Advances in Character Recognition

This book presents advances in character recognition, and it consists of 12 chapters that cover wide range of topics on different aspects of character recognition. Hopefully, this book will serve as a reference source for academic research, for professionals working in the character recognition field and for all interested in the subject

Plant recognition, detection, and counting with deep learning

In agricultural and farm management, plant recognition, plant detection, and plant counting systems are crucial. We can apply these tasks to several applications, for example, plant disease detection, weed detection, fruit harvest system, and plant species identification. Plants can be identified by looking at their most discriminating parts, such as a leaf, fruit, flower, bark, and the overall plant, by considering attributes as shape, size, or color. However, the identification of plant species from field observation can be complicated, time-consuming, and requires specialized expertise. Computer vision and machine-learning techniques have become ubiquitous and are invaluable to overcome problems with plant recognition in research. Although these techniques have been of great help, image-based plant recognition is still a challenge. There are several obstacles, such as considerable species diversity, intra-class dissimilarity, inter-class similarity, and blurred resource images. Recently, the emerging of deep learning has brought substantial advances in image classification. Deep learning architectures can learn from images and notably increase their predictive accuracy. This thesis provides various techniques, including data augmentation and classification schemes, to improve plant recognition, plant detection, and plant counting system

Game theoretic and machine learning techniques for balancing games

Game balance is the problem of determining the fairness of actions or sets of actions in competitive, multiplayer games. This problem primarily arises in the context of designing board and video games. Traditionally, balance has been achieved through large amounts of play-testing and trial-and-error on the part of the designers. In this thesis, it is our intent to lay down the beginnings of a framework for a formal and analytical solution to this problem, combining techniques from game theory and machine learning. We first develop a set of game-theoretic definitions for different forms of balance, and then introduce the concept of a strategic abstraction. We show how machine classification techniques can be used to identify high-level player strategy in games, using the two principal methods of sequence alignment and Naive Bayes classification. Bioinformatics sequence alignment, when combined with a 3-nearest neighbor classification approach, can, with only 3 exemplars of each strategy, correctly identify the strategy used in 55\% of cases using all data, and 77\% of cases on data that experts indicated actually had a strategic class. Naive Bayes classification achieves similar results, with 65\% accuracy on all data and 75\% accuracy on data rated to have an actual class. We then show how these game theoretic and machine learning techniques can be combined to automatically build matrices that can be used to analyze game balance properties

Multilingual sentiment analysis in social media.

252 p.This thesis addresses the task of analysing sentiment in messages coming from social media. The ultimate goal was to develop a Sentiment Analysis system for Basque. However, because of the socio-linguistic reality of the Basque language a tool providing only analysis for Basque would not be enough for a real world application. Thus, we set out to develop a multilingual system, including Basque, English, French and Spanish.The thesis addresses the following challenges to build such a system:- Analysing methods for creating Sentiment lexicons, suitable for less resourced languages.- Analysis of social media (specifically Twitter): Tweets pose several challenges in order to understand and extract opinions from such messages. Language identification and microtext normalization are addressed.- Research the state of the art in polarity classification, and develop a supervised classifier that is tested against well known social media benchmarks.- Develop a social media monitor capable of analysing sentiment with respect to specific events, products or organizations

Prediction, detection, and correction of misunderstandings in interactive tasks

Technology has allowed all kinds of devices and software to come into our lives. Advances in GPS, Virtual Reality, and wearable computers with increased computing power and Internet connectivity open the doors for interactive systems that were considered science fiction less than a decade ago, and are capable of guiding us in a variety of environments. This increased accessibility comes at the cost of increasing both the scale of problems that can be realistically tackled and the capabilities that we expect from such systems. Indoor navigation is an example of such a task: although guiding a car is a solved problem, guiding humans for instance inside a museum is much more challenging. Unlike cars, pedestrians use landmarks rather than absolute distances. They must discriminate from a larger number of distractors, and expect sentences of higher complexity than those appropriate for a car driver. A car driver prefers short, simple instructions that do not distract them from traffic. A tourist inside a museum on the contrary can afford the mental effort that a detailed grounding process would require. Both car and indoor navigation are specific examples of a wider family of collaborative tasks known as “Instruction Following”. In these tasks, agents with the two clearly defined roles of Instruction Giver and Instruction Follower must cooperate to achieve a joint objective. The former has access to all required information about the environment, including (but not limited to) a detailed map of the environment, a clear list of objectives, and a profound understanding of the effect that specific actions have in the environment. The latter is tasked with following the instructions, interacting with the environment and moving the undertaking forward. It is then the Instruction Giver’s responsibility to assess a detailed plan of action, segment it into smaller subgoals, and present instructions to the Instruction Follower in a language that is clear and understandable. No matter how carefully crafted the Instruction Giver’s utterances are, it is expected that misunderstandings will take place. Although some of these misunderstandings are easy to detect and repair, others can be very difficult or even impossible to solve. It is therefore important for the Instruction Giver to generate instructions that are as clear as possible, to detect misunderstandings as early as possible, and to correct them in the most effective way. This thesis introduces several algorithms and strategies designed to tackle the aforementioned problems from end to end, presenting the individual aspects of a system that successfully predicts, detects, and corrects misunderstandings in interactive Instruction Following tasks. We focus on one particular type of instruction: those involving Referring Expressions. A Referring Expression identifies a single object out of many, such as “the red button” or “the tall plant”. Generating Referring Expressions is a key component of Inst. Following tasks, since any kind of object manipulation is likely to require a description of the object. Due to its importance and complexity, this is one of the most widely studied areas of Natural Language Generation. In this thesis we use Semantically Interpreted Grammars, an approach that integrates both Referring Expression Generation (identifying which properties are required for a unique description) and Surface realization (combining those properties into a concrete Noun Phrase). The complexity of performing, recording, and analyzing Instruction Following tasks in the real world is one of the major challenges of Instruction Following research. In order to simplify both the development of new algorithms and the access to those results by the research community, our work is evaluated in what we call a Virtual Environment—an environment that mimics the main aspects of the real world and abstracts distractions while preserving enough characteristics of the real world to be useful for research. Selecting the appropriate virtual environment for a research task ensures that results will be applicable in the real world. We have selected the Virtual Environment of the GIVE Challenge, an environment designed for an Instruction Following task in which a human Instruction Follower is paired with an automated Instruction Giver in a maze-like 3D world. Completing the task requires navigating the space, avoiding alarms, interacting with objects, generating instructions in Natural Language, and preventing mistakes that can bring the task to a premature end. Even under these simplified conditions, the task presents several computational challenges: performing these tasks in real time require fast algorithms, and ensuring the efficiency of our approaches remains a priority at every step. Our first experimental study identifies the most challenging type of mistakes that our system is expected to find. Creating an Inst. Following system that leverages previously-recorded human data and follows instructions using a simple greedy algorithm, we clearly separate those situations for which no further study is warranted from those that are of interest for our research. We test our algorithm with similarity metrics of varying complexity, ranging from overlap measures such as Jaccard and edit distances to advanced machine learning algorithms such as Support Vector Machines. The best performing algorithms achieve not only good accuracy, but we show in fact that mistakes are highly correlated with situations that are also challenging for human annotators. Going a step further, we also study the type of improvement that can be expected from our system if we give it the chance of retrying after a mistake was made. This system has no prior beliefs on which actions are more likely to be selected next, and our results make a good case for this vision to be one of its weakest points. Moving away from a paradigm where all actions are considered equally likely, and moving towards a model in which the Inst. Follower’s own action is taken into account, our subsequent step is the development of a system that explicitly models listener’s understanding. Given an instruction containing a Referring Expression, we approach the Instruction Follower’s understanding of it with a combination of two probabilistic models. The Semantic model uses features of the Referring Expression to identify which object is more likely to be selected: if the instruction mentions a red button, it is unlikely that the Inst. Follower will select a blue one. The Observational model, on the other hand, predicts which object will be selected by the Inst. Follower based on their behavior: if the user is walking straight towards a specific object, it is very likely that this object will be selected. These two log-linear, probabilistic models were trained with recorded human data from the GIVE Challenge, resulting in a model that can effectively predict that a misunderstanding is about to take place several seconds before it actually happens. Using our Combined model, we can easily detect and predict misunderstandings — if the Inst. Giver tells the Inst. Follower to “click the red button”, and the Combined model detects that the Inst. Follower will select a blue one, we know that a misunderstanding took place, we know what the misunderstood object is, and we know both facts early enough to generate a correction that will stop the Inst. Follower from making the mistake in the first place. A follow-up study extends the Observational model introducing features based on the gaze of the Inst. Follower. Gaze has been shown to correlate with human attention, and our study explores whether gaze-based features can improve the accuracy of the Observational model. Using previouslycollected data from the GIVE Environment in which gaze was recorded using eye-tracking equipment, the resulting Extended Observational model improves the accuracy of predictions in challenging scenes where the number of distractors is high. Having a reliable method for the detection of misunderstandings, we turn our attention towards corrections. A corrective Referring Expression is one designed not only for the identification of a single object out of many, but rather, for identifying a previously-wrongly-identified object. The simplest possible corrective Referring Expression is repetition: if the user misunderstood the expression “the red button” the first time, it is possible that they will understand it correctly the second time. A smarter approach, however, is to reformulate the Referring Expression in a way that makes it easier for the Inst. Follower to understand. We designed and evaluated two different strategies for the generation of corrective feedback. The first of these strategies exploits the pragmatics concept of a Context Set, according to which human attention can be segmented into objects that are being attended to (that is, those inside the Context Set) and those that are ignored. According to our theory, we could virtually ignore all objects outside the Context Set and generate Referring Expressions that would not be uniquely identifying with respect to the entire context, but would still be identifying enough for the Inst. Follower. As an example, if the user is undecided between a red button and a blue one, we could generate the Referring Expression “the red one” even if there are other red buttons on the scene that the user is not paying attention to. Using our probabilistic models as a measure for which elements to include in the Context Set, we modified our Referring Expression Generation algorithm to build sentences that explicitly account for this behavior. We performed experiments over the GIVE Challenge Virtual Environment, crowdsourcing the data collection process, with mixed results: even if our definition of a Context Set were correct (a point that our results can neither confirm nor deny), our strategy generates Referring Expressions that prevents some mistakes, but are in general harder to understand than the baseline approach. The results are presented along with an extensive error analysis of the algorithm. They imply that corrections can cause the Instruction Follower to re-evaluate the entire situation in a new light, making our previous definition of Context Set impractical. Our approach also fails at identifying previously grounded referents, compounding the number of pragmatic effects that conspire against this approach. The second strategy for corrective feedback consists on adding Contrastive focus to a second, corrective Referring Expression In a scenario in which the user receives the Referring Expression “the red button” and yet mistakenly selects a blue one, an approach with contrastive focus would generate “no, the RED button” as a correction. Such a Referring Expression makes it clear to the Inst. Follower that on the one hand their selection of an object of type “button” was correct, and that on the other hand it is the property “color” that needs re-evaluation. In our approach, we model a misunderstanding as a noisy channel corruption: the Inst. Giver generates a correct Referring Expression for a given object, but it is corrupted in transit and reaches the Inst. Follower in the form of an altered, incorrect Referring Expression We correct this misconstrual by generating a new, corrective Referring Expression: starting from the original Referring Expression and the misunderstood object, we identify the constituents of the Referring Expression that were corrupted and place contrastive focus on them. Our hypothesis states that the minimum edit sequence between the original and misunderstood Referring Expression correctly identifies the constituents requiring contrastive focus, a claim that we verify experimentally. We perform crowdsourced preference tests over several variations of this idea, evaluating Referring Expressions that either present contrast side by side (as in “no, not the BLUE button, the RED button”) or attempt to remove redundant information (as in “no, the RED one”). We evaluate our approaches using both simple scenes from the GIVE Challenge and more complicated ones showing pictures from the more challenging TUNA people corpus. Our results show that human users significantly prefer our most straightforward contrastive algorithm. In addition to detailing models and strategies for misunderstanding detection and correction, this thesis also includes practical considerations that must be taken into account when dealing with similar tasks to those discussed here. We pay special attention to Crowdsourcing, a practice in which data about tasks can be collected from participants all over the world at a lower cost than traditional alternatives. Researchers interested in using crowdsourced data must often deal both with unmotivated players and with players whose main motivation is to complete as many tasks as possible in the least amount of time. Designing a crowdsourced experiment requires a multifaceted approach: the task must be designed in such a way as to motivate honest players, discourage other players from cheating, implementing technical measures to detect bad data, and prevent undesired behavior looking at the entire pipeline with a Security mindset. We dedicate a Chapter to this issue, presenting a full example that will undoubtedly be of help for future research. We also include sections dedicated to the theory behind our implementations. Background literature includes the pragmatics of dialogue, misunderstandings, and focus, the link between gaze and visual attention, the evolution of approaches towards Referring Expression Generation, and reports on the motivations of crowdsourced workers that borrow from fields such as psychology and economics. This background contextualizes our methods and results with respect to wider fields of study, enabling us to explain not only that our methods work but also why they work. We finish our work with a brief overview of future areas of study. Research on the prediction, detection, and correction of misunderstandings for a multitude of environments is already underway. With the introduction of more advanced virtual environments, modern spoken, dialoguebased tools revolutionizing the market of home devices, and computing power and data being easily available, we expect that the results presented here will prove useful for researchers in several areas of Natural Language Processing for many years to come.Die Technologie hat alle möglichen Arten von unterstützenden Geräten und Softwares in unsere Leben geführt. Fortschritte in GPS, Virtueller Realität, und tragbaren Computern mit wachsender Rechenkraft und Internetverbindung öffnen die Türen für interaktive Systeme, die vor weniger als einem Jahrzehnt als Science Fiction galten, und die in der Lage sind, uns in einer Vielfalt von Umgebungen anzuleiten. Diese gesteigerte Zugänglichkeit kommt zulasten sowohl des Umfangs der Probleme, die realistisch gelöst werden können, als auch der Leistungsfähigkeit, die wir von solchen Systemen erwarten. Innennavigation ist ein Beispiel einer solcher Aufgaben: obwohl Autonavigation ein gelöstes Problem ist, ist das Anleiten von Meschen zum Beispiel in einem Museum eine größere Herausforderung. Anders als Autos, nutzen Fußgänger eher Orientierungspunkte als absolute Distanzen. Sie müssen von einer größeren Anzahl von Ablenkungen unterscheiden können und Sätze höherer Komplexität erwarten, als die, die für Autofahrer angebracht sind. Ein Autofahrer bevorzugt kurze, einfache Instruktionen, die ihn nicht vom Verkehr ablenken. Ein Tourist in einem Museum dagegen kann die metale Leistung erbringen, die ein detaillierter Fundierungsprozess benötigt. Sowohl Auto- als auch Innennavigation sind spezifische Beispiele einer größeren Familie von kollaborativen Aufgaben bekannt als Instruction Following. In diesen Aufgaben müssen die zwei klar definierten Akteure des Instruction Givers und des Instruction Followers zusammen arbeiten, um ein gemeinsames Ziel zu erreichen. Der erstere hat Zugang zu allen benötigten Informationen über die Umgebung, inklusive (aber nicht begrenzt auf) einer detallierten Karte der Umgebung, einer klaren Liste von Zielen und einem genauen Verständnis von Effekten, die spezifische Handlungen in dieser Umgebung haben. Der letztere ist beauftragt, den Instruktionen zu folgen, mit der Umgebung zu interagieren und die Aufgabe voranzubringen. Es ist dann die Verantwortung des Instruction Giver, einen detaillierten Handlungsplan auszuarbeiten, ihn in kleinere Unterziele zu unterteilen und die Instruktionen dem Instruction Follower in einer klaren, verständlichen Sprache darzulegen. Egal wie sorgfältig die Äußerungen des Instruction Givers erarbeitet sind, ist es zu erwarten, dass Missverständnisse stattfinden. Obwohl einige dieser Missverständnisse einfach festzustellen und zu beheben sind, können anderen sehr schwierig oder gar unmöglich zu lösen sein. Daher ist es wichtig, dass der Instruction Giver die Anweisungen so klar wie möglich formuliert, um Missverständnisse so früh wie möglich aufzudecken, und sie in der effektivstenWeise zu berichtigen. Diese Thesis führt mehrere Algorithmen und Strategien ein, die dazu entworfen wurden, die oben genannten Probleme in einem End-to-End Prozess zu lösen. Dabei werden die individuellen Aspekte eines Systems präsentiert, dass erfolgreich Missverständnisse in interaktiven Instruction Following Aufgaben vorhersagen, feststellen und korrigieren kann.Wir richten unsere Aufmerksamkeit auf eine bestimmte Art von Instruktion: die sogennanten Referring Expressions. Eine Referring Expression idenfiziert ein einzelnes Objekt aus vielen, wie zum Beispiel „der rote Knopf” oder „die große Pflanze”. Das Generieren von Referring Expressions ist eine Schlüsselkomponente von Instruction Following Aufgaben, da jegliche Art von Manipulation sehr wahrscheinlich eine Beschreibung des Objektes erfordert. Wegen derWichtigkeit und Komplexität ist dies eine der am meisten untersuchten Gebiete der Textgenerierung. In dieser Thesis verwenden wir Semantisch Interpretierte Grammatik, eine Methode, die sowohl die Generierung von Referring Expressions (Identifizierung von Eigenschaften für eine eindeutige Beschreibung) als auch Surface Realization (Kombinieren dieser Eigenschaften in eine konkrete Substantivgruppe) integriert. Die Komplexität der Durchführung, Aufzeichnung und Analyse von Instruction Following Aufgaben in der realen Welt ist eine der großen Herausforderungen der Instruction Following Forschung. Um sowohl die Entwicklung neuer Algorithmen und den Zugang zu diesen Ergebnissen durch die Wissenschaftsgemeinde zu vereinfachen, wird unsere Arbeit in einer Virtuellen Umgebung bewertet. Eine virtuelle Umgebung ahmt die Hauptaspekte der realen Welt nach und nimmt Ablenkungen weg, während genug Eigenschaften der realen Welt erhalten bleiben, um verwendbar für die Untersuchung zu sein. Die Auswahl der angebrachten virtuellen Umgebung für eine Forschungsaufgabe gewährleistet, dass die Ergebnisse auch in der realenWelt anwendbar sind. Wir haben eine virtuelle Umgebung der GIVE Challenge ausgesucht â˘A ¸S eine Umgebung, die für eine Instruction Following Aufgabe entworfen wurde, in der ein menschlicher Instruction Follower mit einem automatischen Instruction Giver in einer Labyrinth-artigen 3D Welt verbunden wird. Die Aufgabe zu beenden erfordert Navigation im Raum, Vermeidung von Alarmen, Interagieren mit Objekten, Textgenerierung und Verhindern von Fehlern, die zu einer vorzeitigen Beendung der Aufgabe führen. Sogar unter diesen vereinfachten Bedingungen stellt die Aufgabe mehrere rechentechnische Herausforderungen dar: die Aufgabe in Echtzeit durchzuführen erfordert schnelle Algorithmen, und die Effizienz unserer Methode zu gewährleisten bleibt Priorotät in jedem Schritt. Unser erstes Experiment identifiziert die herausfordernste Art von Fehlern, die unser System erwartungsgemäß finden soll. Durch den Entwurf eines Instruction Following Systems, das sich zuvor aufgezeichnete menschliche Daten zu Nutze macht und durch die Nutzung eines einfachen gierigen Algorithmus Intruktionen folgt, grenzen wir klar die Situationen ab, die keine weitere Studie rechtfertigen, von denen, die interessant für unsere Forschung sind. Wir testen unseren Algorithmus mit Ähnlichkeitsmaßen verschiedener Komplexität, die sich von Überlappungsmaßnahmen wie Jaccard und Editierdistanzen, bis zu fortgeschrittenen Algorithmen des Maschinellen Lernens erstrecken. Die am besten ausführenden Algorithmen erreichen nicht nur gute Genauigkeit sondern tatsächlich zeigen wir, dass Fehler hoch korreliert sind mit Situationen, die auch herausfordernd für menschliche Kommentatoren sind. In einem weiteren Schritt untersuchen wir die Art von Verbesserung, die von unserem System erwartet werden kann wenn wir ihm die Chance geben, es wieder zu versuchen nachdem ein Fehler gemacht wurde. Dieses System macht keine vorherigen Annahmen darüber, welche Aktionen am wahrscheinlichsten als nächstes ausgewählt werden und unsere Ergebnisse liefern gute Argumente dafür, dass dieser Ansatz einer der schwächsten Aspekte ist. Um sich von einem Paradigma wegzubewegen, in dem alle Aktionen gleich wahrscheinlich betrachtet werden, zu einem Model, in dem das Handeln des Instruction Followers in Betracht gezogen wird, ist unser folgender Schritt die Entwicklung eines Systems, dass explizit das Verständnis des Anwenders modelliert. Voraussetzend, dass die Instruktion eine Referring Expression beinhaltet, gehen wir das Verstehen des Instruction Followers mit einer Kombination aus zwei probabilistischen Modellen an. Das semantische Modell verwendet Eigenschaften der Referring Expression um zu identifizieren, welches Objekt wahrscheinlicher a