Miracle-dz/UniFAR-FTUs huggingface dataset:
Stories, Agents and Videotapes: Agents that make up stories

—With our work, we aim to study virtual cameras and how they provide a meaningful way for players to connect to many and diverse types of virtual worlds and games alike. We will explain how we researched virtual cameras in many ﬁelds and with many purposes, with the intent of building a system of our own and incorporate these ideas. All this to study the impact and inﬂuence the camera has on people’s perceptions of these virtual worlds, from event awareness to understanding inter-character relationships. Through our results we were able to pinpoint the Third Person Camera as a strong candidate in leading the best possible experience on character-based interactive storytelling systems and games, by providing the best compromise between world awareness and player attachment to the character she controls. We believe this is a start to understanding people’s engagement with these systems and a ﬁrst step to providing them with a better experience that lets them get immersed in the story with greater ease.
Track It! Enhancing GPS-Enabled Applications for Outdoor Sports and Activities

GPS devices are ubiquitous and widely used in outdoor sports and activities to collect positioning and ﬁtness data. Nevertheless, a survey on state of the art applications shows that none of the existing applications support all the essential features required for a planning session. Thus, we developed a ﬂexible, customisable, and extensible GPS-enabled application for visualising and planning outdoor sports and activities, with support for for digital mapping and the Portuguese Military Survey Maps. We also addressed planning algorithms such as grade calculation, climb detection and classiﬁcation, variable pacing or track simpliﬁca-tion. The solution was subject to usability tests in order to verify its eﬀectiveness and results have shown that even inexperienced users are able to execute complex planning tasks. We found the climb detection and classiﬁcation algorithms to be ﬂexible and accurate, and to produce reliable results when compared with cycling races data. The track sim-pliﬁcation algorithm proved to be eﬀective in preserving track shape and maintaining an higher track point density on climb segments.
BEAUFORD: A Benchmark for Evaluation of Formalisation of Definitions in OWL

In this paper we present BEAUFORD, a benchmark for methods which aim to provide formal expressions of concepts using the natural language (NL) definition of these concepts. Adding formal expressions of concepts to a given ontology allows reasoners to infer more useful pieces of information or to detect inconsistencies in this given ontology. To the best of our knowledge, BEAUFORD is the first benchmark to tackle this ontology enrichment problem. BEAUFORD allows the breaking down of a given formalisation approach by identifying its key features. In addition, BEAUFORD provides strong mechanisms to evaluate efficiently an approach even in case of ambiguity which is a major challenge in formalisation of NL resources. Indeed, BEAUFORD takes into account the fact that a given NL phrase can be formalised in many ways. Hence, it proposes a suitable specification to represent these multiple formalisations. Taking advantage of this specification, BEAUFORD redefines classical precision and recall and introduces other metrics to take into account the fact that there is not only one unique way to formalise a definition. Finally, BEAUFORD comprises a well-suited dataset to concretely judge of the efficiency of methods of formalisation. Using BEAUFORD, current approaches of formalisation of definitions can be compared accurately using a suitable gold standard.
UNITED NATIONS OFFICE ON DRUGS AND CRIME-INDEPENDENT EVALUATION SECTION

GLOT60 was developed to provide sustained support to the work of the Conference of Parties (COP) of the UN Convention against Transnational Organised Crime (UNTOC); provide for the review of the implementation of the Convention; and provide a framework for the delivery of technical support. Initially, the programme focused on the development of the UNTOC’s review mechanism but its focus changed in 2012. Its current objective is to enhance political commitment and capacities of states to prevent and combat transnational organized crime and build related legal, institutional and operational frameworks. The programme provides technical assistance to encourage ratification and implementation of UNTOC; promotes international cooperation; and knowledge management and sharing through the SHERLOC platform (Sharing Electronic Resources and Laws on Crime). The programme is implemented by the Conference Support Section (CSS) in the Organised Crime and Illicit Trafficking Branch (OCB). As of 2019, its overall budget was USD 5,909,144. The programme is coming to an end and a successor programme will be designed to complement the newly adopted UNTOC Review Mechanism.
Computer aided diagnosis of Alzheimer’s disease from brain images: a method robust to registration errors

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease aﬀecting millions of people worldwide and prominently the elderly. While there is still no cure for AD, its early detection is crucial, as an eﬀective management of the disease may help prevent the progression to more severe stages. The inherent uncertainty in the clinical diagnosis of AD has driven a search for biomarkers, where brain imaging, as positron emission tomography (PET), assumes a key role. For the acquired data to be used for computer aidded diagnosis of AD, however, it’s usually necessary to perform image registration to a standard spatial coordinate system. This can be troublesome, as many challenges, including inter-subject anatomical variability, are encountered, so that possible misclassiﬁcation errors might result from a poor coordinate transformation. In this work, in the attempt to ﬁnd a method robust to such registration errors, and building from the texton-based approach, several methods were considered and applied on both registered and non-registered datasets. Other feature representations were considered, namely learned using a stacked sparse autoencoder (SSAE) and the raw voxel intensity values, either extracted from the whole brain, patches or identiﬁed regions of interest. Binary classiﬁcation among cognitively normal subjects, AD and mild cognitive impairment (MCI) patients was performed in a dichotomous fashion and conclusions regarding the accuracy and robustness of the diferent methods were drawn, particularly conﬁrming the robustness of the texton-based approach applied on the whole brain images and of the learned feature representations using SSAE.
Domotic Module for the Internet-of-Things

With the exponential growth of Internet-of-Things (IoT) usage and devices and with the increasing interest of users in acquiring these devices to improve comfort and optimize their household chores, it is necessary to develop a robust and secure home automation system that allows users to connect all these devices and manage their information in a ﬂexible way, with low implementation costs. Thus, this paper presents a solution for a smart home system that only uses WiFi network for communication, avoiding intrusive installations (it does not need to add physical wiring), and allows to connect and control diﬀerent devices, from any vendor, remotely from a mobile phone, a tablet or any other device with an internet connection. A complex and modular system has been developed, which uses standard technologies and protocols and consists in a central server (smart home server), which oﬀers a set of services to control and manage the system; an auxiliary database to minimize information that devices need to store; a MQTT broker that allows the devices to communicate using the MQTT protocol; and home appliance devices whose requirements are minimum, they only must able to connect to an WiFi network.
Flow-Z: A Flow-based Adaptable Game to Maintain Optimal Challenge

—Often video games fail to attract a wider range of consumers because people become uninvolved when they fail to meet the in-game difﬁculty. This dissertation addresses the problem of the in-game difﬁculty not being correctly adapted to the gamers, leading to their uninterest for not having their skills balanced with the challenge of the game. This balance is one of the conditions that lead people to ﬂow, which is the mental state associated with optimal enjoyment of an activity. In our work, we study if ﬂow may be relevant for gameplay adaptability and may offer a better gaming experience, since it provides a better enjoyment of an activity. We created a hypothesis to verify if a game that adapts its parameters to a representation of the mental state of the player following the ﬂow theory can provide a better gaming experience compared to a game that adapts to their performance. We developed a ﬁrst-person shooter video game that adapts its in-game difﬁculty and environmental settings based on a representation of their mental state to keep a balance between the skills of the player and the challenge of the game. The mental state of the player is measured with their physiological signals, namely the heart rate and the beta band of the brainwaves, and we distinguish the mental state of the player with an accuracy of 87%. We also conducted an evaluation using self-perceived ﬂow and in-game scores as metrics to compare the mental state-based adaptability with a performance-based version. Results show that the latter provided a better gaming experience.
Mental Wellbeing: Future Agenda Drawing from Design, HCI and Big Data

Most HCI work on the exploration and support of mental wellbeing involves mobiles, sensors, and various on-line systems which focus on tracking users. However, adoption of, and adherence to such systems is not ideal. Are there innovative ways to better design for mental wellbeing? A promising novel approach is to encourage changes to behavior through the use of tailored feedback informed by machine learning algorithms applied to large sets of use data. This one day workshop aims to explore novel ways to actively engage participants through interactive systems, with an overall aim to shape the research agenda of future HCI work on mental wellbeing. The workshop is designed in an innovative format offering a mixture of traditional presentation, hands-on design and future-thinking activities. The workshop brings together both practitioners and HCI researchers from across a range areas addressing mental wellbeing.
Open Information Extraction from Dialogue Transcriptions

—Open Information Extraction (OpenIE) is the task of ﬁnding a structured representation for the relations and assertions present in natural language text, without using a predeﬁned categorization of the relationship types that are to be extracted. OpenIE tools exploit word tokens, together with syntactic and/or semantic information from sentences, to search for relations in them. Applying OpenIE methods to dialogue transcriptions, a speciﬁc kind of text that is less structured and consistent than formal text, results in a decrease in performance. To address this issue we ﬁrst present a comparative study between four OpenIE systems, namely ReVerb, OLLIE, Stanford OIE, and OpenIE4, intended to analyze and justify the difference in results; second we implement an application that, used together with the aforementioned Open IE tools, increases precision by 11 percentage points (pps), recall by up to 12 pps, and F1-score by 11 pps for the best obtained results, which improved the quality of extractions. Our system pre-processes dialogue text before being extracted, by simplifying and dividing the sentences using several natural language processing techniques.
Supporting Affective Expression in Multi-party Interactions

—When designing synthetic characters, it is imperative to consider the expression of affective states, in order to achieve believable communications. This work implements an animation module, using Unity 3D’s Mechanim system, capable of supporting interactions between two synthetic characters and a human. To accomplish this, we coordinated head motion and emotional expression, via the use of blending and layering of simple animations, to create more dynamic and believable dialogs. Two sets of experiments were conducted on the system, which revealed that while the animation quality was improved, not all occurrences of emotional ambiguity were able to be resolved, and further work needs to be done to balance emotional believability and recognition.
Multi-Layer Level of Detail For Character Animation

Real-time animation of human-like characters has been an active research area in computer graphics. Nowadays, more and more applications need to render various realistic scenes with human motion in crowds for interactive virtual environments. Animation and level of detail are well explored fields but little has been done to generate level of detail automatically for dynamic articulated meshes. Our approach is based on the combination of three interesting layers for run-time level of detail in character crowd animation: the skeleton, the mesh and the motion. We build a Multiresolution Skeletal Graph to simplify the skeleton topology progressively. In contrast with previous works, we use a Dual-Graph Based Simplification for articulated meshes, where the triangle decimation is driven by triangle compactness, to build a dynamic, continuous, progressive and selective mesh level of detail. We also present Power Skinning to ensure the stability of Linear Smooth Skinning, during the simplification, with an efficient multi-weight update rule.
TrustFrame, a Software Development Framework for TrustZone-enabled Hardware

—With the continuous evolution of technology ﬁelds like mobile, embedded systems and ubiquitous computing, the way we interact with several types of devices is ever changing. Today, applications with sensitive data are increasingly used in mobile devices by users, which brings huge security issues. In an attempt to overcome the growing security concerns, some hardware manufacturers are starting to use ARM TrustZone technology. This technology presents itself with enormous appliance potential, since it allows the development of more robust operating systems, achieving better application security. However, research on this matter is quite complicated, due to various incompatible software and hardware solutions, as well as the lack of documentation and support to software development for TrustZone-enabled hardware. Development initiation on any TrustZone-based solution has many barriers: framework selection, choosing compatible hardware, initial environment conﬁguration, programming APIs study and start of development. In this work we study the current state of the art in TrustZone-based solutions. This work paves the way for the development of a complete framework (documentation, development environment and compatibility support) to ease the bootstrap of TrustZone-based software development projects.
Translation and Validation of the Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia (ASTA) to the Brazilian Context: An Instrument Focusing on Arrhythmia Symptoms

Introduction The wide variety of symptoms in patients with cardiac arrhythmias can affect daily living activities. The evaluation of symptoms with patient-reported outcome measures (PROMs), with validated instruments, can provide information that contributes to clinical decisions and treatment. In Brazil, however, there is no available scale that evaluates symptoms in different types of arrhythmias. Purpose This study aimed to translate the Arrhythmia-Specific Questionnaire in Tachycardia and Arrhythmia symptom scale (ASTA-symptom scale) and then validate the questionnaire in terms of Brazilian culture. Method The methodological process of cultural adaptation used was based on international literature guidelines consisting of forward translation, synthesis, back translation, review by an expert committee, and pretest. Psychometric analyses were conducted with 140 patients. These included measuring internal consistency (Cronbach's alpha), construct validity with item-total correlations, and convergent construct validity with correlations with the quality of life questionnaire for patients with atrial fibrillation-version 2 (QVFA-v2). Usability and understandability were evaluated through the usability evaluation of instruments. Results The translation and adaptation processes were performed by obtaining the Brazilian Portuguese version of the original Swedish instrument. This version presented the internal consistency of items, evaluated through Cronbach's α (0.79). Construct validity was demonstrated by item-total correlations for the nine items, all except one reached the level of >0.30 (0.24). Convergent validity showed a high correlation with QVFA-v2 (0.89). As for the evaluation of usability and understanding, after two small suggested changes, no additional alterations were necessary. Conclusion The psychometric properties of the Brazilian version of ASTA-symptom scale evaluated in this study were satisfactory, and the scale was proved to be a valid and reliable tool to assess the symptom burden in patients with different forms of tachyarrhythmia. The ASTA-Br-symptom scale questionnaire can be an important addition to PROMs for patients with arrhythmias and could help healthcare professionals in decision-making.
Development of multiplex suspension microarrays for the detection of alphaviruses and flaviviruses of public health significance

II DECLARATION BY AUTHOR . IV PUBLICATIONS DURING CANDIDATURE . V PUBLICATIONS INCLUDED IN THIS THESIS . V CONTRIBUTIONS BY OTHERS TO THE THESIS . VI STATEMENT OF PARTS OF THE THESIS SUBMITTED TO QUALIFY FOR THE AWARD OF ANOTHER DEGREE. VI ACKNOWLEDGEMENTS . VII
Making the Production of Learning at Scale More Open and Flexible

Professional learning is a critical component of the ongoing improvement, innovation and adoption of new practices that support learning at scale. In this context, educators must learn how to apply digital technologies and work effectively in digital networks. This study examines how higher education professionals adapted their practice to enable more open and flexible work processes. A case study carried out using Activity Theory showed that teams involved in the development of a module all need access to a range of expertise both practical and academic. At each stage, they need to be clear about the learning outcomes of the module, the responsibilities of each team and its constraints. Teams need to be willing to agree ways to shift those constraints in order to develop a module effectively.
KEYNOTE: VACCINE QUALITY TESTS: THE VALUE OF ALTERNATIVE METHODS?

In vivo vaccine potency and cross-protection tests for foot-and-mouth disease are highly variable. This is equally true for the European Pharmacopoeia 50% protective dose test and the SouthAmerican “Protection against Podal Generalisation” test. In light of the 3Rs (Reduction, Refinement, Replacement), alternative potency and matching tests have been described based on serology for instance. Promising prospects include correlating innate immunity to protection. From serological potency data it is evident that each laboratory needs to set up its own pass-level for protection. This may not be feasible and the use of standardised/harmonised assays and reference standards should thus be explored. In vitro measures of relatedness between vaccine and field strains should also be further harmonised. Guidelines for the selection of sera for vaccine matching purposes are urgently needed as well as standardised assay protocols. Other in vitro tests for vaccine quality (vaccine stability, measurement of 146S content etc) need further research.
A Trenchant Tracking in Car with Smart Emergency Lifesaving System

: Internet of things is an emerging technology having the ability to change the way we live. Immediately after an accident or an emergency, the system either starts automatically or may be triggered manually. Depending upon type of emergency it initiates communication and shares information e.g. location information, a set of relevant images taken from prefixed angles etc. with appropriate authority. The system prototype has been designed with Raspberry Pi 3 Model B. IOT with enable of GPS, can able to track any location or any emergency and more over with camera and object detect sensor, If the object is detected the end result will be the accident and if the object is not detected the end result will be no accident, If the result is accident the camera will capture images and sends the result to the rescue center using Harveshine Algorithm
Relating Linguistic Gender Bias, Gender Values, and Gender Gaps: An International Analysis (cid:63)

Recent research in machine learning has shown that many machine-learned language models contain pervasive racial and gender biases, rooting from biases in their textual training data. While these biases produce sub-optimal parsing and inferences, they may help us characterize and predict statistical gender gaps and gender values in the culture(s) that produced the training text, thereby helping us understand cultural context through big data. This paper presents an approach to (1) quantify gender bias in word embeddings (i.e., vector-based lexical semantics), (2) correlate gender biases with survey responses and statistical gender gaps in education, politics, economics, and health, and (3) integrate numerical biases and statistics to model diﬀerent cultures’ survey results more accurately than either in isolation. We validate this approach us-ing 2018 Twitter data spanning 99 countries, 18 Global Gender Gap statistics from the World Economic Forum, and 8 international survey results from the World Value Survey. Integrating these heterogeneous data across cultures is an important step toward building computational models to understand group bias.
Indagare - Vector Drawings Search Engine

Currently, there are large collections of clip art vector drawings from which users can select the desired ﬁgures to insert in their documents. However, to locate a particular drawing among thousands is not easy. This thesis presents a former approach adapted to the human perception model, based in geometric matching of two drawings, which takes into account the drawing as a whole and each of the shapes in it. We developed a web search engine for clip art drawings, where we included this new technique. Experimental evaluation reveals that this new geometric matching algorithm, applied to the problem of ClipArt drawings retrieval based on sketches, conducts to better results than some solutions based on topological information.