13 research outputs found
On the Reliability, Validity and Sensitivity of Three Mental Workload Assessment Techniques for the Evaluation of Instructional Designs: A Case Study in a Third-level Course
Cognitive Load Theory (CLT) has been conceived for instructional designers eager to create instructional resources that are presented in a way that encourages the activities of the learners and optimise their performance, thus their learning. Although it has been researched for many years, it has been criticised because of its theoretical clarity and its methodological approach. In particular, one fundamental and open problem is the measurement of its cognitive load types and the measurement of the overall cognitive load of learners during learning tasks. This paper is aimed at investigating the reliability, validity and sensitivity of existing mental workload assessment techniques, borrowed from the discipline of Ergonomics, when applied to the field of Education, Teaching and Learning. In details, a primary research involved the application of three subjective mental workload assessment techniques, namely the NASA Task Load Index, the Workload Profile and the Rating Scale Mental Effort, in a typical third-level classroom for the evaluation of two instructional design conditions. The Cognitive Theory of Multimedia Learning and its design principles have been used as the underlying theoretical framework for the design of the two conditions. Evidence strongly suggests that the three selected mental workload measures are highly reliable within Education and their moderate validity is in line with results obtained in Ergonomics
Named Entity Recognition and Text Compression
Import 13/01/2017In recent years, social networks have become very popular. It is easy for users
to share their data using online social networks. Since data on social networks is
idiomatic, irregular, brief, and includes acronyms and spelling errors, dealing with
such data is more challenging than that of news or formal texts. With the huge
volume of posts each day, effective extraction and processing of these data will bring
great benefit to information extraction applications.
This thesis proposes a method to normalize Vietnamese informal text in social
networks. This method has the ability to identify and normalize informal text
based on the structure of Vietnamese words, Vietnamese syllable rules, and a trigram
model. After normalization, the data will be processed by a named entity
recognition (NER) model to identify and classify the named entities in these data.
In our NER model, we use six different types of features to recognize named entities
categorized in three predefined classes: Person (PER), Location (LOC), and
Organization (ORG).
When viewing social network data, we found that the size of these data are very
large and increase daily. This raises the challenge of how to decrease this size. Due
to the size of the data to be normalized, we use a trigram dictionary that is quite
big, therefore we also need to decrease its size. To deal with this challenge, in this
thesis, we propose three methods to compress text files, especially in Vietnamese
text. The first method is a syllable-based method relying on the structure of
Vietnamese morphosyllables, consonants, syllables and vowels. The second method
is trigram-based Vietnamese text compression based on a trigram dictionary. The
last method is based on an n-gram slide window, in which we use five dictionaries
for unigrams, bigrams, trigrams, four-grams and five-grams. This method achieves
a promising compression ratio of around 90% and can be used for any size of text file.In recent years, social networks have become very popular. It is easy for users
to share their data using online social networks. Since data on social networks is
idiomatic, irregular, brief, and includes acronyms and spelling errors, dealing with
such data is more challenging than that of news or formal texts. With the huge
volume of posts each day, effective extraction and processing of these data will bring
great benefit to information extraction applications.
This thesis proposes a method to normalize Vietnamese informal text in social
networks. This method has the ability to identify and normalize informal text
based on the structure of Vietnamese words, Vietnamese syllable rules, and a trigram
model. After normalization, the data will be processed by a named entity
recognition (NER) model to identify and classify the named entities in these data.
In our NER model, we use six different types of features to recognize named entities
categorized in three predefined classes: Person (PER), Location (LOC), and
Organization (ORG).
When viewing social network data, we found that the size of these data are very
large and increase daily. This raises the challenge of how to decrease this size. Due
to the size of the data to be normalized, we use a trigram dictionary that is quite
big, therefore we also need to decrease its size. To deal with this challenge, in this
thesis, we propose three methods to compress text files, especially in Vietnamese
text. The first method is a syllable-based method relying on the structure of
Vietnamese morphosyllables, consonants, syllables and vowels. The second method
is trigram-based Vietnamese text compression based on a trigram dictionary. The
last method is based on an n-gram slide window, in which we use five dictionaries
for unigrams, bigrams, trigrams, four-grams and five-grams. This method achieves
a promising compression ratio of around 90% and can be used for any size of text file.460 - Katedra informatikyvyhově
“How cavemen did social media”: A comparative case study of social movement organisations using Twitter to mobilise on climate change
In the face of widespread public disillusionment with traditional politics the internet is emerging as a popular tool for increasing public participation in social and political activism. Little research has been performed, however, on how social movement organisations are using the internet and in particular increasingly popular social networking services to mobilise individuals. Accordingly, this thesis presents a comparative case study of three climate change campaigns’ Twitter accounts aiming to identify and analyse the ways they are using it as part of their mobilisation efforts. Use of Twitter varied across all three, reflecting campaign design. However, each case displayed efforts to establish and use online ties and networks to facilitate and sustain participation in low-risk, moderate and symbolic forms of online and offline action. Such findings will provide inspiration for movement activists seeking to use the internet to mobilise on climate change, and open up to greater academic attention the role of social networking services in movement mobilisation
Analysis and Modular Approach for Text Extraction from Scientific Figures on Limited Data
Scientific figures are widely used as compact, comprehensible representations of important information. The re-usability of these figures is however limited, as one can rarely search directly for them, since they are mostly indexing by their surrounding text (e. g., publication or website) which often does not contain the full-message of the figure. In this thesis, the focus is on making the content of scientific figures accessible by extracting the text from these figures. A modular pipeline for unsupervised text extraction from scientific figures, based on a thorough analysis of the literature, was built to address the problem. This modular pipeline was used to build several unsupervised approaches, to evaluate different methods from the literature and new methods and method combinations. Some supervised approaches were built as well for comparison. One challenge, while evaluating the approaches, was the lack of annotated data, which especially needed to be considered when building the supervised approach. Three existing datasets were used for evaluation as well as two datasets of 241 scientific figures which were manually created and annotated. Additionally, two existing datasets for text extraction from other types of images were used for pretraining the supervised approach. Several experiments showed the superiority of the unsupervised pipeline over common Optical Character Recognition engines and identified the best unsupervised approach. This unsupervised approach was compared with the best supervised approach, which, despite of the limited amount of training data available, clearly outperformed the unsupervised approach.Infografiken sind ein viel verwendetes Medium zur kompakten Darstellung von Kernaussagen. Die Nachnutzbarkeit dieser Abbildungen ist jedoch häufig limitiert, da sie schlecht auffindbar sind, da sie meist über die umschließenden Medien, wie beispielsweise Publikationen oder Webseiten, und nicht über ihren Inhalt indexiert sind. Der Fokus dieser Arbeit liegt auf der Extraktion der textuellen Inhalte aus Infografiken, um deren Inhalt zu erschließen. Ausgehend von einer umfangreichen Analyse verwandter Arbeiten, wurde ein generalisierender, modularer Ansatz für die unüberwachte Textextraktion aus wissenschaftlichen Abbildungen entwickelt. Mit diesem modularen Ansatz wurden mehrere unüberwachte Ansätze und daneben auch noch einige überwachte Ansätze umgesetzt, um diverse Methoden aus der Literatur sowie neue und bisher noch nicht genutzte Methoden zu vergleichen. Eine Herausforderung bei der Evaluation war die geringe Menge an annotierten Abbildungen, was insbesondere beim überwachten Ansatz Methoden berücksichtigt werden musste. Für die Evaluation wurden drei existierende Datensätze verwendet und zudem wurden zusätzlich zwei Datensätze mit insgesamt 241 Infografiken erstellt und mit den nötigen Informationen annotiert, sodass insgesamt 5 Datensätze für die Evaluation verwendet werden konnten. Für das Pre-Training des überwachten Ansatzes wurden zudem zwei Datensätze aus verwandten Textextraktionsbereichen verwendet. In verschiedenen Experimenten wird gezeigt, dass der unüberwachte Ansatz besser funktioniert als klassische Texterkennungsverfahren und es wird aus den verschiedenen unüberwachten Ansätzen der beste ermittelt. Dieser unüberwachte Ansatz wird mit dem überwachten Ansatz verglichen, der trotz begrenzter Trainingsdaten die besten Ergebnisse liefert
Formalising Human Mental Workload as a Defeasible Computational Concept
Human mental workload has gained importance, in the last few decades, as a fundamental design concept in human-computer interaction. It can be intuitively defined as the amount of mental work necessary for a person to complete a task over a given period of time. For people interacting with interfaces, computers and technological devices in general, the construct plays an important role. At a low level, while processing information, often people feel annoyed and frustrated; at higher level, mental workload is critical and dangerous as it leads to confusion, it decreases the performance of information processing and it increases the chances of errors and mistakes. It is extensively documented that either mental overload or underload negatively affect performance. Hence, designers and practitioners who are ultimately interested in system or human performance need answers about operator workload at all stages of system design and operation. At an early system design phase, designers require some explicit model to predict the mental workload imposed by their technologies on end-users so that alternative system designs can be evaluated. However, human mental workload is a multifaceted and complex construct mainly applied in cognitive sciences. A plethora of ad-hoc definitions can be found in the literature. Generally, it is not an elementary property, rather it emerges from the interaction between the requirements of a task, the circumstances under which it is performed and the skills, behaviours and perceptions of the operator. Although measuring mental workload has advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. Many researchers agree that too many ad-hoc models are present in the literature and that they are applied subjectively by mental workload designers thereby limiting their application in different contexts and making comparison across different models difficult. This thesis introduces a novel computational framework for representing and assessing human mental workload based on defeasible reasoning. The starting point is the investigation of the nature of human mental workload that appears to be a defeasible phenomenon. A defeasible concept is a concept built upon a set of arguments that can be defeated by adding additional arguments. The word ‘defeasible’ is inherited from defeasible reasoning, a form of reasoning built upon reasons that can be defeated. It is also known as non-monotonic reasoning because of the technical property (non-monotonicity) of the logical formalisms that are aimed at modelling defeasible reasoning activity. Here, a conclusion or claim, derived from the application of previous knowledge, can be retracted in the light of new evidence. Formally, state-of-the-art defeasible reasoning models are implemented employing argumentation theory, a multi-disciplinary paradigm that incorporates elements of philosophy, psychology and sociology. It systematically studies how arguments can be built, sustained or discarded in a reasoning process, and it investigates the validity of their conclusions.
Since mental workload can be seen as a defeasible phenomenon, formal defeasible argumentation theory may have a positive impact in its representation and assessment. Mental workload can be captured, analysed, and measured in ways that increase its understanding allowing its use for practical activities. The research question investigated here is whether defeasible argumentation theory can enhance the representation of the construct of mental workload and improve the quality of its assessment in the field of human-computer interaction.
In order to answer this question, recurrent knowledge and evidence employed in state-of-the-art mental workload measurement techniques have been reviewed in the first place as well as their defeasible and non-monotonic properties. Secondly, an investigation of the state-of-the-art computational techniques for implementing defeasible reasoning has been carried out. This allowed the design of a modular framework for mental workload representation and assessment. The proposed solution has been evaluated by comparing the properties of sensitivity, diagnosticity and validity of the assessments produced by two instances of the framework against the ones produced by two well known subjective mental workload assessments techniques (the Nasa Task Load Index and the Workload Profile) in the context of human-web interaction. In detail, through an empirical user study, it has been firstly demonstrated how these two state-of-the-art techniques can be translated into two particular instances of the framework while still maintaining the same validity. In other words, the indexes of mental workload inferred by the two original instruments, and the ones generated by their corresponding translations (instances of the framework) showed a positive and nearly perfect statistical correlation. Additionally, a new defeasible instance built with the framework showed a better sensitivity and a higher diagnosticity capacity than the two selected state-of-the art techniques. The former showed a higher convergent validity with the latter techniques, but a better concurrent validity with performance measures. The new defeasible instance generated indexes of mental workload that better correlated with the objective time for task completion compared to the two selected instruments. These findings support the research question thereby demonstrating how defeasible argumentation theory can be successfully adopted to support the representation of mental workload and to enhance the quality of its assessments.
The main contribution of this thesis is the presentation of a methodology, developed as a formal modular framework, to represent mental workload as a defeasible computational concept and to assess it as a numerical usable index. This research contributes to the body of knowledge by providing a modular framework built upon defeasible reasoning and formalised through argumentation theory in which workload can be optimally measured, analysed, explained and applied in different contexts
Contributions to the privacy provisioning for federated identity management platforms
Identity information, personal data and user’s profiles are key assets for organizations
and companies by becoming the use of identity management (IdM) infrastructures a prerequisite
for most companies, since IdM systems allow them to perform their business
transactions by sharing information and customizing services for several purposes in more
efficient and effective ways.
Due to the importance of the identity management paradigm, a lot of work has been done
so far resulting in a set of standards and specifications. According to them, under the
umbrella of the IdM paradigm a person’s digital identity can be shared, linked and reused
across different domains by allowing users simple session management, etc. In this way,
users’ information is widely collected and distributed to offer new added value services
and to enhance availability. Whereas these new services have a positive impact on users’
life, they also bring privacy problems.
To manage users’ personal data, while protecting their privacy, IdM systems are the ideal
target where to deploy privacy solutions, since they handle users’ attribute exchange.
Nevertheless, current IdM models and specifications do not sufficiently address comprehensive
privacy mechanisms or guidelines, which enable users to better control over the
use, divulging and revocation of their online identities. These are essential aspects, specially
in sensitive environments where incorrect and unsecured management of user’s data
may lead to attacks, privacy breaches, identity misuse or frauds.
Nowadays there are several approaches to IdM that have benefits and shortcomings, from
the privacy perspective.
In this thesis, the main goal is contributing to the privacy provisioning for federated
identity management platforms. And for this purpose, we propose a generic architecture
that extends current federation IdM systems. We have mainly focused our contributions
on health care environments, given their particularly sensitive nature. The two main
pillars of the proposed architecture, are the introduction of a selective privacy-enhanced
user profile management model and flexibility in revocation consent by incorporating an
event-based hybrid IdM approach, which enables to replace time constraints and explicit
revocation by activating and deactivating authorization rights according to events. The
combination of both models enables to deal with both online and offline scenarios, as well
as to empower the user role, by letting her to bring together identity information from
different sources.
Regarding user’s consent revocation, we propose an implicit revocation consent mechanism
based on events, that empowers a new concept, the sleepyhead credentials, which
is issued only once and would be used any time. Moreover, we integrate this concept
in IdM systems supporting a delegation protocol and we contribute with the definition
of mathematical model to determine event arrivals to the IdM system and how they are
managed to the corresponding entities, as well as its integration with the most widely
deployed specification, i.e., Security Assertion Markup Language (SAML).
In regard to user profile management, we define a privacy-awareness user profile management
model to provide efficient selective information disclosure. With this contribution a
service provider would be able to accesses the specific personal information without being
able to inspect any other details and keeping user control of her data by controlling
who can access. The structure that we consider for the user profile storage is based on
extensions of Merkle trees allowing for hash combining that would minimize the need of
individual verification of elements along a path. An algorithm for sorting the tree as we
envision frequently accessed attributes to be closer to the root (minimizing the access’
time) is also provided.
Formal validation of the above mentioned ideas has been carried out through simulations
and the development of prototypes. Besides, dissemination activities were performed in
projects, journals and conferences.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: María Celeste Campo Vázquez.- Secretario: María Francisca Hinarejos Campos.- Vocal: Óscar Esparza Martí