Collective Intelligence and the Mapping of Accessible Ways in the City: a Systematic Literature Review

This paper has the objective of assessing how ICTs are being used to provide accessibility in urban mobility, with special interest to collective intelligence approaches. A systematic literature review (SLR) was performed, using several different criteria to filter down the 500+ academic papers that were originally obtained from a search for “accessible maps” to the 43 papers that finally remained in the corpus of the SLR. Among the findings, it was noticed that (i) few studies explored the motivations of users that actively contribute, providing information to feed maps, and they restricted themselves to exploring three techniques: gaming, monetary reward and ranking; (ii) social networks are rarely used as a source of data for building and updating maps; and (iii) the literature does not discuss any initiative that aims to support the needs of physically and visually impaired citizens at the same time

Juegos serios, evaluación de tecnologías y ámbitos de aplicación

El presente trabajo gira alrededor del mundo de los Serious Games (Juegos Serios) abarcando aspectos tales como campos de aplicación, herramientas disponibles para el desarrollo y un paneo acerca de las plataformas utilizadas para el desarrollo y para su utilización. En el capítulo 1 se explicará la organización del presente trabajo, en el capítulo 2 se analizarán algunos de los campos de aplicación donde se ve un importante desarrollo de los juegos serios, en el capítulo 3 se describirán algunas herramientas privativas y otras FLOSS (Free/Libre Open Source Software), en el capítulo 4 se hablará acerca de la ludificación/gamificación y en el capítulo 5 se describirán las conclusiones y posibles trabajos futuros.Facultad de Informátic

Location reliability and gamification mechanisms for mobile crowd sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the sensors on the phones. This new type of sensing can be a scalable and cost-effective alternative to deploying static wireless sensor networks for dense sensing coverage across large areas. However, mobile people-centric sensing has two main issues: 1) Data reliability in sensed data and 2) Incentives for participants. To study these issues, this dissertation designs and develops McSense, a mobile crowd sensing system which provides monetary and social incentives to users. This dissertation proposes and evaluates two protocols for location reliability as a step toward achieving data reliability in sensed data, namely, ILR (Improving Location Reliability) and LINK (Location authentication through Immediate Neighbors Knowledge). ILR is a scheme which improves the location reliability of mobile crowd sensed data with minimal human efforts based on location validation using photo tasks and expanding the trust to nearby data points using periodic Bluetooth scanning. LINK is a location authentication protocol working independent of wireless carriers, in which nearby users help authenticate each other’s location claims using Bluetooth communication. The results of experiments done on Android phones show that the proposed protocols are capable of detecting a significant percentage of the malicious users claiming false location. Furthermore, simulations with the LINK protocol demonstrate that LINK can effectively thwart a number of colluding user attacks. This dissertation also proposes a mobile sensing game which helps collect crowd sensing data by incentivizing smart phone users to play sensing games on their phones. We design and implement a first person shooter sensing game, “Alien vs. Mobile User”, which employs techniques to attract users to unpopular regions. The user study results show that mobile gaming can be a successful alternative to micro-payments for fast and efficient area coverage in crowd sensing. It is observed that the proposed game design succeeds in achieving good player engagement

Testing quality in interlingual respeaking and other methods of interlingual live subtitling

La sottotitolazione in tempo reale (Live Subtitling, LS), trova le sue fondamenta nella sottotitolazione preregistrata per non udenti e ipoudenti per la produzione di sottotitoli per eventi o programmi televisivi dal vivo. La sottotitolazione live comporta il trasferimento da un contenuto orale a uno scritto (traduzione intersemiotica) e può essere effettuata da e verso la stessa lingua (intralinguistica), o da una lingua a un’altra (interlinguistica), fornendo così accessibilità per soggetti non udenti e al tempo stesso garantendo accesso multilingue ai contenuti audiovisivi. La sottotitolazione interlinguistica in tempo reale (d'ora in poi indicata come ILS, Interlingual Live Subtitling) viene attualmente realizzata con diversi metodi: l'attenzione è qui posta sulla tecnica del respeaking interlinguistico, uno dei metodi di sottotitolazione in tempo reale o speech-to-text interpreting (STTI) che ha suscitato negli ultimi anni un crescente interesse, anche nel panorama italiano. Questa tesi di Dottorato intende fornire un quadro della letteratura e della ricerca sul respeaking intralinguistico e interlinguistico fino ad oggi, con particolare enfasi sulla situazione attuale in Italia di questa pratica. L'obiettivo della ricerca è stato quello di esplorare diversi metodi di ILS, mettendone in luce i punti di forza e le debolezze nel tentativo di informare il settore delle potenzialità e dei rischi che possono riflettersi sulla qualità complessiva finale dei sottotitoli attraverso l’utilizzo di diverse tecniche. Per fare ciò, sono stati testati in totale cinque metodi di ILS con diversi gradi di interazione uomo-macchina; ciascun metodo è stato analizzato in termini di qualità, quindi non solo dal punto di vista dell'accuratezza linguistica, ma anche considerando un altro fattore cruciale quale il ritardo nella trasmissione dei sottotitoli stessi. Nello svolgimento della ricerca sono stati condotti due casi di studio con diverse coppie linguistiche: il primo esperimento (dall'inglese all'italiano) ha testato e valutato la qualità di respeaking interlinguistico, interpretazione simultanea insieme a respeaking intralinguistico e, infine, interpretazione simultanea e sistema di riconoscimento automatico del parlato (Automatic Speech Recognition, ASR). Il secondo esperimento (dallo spagnolo all'italiano) ha valutato e confrontato cinque i metodi: i primi tre appena menzionati e altri due in cui la macchina svolgeva la maggior parte se non la totalità del lavoro: respeaking intralinguistico e traduzione automatica (Machine Translation, MT), e ASR con MT. Sono stati offerti due laboratori di respeaking interlinguistico nel Corso magistrale in Traduzione e Interpretazione dell'Università di Genova per preparare gli studenti agli esperimenti, volti a testare diversi moduli di formazione sull'ILS e la loro efficacia sull’apprendimento degli studenti. Durante le fasi di test, agli studenti sono stati assegnati diversi ruoli per ogni metodo, producendo sottotitoli interlinguistici live a partire dallo stesso testo di partenza: un video di un discorso originale completo durante un evento dal vivo. Le trascrizioni ottenute, sotto forma di sottotitoli, sono state analizzate utilizzando il modello NTR (Romero-Fresco & Pöchhacker, 2017) e per ciascun metodo è anche stato calcolato il ritardo. I risultati quantitativi preliminari derivanti dalle analisi NTR e dal calcolo del ritardo sono stati confrontati con altri due casi di studio condotti dall'Università di Vigo (Spagna) e dall'Università del Surrey (Gran Bretagna), sottolineando come i flussi di lavoro più automatizzati o completamente automatizzati siano effettivamente più veloci degli altri, ma al contempo presentino ancora diversi problemi di traduzione e di punteggiatura. Anche se su scala ridotta, la ricerca dimostra anche quanto sia urgente e possa potenzialmente essere facile formare i traduttori e gli interpreti sul respeaking durante il loro percorso accademico, grazie anche al loro spiccato interesse per la materia. Si spera che i risultati ottenuti possano meglio mettere in luce le ripercussioni dell'uso dei diversi metodi a confronto, nonché indurre un'ulteriore riflessione sull'importanza dell'interazione umana con i sistemi automatici di traduzione e di riconoscimento del parlato nel fornire accessibilità di alta qualità per eventi dal vivo. Si spera inoltre che l’interesse degli studenti in questo campo, che era a loro completamente sconosciuto prima di questa ricerca, possa informare sull'urgenza di sensibilizzare gli studenti nel campo della sottotitolazione dal vivo attraverso il respeaking.Live subtitling (LS) finds its foundations in pre-recorded subtitling for the d/Deaf and hard of hearing (SDH) to produce real-time subtitles for live events and programs. LS implies the transfer from oral into written content (intersemiotic translation) and can be carried out from and to the same language (intralingual), or from one language to another (interlingual) to provide full accessibility for all, therefore combining SDH to the need of guaranteeing multilingual access as well. Interlingual Live Subtitling (from now on referred to as ILS) in real-time is currently being achieved by using different methods: the focus here is placed on interlingual respeaking as one of the currently used methods of LS – also referred to in this work as speech-to-text interpreting (STTI) – which has triggered growing interest also in the Italian industry over the past years. The hereby presented doctoral thesis intends to provide a wider picture of the literature and the research on intralingual and interlingual respeaking to the date, emphasizing the current situation in Italy in this practice. The aim of the research was to explore different ILS methods through their strengths and weaknesses, in an attempt to inform the industry on the impact that both potentialities and risks can have on the final overall quality of the subtitles with the involvement of different techniques in producing ILS. To do so, five ILS workflows requiring human and machine interaction to different extents were tested overall in terms of quality, thus not only from a linguistic accuracy point of view, but also considering another crucial factor such as delay in the broadcast of the subtitles. Two case studies were carried out with different language pairs: a first experiment (English to Italian) tested and assessed quality in interlingual respeaking on one hand, then simultaneous interpreting (SI) combined with intralingual respeaking, and SI and Automatic Speech Recognition (ASR) on the other. A second experiment (Spanish to Italian) evaluated and compared all the five methods: the first three again, and two others more machine-centered: intralingual respeaking combined with machine translation (MT), and ASR with MT. Two workshops in interlingual respeaking were offered at the master’s degree in Translation and Interpreting from the University of Genova to prepare students for the experiments, aimed at testing different training modules on ILS and their effectiveness on students’ learning outcomes. For the final experiments, students were assigned different roles for each tested method and performed different required tasks producing ILS from the same source text: a video of a full original speech at a live event. The obtained outputs were analyzed using the NTR model (Romero-Fresco & Pöchhacker, 2017) and the delay was calculated for each method. Preliminary quantitative results deriving from the NTR analyses and the calculation of delay were compared to other two case studies conducted by the University of Vigo and the University of Surrey, showing that more and fully-automated workflows are, indeed, faster than the others, while they still present several important issues in translation and punctuation. Albeit on a small scale, the research also shows how urgent and potentially easy could be to educate translators and interpreters in respeaking during their training phase, given their keen interest in the subject matter. It is hoped that the results obtained can better shed light on the repercussions of the use of different methods and induce further reflection on the importance of human interaction with automatic machine systems in providing high quality accessibility at live events. It is also hoped that involved students’ interest in this field, which was completely unknown to them prior to this research, can inform on the urgency of raising students’ awareness and competence acquisition in the field of live subtitling through respeaking

Gender differences in navigation dialogues with computer systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Gender is among the most influential of the factors underlying differences in spatial abilities, human communication and interactions with and through computers. Past research has offered important insights into gender differences in navigation and language use. Yet, given the multidimensionality of these domains, many issues remain contentious while others unexplored. Moreover, having been derived from non-interactive, and often artificial, studies, the generalisability of this research to interactive contexts of use, particularly in the practical domain of Human-Computer Interaction (HCI), may be problematic. At the same time, little is known about how gender strategies, behaviours and preferences interact with the features of technology in various domains of HCI, including collaborative systems and systems with natural language interfaces. Targeting these knowledge gaps, the thesis aims to address the central question of how gender differences emerge and operate in spatial navigation dialogues with computer systems. To this end, an empirical study is undertaken, in which, mixed-gender and same-gender pairs communicate to complete an urban navigation task, with one of the participants being under the impression that he/she interacts with a robot. Performance and dialogue data were collected using a custom system that supported synchronous navigation and communication between the user and the robot. Based on this empirical data, the thesis describes the key role of the interaction of gender in navigation performance and communication processes, which outweighed the effect of individual gender, moderating gender differences and reversing predicted patterns of performance and language use. This thesis has produced several contributions; theoretical, methodological and practical. From a theoretical perspective, it offers novel findings in gender differences in navigation and communication. The methodological contribution concerns the successful application of dialogue as a naturalistic, and yet experimentally sound, research paradigm to study gender and spatial language. The practical contributions include concrete design guidelines for natural language systems and implications for the development of gender-neutral interfaces in specific domains of HCI

Characterising and modeling the co-evolution of transportation networks and territories

The identification of structuring effects of transportation infrastructure on territorial dynamics remains an open research problem. This issue is one of the aspects of approaches on complexity of territorial dynamics, within which territories and networks would be co-evolving. The aim of this thesis is to challenge this view on interactions between networks and territories, both at the conceptual and empirical level, by integrating them in simulation models of territorial systems.Comment: Doctoral dissertation (2017), Universit\'e Paris 7 Denis Diderot. Translated from French. Several papers compose this PhD thesis; overlap with: arXiv:{1605.08888, 1608.00840, 1608.05266, 1612.08504, 1706.07467, 1706.09244, 1708.06743, 1709.08684, 1712.00805, 1803.11457, 1804.09416, 1804.09430, 1805.05195, 1808.07282, 1809.00861, 1811.04270, 1812.01473, 1812.06008, 1908.02034, 2012.13367, 2102.13501, 2106.11996

The Multimodal Tutor: Adaptive Feedback from Multimodal Experiences

This doctoral thesis describes the journey of ideation, prototyping and empirical testing of the Multimodal Tutor, a system designed for providing digital feedback that supports psychomotor skills acquisition using learning and multimodal data capturing. The feedback is given in real-time with machine-driven assessment of the learner's task execution. The predictions are tailored by supervised machine learning models trained with human annotated samples. The main contributions of this thesis are: a literature survey on multimodal data for learning, a conceptual model (the Multimodal Learning Analytics Model), a technological framework (the Multimodal Pipeline), a data annotation tool (the Visual Inspection Tool) and a case study in Cardiopulmonary Resuscitation training (CPR Tutor). The CPR Tutor generates real-time, adaptive feedback using kinematic and myographic data and neural networks

Safety-critical scenarios and virtual testing procedures for automated cars at road intersections

This thesis addresses the problem of road intersection safety with regard to a mixed population of automated vehicles and non-automated road users. The work derives and evaluates safety-critical scenarios at road junctions, which can pose a particular safety problem involving automated cars. A simulation and evaluation framework for car-to-car accidents is presented and demonstrated, which allows examining the safety performance of automated driving systems within those scenarios. Given the recent advancements in automated driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual testing environments or on real-world test tracks. Since it is unrealistic to cover all possible combinations of traffic situations and environment conditions, the challenge is to find the key driving situations to be evaluated at junctions. Against this background, a novel method to derive critical pre-crash scenarios from historical car accident data is presented. It employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1,056 junction crashes in the UK, which were exported from the in-depth On-the-Spot database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. As a follow-up to the scenario generation, the thesis further presents a novel, modular framework to transfer the derived collision scenarios to a sub-microscopic traffic simulation environment. The software CarMaker is used with MATLAB/Simulink to simulate realistic models of vehicles, sensors and road environments and is combined with an advanced Monte Carlo method to obtain a representative set of parameter combinations. The analysis of different safety performance indicators computed from the simulation outputs reveals collision and near-miss probabilities for selected scenarios. The usefulness and applicability of the simulation and evaluation framework is demonstrated for a selected junction scenario, where the safety performance of different in-vehicle collision avoidance systems is studied. The results show that the number of collisions and conflicts were reduced to a tenth when adding a crossing and turning assistant to a basic forward collision avoidance system. Due to its modular architecture, the presented framework can be adapted to the individual needs of future users and may be enhanced with customised simulation models. Ultimately, the thesis leads to more efficient workflows when virtually testing automated driving at intersections, as a complement to field operational tests on public roads

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms