2,053 research outputs found
Do-it-yourself instruments and data processing methods for developing marine citizen observatories
Water is the most important resource for living on planet Earth, covering more than 70% of its surface. The oceans represent more than 97% of the planet total water and they are where more than the 99.5% of the living beings are concentrated. A great number
of ecosystems depend on the health of these oceans; their study and protection are necessary.
Large datasets over long periods of time and over wide geographical areas can be required to assess the health of aquatic ecosystems. The funding needed for data collection is considerable and limited, so it is important to look at new cost-effective
ways of obtaining and processing marine environmental data.
The feasible solution at present is to develop observational infrastructures that may increase significantly the conventional sampling capabilities. In this study we promote to achieve this solution with the implementation of Citizen Observatories, based on
volunteer participation.
Citizen observatories are platforms that integrate the latest information technologies to digitally connect citizens, improving observation skills for developing a new type of research known as Citizen Science. Citizen science has the potential to increase
the knowledge of the environment, and aquatic ecosystems in particular, through the use of people with no specific scientific training to collect and analyze large data sets.
We believe that citizen science based tools -open source software coupled with low-cost do-it-yourself hardware- can help to close the gap between science and citizens in the oceanographic field. As the public is actively engaged in the analysis of data, the research also provides a strong avenue for public education.
This is the objective of this thesis, to demonstrate how open source software and low-cost do-it-yourself hardware are effectively applied to oceanographic research and how can it develop into citizen science. We analyze four different scenarios where this idea
is demonstrated: an example of using open source software for video analysis where lobsters were monitored; a demonstration of using similar video processing techniques on in-situ low-cost do-it-yourself hardware for submarine fauna monitoring; a study using
open source machine learning software as a method to improve biological observations; and last but not least, some preliminar results, as proof of concept, of how manual water sampling could be replaced by low-cost do-it-yourself hardware with optical sensors.L’aigua és el recurs més important per la vida al planeta Terra, cobrint més del 70% de la seva superfÃcie. Els oceans representen més del 70% de tota l'aigua del planeta, i és on estan concentrats més del 99.5% dels éssers vius. Un gran nombre d'ecosistemes depenen de la salut d'aquests oceans; el seu estudi i protecció són necessaris. Grans conjunts de dades durant llargs perÃodes de temps i al llarg d’amples à rees geogrà fiques poden ser necessaris per avaluar la salut dels ecosistemes aquà tics. El finançament necessari per aquesta recol·lecció de dades és considerable però limitat, i per tant és important trobar noves formes més rendibles d’obtenir i processar dades mediambientals marines. La solució factible actualment és la de desenvolupar infraestructures observacionals que puguin incrementar significativament les capacitats de mostreig convencionals. En aquest estudi promovem que es pot assolir aquesta solució amb la implementació d’Observatoris Ciutadans, basats en la participació de voluntaris. Els observatoris ciutadans són plataformes que integren les últimes tecnologies de la informació amb ciutadans digitalment connectats, millorant les capacitats d’observació, per desenvolupar un nou tipus de recerca coneguda com a Ciència Ciutadana. La ciència ciutadana té el potencial d’incrementar el coneixement del medi ambient, i dels ecosistemes aquà tics en particular, mitjançant l'ús de persones sense coneixement cientÃfic especÃfic per recollir i analitzar grans conjunts de dades. Creiem que les eines basades en ciència ciutadana -programari lliure juntament amb maquinari de baix cost i del tipus "fes-ho tu mateix" (do-it-yourself en anglès)- poden ajudar a apropar la ciència del camp oceanogrà fic als ciutadans. A mesura que el gran públic participa activament en l'anà lisi de dades, la recerca esdevé també una nova via d’educació pública. Aquest és l’objectiu d’aquesta tesis, demostrar com el programari lliure i el maquinari de baix cost "fes-ho tu mateix" s’apliquen de forma efectiva a la recerca oceanogrà fica i com pot desenvolupar-se cap a ciència ciutadana. Analitzem quatre escenaris diferents on es demostra aquesta idea: un exemple d’ús de programari lliure per anà lisi de vÃdeos de monitoratge de llagostes; una demostració utilitzant tècniques similars de processat de vÃdeo en un dispositiu in-situ de baix cost "fes-ho tu mateix" per monitoratge de fauna submarina; un estudi utilitzant programari lliure d’aprenentatge automà tic (machine learning en anglès) com a mètode per millorar observacions biològiques; i finalment uns resultats preliminars, com a prova de la seva viabilitat, de com un mostreig manual de mostres d’aigua podria ser reemplaçat per maquinari de baix cost "fes-ho tu mateix" amb sensors òptics
Crowdsourcing Cognitive Presence: A Quantitative Content Analysis of a K12 Educator MOOC Discussion Forum
Massively Open Online Courses (MOOCs) offer participants opportunities to engage with content and discussion forums similar to other online courses. Pedagogical components of MOOCs and the nature of learning are worth of examining due to issues involving scale, interaction and the role of the instructor (Ross, Sinclair, Know, Bayne & McLeod, 2014). The Community of Inquiry (CoI) framework provides a basis for measuring cognitive presence in online discussion forums. As voluntary point of entry to a community of learners, it is important to consider the nature of participant contributions in terms of cognitive presence. This study focused on an educator MOOC because MOOCs have been proposed as an efficient vehicle for providing professional development due to the significant self-identification of participants as educators (Ho et al. 2014).
Participant attributes have been categorized, however the discussion forum is difficult to study on a massive scale (Kizilcec, Piech, & Schulz, 2013). Automated measures of cognitive presence may not provide the full view of learning behaviors implicit in messages posted to the forums (Wong, Pursel, Divinsky & Jansen, 2015). To address this gap, the forum messages were hand-coded and analyzed using quantitative content analysis (Neuendorf, 2002). The study found that the measure of exploration increased over the duration of the course. Viewing cognitive presence over time provided a new metaphor for explaining the proportions of cognitive presence in the discussion forum of an educator MOOC. This finding suggests that increased instructor presence during the later stages of the course may increase cognitive presence over time (Akyol & Garrison, 2007; Garrison & Cleveland-Innes, 2005)
Do-it-yourself instruments and data processing methods for developing marine citizen observatories
La consulta Ãntegra de la tesi, inclosos els articles no comunicats públicament per drets d'autor, es pot realitzar prèvia petició a l'Arxiu de la UPCWater is the most important resource for living on planet Earth, covering more than 70% of its surface. The oceans represent more than 97% of the planet total water and they are where more than the 99.5% of the living beings are concentrated. A great number
of ecosystems depend on the health of these oceans; their study and protection are necessary.
Large datasets over long periods of time and over wide geographical areas can be required to assess the health of aquatic ecosystems. The funding needed for data collection is considerable and limited, so it is important to look at new cost-effective
ways of obtaining and processing marine environmental data.
The feasible solution at present is to develop observational infrastructures that may increase significantly the conventional sampling capabilities. In this study we promote to achieve this solution with the implementation of Citizen Observatories, based on
volunteer participation.
Citizen observatories are platforms that integrate the latest information technologies to digitally connect citizens, improving observation skills for developing a new type of research known as Citizen Science. Citizen science has the potential to increase
the knowledge of the environment, and aquatic ecosystems in particular, through the use of people with no specific scientific training to collect and analyze large data sets.
We believe that citizen science based tools -open source software coupled with low-cost do-it-yourself hardware- can help to close the gap between science and citizens in the oceanographic field. As the public is actively engaged in the analysis of data, the research also provides a strong avenue for public education.
This is the objective of this thesis, to demonstrate how open source software and low-cost do-it-yourself hardware are effectively applied to oceanographic research and how can it develop into citizen science. We analyze four different scenarios where this idea
is demonstrated: an example of using open source software for video analysis where lobsters were monitored; a demonstration of using similar video processing techniques on in-situ low-cost do-it-yourself hardware for submarine fauna monitoring; a study using
open source machine learning software as a method to improve biological observations; and last but not least, some preliminar results, as proof of concept, of how manual water sampling could be replaced by low-cost do-it-yourself hardware with optical sensors.L’aigua és el recurs més important per la vida al planeta Terra, cobrint més del 70% de la seva superfÃcie. Els oceans representen més del 70% de tota l'aigua del planeta, i és on estan concentrats més del 99.5% dels éssers vius. Un gran nombre d'ecosistemes depenen de la salut d'aquests oceans; el seu estudi i protecció són necessaris. Grans conjunts de dades durant llargs perÃodes de temps i al llarg d’amples à rees geogrà fiques poden ser necessaris per avaluar la salut dels ecosistemes aquà tics. El finançament necessari per aquesta recol·lecció de dades és considerable però limitat, i per tant és important trobar noves formes més rendibles d’obtenir i processar dades mediambientals marines. La solució factible actualment és la de desenvolupar infraestructures observacionals que puguin incrementar significativament les capacitats de mostreig convencionals. En aquest estudi promovem que es pot assolir aquesta solució amb la implementació d’Observatoris Ciutadans, basats en la participació de voluntaris. Els observatoris ciutadans són plataformes que integren les últimes tecnologies de la informació amb ciutadans digitalment connectats, millorant les capacitats d’observació, per desenvolupar un nou tipus de recerca coneguda com a Ciència Ciutadana. La ciència ciutadana té el potencial d’incrementar el coneixement del medi ambient, i dels ecosistemes aquà tics en particular, mitjançant l'ús de persones sense coneixement cientÃfic especÃfic per recollir i analitzar grans conjunts de dades. Creiem que les eines basades en ciència ciutadana -programari lliure juntament amb maquinari de baix cost i del tipus "fes-ho tu mateix" (do-it-yourself en anglès)- poden ajudar a apropar la ciència del camp oceanogrà fic als ciutadans. A mesura que el gran públic participa activament en l'anà lisi de dades, la recerca esdevé també una nova via d’educació pública. Aquest és l’objectiu d’aquesta tesis, demostrar com el programari lliure i el maquinari de baix cost "fes-ho tu mateix" s’apliquen de forma efectiva a la recerca oceanogrà fica i com pot desenvolupar-se cap a ciència ciutadana. Analitzem quatre escenaris diferents on es demostra aquesta idea: un exemple d’ús de programari lliure per anà lisi de vÃdeos de monitoratge de llagostes; una demostració utilitzant tècniques similars de processat de vÃdeo en un dispositiu in-situ de baix cost "fes-ho tu mateix" per monitoratge de fauna submarina; un estudi utilitzant programari lliure d’aprenentatge automà tic (machine learning en anglès) com a mètode per millorar observacions biològiques; i finalment uns resultats preliminars, com a prova de la seva viabilitat, de com un mostreig manual de mostres d’aigua podria ser reemplaçat per maquinari de baix cost "fes-ho tu mateix" amb sensors òptics.Postprint (published version
Automatic Sensor-free Affect Detection: A Systematic Literature Review
Emotions and other affective states play a pivotal role in cognition and,
consequently, the learning process. It is well-established that computer-based
learning environments (CBLEs) that can detect and adapt to students' affective
states can enhance learning outcomes. However, practical constraints often pose
challenges to the deployment of sensor-based affect detection in CBLEs,
particularly for large-scale or long-term applications. As a result,
sensor-free affect detection, which exclusively relies on logs of students'
interactions with CBLEs, emerges as a compelling alternative. This paper
provides a comprehensive literature review on sensor-free affect detection. It
delves into the most frequently identified affective states, the methodologies
and techniques employed for sensor development, the defining attributes of
CBLEs and data samples, as well as key research trends. Despite the field's
evident maturity, demonstrated by the consistent performance of the models and
the application of advanced machine learning techniques, there is ample scope
for future research. Potential areas for further exploration include enhancing
the performance of sensor-free detection models, amassing more samples of
underrepresented emotions, and identifying additional emotions. There is also a
need to refine model development practices and methods. This could involve
comparing the accuracy of various data collection techniques, determining the
optimal granularity of duration, establishing a shared database of action logs
and emotion labels, and making the source code of these models publicly
accessible. Future research should also prioritize the integration of models
into CBLEs for real-time detection, the provision of meaningful interventions
based on detected emotions, and a deeper understanding of the impact of
emotions on learning
A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions
In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content. Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media
Automatic Context-Driven Inference of Engagement in HMI: A Survey
An integral part of seamless human-human communication is engagement, the
process by which two or more participants establish, maintain, and end their
perceived connection. Therefore, to develop successful human-centered
human-machine interaction applications, automatic engagement inference is one
of the tasks required to achieve engaging interactions between humans and
machines, and to make machines attuned to their users, hence enhancing user
satisfaction and technology acceptance. Several factors contribute to
engagement state inference, which include the interaction context and
interactants' behaviours and identity. Indeed, engagement is a multi-faceted
and multi-modal construct that requires high accuracy in the analysis and
interpretation of contextual, verbal and non-verbal cues. Thus, the development
of an automated and intelligent system that accomplishes this task has been
proven to be challenging so far. This paper presents a comprehensive survey on
previous work in engagement inference for human-machine interaction, entailing
interdisciplinary definition, engagement components and factors, publicly
available datasets, ground truth assessment, and most commonly used features
and methods, serving as a guide for the development of future human-machine
interaction interfaces with reliable context-aware engagement inference
capability. An in-depth review across embodied and disembodied interaction
modes, and an emphasis on the interaction context of which engagement
perception modules are integrated sets apart the presented survey from existing
surveys
Do I Have Your Attention: A Large Scale Engagement Prediction Dataset and Baselines
The degree of concentration, enthusiasm, optimism, and passion displayed by
individual(s) while interacting with a machine is referred to as `user
engagement'. Engagement comprises of behavioral, cognitive, and affect related
cues. To create engagement prediction systems that can work in real-world
conditions, it is quintessential to learn from rich, diverse datasets. To this
end, a large scale multi-faceted engagement in the wild dataset EngageNet is
proposed. 31 hours duration data of 127 participants representing different
illumination conditions are recorded. Thorough experiments are performed
exploring the applicability of different features, action units, eye gaze, head
pose, and MARLIN. Data from user interactions (question-answer) are analyzed to
understand the relationship between effective learning and user engagement. To
further validate the rich nature of the dataset, evaluation is also performed
on the EngageWild dataset. The experiments show the usefulness of the proposed
dataset. The code, models, and dataset link are publicly available at
https://github.com/engagenet/engagenet_baselines
Earth Observation Open Science and Innovation
geospatial analytics; social observatory; big earth data; open data; citizen science; open innovation; earth system science; crowdsourced geospatial data; citizen science; science in society; data scienc
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