Evolving artificial pain from fault detection through pattern data analysis

© 2017 IEEE. Fault detection is a classical area of study in robotics and extensive research works have been dedicated to investigate its broad applications. As the breath of robots applications requiring human interaction grow, it is important for robots to acquire sophisticated social skills such as empathy towards pain. However, it turns out that this is difficult to achieve without having an appropriate concept of pain that relies on robots being aware of their own body machinery aspects. This paper introduces the concept of pain, based on the ability to develop a state of awareness of robots own body and the use of the fault detection approach to generate artificial robot pain. Faults provide the stimulus and defines a classified magnitude value, which constitutes artificial pain generation, comprised of synthetic pain classes. Our experiment evaluates some of synthetic pain classes and the results show that the robot gains awareness of its internal state through its ability to predict its joint motion and generate appropriate artificial pain. The robot is also capable of alerting humans whenever a task will generate artificial pain, or whenever humans fails to acknowledge the alert, the robot can take a considerable preventive actions through joint stiffness adjustment

Discovering Unexpected Patterns in Temporal Data Using Temporal Logic

There has been much attention given recently to the task of finding interesting patterns in temporal databases. Since there are so many different approaches to the problem of discovering temporal patterns, we first present a characterization of different discovery tasks and then focus on one task of discovering interesting patterns of events in temporal sequences. Given an (infinite) temporal database or a sequence of events one can, in general, discover an infinite number of temporal patterns in this data. Therefore, it is important to specify some measure of interestingness for discovered patterns and then select only the patterns interesting according to this measure. We present a probabilistic measure of interestingness based on unexpectedness, whereby a pattern P is deemed interesting if the ratio of the actual number of occurrences of P exceeds the expected number of occurrences of P by some user defined threshold. We then make use of a subset of the propositional, linear temporal logic and present an efficient algorithm that discovers unexpected patterns in temporal data. Finally, we apply this algorithm to synthetic data, UNIX operating system calls, and Web logfiles and present the results of these experiments.Information Systems Working Papers Serie

Analysing the temporal association among financial news using concept space model.

Law Yee-shan, Carol.Thesis (M.Phil.)--Chinese University of Hong Kong, 2001.Includes bibliographical references (leaves 81-89).Abstracts in English and Chinese.Chapter CHAPTER ONE --- INTRODUCTION --- p.1Chapter 1.1 --- Research Contributions --- p.5Chapter 1.2 --- Organization of the thesis --- p.5Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.7Chapter 2.1 --- Temporal data Association --- p.7Chapter 2.1.1 --- Association Rule Mining --- p.8Chapter 2.1.2 --- Sequential Patterns Mining --- p.10Chapter 2.2 --- Information Retrieval Techniques --- p.11Chapter 2.2.1 --- Vector Space model --- p.12Chapter 2.2.2 --- Probabilistic model --- p.75Chapter CHAPTER THREE --- AN OVERVIEW OF THE PROPOSED APPROACH --- p.16Chapter 3.1 --- The Test Bed --- p.19Chapter 3.2 --- General Concept Term Identification........................................……… --- p.19Chapter 3.3 --- Anchor Document Selection --- p.21Chapter 3.4 --- Specific Concept Term Identification --- p.22Chapter 3.5 --- Establishment of Associations --- p.22Chapter CHAPTER FOUR --- GENERAL CONCEPT TERM IDENTIFICATION --- p.24Chapter 4.1 --- Document Pre-processing --- p.25Chapter 4.2 --- Stopwording and stemming --- p.29Chapter 4.3 --- Word-phrase formation --- p.29Chapter 4.4 --- Automatic Indexing of Words and Sentences --- p.30Chapter 4.5 --- Relevance Weighting --- p.31Chapter 4.5.1 --- Term Frequency and Document Frequency Computation --- p.31Chapter 4.5.2 --- Uncommon Data Removal --- p.32Chapter 4.5.3 --- Combined Weight Computation --- p.32Chapter 4.5.4 --- Cluster Analysis --- p.33Chapter 4.6 --- Hopfield Network Classification --- p.35Chapter CHAPTER FIVE --- ANCHOR DOCUMENT SELECTION --- p.37Chapter 5.1 --- What is an anchor document? --- p.37Chapter 5.2 --- Selection Criteria of an anchor document --- p.40Chapter CHAPTER SIX --- DISCOVERY OF NEWS ASSOCIATION --- p.44Chapter 6.1 --- Specific Concept Term Identification --- p.44Chapter 6.2 --- Establishment of Associations --- p.45Chapter 6.2.1 --- Anchor document representation --- p.46Chapter 6.2.2 --- Similarity measurement --- p.47Chapter 6.2.3 --- Formation of a link of news --- p.48Chapter CHAPTER SEVEN --- EXPERIMENTAL RESULTS AND ANALYSIS --- p.54Chapter 7.1 --- Objective of Experiments --- p.54Chapter 7.2 --- Background of Subjects --- p.55Chapter 7.3 --- Design of Experiments --- p.55Chapter 7.3.1 --- Experimental Data --- p.55Chapter 7.3.2 --- Methodology --- p.55Anchor document selection --- p.57Specific concept term identification --- p.55News association --- p.59Chapter 7.4 --- Results and Analysis --- p.60Anchor document selection --- p.60Specific concept term identification --- p.64News association --- p.68Chapter CHAPTER EIGHT --- CONCLUSIONS AND FUTURE WORK --- p.72Chapter 8.1 --- Conclusions --- p.72Chapter 8.2 --- Future work --- p.74APPENDIX A --- p.76APPENDIX B --- p.78BIBLIOGRAPHY --- p.8

Extraction et partitionnement pour la recherche de régularités : application à l’analyse de dialogues

In the context of dialogue analysis, a corpus of dialogues can be represented as a set of arrays of annotations encoding the dialogue utterances. In order to identify the frequently used dialogue schemes, we design a two-step methodology in which recurrent patterns are first extracted and then partitioned into homogenous classes constituting the regularities. Two methods are developed to extract recurrent patterns: LPCA-DC and SABRE. The former is an adaptation of a dynamic programming algorithm whereas the latter is obtained from a formal modeling of the extraction of local alignment problem in annotations arrays.The partitioning of recurrent patterns is realised using various heuristics from the literature as well as two original formulations of the K-partitioning problem in the form of mixed integer linear programs. Throughout a polyhedral study of a polyhedron associated to these formulations, facets are characterized (in particular: 2-chorded cycle inequalities, 2-partition inequalities and general clique inequalities). These theoretical results allow the establishment of an efficient cutting plane algorithm.We developed a decision support software called VIESA which implements these different methods and allows their evaluation during two experiments realised by a psychologist. Thus, regularities corresponding to dialogical strategies that previous manual extractions failed to identify are obtained.Dans le cadre de l’aide à l’analyse de dialogues, un corpus de dialogues peut être représenté par un ensemble de tableaux d’annotations encodant les différents énoncés des dialogues. Afin d’identifier des schémas dialogiques mis en oeuvre fréquemment, nous définissons une méthodologie en deux étapes : extraction de motifs récurrents, puis partitionnement de ces motifs en classes homogènes constituant ces régularités. Deux méthodes sont développées afin de réaliser l’extraction de motifs récurrents : LPCADC et SABRE. La première est une adaptation d’un algorithme de programmation dynamique tandis que la seconde est issue d’une modélisation formelle du problème d’extraction d’alignements locaux dans un couple de tableaux d’annotations.Le partitionnement de motifs récurrents est réalisé par diverses heuristiques de la littérature ainsi que deux formulations originales du problème de K-partitionnement sous la forme de programmes linéaires en nombres entiers. Lors d’une étude polyèdrale, nous caractérisons des facettes d’un polyèdre associé à ces formulations (notamment les inégalités de 2-partitions, les inégalités 2-chorded cycles et les inégalités de clique généralisées). Ces résultats théoriques permettent la mise en place d’un algorithme de plans coupants résolvant efficacement le problème.Nous développons le logiciel d’aide à la décision VIESA, mettant en oeuvre ces différentes méthodes et permettant leur évaluation au cours de deux expérimentations réalisées par un expert psychologue. Des régularités correspondant à des stratégies dialogiques que des extractions manuelles n’avaient pas permis d’obtenir sont ainsi identifiées

Attribute, Event Sequence, and Event Type Similarity Notions for Data Mining

In data mining and knowledge discovery, similarity between objects is one of the central concepts. A measure of similarity can be user-defined, but an important problem is defining similarity on the basis of data. In this thesis we consider three kinds of similarity notions: similarity between binary attributes, similarity between event sequences, and similarity between event types occurring in sequences. Traditional approaches for defining similarity between two attributes typically consider only the values of those two attributes, not the values of any other attributes in the relation. Such similarity measures are often useful, but unfortunately they cannot describe all important types of similarity. Therefore, we introduce a new attribute similarity measure that takes into account the values of other attributes in the relation. The behavior of the different measures of attribute similarity is demonstrated by giving empirical results on two real-life data sets. We also present a si..