Neurocognitive Informatics Manifesto.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given

KENTUCKY\u27S ADAPTER FOR PARALLEL EXECUTION AND RAPID SYNCHRONIZATION

As network hardware has become faster, inefficient communication and synchronization mechanisms often have proven to be fast enough but better models are needed in order to support future systems. The aggregate function communication model, and the KAPERS design and implementation presented in this thesis, provide more efficient ways to implement a wide range of higher-level communication and synchronization operations. The main contributions of this work center on a new way to use FPGA-based memory in an aggregate function network (AFN). The basic functions were designed and implemented with modal encoding to create a global memory that allows variable length objects and object addresses. New and enhanced algorithms were written for use with the new AFN architecture. This thesis also details the KAPERS prototype hardware implementation

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field

An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management

An Embodied Conversational Agent (ECA) is an intelligent agent that works as the front end of software applications to interact with users through verbal/nonverbal expressions and to provide online assistance without the limits of time, location, and language. To help to improve the experience of human-computer interaction, there is an increasing need to empower ECA with not only the realistic look of its human counterparts but also a higher level of intelligence. This thesis first highlights the main topics related to the construction of ECA, including different approaches of dialogue management, and then discusses existing techniques of trend analysis for its application in user classification. As a further refinement and enhancement to our prior work on ECA, this thesis research proposes a cohesive framework to integrate emotion-based facial animation with improved intention discovery. In addition, a machine learning technique modelled from Q-learning (Quality-Learning) technique is introduced to support sentiment analysis for the adjustment of policy design in POMDP-based dialogue management. It is anticipated that the proposed research work is going to improve the accuracy of intention discovery while reducing the length of dialogues. Un agent de conversation incorporé (ECA) est un agent intelligent fonctionnant en amont des applications logicielles pour interagir avec les utilisateurs par le biais d\u27expressions verbales / non verbales et pour fournir une assistance en ligne sans limite de temps, de lieu et de langage. Pour aider à améliorer l\u27expérience de l\u27interaction homme-machine, il est de plus en plus nécessaire de doter la CEA de droits non seulement vis-à-vis de ses homologues humains, mais également d\u27un niveau de renseignement supérieur. Cette thèse aborde d’abord les principaux sujets liés à la construction de la CEA, y compris différentes approches de la gestion du dialogue, puis aborde les techniques existantes d’analyse des tendances pour son application à la classification des utilisateurs. Pour affiner et améliorer nos travaux antérieurs sur ECA, cette thèse de recherche propose un cadre cohérent pour intégrer une animation faciale basée sur les émotions avec une découverte de l’intention améliorée. En outre, une technique d\u27apprentissage automatique modélisée à partir de la technique Q-learning (Quality-Learning) est introduite pour prendre en charge l\u27analyse des sentiments afin d\u27ajuster la conception des stratégies dans la gestion du dialogue basée sur POMDP. On s’attend à ce que les travaux de recherche proposés améliorent la précision de la découverte de l’intention tout en réduisant la durée des dialogues