Implementation and analysis of the ISO/IEC/IEEE P21451-1 draft standard for a smart transducer interface common network services and its applications in the Internet of Things

The Internet of Things (IoT) has rapidly become the paradigm for the creation and improvement of new and old Cyber Physical Systems (CPS), but how much longer can this development of IoT devices, networks, and services be sustained? The past decade has seen incredible growth in internet connected devices, with current estimates placing the number of such devices at about 20 billion in 2017, not including personal computers, smart phones, and tablets. This has created a massive market for these devices, with each company making their own applications, protocols, and services. Since these markets are competitive, there originally was no incentive to design systems, which were built to have a common protocol to enable interoperability between systems. This can pose a large integration effort if two or more of these systems need to communicate together as part of a larger system. The problem is compounded if these systems utilize two different physical layers or talk using two different protocols. The revitalization of the IEEE 1451 family of standards can solve this problem. The work in this thesis proposes to solve the integration problem by providing a common set of services and protocols for devices. This work provides the basis for a common architectural foundation for future IoT development. The contributions of this thesis include a renewal of the language and intent of the IEEE P21451-1 draft standard, development of example implementations to be included in the standard, and the development of Open Source hardware and software aimed at lowering the cost of adopting this standard

UI Construction for a Web-Based IDE on an Industrial IoT System

ABB as one of the leading power and automation company is connecting millions of electrical devices and systems to industrial internet of things called ABB Ability(TM). ABB Ability(TM) is refining the measured real-time data with calculations to additional soft sensors signals and Key Performance Indicators (KPIs) at the various levels from the system edges to central cloud. The engineering of the calculations requires web based Integrated Development Environment (IDE) that provides good developer experience for the subject matter experts to be productive in their work. This thesis aims to construct a user interface for ABB calculation engine that will help subject matter expert to work on the calculation engine more efficiently. As the output of this thesis work, a web-based IDE is developed on top of ABB's internal front end dashboard framework. The developed user interface lets user to operate the calculation engine more efficiently and follows their natural work flow

Transmission adaptative de modèles 3D massifs

Avec les progrès de l'édition de modèles 3D et des techniques de reconstruction 3D, de plus en plus de modèles 3D sont disponibles et leur qualité augmente. De plus, le support de la visualisation 3D sur le web s'est standardisé ces dernières années. Un défi majeur est donc de transmettre des modèles massifs à distance et de permettre aux utilisateurs de visualiser et de naviguer dans ces environnements virtuels. Cette thèse porte sur la transmission et l'interaction de contenus 3D et propose trois contributions majeures. Tout d'abord, nous développons une interface de navigation dans une scène 3D avec des signets -- de petits objets virtuels ajoutés à la scène sur lesquels l'utilisateur peut cliquer pour atteindre facilement un emplacement recommandé. Nous décrivons une étude d'utilisateurs où les participants naviguent dans des scènes 3D avec ou sans signets. Nous montrons que les utilisateurs naviguent (et accomplissent une tâche donnée) plus rapidement en utilisant des signets. Cependant, cette navigation plus rapide a un inconvénient sur les performances de la transmission : un utilisateur qui se déplace plus rapidement dans une scène a besoin de capacités de transmission plus élevées afin de bénéficier de la même qualité de service. Cet inconvénient peut être atténué par le fait que les positions des signets sont connues à l'avance : en ordonnant les faces du modèle 3D en fonction de leur visibilité depuis un signet, on optimise la transmission et donc, on diminue la latence lorsque les utilisateurs cliquent sur les signets. Deuxièmement, nous proposons une adaptation du standard de transmission DASH (Dynamic Adaptive Streaming over HTTP), très utilisé en vidéo, à la transmission de maillages texturés 3D. Pour ce faire, nous divisons la scène en un arbre k-d où chaque cellule correspond à un adaptation set DASH. Chaque cellule est en outre divisée en segments DASH d'un nombre fixe de faces, regroupant des faces de surfaces comparables. Chaque texture est indexée dans son propre adaptation set à différentes résolutions. Toutes les métadonnées (les cellules de l'arbre k-d, les résolutions des textures, etc.) sont référencées dans un fichier XML utilisé par DASH pour indexer le contenu: le MPD (Media Presentation Description). Ainsi, notre framework hérite de la scalabilité offerte par DASH. Nous proposons ensuite des algorithmes capables d'évaluer l'utilité de chaque segment de données en fonction du point de vue du client, et des politiques de transmission qui décident des segments à télécharger. Enfin, nous étudions la mise en place de la transmission et de la navigation 3D sur les appareils mobiles. Nous intégrons des signets dans notre version 3D de DASH et proposons une version améliorée de notre client DASH qui bénéficie des signets. Une étude sur les utilisateurs montre qu'avec notre politique de chargement adaptée aux signets, les signets sont plus susceptibles d'être cliqués, ce qui améliore à la fois la qualité de service et la qualité d'expérience des utilisateur

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Natural Language Processing: Analysis of Information Technology Students’ Spoken Language

Tato bakalářská práce se zabývá problematikou nových technologií umělé inteligence při zpracování přirozeného jazyka. Práce je rozdělena na teoretickou a analytickou část. Teoretická část přistupuje k problému rozdělením do tří kapitol: umělá inteligence a statistika, zpracování přirozeného jazyka a IBM Watson Natural Language Understanding. Každá z těchto kapitol je rozpracována včetně uvedení alespoň jednoho příkladu z praxe. V první kapitole je hlavním cílem vymezit teoretický rámec umělé inteligence a jejích postupů, zatímco ve druhé kapitole je vysvětlena problematika zpracování přirozeného jazyka a jeho primární funkce včetně jeho vztahu k samotné umělé inteligenci. Cílem třetí kapitoly je představit porozumění přirozenému jazyku jako primární nástroj pro analýzu, která je realizována v analytické části práce. Analytická část se zabývá analýzou mluveného jazyka studentů prostřednictvím různých metod. Transkripce shromážděných vzorků videí je provedena strojovým překladem jako aplikací zpracování přirozeného jazyka, zatímco textový výstup je analyzován prostřednictvím nástroje porozumění přirozenému jazyku. V analytické části, která popisuje výzkumnou metodologii, prezentuje a interpretuje výsledky výzkumu, jsou využívány aplikované znalosti z teoretické části práce.This bachelor’s thesis deals with the issue of new artificial intelligence technologies in natural language processing. The thesis consists of a theoretical part and an analytical part. The theoretical part approaches the issue by dividing it into three chapters: artificial intelligence and statistics, natural language processing, and IBM Watson Natural Language Understanding. Each of these chapters is elaborated on by using at least one example from the real world. In the first chapter, the main aim is to frame the theoretical framework of artificial intelligence and its practices, while in the second chapter, natural language processing and its primary functions are explained as well as its relation to artificial intelligence itself. The aim of the third chapter is to introduce natural language understanding as the primary tool for analysis which is done in the analytical part. The analytical part deals with the analysis of students’ spoken language using various methods. Collected video samples are transcribed by means of a machine translator as a natural language processing application, while the textual output is analysed through a natural language understanding engine. The applied knowledge from the theoretical part is used in the analytical part that includes the description of research methodology, presentation and interpretation of research results.

Knowledge acquisition for coreference resolution

Diese Arbeit befasst sich mit dem Problem der statistischen Koreferenzauflösung. Theoretische Studien bezeichnen Koreferenz als ein vielseitiges linguistisches Phänomen, das von verschiedenen Faktoren beeinflusst wird. Moderne statistiche Algorithmen dagegen basieren sich typischerweise auf einfache wissensarme Modelle. Ziel dieser Arbeit ist das Schließen der Lücke zwischen Theorie und Praxis. Ausgehend von den Erkentnissen der theoretischen Studien erfolgt die Bestimmung der linguistischen Faktoren die fuer die Koreferenz besonders relevant erscheinen. Unterschiedliche Informationsquellen werden betrachtet: von der Oberflächenübereinstimmung bis zu den tieferen syntaktischen, semantischen und pragmatischen Merkmalen. Die Präzision der untersuchten Faktoren wird mit korpus-basierten Methoden evaluiert. Die Ergebnisse beweisen, dass die Koreferenz mit den linguistischen, in den theoretischen Studien eingebrachten Merkmalen interagiert. Die Arbeit zeigt aber auch, dass die Abdeckung der untersuchten theoretischen Aussagen verbessert werden kann. Die Merkmale stellen die Grundlage für den Aufbau eines einerseits linguistisch gesehen reichen andererseits auf dem Machinellen Lerner basierten, d.h. eines flexiblen und robusten Systems zur Koreferenzauflösung. Die aufgestellten Untersuchungen weisen darauf hin dass das wissensreiche Model erfolgversprechende Leistung zeigt und im Vergleich mit den Algorithmen, die sich auf eine einzelne Informationsquelle verlassen, sowie mit anderen existierenden Anwendungen herausragt. Das System erreicht einen F-wert von 65.4% auf dem MUC-7 Korpus. In den bereits veröffentlichen Studien ist kein besseres Ergebnis verzeichnet. Die Lernkurven zeigen keine Konvergenzzeichen. Somit kann der Ansatz eine gute Basis fuer weitere Experimente bilden: eine noch bessere Leistung kann dadurch erreicht werden, dass man entweder mehr Texte annotiert oder die bereits existierende Daten effizienter einsetzt. Diese Arbeit beweist, dass statistiche Algorithmen fuer Koreferenzauflösung stark von den theoretischen linguistischen Studien profitiern können und sollen: auch unvollständige Informationen, die automatische fehleranfällige Sprachmodule liefern, können die Leistung der Anwendung signifikant verbessern.This thesis addresses the problem of statistical coreference resolution. Theoretical studies describe coreference as a complex linguistic phenomenon, affected by various different factors. State-of-the-art statistical approaches, on the contrary, rely on rather simple knowledge-poor modeling. This thesis aims at bridging the gap between the theory and the practice. We use insights from linguistic theory to identify relevant linguistic parameters of co-referring descriptions. We consider different types of information, from the most shallow name-matching measures to deeper syntactic, semantic, and discourse knowledge. We empirically assess the validity of the investigated theoretic predictions for the corpus data. Our data-driven evaluation experiments confirm that various linguistic parameters, suggested by theoretical studies, interact with coreference and may therefore provide valuable information for resolution systems. At the same time, our study raises several issues concerning the coverage of theoretic claims. It thus brings feedback to linguistic theory. We use the investigated knowledge sources to build a linguistically informed statistical coreference resolution engine. This framework allows us to combine the flexibility and robustness of a machine learning-based approach with wide variety of data from different levels of linguistic description. Our evaluation experiments with different machine learners show that our linguistically informed model, on the one side, outperforms algorithms, based on a single knowledge source and, on the other side, yields the best result on the MUC-7 data, reported in the literature (F-score of 65.4% with the SVM-light learning algorithm). The learning curves for our classifiers show no signs of convergence. This suggests that our approach makes a good basis for further experimentation: one can obtain even better results by annotating more material or by using the existing data more intelligently. Our study proves that statistical approaches to the coreference resolution task may and should benefit from linguistic theories: even imperfect knowledge, extracted from raw text data with off-the-shelf error-prone NLP modules, helps achieve significant improvements

A Study on Structural and Semantic Analysis for Presentation Content Management

兵庫県立大学大学院201

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output