Unified Model of Cognition, Emotion and Action

Overview: The unified model of cognition, emotion, and action suggests that cognitive processes that steer and organise Our model suggests that the system"s control space be defined by three dimensions: valence, arousal, and potency. These have so far been considered as intrinsically affectiv

Epigenetic adaptation in action selection environments with temporal dynamics

John Lones, Lola Canamero, and Andrew Lewis, 'Epigenetic adaptation in action selection environments with temporal dynamics' in Pietro Lio, et al, eds., Advances in Artificial Life ECAL 2013, Proceedings of the twelfth European conference on the synthesis and simulation of living systems, (Massachussetts: MIT, 2013), available at doi: 10.7551/978-0-262-31709-2-ch073. This is an open access publication under the CC Attribution-NonCommercial-NoDerivs 3.0 United States license. http://creativecommons.org/licenses/by/nc/nd/3.0/us/. You are free to share - to copy, distribute and transmit the work under the following conditions: Attribution: You must attribute the work in the manner specified by the author or licensor; Noncommercial: You may not use this work for commercial purposes; No Derivative Works: You may not alter, transform, or build upon this work.To operate in dynamic environments robots must be able to adapt their behaviour to meet the challenges that these pose while being constrained by their physical and computational limitation. In this paper we continue our study into using biologically inspired epigenetic adaptation through hormone modulation as a way to accommodate the needed flexibility in robots’ behaviour, focusing on problems of temporal dynamics. We have specifically framed our study in three variants of dynamic three-resource action selection environment. The challenges posed by these environments include: moving resources, temporal and increasing unavailability of resources, and cyclic changes in type and availability of resources related to cyclic environmental changes

The Triumph of Tokenism: The Voting Rights Act and the Theory of Black Electoral Success

In this article, my goal is to organize the divergent themes of black electoral success strategy within one conceptual framework in order to give the themes more cogency and attention. Having exposed the existence of a coherent theory, I then argue that the theory posits many of the correct goals but fails to provide a realistic mechanism for achieving them. The article proceeds in three Parts. In Part I, I develop the ideological and statutory roots of black electoral success theory. In Part II, I analyze the inadequacies of current voting rights litigation and its failure to realize the statute\u27s original goals. I conclude in Part II by arguing that contemporary preoccupation with black electoral success stifles rather than empowers black political participation for three reasons. In Part III, based on my critique of the black electoral success theory, I put forth suggestions for a different approach to voting rights reform. Relying on what I tentatively call proportionate interest representation for self-identified communities of interest, I propose to reconsider the ways in which representatives are elected and the rules under which legislative decisions are made

The State of the Parties (Seventh Edition)

Continuing a three-decade tradition, The State of the Parties 7th edition brings together leading experts to evaluate change and continuity in American electoral politics. Political parties in America have never been more contentious and divided than they are right now. Even splits within the parties themselves have the power to elevate relatively unknown candidates to power and topple established incumbents. With sections devoted to polarization and the electorate, polarization and political elites, tea party politics, super PACS, and partisan resources and partisan activities, the contributors survey the American political landscape. They pay special attention to polarization between and within the parties in the aftermath of the 2012 election, demographic changes to America\u27s political parties, the effects of new media and campaign finance laws on national and local electoral results, the Tea Party\u27s rise and, as always, the implications of all these factors on future policymaking and electoral prospects. The State of the Parties 7th edition offers an indispensable guide to American politics for scholars, students, and practitioners.https://ideaexchange.uakron.edu/state_of_the_parties7/1000/thumbnail.jp

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Enhancing Computer Network Security through Improved Outlier Detection for Data Streams

V několika posledních letech se metody strojového učení (zvláště ty zabývající se detekcí odlehlých hodnot - OD) v oblasti kyberbezpečnosti opíraly o zjišťování anomálií síťového provozu spočívajících v nových schématech útoků. Detekce anomálií v počítačových sítích reálného světa se ale stala stále obtížnější kvůli trvalému nárůstu vysoce objemných, rychlých a dimenzionálních průběžně přicházejících dat (SD), pro která nejsou k dispozici obecně uznané a pravdivé informace o anomalitě. Účinná detekční schémata pro vestavěná síťová zařízení musejí být rychlá a paměťově nenáročná a musejí být schopna se potýkat se změnami konceptu, když se vyskytnou. Cílem této disertace je zlepšit bezpečnost počítačových sítí zesílenou detekcí odlehlých hodnot v datových proudech, obzvláště SD, a dosáhnout kyberodolnosti, která zahrnuje jak detekci a analýzu, tak reakci na bezpečnostní incidenty jako jsou např. nové zlovolné aktivity. Za tímto účelem jsou v práci navrženy čtyři hlavní příspěvky, jež byly publikovány nebo se nacházejí v recenzním řízení časopisů. Zaprvé, mezera ve volbě vlastností (FS) bez učitele pro zlepšování již hotových metod OD v datových tocích byla zaplněna navržením volby vlastností bez učitele pro detekci odlehlých průběžně přicházejících dat označované jako UFSSOD. Následně odvozujeme generický koncept, který ukazuje dva aplikační scénáře UFSSOD ve spojení s online algoritmy OD. Rozsáhlé experimenty ukázaly, že UFSSOD coby algoritmus schopný online zpracování vykazuje srovnatelné výsledky jako konkurenční metoda upravená pro OD. Zadruhé představujeme nový aplikační rámec nazvaný izolovaný les založený na počítání výkonu (PCB-iForest), jenž je obecně schopen využít jakoukoliv online OD metodu založenou na množinách dat tak, aby fungovala na SD. Do tohoto algoritmu integrujeme dvě varianty založené na klasickém izolovaném lese. Rozsáhlé experimenty provedené na 23 multidisciplinárních datových sadách týkajících se bezpečnostní problematiky reálného světa ukázaly, že PCB-iForest jasně překonává už zavedené konkurenční metody v 61 % případů a dokonce dosahuje ještě slibnějších výsledků co do vyváženosti mezi výpočetními náklady na klasifikaci a její úspěšností. Zatřetí zavádíme nový pracovní rámec nazvaný detekce odlehlých hodnot a rozpoznávání schémat útoku proudovým způsobem (SOAAPR), jenž je na rozdíl od současných metod schopen zpracovat výstup z různých online OD metod bez učitele proudovým způsobem, aby získal informace o nových schématech útoku. Ze seshlukované množiny korelovaných poplachů jsou metodou SOAAPR vypočítány tři různé soukromí zachovávající podpisy podobné otiskům prstů, které charakterizují a reprezentují potenciální scénáře útoku s ohledem na jejich komunikační vztahy, projevy ve vlastnostech dat a chování v čase. Evaluace na dvou oblíbených datových sadách odhalila, že SOAAPR může soupeřit s konkurenční offline metodou ve schopnosti korelace poplachů a významně ji překonává z hlediska výpočetního času . Navíc se všechny tři typy podpisů ve většině případů zdají spolehlivě charakterizovat scénáře útoků tím, že podobné seskupují k sobě. Začtvrté představujeme algoritmus nepárového kódu autentizace zpráv (Uncoupled MAC), který propojuje oblasti kryptografického zabezpečení a detekce vniknutí (IDS) pro síťovou bezpečnost. Zabezpečuje síťovou komunikaci (autenticitu a integritu) kryptografickým schématem s podporou druhé vrstvy kódy autentizace zpráv, ale také jako vedlejší efekt poskytuje funkcionalitu IDS tak, že vyvolává poplach na základě porušení hodnot nepárového MACu. Díky novému samoregulačnímu rozšíření algoritmus adaptuje svoje vzorkovací parametry na základě zjištění škodlivých aktivit. Evaluace ve virtuálním prostředí jasně ukazuje, že schopnost detekce se za běhu zvyšuje pro různé scénáře útoku. Ty zahrnují dokonce i situace, kdy se inteligentní útočníci snaží využít slabá místa vzorkování.ObhájenoOver the past couple of years, machine learning methods - especially the Outlier Detection (OD) ones - have become anchored to the cyber security field to detect network-based anomalies rooted in novel attack patterns. Due to the steady increase of high-volume, high-speed and high-dimensional Streaming Data (SD), for which ground truth information is not available, detecting anomalies in real-world computer networks has become a more and more challenging task. Efficient detection schemes applied to networked, embedded devices need to be fast and memory-constrained, and must be capable of dealing with concept drifts when they occur. The aim of this thesis is to enhance computer network security through improved OD for data streams, in particular SD, to achieve cyber resilience, which ranges from the detection, over the analysis of security-relevant incidents, e.g., novel malicious activity, to the reaction to them. Therefore, four major contributions are proposed, which have been published or are submitted journal articles. First, a research gap in unsupervised Feature Selection (FS) for the improvement of off-the-shell OD methods in data streams is filled by proposing Unsupervised Feature Selection for Streaming Outlier Detection, denoted as UFSSOD. A generic concept is retrieved that shows two application scenarios of UFSSOD in conjunction with online OD algorithms. Extensive experiments have shown that UFSSOD, as an online-capable algorithm, achieves comparable results with a competitor trimmed for OD. Second, a novel unsupervised online OD framework called Performance Counter-Based iForest (PCB-iForest) is being introduced, which generalized, is able to incorporate any ensemble-based online OD method to function on SD. Two variants based on classic iForest are integrated. Extensive experiments, performed on 23 different multi-disciplinary and security-related real-world data sets, revealed that PCB-iForest clearly outperformed state-of-the-art competitors in 61 % of cases and even achieved more promising results in terms of the tradeoff between classification and computational costs. Third, a framework called Streaming Outlier Analysis and Attack Pattern Recognition, denoted as SOAAPR is being introduced that, in contrast to the state-of-the-art, is able to process the output of various online unsupervised OD methods in a streaming fashion to extract information about novel attack patterns. Three different privacy-preserving, fingerprint-like signatures are computed from the clustered set of correlated alerts by SOAAPR, which characterize and represent the potential attack scenarios with respect to their communication relations, their manifestation in the data's features and their temporal behavior. The evaluation on two popular data sets shows that SOAAPR can compete with an offline competitor in terms of alert correlation and outperforms it significantly in terms of processing time. Moreover, in most cases all three types of signatures seem to reliably characterize attack scenarios to the effect that similar ones are grouped together. Fourth, an Uncoupled Message Authentication Code algorithm - Uncoupled MAC - is presented which builds a bridge between cryptographic protection and Intrusion Detection Systems (IDSs) for network security. It secures network communication (authenticity and integrity) through a cryptographic scheme with layer-2 support via uncoupled message authentication codes but, as a side effect, also provides IDS-functionality producing alarms based on the violation of Uncoupled MAC values. Through a novel self-regulation extension, the algorithm adapts its sampling parameters based on the detection of malicious actions on SD. The evaluation in a virtualized environment clearly shows that the detection rate increases over runtime for different attack scenarios. Those even cover scenarios in which intelligent attackers try to exploit the downsides of sampling

Citizen Responses to the Global Financial Crisis: A Comparative Study of Participation and Democratic Support

The global financial crisis was the greatest economic crisis since the Great Depression of the 1930s. The crisis, which had its origins in the United States' housing market crash of 2007, led to global impacts including rising unemployment and underemployment, home foreclosures, fewer opportunities for young people, and a loss of retirement savings. Previous research has examined the role of economic conditions in influencing various types of political behaviors and attitudes, however this has primarily pertained to fluctuations in economic performance during ordinary times. The magnitude of the recent crisis presents an unprecedented opportunity to examine how citizen political engagement in democratic societies is affected by a major economic shock. This thesis investigates how the global financial crisis has affected citizen political behavior - including voting behavior, civic engagement, and political protest - as well as democratic attitudes. To investigate the impact of the crisis, the study uses cross-national survey data fielded before and after the crisis in countries affected by the crisis to varying degrees. This enables a comparison both over time and across countries. Data from the previous two waves of the World Values Survey in 18 democratic countries is used to investigate the crisis impacts on civic engagement, political protest, and democratic support, while data from the Comparative Study of Electoral Systems in 13 countries facilitates an analysis of electoral behavior. Multilevel modeling and other quantitative methods are used to assess how factors at the country and individual level affected citizen political engagement before and after the crisis hit. The thesis tests a number of theories regarding the relationship between economic conditions and political behavior, including grievances and resources approaches. The analysis finds that countries harder hit by the crisis were more likely to experience declines in voter turnout, civic engagement, political protest and democratic support, suggesting the crisis had a demobilizing effect on participation. Similarly, at the individual level there was no evidence of a mobilization amongst those most vulnerable to the crisis, rather it continued to be those with resources that were most likely to participate in politics. The study contributes to our understanding of how economic conditions influence political attitudes and behaviors, and more broadly speaks to the political ramifications of major economic shocks

A collaborative, multi-agent based methodology for abnormal events management

Ph.DDOCTOR OF PHILOSOPH

Bio-inspired decision making system for an autonomous social robot: the role of fear

Robotics is an emergent field which is currently in vogue. In the near future, many researchers anticipate the spread of robots coexisting with humans in the real world. This requires a considerable level of autonomy in robots. Moreover, in order to provide a proper interaction between robots and humans without technical knowledge, these robots must behave according to the social and cultural norms. This results in social robots with cognitive capabilities inspired by biological organisms such as humans or animals. The work presented in this dissertation tries to extend the autonomy of a social robot by implementing a biologically inspired decision making system which allows the robot to make its own decisions. Considering this kind of decision making system, the robot will not be considered as a slave any more, but as a partner. The decisionmaking systemis based on drives,motivations, emotions, and self-learning. According to psychological theories, drives are deficits of internal variables or needs (e.g. energy) and the urge to correct these deficits are the motivations (e.g. survival). Following a homeostatic approach, the goal of the robot is to satisfy its drives maintaining its necessities within an acceptable range, i.e. to keep the robot’s wellbeing as high as possible. The learning process provides the robot with the proper behaviors to cope with each motivation in order to achieve the goal. In this dissertation, emotions are individually treated following a functional approach. This means that, considering some of the different functions of emotions in animals or humans, each artificial emotion plays a different role. Happiness and sadness are employed during learning as the reward or punishment respectively, so they evaluate the performance of the robot. On the other hand, fear plays a motivational role, that is, it is considered as a motivation which impels the robot to avoid dangerous situations. The benefits of these emotions in a real robot are detailed and empirically tested. The robot decides its future actions based on what it has learned from previous experiences. Although the current context of this robot is limited to a laboratory, the social robot cohabits with humans in a potentially non-deterministic environment. The robot is endowed with a repertory of actions but, initially, it does not know what action to execute either when to do it. Actually, it has to learn the policy of behavior, i.e. what action to execute in different world configuration, that is, in every state, in order to satisfy the drive related to the highest motivation. Since the robot will be learning in a real environment interacting with several objects, it is desired to achieve the policy of behavior in an acceptable range of time. The learning process is performed using a variation of the well-known Q-Learning algorithm, the Object Q-Learning. By using this algorithm, the robot learns the value of every state-action pair through its interaction with the environment. This means, it learns the value that every action has in every possible state; the higher the value, the better the action is in that state. At the beginning of the learning process these values, called the Q values, can all be set to the same value, or some of them can be fixed to another value. In the first case, this implies that the robot will learn from scratch; in the second case, the robot has some kind of previous information about the action selection. These values are updated during the learning process. The emotion of fear is particularly studied. The generation process of this emotion (the appraisal) and the reactions to fear are really useful to endow the robot with an adaptive reliable mechanism of “survival”. This dissertation presents a social robot which benefits from a particular learning process of new releasers of fear, i.e. the capacity to identify new dangerous situations. In addition, by means of the decision making system, the robot learns different reactions to prevent danger according to different unpredictable events. In fact, these reactions to fear are quite similar to the fear reactions observed in nature. Another challenge is to design a solution for the decision making system in such a way that it is flexible enough to easily change the configuration or even apply it to different robots. Considering the bio-inspiration of this work, this research (and other related works) was born as a try to better understand the brain processes. It is the author’s hope that it sheds some light in the study of mental processes, in particular those which may lead to mental or cognitive disorders. -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------La robótica es un área emergente que actualmente se encuentra en boga. Muchos científicos pronostican que, en un futuro próximo, los robot cohabitarán con las personas en el mundo real. Para que esto llegue a suceder, se necesita que los robots tengan un nivel de autonomía considerable. Además, para que exista una interacción entre robots y personas sin conocimientos técnicos, estos robots deben comportarse de acuerdo a las normas sociales y culturales. Esto nos lleva a robots sociales con capacidades cognitivas inspiradas en organismos biológicos, como los humanos o los animales. El trabajo que se presenta en esta tesis pretende aumentar la autonomía de un robot social mediante la implementación de un sistema de toma de decisiones bioinspirado que permita a un robot tomar sus propias decisiones. Desde este punto de vista, el robot no se considerará más como un esclavo, sino como un compañero. El sistema de toma de decisiones está basado en necesidades (drives), motivaciones, emociones y auto-aprendizaje. De acuerdo a diversas teorías psicológicas, las necesidades son carencias o déficits de variables internas (por ejemplo, la energía) y el impulso para corregir estas necesidades son las motivaciones (como por ejemplo la supervivencia). Considerando un enfoque homeostático, el objetivo del robot es satisfacer sus carencias manteniéndolas en un nivel aceptable. Esto quiere decir que el bienestar del robot debe ser lo más alto posible. El proceso de aprendizaje permite al robot desarrollar el comportamiento necesario según las distintas motivaciones para lograr su objetivo. En esta tesis, las emociones son consideradas de forma individual desde un punto de vista funcional. Esto significa que, considerando las diferentes funciones de las emociones en animales y humanos, cada una de las emociones artificiales juega un papel diferente. Por un lado, la felicidad y la tristeza se usan durante el aprendizaje como refuerzo o castigo respectivamente y, por tanto, evaluan el comportamiento del robot. Por otro lado, el miedo juega un papel motivacional, es decir, es considerado como una motivación la cual “empuja” el robot a evitar las situaciones peligrosas. Los detalles y las ventajas de estas emociones en un robot real se muestran empíricamente a lo largo de este libro. El robot decide sus acciones futuras en base a lo que ha aprendido en experiencias pasadas. A pesar de que el contexto actual del robot está limitado a un laboratorio, el robot social cohabita con personas en un entorno potencialmente no-determinístico. El robot está equipado con un repertorio de acciones pero, inicialmente, no sabe qué acción ejecutar ni cuando hacerlo. De echo, tiene que aprender la política de comportamiento, esto es, qué acción ejecutar en diferentes configuraciones del mundo (en cada estado) para satisfacer la necesidad relacionada con la motivación más alta. Puesto que el robot aprende en un entorno real interaccionando con distintos objetos, es necesario que este aprendizaje se realice en un tiempo aceptable. El algoritmo de aprendizaje que se utiliza es una variación del conocido Q-Learning, el Object Q-Learning. Mediante este algoritmo el robot aprende el valor de cada par estadoacción a través de interacción con el entorno. Esto significa, que aprende el valor de cada acción in cada posible estado. Cuanto más alto sea el valor, mejor es la acción en ese estado. Al inicio del proceso de aprendizaje, estos valores, llamados valores Q, pueden tener todos el mismo valor o pueden pueden tener asignados distintos valores. En el primer caso, el robot no dispone de conocimientos previos; en el segundo, el robot dispone de cierta información sobre la acción a elegir. Estos valores serán actualizados durante el aprendizaje. La emoción de miedo es especialmente estudiada en esta tesis. La forma de generarse esta emoción (el appraisal) y las reacciones al miedo resultan realmente útiles a la hora de dotar al robot con un mecanismo de supervivencia adaptable y fiable. Esta tesis presenta un robot social que utiliza un proceso particular para el aprendizaje de nuevos “liberadores” del miedo, es decir, dispone de la capacidad de identificar nuevas situaciones peligrosas. Además, mediante el sistema de toma de decisiones, el robot aprende diferente reacciones para protegerse ante posibles daños causados por diversos eventos impredecibles. De echo, estas reacciones al miedo son bastante similares a las reacciones al miedo que se pueden observar en la naturaleza. Otro reto importante es el diseño de la solución: el sistema de toma de decisiones tiene que diseñarse de forma que sea suficientemente flexible para permitir cambiar fácilmente la configuración o incluso para aplicarse a distintos robots. Teniendo en cuenta el enfoque bioinspirado de este trabajo, esta investigación (y muchos otros trabajos relacionados) surge como un intento de entender un poco más lo que sucede en el cerebro. El autor espera que esta tesis pueda ayudar en el estudio de los procesos mentales, en particular aquellos que pueden llevar a desórdenes mentales o cognitivos