Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Wonderful Life Utility Functions in Multi-Agent Systems

The mathematics of Collective Intelligence (COINs) is concerned with the design of multi-agent systems so as to optimize an overall global utility function when those systems lack centralized communication and control. Typically in COINs each agent runs a distinct Reinforcement Learning (RL) algorithm, so that much of the design problem reduces to how best to initialize/update each agent's private utility function, as far as the ensuing value of the global utility is concerned. Traditional team game solutions to this problem assign to each agent the global utility as its private utility function. In previous work we used the COIN framework to derive the alternative Wonderful Life Utility (WLU), and experimentally established that having the agents use it induces global utility performance up to orders of magnitude superior to that induced by use of the team game utility. The WLU has a free parameter (the clamping parameter) which we simply set to zero in that previous work. Here we derive the optimal value of the clamping parameter, and demonstrate experimentally that using that optimal value can result in significantly improved performance over that of clamping to zero, over and above the improvement beyond traditional approaches.
Fuzzy Oscillations: a Novel Model for Solving Pattern Segmentation

In this thesis we develop a novel network model that extends the traditional artificial neural network (ANN) model to include oscillatory behaviour. This model is able to correctly classify combinations of previously learned input patterns by grouping features that belong to the same category. This grouping process is termed segmentation and we show how synchrony of oscillations is the necessary missing component of ANNs to be able to perform this segmentation. Using this model we go on to show that top-down modulatory feedback is necessary to enable separation of multiple objects in a scene and segmentation of their individual features. This type of feedback is distinctly different than recurrency and is what enables the rich dynamics between the nodes of our network. Additionally, we show how our model's dynamics avoid the combinatorial explosion in required training repetitions of traditional feed-forward classification networks. In these networks, relations between objects must explicitly be learned. In contrast, the dynamics of modulatory feedback allow us to defer calculation of these relations until run-time, thus creating a more robust system. We call our model Fuzzy Oscillations, and it achieves good results when compared to existing models. However, oscillatory neural network models successful in achieving segmentation are a relatively recent development. We thus feel that our model is a contribution to the field of oscillatory neural networks.
The use of cloud computing in mobile robotics

The increasing popularity of mobile service robots results in the development of technologies aimed at improving the performance of computing while simultaneously reducing energy consumption of processing units. Projects are proposed for offloading complex computation from robots into other platforms. The migration of data for storage, computing, and other purposes is described as cloud computing. This solution has a number of advantages over traditional approach, where all actions are performed on a single machine. The main advantages include the increase of operating time and decrease of mass by reducing the capacity of the required energy sources. This is thanks to the fact that the mobile agent can be tasked with no or only simple data processing. In the paper, an analysis of the capability of the most popular cloud robotic platforms is presented. Furthermore, descriptions of typical cloud architectures as well as factors in favour of applying cloud systems into mobile robots are described. Short descriptions of a number of popular cloud-based platforms including the DAvinCi project, Cloud-Based Robot Grasping Project, and RoboEarth are provided. RoboEarth is further described in greater detail including its main advantages. Basic tests using the Kinect sensor and RoboEarth object recognition software were performed. Lastly, a potential application for the system based on available mobile platforms is described.
STAC: a comprehensive sensor fusion model for scene characterization

We are interested in data fusion strategies for Intelligence, Surveillance, and Reconnaissance (ISR) missions. Advances in theory, algorithms, and computational power have made it possible to extract rich semantic information from a wide variety of sensors, but these advances have raised new challenges in fusing the data. For example, in developing fusion algorithms for moving target identification (MTI) applications, what is the best way to combine image data having different temporal frequencies, and how should we introduce contextual information acquired from monitoring cell phones or from human intelligence? In addressing these questions we have found that existing data fusion models do not readily facilitate comparison of fusion algorithms performing such complex information extraction, so we developed a new model that does. Here, we present the Spatial, Temporal, Algorithm, and Cognition (STAC) model. STAC allows for describing the progression of multi-sensor raw data through increasing levels of abstraction, and provides a way to easily compare fusion strategies. It provides for unambiguous description of how multi-sensor data are combined, the computational algorithms being used, and how scene understanding is ultimately achieved. In this paper, we describe and illustrate the STAC model, and compare it to other existing models.
Prediction of photo shareability using supervisedlearning

Many photos are taken every day. Some of them are shared to friends, acquaintances or family. Automatic prediction of what photos are shareable is investigated. The prediction techniques use supervised machine learning, trying to separate shareable images from non-shareable images. High performing prediction is shown to be hard to solve accurately using the chosen approach.
Soft-Switching PWM Full-Bridge Converters: Topologies, Control, and Design

Soft-switching PWM full-bridge converters have been widely used in medium-to-high power dc-dc conversions for topological simplicity, easy control and high efficiency. Early works on soft-switching PWM full-bridge converter by many researchers included various topologies and modulation strategies. However, these works were scattered, and the relationship among these topologies and modulation strategies had not been revealed. This book intends to describe systematically the soft-switching techniques for pulsewidth modulation (PWM) full-bridge converters, including the topologies, control and design, and it reveals the relationship among the various topologies and PWM strategies previously proposed by other researchers. The book not only presents theoretical analysis, but also gives many detailed design examples of the converters.
A Semiotic Analysis of Selected British American Tobacco Print Advertisements in Nigeria

Advertisements are created for specific target audiences. Therefore, there are meanings usually embedded in such ads. This paper examines in detail six British American Tobacco Nigerian Print advertisements. It seeks to identify the cultural values reflected in the ads. Using a semiological framework of how images construct meanings, the paper sheds light on the various messages behind the ads and how they promote the Nigerian culture. The cultural values identified are love for fashion, education, promotion of locally made products, development of culture through drama and agriculture. The paper concludes by asserting that culture is a driving force for successful multinational advertisements. Keywords: Advertisements, Culture, Print, Signs
LFG Parsebanker: A Tool for Building and Searching a Treebank as a Parsed Corpus

We present the LFG PARSEBANKER, a comprehensive toolkit for interactive incremental construction of a treebank as a parsed corpus. This web-based toolkit offers an environment for batch and interactive parsing, versioning, inspection of structures, discriminant-based disambiguation, and statistics. It has recently been extended with a structural search facility.
Macroanalysis: Digital Methods and Literary History

In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands of texts in digital libraries, researchers can draw conclusions based on quantifiable evidence regarding how literary trends are employed over time, across periods, within regions, or within demographic groups, as well as how cultural, historical, and societal linkages may bind individual authors, texts, and genres into an aggregate literary culture. Moving beyond the limitations of literary interpretation based on the "close-reading" of individual works, Jockers describes how this new method of studying large collections of digital material can help us to better understand and contextualize the individual works within those collections.
Deblurring Algorithms for Out-of-focus Infrared Images

An image that has been subject to the out-of-focus phenomenon has reducedsharpness, contrast and level of detail depending on the amount of defocus. Torestore out-of-focused images is a complex task due to the information loss thatoccurs. However there exist many restoration algorithms that attempt to revertthis defocus by estimating a noise model and utilizing the point spread function.The purpose of this thesis, proposed by FLIR Systems, was to ﬁnd a robustalgorithm that can restore focus and from the customer’s perspective be userfriendly. The thesis includes three implemented algorithms that have been com-pared to MATLABs built-in. Three image series were used to evaluate the limitsand performance of each algorithm, based on deblurring quality, implementationcomplexity, computation time and usability.Results show that the Alternating Direction Method for total variation de-convolution proposed by Tao et al. [29] together with its the modiﬁed discretecosines transform version restores the defocused images with the highest qual-ity. These two algorithms include features such as, fast computational time, fewparameters to tune and a powerful noise reduction.
Aplicaciones distribuidas: coordinación y sincronización

Una aplicacion distribuida esta compuesta por un conjunto de procesos. Los procesos pueden cooperar para realizar una tarea o competir por la utilizacion de un recurso. Un proceso cooperativo es aquel que puede afectar o ser afectado por otros procesos que se encuentran en ejecucion en el sistema. Los procesos cooperativos pueden directamente compartir un espacio de direccion logico (esto es, datos y codigo), o solamente comparte los datos a traves de archivos. El acceso concurrente a los datos compartidos puede resultar en un inconsistencia de los mismos. Se necesitan mecanismos para asegurar un ordenamiento en la ejecucion de los procesos cooperativos que comparten un espacio de direccionamiento logico, tal que la consistencia en los datos sea respetada. Los mecanismos para asegurar el ordenamiento en la ejecucion de procesos son: sincronizacion, exclusion mutua en las secciones cŕiticas, alocacion de recursos. El proyecto en estudio esta basado en el analisis, comparacion y busqueda de nuevas alternativas de algoritmos distribuidos que se pueden aplicar para soportar la exclusion mutua o cooperacion entre los procesos.
Satellite communication applied in a distributed application

The paper describes the development of a prototype application for access to vital weather information from the Northern Atlantic sea region. The application gives meteorologists access to weather observations measured on sea vessels. Today the available information is very limited. On a daily basis, only 4-5 weather observations in the whole arctic sea-region are conducted. It is therefore suggested to use the available fishing-float and the coast-guard as a basis for a full scale application. Communication is based on the Inmarsat-C global satellite system. This kind of communication offers limited data bandwidth, currently 600 bit/sec. In addition delays are introduced by the coast/earth-station due to message-queues and protocol-transformations. Based on the services offered by the Inmarsat-C system, a simple prototype communication mechanism for data communication between terrestrial computers and computers on sea-vessels has been developed. It is, partly due to the nature of the satellite system, based on unreliable multicast of messages. A message may consist of a request which requires a reply (multicast RPC). Test results showed that doing a request and getting a reply takes from 1.5 4 minutes. The results depend on the type of message (supported by the Inmarsat-C system) which is used. The sea-vessels send weather observations in set intervals. This means higher availability on weather observations, and again a big step for meteorologists which today have to rely on observation which may be several hours old (if any at all).
Priority oriented channel access for cel-lular systems serving vehicular and portable radio telephon

A priority oriented channel access scheme for cellular mobile radio telephone systems serving vehicular and portable radio telephone users is described. The calls generated are divided into three different classes: new vehicular calls, new portable calls and hand-off calls. Priority is given to hand-off calls over new calls and to vehicular calls over portable calls. Appropriate analytical models and criteria are developed and used to derive performance characteristics. Blocking and forced termination probabilities for each kind of user are determined as functions of system parameters. Noncompleted call probability is defined as a useful performance measure for each kind of user. The effects of priorities and hand-off procedures on performance characteristics are investigated. General formulas are given and specific numerical results for nominal system parameters are presented.
Empirical Evaluation Methods in Computer Vision

Automated performance evaluation of range image segmentation algorithms training/test data partitioning for empirical performance evaluation analyzing PCA-based face recognition algorithms -eigenvector selection and distance measures design of a visual system for detecting natural events by the use of an independent visual estimate - a human fall detector task-based evaluation of image filtering within a class of geometry-driven-diffusion algorithms a comparative analysis of cross-correlation matching algorithms using a pyramidal resolution approach performance evaluation of medical image processing algorithms.
Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics and Writing: Writing Process

Writing today, whether professional, academic, or private, relies heavily on computers. Most texts composed in the 21st century are probably written on computers or other electronic devices, such as mobile phones. People compose texts in word processors, text editors, content management systems, blogs, wikis, e-mail clients, and instant messaging applications. Each of these tools supports authors in different ways and to different degrees. Writing research has been concerned with word processing since the 1970s. Writing researchers today investigate specific characteristics of writing with computers and the effect of tools on writing processes. The current rise of new writing environments and genres (e.g., blogging) has prompted new studies in this area of research. During the last few decades, computational linguistics has mostly been concerned with static or finished texts. We believe there is now a growing need to explore how computational linguistics can support human text production and word processing. However, there are still very few projects where computational linguists and writing researchers work together here.
The Second & Third Sapporo Symposium on Complex Systems Sapporo

This paper studies the essential dynamical structure that arises in two different classes of learning of the sensory-based navigation, namely skill-based learning and model-based learning. In skill-based learning a robot learns navigational skills for a fixed navigational task such as homing, while in modclbased learning a robot learns a model of the environment, then conducts plmming on the model to reach an arbitrary goal. We formulated that the former is achieved by learning the state-action map, and the latter does by learning the forward model of the environment, using recurrent neural learning scheme. The analysis of the dynamical structure from the coupling of the internal neural dynamics and the environment showed that generation of the global attractor is crucial for both learning cases. Experiments were conducted using a mobile robot with a laser range sensor, which verified our assertions in a simple obstacle environment.
Machine learning for translational medicine

In biomedical sciences, the increasing amount of available high throughput data brings many challenges. The collection of such data usually results in large number of predictor genes and few samples, possibly also with high noise levels. Such problems are associated to the socalled “curse of dimensionality”, i.e. the small n large p problem. Therefore, the development and application of computational protocols in bioinformatics is necessary in order to tackle these problems, translate knowledge discovery from genome-scale studies and infer new knowledge combining the different types of post-genomics data. Data mining methods, including machine learning approaches, aim to identify patterns in high-throughput data and extract information about the underlying biological interactions. Research questions that are discussed in this thesis are disease stratification, biomarker discovery, network inference and data integration. The methodological contributions of this thesis focus on the problem encountered, nowadays, by clinicians where patients appearing to have the same disease may not respond to the same treatment. First, using supervised and unsupervised learning techniques, a machine learning strategy based on ensembles of decision trees was used to define subphenotypes based on gene expression patterns and generate potential biomarkers for disease progression. Second, we developed a network inference algorithm (NetCFS) that uses feature selection to select a number of genes (n) that are highly correlated with the phenotype of interest so as to generate n different regression problems. Third, a “top-down” approach was implemented where gene sets corresponding to biochemical pathways are used to develop a disease classification framework. A multi-stage procedure was developed to uncover functional modules that are closely associated to the phenotype of interest and relevant to disease pathology. Phenotype-Responsive Genes (PRGs) are identified based on non-overlapping constraints of the classification procedure and association rules are used to estimate the activity level of each pathway. Applications discussed in this thesis include skin inflammation where an integrative approach combining clinically relevant in vivo models with molecular network analysis was developed to infer disease biomarkers and to translate the rapidly growing body of data into knowledge usable at the bedside. Other disease cases studied involve cancer analyses to illustrate contributions in systems medicine. Overall, this thesis presents methodological contributions on predictive models based on machine learning techniques and mathematical programming together with relevant insights in disease mechanisms and potential treatment options.
Constraints on the Design of a High-Level Model of Cognition

The TacAir-Soar system is a computer program that generates human-like behavior flying simulated aircraft in tactical air combat training scenarios. The design of the system has been driven by functional concerns, allowing the system to generate a wide range of appropriate behaviors in severely time-limited situations. The combination of constraints from the complexity and dynamics of the domain with the overall goal of humanlike behavior led to a system that can be viewed as a model of cognition for high-level, complex tasks. This paper analyzes the system in such a light, and describes how the functional design constraints map on to cognitively plausible representations and mechanisms, sometimes in surprising ways.
Operations Research Prospects for Libraries: The Realities and Ideals

Operations research can be described in terms of a specific strategy of research. The purpose of this paper is to describe and criticize a very common strategy in which the operations researcher relies strongly on managerial judgment and the existing system as a basis for his inquiry. In this strategy, the operations researcher takes the libraries and specifically the user behavior as givens and relies heavily on library administrators as a basis for his models and his data. The current strategy is criticized in idealistic terms. It may very well be that operations researchers are solving the wrong problems because the existing system--for example, the library--may itself be seriously faulty in its design with respect to the real clients.
Continuous School Improvement

Foreword by Willis D. Hawley and E. Joseph Schneider Preface Acknowledgments About the Author 1. The Context and Call for Continuous Improvement 2. The Meaning of Continuous Improvement with Kelly Hallberg 3. Evidence of the Effectiveness of Continuous Improvement 4. Processes of Continuous Improvement 5. Organizational Design for Continuous Improvement 6. Continuous Improvement "In Action" with Cristal Mendlen 7. Becoming a Continuously Improving School Endnotes References Index
Research and Trends in Data Mining Technologies and Applications

Activities in data warehousing and mining are constantly emerging. Data mining methods, algorithms, online analytical processes, data mart and practical issues consistently evolve, providing a challenge for professionals in the field. "Research and Trends in Data Mining Technologies and Applications" focuses on the integration between the fields of data warehousing and data mining, with emphasis on the applicability to real-world problems. This book provides an international perspective, highlighting solutions to some of researchers' toughest challenges. Developments in the knowledge discovery process, data models, structures, and design serve as answers and solutions to these emerging challenges.
Principles Of Wireless Networks

Over the past few years, a new understanding of wireless networks is emerging by focussing on signal interactions arising due to the broadcast nature of wireless channels. This allows some approximate characterizations of information flow over wireless networks. Underlying these developments are deterministic modeling techniques that approximately capture these interactions. This doctoral school class develops these ideas and applies them to (practical) methods of communication over wireless networks. It develops many of the underlying principles of communication of wireless networks from this viewpoint, with the goal of introducing these ideas to advanced students in the School of Computer and Communication Sciences. A brief outline of the class is as follows.