from the high-dimensional spectral dataset via the formation of the agglomerative hierarchical tree. However, to date, there has been very limited implementation of HCA in the field of cultural heritage. Data used in this experiment were acquired on real historic film samples with various degradation degrees, using a custom-made push-broom VNIR hyperspectral camera (380–780nm). With the proposed HCA workflow, multiple samples in the entire dataset were processed simultaneously and the degradation areas with distinctive characteristics were successfully segmented into clusters with various hierarchies. A range of algorithmic parameters was tested, including the grid sizes, metrics, and agglomeration methods, and the best combinations were proposed at the end. This novel application of the semi-automating and unsupervised HCA could provide a basis for future digital unfading, and show the potential to solve other CH problems such as pigment mapping.
Journal Article
Picturing and modeling catchments by representative hillslopes
This study explores the suitability of a single hillslope as a parsimonious representation of a catchment in a physically based model. We test this hypothesis by picturing two distinctly different catchments in perceptual models and translating these pictures into parametric setups of 2-D physically based hillslope models. The model parametrizations are based on a comprehensive field data set, expert knowledge and process-based reasoning. Evaluation against streamflow data highlights that both models predicted the annual pattern of streamflow generation as well as the hydrographs acceptably. However, a look beyond performance measures revealed deficiencies in streamflow simulations during the summer season and during individual rainfall–runoff events as well as a mismatch between observed and simulated soil water dynamics. Some of these shortcomings can be related to our perception of the systems and to the chosen hydrological model, while others point to limitations of the representative hillslope concept itself. Nevertheless, our results confirm that representative hillslope models are a suitable tool to assess the importance of different data sources as well as to challenge our perception of the dominant hydrological processes we want to represent therein. Consequently, these models are a promising step forward in the search for the optimal representation of catchments in physically based models.
Journal Article