Interpreting Judgements using Knowledge Representation Methods and Computational Models of Argument

The goal of the present research is to define a Semantic Web framework for precedent modelling, by using knowledge extracted from text, metadata, and rules, while maintaining a strong text-to-knowledge morphism between legal text and legal concepts, in order to fill the gap between legal document and its semantics. The framework is composed of four different models that make use of standard languages from the Semantic Web stack of technologies: a document metadata structure, modelling the main parts of a judgement, and creating a bridge between a text and its semantic annotations of legal concepts; a legal core ontology, modelling abstract legal concepts and institutions contained in a rule of law; a legal domain ontology, modelling the main legal concepts in a specific domain concerned by case-law; an argumentation system, modelling the structure of argumentation. The input to the framework includes metadata associated with judicial concepts, and an ontology library representing the structure of case-law. The research relies on the previous efforts of the community in the field of legal knowledge representation and rule interchange for applications in the legal domain, in order to apply the theory to a set of real legal documents, stressing the OWL axioms definitions as much as possible in order to enable them to provide a semantically powerful representation of the legal document and a solid ground for an argumentation system using a defeasible subset of predicate logics. It appears that some new features of OWL2 unlock useful reasoning features for legal knowledge, especially if combined with defeasible rules and argumentation schemes. The main task is thus to formalize legal concepts and argumentation patterns contained in a judgement, with the following requirement: to check, validate and reuse the discourse of a judge - and the argumentation he produces - as expressed by the judicial text.

Phage therapy: A software system for phage quantification and kinetic model inference.

One of the most serious problems of the modern medicine is the growing emergence of antibiotic resistance among pathogenic bacteria. In this circumstance, different and innovative approaches for treating infections caused by multidrug-resistant bacteria are imperatively required. Bacteriophage Therapy is one among the fascinating approaches to be taken into account. This consists of the use of bacteriophages, viruses that infect bacteria, in order to defeat specific bacterial pathogens. Phage therapy is not an innovative idea, indeed, it was widely used around the world in the 1930s and 1940s, in order to treat various infection diseases, and it is still used in Eastern Europe and the former Soviet Union. Nevertheless, Western scientists mostly lost interest in further use and study of phage therapy and abandoned it after the discovery and the spread of antibiotics. The advancement of scientific knowledge of the last years, together with the encouraging results from recent animal studies using phages to treat bacterial infections, and above all the urgent need for novel and effective antimicrobials, have given a prompt for additional rigorous researches in this field. In particular, in the laboratory of synthetic biology of the department of Life Sciences at the University of Warwick, a novel approach was adopted, starting from the original concept of phage therapy, in order to study a concrete alternative to antibiotics. The innovative idea of the project consists in the development of experimental methodologies, which allow to engineer a programmable synthetic phage system using a combination of directed evolution, automation and microfluidics. The main aim is to make “the therapeutics of tomorrow individualized, specific, and self-regulated” (Jaramillo, 2015). In this context, one of the most important key points is the Bacteriophage Quantification. Therefore, in this research work, a mathematical model describing complex dynamics occurring in biological systems involving continuous growth of bacteriophages, modulated by the performance of the host organisms, was implemented as algorithms into a working software using MATLAB. The developed program is able to predict different unknown concentrations of phages much faster than the classical overnight Plaque Assay. What is more, it gives a meaning and an explanation to the obtained data, making inference about the parameter set of the model, that are representative of the bacteriophage-host interaction.

Experimental characterization and modelling of a servo-pneumatic system for a knee loading apparatus

The new knee test rig developed in University of Bologna used pneumatic cylinder as actuator system. Specific characterization and modelling about the pneumatic cylinder and the related devices are needed in better controlling the test rig. In this thesis, an experimental environment for the related device is set up with data acquisition system using Real-time Windows Target, Simulink, MatLab. Based on the experimental data, a fitted model for the pneumatic cylinder friction is found.

Un’applicazione di elaborazione del linguaggio naturale per il gioco di "Indovina chi?" su robot umanoide

La tesi è stata incentrata sul gioco «Indovina chi?» per l’identificazione da parte del robot Nao di un personaggio tramite la sua descrizione. In particolare la descrizione avviene tramite domande e risposte L’obiettivo della tesi è la progettazione di un sistema in grado di capire ed elaborare dei dati comunicati usando un sottoinsieme del linguaggio naturale, estrapolarne le informazioni chiave e ottenere un riscontro con informazioni date in precedenza. Si è quindi programmato il robot Nao in modo che sia in grado di giocare una partita di «Indovina chi?» contro un umano comunicando tramite il linguaggio naturale. Sono state implementate regole di estrazione e categorizzazione per la comprensione del testo utilizzando Cogito, una tecnologia brevettata dall'azienda Expert System. In questo modo il robot è in grado di capire le risposte e rispondere alle domande formulate dall'umano mediante il linguaggio naturale. Per il riconoscimento vocale è stata utilizzata l'API di Google e PyAudio per l'utilizzo del microfono. Il programma è stato implementato in Python e i dati dei personaggi sono memorizzati in un database che viene interrogato e modificato dal robot. L'algoritmo del gioco si basa su calcoli probabilistici di vittoria del robot e sulla scelta delle domande da proporre in base alle risposte precedentemente ricevute dall'umano. Le regole semantiche realizzate danno la possibilità al giocatore di formulare frasi utilizzando il linguaggio naturale, inoltre il robot è in grado di distinguere le informazioni che riguardano il personaggio da indovinare senza farsi ingannare. La percentuale di vittoria del robot ottenuta giocando 20 partite è stata del 50%. Il data base è stato sviluppato in modo da poter realizzare un identikit completo di una persona, oltre a quello dei personaggi del gioco. È quindi possibile ampliare il progetto per altri scopi, oltre a quello del gioco, nel campo dell'identificazione.

Semantic memory involvement in the default mode network: a functional neuroimaging study using independent component analysis

The Default Mode Network (DMN) is a higher order functional neural network that displays activation during passive rest and deactivation during many types of cognitive tasks. Accordingly, the DMN is viewed to represent the neural correlate of internally-generated self-referential cognition. This hypothesis implies that the DMN requires the involvement of cognitive processes, like declarative memory. The present study thus examines the spatial and functional convergence of the DMN and the semantic memory system. Using an active block-design functional Magnetic Resonance Imaging (fMRI) paradigm and Independent Component Analysis (ICA), we trace the DMN and fMRI signal changes evoked by semantic, phonological and perceptual decision tasks upon visually-presented words. Our findings show less deactivation during semantic compared to the two non-semantic tasks for the entire DMN unit and within left-hemispheric DMN regions, i.e., the dorsal medial prefrontal cortex, the anterior cingulate cortex, the retrosplenial cortex, the angular gyrus, the middle temporal gyrus and the anterior temporal region, as well as the right cerebellum. These results demonstrate that well-known semantic regions are spatially and functionally involved in the DMN. The present study further supports the hypothesis of the DMN as an internal mentation system that involves declarative memory functions.

ZeroG: overground gait and balance training system

A new overground body-weight support system called ZeroG has been developed that allows patients with severe gait impairments to practice gait and balance activities in a safe, controlled manner. The unloading system is capable of providing up to 300 lb of static support and 150 lb of dynamic (or constant force) support using a custom-series elastic actuator. The unloading system is mounted to a driven trolley, which rides along an overhead rail. We evaluated the performance of ZeroG's unloading system, as well as the trolley tracking system, using benchtop and human-subject testing. Average root-mean-square and peak errors in unloading were 2.2 and 7.2 percent, respectively, over the range of forces tested while trolley tracking errors were less than 3 degrees, indicating the system was able to maintain its position above the subject. We believe training with ZeroG will allow patients to practice activities that are critical to achieving functional independence at home and in the community.

Mandibular reconstruction using a combination graft of rhBMP-2 with bone marrow cells expanded in vitro

BACKGROUND: The aim of this study was to evaluate the efficacy of a combination graft, using recombinant human bone morphogenetic protein-2 (rhBMP-2) and culture-expanded cells derived from bone marrow, for bone regeneration in a nonhuman primate mandible. METHODS: Five Japanese monkeys were used. Three milliliters of bone marrow was obtained from the tibia and plated into culture flasks. Adherent cells were cultured until near confluence; then, the proliferated cells were transferred to a three-dimensional culture system using collagen beads as the cell carrier. The medium was supplemented with ascorbic acid, beta-glycerophosphate, and dexamethasone to promote osteoblastic differentiation. After further proliferation on beads, the cells were mixed with a collagen sponge that was impregnated with rhBMP-2 and grafted into surgically created segmental bone defects of the mandibles. Three animals received this treatment, and either culture-expanded cells alone or collagen beads without cells were implanted into the remaining two monkeys as controls. The animals were killed 24 weeks after surgery, and the results were assessed by radiographic and histologic evaluation. RESULTS: The combination graft of culture-expanded bone marrow cells with rhBMP-2 in a collagen sponge regenerated the mandibular bone completely. By contrast, the graft of culture-expanded cells alone resulted in only a small amount of bone formation, and the implantation of collagen beads alone led to no bone formation. CONCLUSION: The combination graft of rhBMP-2 and culture-expanded cells, which requires only a small amount of bone marrow, is a reliable method for the reconstruction of segmental bone defects of the mandible.

Time-domain models for power system stability and unbalance

It is an important and difficult challenge to protect modern interconnected power system from blackouts. Applying advanced power system protection techniques and increasing power system stability are ways to improve the reliability and security of power systems. Phasor-domain software packages such as Power System Simulator for Engineers (PSS/E) can be used to study large power systems but cannot be used for transient analysis. In order to observe both power system stability and transient behavior of the system during disturbances, modeling has to be done in the time-domain. This work focuses on modeling of power systems and various control systems in the Alternative Transients Program (ATP). ATP is a time-domain power system modeling software in which all the power system components can be modeled in detail. Models are implemented with attention to component representation and parameters. The synchronous machine model includes the saturation characteristics and control interface. Transient Analysis Control System is used to model the excitation control system, power system stabilizer and the turbine governor system of the synchronous machine. Several base cases of a single machine system are modeled and benchmarked against PSS/E. A two area system is modeled and inter-area and intra-area oscillations are observed. The two area system is reduced to a two machine system using reduced dynamic equivalencing. The original and the reduced systems are benchmarked against PSS/E. This work also includes the simulation of single-pole tripping using one of the base case models. Advantages of single-pole tripping and comparison of system behavior against three-pole tripping are studied. Results indicate that the built-in control system models in PSS/E can be effectively reproduced in ATP. The benchmarked models correctly simulate the power system dynamics. The successful implementation of a dynamically reduced system in ATP shows promise for studying a small sub-system of a large system without losing the dynamic behaviors. Other aspects such as relaying can be investigated using the benchmarked models. It is expected that this work will provide guidance in modeling different control systems for the synchronous machine and in representing dynamic equivalents of large power systems.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Documenting sound change with smartphone apps

Crowdsourcing linguistic phenomena with smartphone applications is relatively new. Apps have been used to train acoustic models for automatic speech recognition (de Vries et al. 2014) and to archive endangered languages (Iwaidja Inyaman Team 2012). Leemann and Kolly (2013) developed a free app for iOS—Dialäkt Äpp (DÄ) (>78k downloads)—to document language change in Swiss German. Here, we present results of sound change based on DÄ data. DÄ predicts the users’ dialects: for 16 variables, users select their dialectal variant. DÄ then tells users which dialect they speak. Underlying this prediction are maps from the Linguistic Atlas of German-speaking Switzerland (SDS, 1962-2003), which documents the linguistic situation around 1950. If predicted wrongly, users indicate their actual dialect. With this information, the 16 variables can be assessed for language change. Results revealed robustness of phonetic variables; lexical and morphological variables were more prone to change. Phonetic variables like to lift (variants: /lupfə, lʏpfə, lipfə/) revealed SDS agreement scores of nearly 85%, i.e., little sound change. Not all phonetic variables are equally robust: ladle (variants: /xælə, xællə, xæuə, xæɫə, xæɫɫə/) exhibited significant sound change. We will illustrate the results using maps that show details of the sound changes at hand.

The contributions of the hippocampal formation, perirhinal cortex and areas TH/TF to object, spatial and contextual recognition memory in primates

Adult monkeys (Macaca mulatta) with lesions of the hippocampal formation, perirhinal cortex, areas TH/TF, as well as controls were tested on tasks of object, spatial and contextual recognition memory. ^ Using a visual paired-comparison (VPC) task, all experimental groups showed a lack of object recognition relative to controls, although this impairment emerged at 10 sec with perirhinal lesions, 30 sec with areas TH/TF lesions and 60 sec with hippocampal lesions. In contrast, only perirhinal lesions impaired performance on delayed nonmatching-to-sample (DNMS), another task of object recognition memory. All groups were tested on DNMS with distraction (dDNMS) to examine whether the use of active cognitive strategies during the delay period could enable good performance on DNMS in spite of impaired recognition memory (revealed by the VPC task). Distractors affected performance of animals with perirhinal lesions at the 10-sec delay (the only delay in which their DNMS performance was above chance). They did not affect performance of animals with areas TH/TF lesions. Hippocampectomized animals were impaired at the 600-sec delay (the only delay at which prevention of active strategies would likely affect their behavior). ^ While lesions of areas TH/TF impaired spatial location memory and object-in-place memory, hippocampal lesions impaired only object-in-place memory. The pattern of results for perirhinal cortex lesions on the different task conditions indicated that this cortical area is not critical for spatial memory. ^ Finally, all three lesions impaired contextual recognition memory processes. The pattern of impairment appeared to result from the formation of only a global representation of the object and background, and suggests that all three areas are recruited for associating information across sources. ^ These results support the view that (1) the perirhinal cortex maintains storage of information about object and the context in which it is learned for a brief period of time, (2) areas TH/TF maintain information about spatial location and form associations between objects and their spatial relationship (a process that likely requires additional time) and (3) the hippocampal formation mediates associations between objects, their spatial relationship and the general context in which these associations are formed (an integrative function that requires additional time). ^

Técnicas de análisis, caracterización y detección de señales de voz en entornos acústicos adversos

Este trabajo de Tesis ha abordado el objetivo de dar robustez y mejorar la Detección de Actividad de Voz en entornos acústicos adversos con el fin de favorecer el comportamiento de muchas aplicaciones vocales, por ejemplo aplicaciones de telefonía basadas en reconocimiento automático de voz, aplicaciones en sistemas de transcripción automática, aplicaciones en sistemas multicanal, etc. En especial, aunque se han tenido en cuenta todos los tipos de ruido, se muestra especial interés en el estudio de las voces de fondo, principal fuente de error de la mayoría de los Detectores de Actividad en la actualidad. Las tareas llevadas a cabo poseen como punto de partida un Detector de Actividad basado en Modelos Ocultos de Markov, cuyo vector de características contiene dos componentes: la energía normalizada y la variación de la energía. Las aportaciones fundamentales de esta Tesis son las siguientes: 1) ampliación del vector de características de partida dotándole así de información espectral, 2) ajuste de los Modelos Ocultos de Markov al entorno y estudio de diferentes topologías y, finalmente, 3) estudio e inclusión de nuevas características, distintas de las del punto 1, para filtrar los pulsos de pronunciaciones que proceden de las voces de fondo. Los resultados de detección, teniendo en cuenta los tres puntos anteriores, muestran con creces los avances realizados y son significativamente mejores que los resultados obtenidos, bajo las mismas condiciones, con otros detectores de actividad de referencia. This work has been focused on improving the robustness at Voice Activity Detection in adverse acoustic environments in order to enhance the behavior of many vocal applications, for example telephony applications based on automatic speech recognition, automatic transcription applications, multichannel systems applications, and so on. In particular, though all types of noise have taken into account, this research has special interest in the study of pronunciations coming from far-field speakers, the main error source of most activity detectors today. The tasks carried out have, as starting point, a Hidden Markov Models Voice Activity Detector which a feature vector containing two components: normalized energy and delta energy. The key points of this Thesis are the following: 1) feature vector extension providing spectral information, 2) Hidden Markov Models adjustment to environment and study of different Hidden Markov Model topologies and, finally, 3) study and inclusion of new features, different from point 1, to reject the pronunciations coming from far-field speakers. Detection results, taking into account the above three points, show the advantages of using this method and are significantly better than the results obtained under the same conditions by other well-known voice activity detectors.

Speaker Diarization Based on Intensity Channel Contribution

The time delay of arrival (TDOA) between multiple microphones has been used since 2006 as a source of information (localization) to complement the spectral features for speaker diarization. In this paper, we propose a new localization feature, the intensity channel contribution (ICC) based on the relative energy of the signal arriving at each channel compared to the sum of the energy of all the channels. We have demonstrated that by joining the ICC features and the TDOA features, the robustness of the localization features is improved and that the diarization error rate (DER) of the complete system (using localization and spectral features) has been reduced. By using this new localization feature, we have been able to achieve a 5.2% DER relative improvement in our development data, a 3.6% DER relative improvement in the RT07 evaluation data and a 7.9% DER relative improvement in the last year's RT09 evaluation data.

Using attributed variables in the implementation of concurrent and parallel logic programming systems

Incorporating the possibility of attaching attributes to variables in a logic programming system has been shown to allow the addition of general constraint solving capabilities to it. This approach is very attractive in that by adding a few primitives any logic programming system can be turned into a generic constraint logic programming system in which constraint solving can be user deñned, and at source level - an extreme example of the "glass box" approach. In this paper we propose a different and novel use for the concept of attributed variables: developing a generic parallel/concurrent (constraint) logic programming system, using the same "glass box" flavor. We argüe that a system which implements attributed variables and a few additional primitives can be easily customized at source level to implement many of the languages and execution models of parallelism and concurrency currently proposed, in both shared memory and distributed systems. We illustrate this through examples and report on an implementation of our ideas.

Using Buildroot for building Embedded Linux Systems

This document describes the basic steps to developed and embedded Linux-based system using the BeagleBoard. The document has been specifically written to use a BeagleBoard development system based on the OMAP `processor.