Different ways to measure human affective and deceptive reactions to stimulus have been developed. One method is a multimodal approach using web camera, thermal imaging camera and physiological sensors data to extract different features in the human face (verbal and non-verbal behavior) such as breathing rate, heart rate, face temperature, skin conductance, eye tracking, language analysis and facial expressions among others. Human subjects from different ages and ethnicity were exposed to two different experiments were they watched videos (affection recognition) and others answered an interview session (deception recognition). With the data collected from videos (thermal and visual), different regions of interest (ROI) of the face were selected as well as the whole picture. The ROI were determined based on the most sensitive parts of the face where larger changes of temperature or other physiological features are recorded. It was also analyzed the language (written and spoken) in order to obtain the verbal modalities. The data has been compared among the subjects to determine whether the deceptive and affective reactions of a person can be predicted using multimodal approach. From the multiple data obtained, a characterization of reactions is proposed when subjects are exposed to different stimulus, positive or negative, as well as deceptive behavior and later on recognize if the person is happy, sad, nervous, anxious, telling the truth, lying etc. Using the multimodal approach we were able to predict automatically, with higher accuracy than the baseline, affective and deceptive states of a person. In the affective state recognition, the classifier software differentiated affective state versus neutral state with 92.85% accuracy. Then it differentiated Positive State, Negative State and Neutral State with 57.14% accuracy. Additionally, it differentiated Positive State versus Negative State with 73.21% accuracy. Finally, the classifier was able to predict Deceptive State (people lying) and Non Deceptive State (people telling the truth) with 72.72% accuracy.

Driven by California State legislation, the City of Bakersfield is taking steps to introduce Complete Streets to the community. Working in collaboration with the Bakersfield City Planning Department, this project was prompted by the CIty's forthcoming update of its Metropolitan Bakersfield General Plan Circulation Element, with purpose to meet the California Complete Streets Act (AB1358) requirement to plan for a balanced, multimodal transportation network. This professional project intends to provide Bakersfield city officials, staff, and residents with tools and information needed to assess and implement Complete Streets within the Community. The project provides in depth background research on the Complete Street concept, related legislation, design features, and benefits. It reviews three Complete Street projects from similar cities located in the Central Valley with purpose to deliver guiding principles Bakersfield can utilize for successful implementation. The project provides new conceptual street standards with recommended design feature tables based on existing street types. In conclusion, this project evaluated the newly adopted Bakersfield Complete Street Policy through a strategic approach with final recommendations to build a stronger Complete Street network.

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Possession is an asymmetric semantic relation between two entities, where one entity (the possessee) belongs to the other entity (the possessor). Automatically extracting possessions are useful in identifying skills, recommender systems and in natural language understanding. Possessions can be found in different communication modalities including text, images, videos, and audios. In this dissertation, I elaborate on the techniques I used to extract possessions. I begin with extracting possessions at the sentence level including the type and temporal anchors. Then, I extract the duration of possession and co-possessions (if multiple possessors possess the same entity). Next, I extract possessions from an entire Wikipedia article capturing the change of possessors over time. I extract possessions from social media including both text and images. Finally, I also present dense annotations generating possession timelines. I present separate datasets, detailed corpus analysis, and machine learning models for each task described above.

Memoria (licenciado en ciencias jurídicas y sociales) / El éxito de las transacciones comerciales nacionales e internacionales depende, entre muchos otros factores, de la eficiencia de las cadenas de transporte de las mercaderías transadas. En la actualidad el transporte ha tenido que adaptarse a las exigencias de la “nueva economía”, cuya demanda por la integración económica, permiten la creación de la figura jurídica del transporte multimodal de mercancías. El transporte multimodal de mercancías es aquel por el cual se transportan mercancías, por dos o más modos diferentes de transporte, de acuerdo a un sólo contrato. La presente obra tiene como objetivo presentar e introducir el novedoso concepto del transporte multimodal, diferenciándolo del tradicional transporte unimodal y segmentado; plantear sus actuales dificultades y divergencias, como también proponer los cambios necesarios para la implementación del mismo como nuevo sistema de transporte que aporte al crecimiento económico de los países en vías de desarrollo.

Syftet med studien är att genom en systematisk litteraturstudie kartlägga vad aktuell forskning säger om sambandet mellan multimodalitet och högstadieelevers läslust. I studien ingår både vetenskapliga artiklar och avhandlingar. Mot teori om elevers läsrepertoarer på fritiden och mot bakgrund om att läslust har med förkroppsligande att göra, har relevant vetenskaplig litteratur analyserats. Resultatet visar att det går att urskilja fem kategorier av multimodala textformer som användbara i undervisning för att skapa läslust. Resultatet visar också på hur man kan lyfta in multimodala texter i undervisningen men att för att göra det krävs mer kunskap om andra texter än den skönlitterära hos svensklärare. Studien visar också på att unga idag läser och lyssnar mer på digitala texter än tidigare generationer och att svenskundervisningen är bristfällig i att fånga upp elevers läserfarenheter utanför skolan.

Autobiographical memory retrieval implies different phenomenological features. Given the lack of previous work in Hispanic-speaking populations, 34 in depth interview were carried out in individuals with and without Major Depressive Disorder in Buenos Aires, Argentina. Phenomenological components during the evocation of autobiographical memories were explored. Data was qualitatively analyzed using Grounded Theory. During the descriptive analyses, seven phenomenological categories were detected as emerging from the discourse. The axial and selective analyses revealed two main discursive axles areas; rhetoric-propo­ sitional and specificity- generalized. The impact on affective regulation processes, derived from the assumption of an amodal or multimodal style of processing autobiographical infor­ mation, merits further attention. / La evocación de recuerdos autobiográficos se caracteriza por presentar distintos compo­ nentes fenomenológicos. Dada la ausencia de trabajos previos realizados en poblaciones hispanoparlantes, se realizaron 34 entrevistas en profundidad a individuos con y sin tras­ torno depresivo mayor de la ciudad de Buenos Aires (Argentina). Fueron explorados los componentes fenomenológicos presentes en la evocación de recuerdos autobiográficos significativos. Los datos fueron analizados cualitativamente por medio de la Teoría Fun­ damentada en los Hechos. Durante el análisis descriptivo, se detectaron siete categorías fenomenológicas emergentes del discurso. Del análisis axial y selectivo fueron identificados dos ejes discursivos: retórico-proposicional y especificidad-generalidad. Las implicancias, en la regulación afectiva, derivadas de la asunción de un estilo amodal o multimodal de proce­ samiento de información autobiográfica merecen mayor atención. / A evocação de memórias autobiográficas é caracterizada por diferentes componentes feno­ menológicos. Dada a falta de trabalhos prévios sobre o tema em populações de língua espanhola, 34 entrevistas em profundidade foram conduzidas em indivíduos com e sem transtorno depressivo maior na cidade de Buenos Aires (Argentina). Foram explorados os componentes fenomenológicos presentes na evocação de memórias autobiográficas signi­ ficativas. Os dados foram analisados qualitativamente através da Teoria Fundamentada. Durante a análise descritiva, foram detectadas sete categorias fenomenológicas emer­ gentes no discurso. Dos analises axial e seletivo foram identificados dois eixos discursivos: retórico-proposicional e especificidade-generalidade. As implicações, na regulação afetiva, decorrentes da assunção de um estilo amodal ou um estilo multimodal no processamento de informações autobiográficas merecem mais atenção.

Studies show that instruction manuals are rarely read, and Volvo CE often finds the manual in the bookshelf at the office at customer visits. The instruction books are thus far from the users. How will information reach users? In this study I have worked with Volvo CE, Eskilstuna based on the question "How can information about how best driving economy be achieved and be designed to motivate drivers of wheel loaders to drive fuel efficiently?". Based on literature studies, interviews, analyzes and hearings, a prototype with accompanying concepts has been developed for Volvo CE for continued development. The prototype consists of a design where three factors that affect fuel efficient driving have been selected and constructed. The elements are text and image-based and then placed in an ECO OPERATOR program in the Volvo CE Co-pilot. Volvo CE already works with information via Co-pilot, which is a display located in the wheel loaders cab. Conclusions are that human centered design where the user is put in focus can be used to generate fuel efficient driving information adapted for wheel loaders.

Biometrics is constantly evolving technology which has been widely used in many official and commercial identification applications. In fact in recent years biometric-based authentication techniques received more attention due to increased concerns in security. Most biometric systems that are currently in use typically employ a single biometric trait. Such systems are called unibiometric systems. Despite considerable advances in recent years, there are still challenges in authentication based on a single biometric trait, such as noisy data, restricted degree of freedom, intra-class variability, non-universality, spoof attack and unacceptable error rates. Some of the challenges can be handled by designing a multimodal biometric system. Multimodal biometric systems are those which utilize or are capable of utilizing, more than one physiological or behavioural characteristic for enrolment, verification, or identification. In this thesis, we propose a novel fusion approach at a hybrid level between iris and online signature traits. Online signature and iris authentication techniques have been employed in a range of biometric applications. Besides improving the accuracy, the fusion of both of the biometrics has several advantages such as increasing population coverage, deterring spoofing activities and reducing enrolment failure. In this doctoral dissertation, we make a first attempt to combine online signature and iris biometrics. We principally explore the fusion of iris and online signature biometrics and their potential application as biometric identifiers. To address this issue, investigations is carried out into the relative performance of several statistical data fusion techniques for integrating the information in both unimodal and multimodal biometrics. We compare the results of the multimodal approach with the results of the individual online signature and iris authentication approaches. This dissertation describes research into the feature and decision fusion levels in multimodal biometrics.

The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy.