Showing papers in "Cognitive Computation in 2012"

A Standardised Procedure for Evaluating Creative Systems: Computational Creativity Evaluation Based on What it is to be Creative

Abstract: Computational creativity is a flourishing research area, with a variety of creative systems being produced and developed. Creativity evaluation has not kept pace with system development with an evident lack of systematic evaluation of the creativity of these systems in the literature. This is partially due to difficulties in defining what it means for a computer to be creative; indeed, there is no consensus on this for human creativity, let alone its computational equivalent. This paper proposes a Standardised Procedure for Evaluating Creative Systems (SPECS). SPECS is a three-step process: stating what it means for a particular computational system to be creative, deriving and performing tests based on these statements. To assist this process, the paper offers a collection of key components of creativity, identified empirically from discussions of human and computational creativity. Using this approach, the SPECS methodology is demonstrated through a comparative case study evaluating computational creativity systems that improvise music.

Sentic Album: Content-, Concept-, and Context-Based Online Personal Photo Management System

Abstract: The world of online personal photo management has come a long way in the past few years, but today, there are still huge gaps in annotating, organizing, and retrieving online pictures in such a way that they can be easily queried and visualized. Existing content-based image retrieval systems apply statistics, pattern recognition, signal processing, and computer vision techniques but these are still too weak to ‘bridge the semantic gap’ between the low-level data representation and the high-level concepts the user associates with images. Image meta search engines, on the other hand, rely on tags associated with online pictures but results are often too inaccurate since they mainly depend on keyword-based rather than concept-based algorithms. Sentic Album is a novel content-, concept-, and context-based online personal photo management system that exploits both data and metadata of online personal pictures to intelligently annotate, organize, and retrieve them. Many salient features of pictures, in fact, are only noticeable in the viewer’s mind, and the cognitive ability to grasp such features is a key aspect for accordingly analyzing and classifying personal photos. To this end, Sentic Album exploits not just colors and texture of online images (content), but also the cognitive and affective information associated with their metadata (concept), and their relative timestamp, geolocation, and user interaction metadata (context).

An Information Analysis of In-Air and On-Surface Trajectories in Online Handwriting

Abstract: This paper is aimed at analysing, from an information theory perspective, the gestures produced by human beings when handwriting a text. Modern capturing devices allow the gathering of data not only from the on-surface movements of the hand, but also from the in-air trajectories performed when the hand moves in the air from one stroke to the next. Our past research with isolated uppercase words clearly suggests that both types of trajectories have a biometric potential to perform writer recognition and that they can be effectively combined to enhance the recognition accuracy. With samples from the BiosecurID database, we have analysed the entropy of each kind of trajectories, as well as the amount of information they share, and the difference between intra- and inter-writer measures of the mutual information. The results show that when pressure is not taken into account, the amount of information is similar in both types of trajectories. Furthermore, even if they share some information, in-air and on-surface trajectories appear to be notably non-redundant.

Creativity and Autonomy in Swarm Intelligence Systems

Abstract: This work introduces two swarm intelligence algorithms—one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (a ‘stochastic diffusion search’, SDS) and the other algorithm mimicking the behaviour of birds flocking (a ‘particle swarm optimiser’, PSO)—and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour. The resulting hybrid algorithm is used to sketch novel drawings of an input image, exploiting an artistic tension between the local behaviour of the ‘birds flocking’—as they seek to follow the input sketch—and the global behaviour of the ‘ants foraging’—as they seek to encourage the flock to explore novel regions of the canvas. The paper concludes by exploring the putatve ‘creativity’ of this hybrid swarm system in the philosophical light of the ‘rhizome’ and Deleauze’s well-known ‘Orchid and Wasp’ metaphor.

Towards Autonomous Creative Systems: A Computational Approach

Abstract: This paper reviews the long-standing debate surrounding the nature of machine intelligence, autonomy and creativity and argues for an approach to developing autonomous computational creativity that models personal motivations, social interactions and the evolution of domains. The implications of this argument on the types of cognitive processes that are required for the development of autonomous computational creativity are explored and a possible approach to achieving the goal is described. In particular, this paper describes the development of artificial creative systems composed of intrinsically motivated agents engaging in language games to interact with a shared social and cultural environment. The paper discusses the implications that this type of approach may have for the development of autonomous creative systems.

Supervised Learning and Codebook Optimization for Bag-of-Words Models

Abstract: In this paper, we present a novel approach for supervised codebook learning and optimization for bag-of-words models. This type of models is frequently used in visual recognition tasks like object class recognition or human action recognition. An entity is represented as a histogram of codewords, which are traditionally clustered with unsupervised methods like k-means or random forests and then classified in a supervised way. We propose a new supervised method for joint codebook creation and class learning, which learns the cluster centers of the codebook in a goal-directed way using the class labels of the training set. As a result, the codebook is highly correlated to the recognition problem, leading to a more discriminative codebook. We propose two different learning algorithms, one based on error backpropagation and the other based on cluster label reassignment. We apply the proposed method to human action recognition from video sequences and evaluate it on the KTH data set, reporting very promising results. The proposed technique allows us to improve the discriminative power of an unsupervised learned codebook or to keep the discriminative power while decreasing the size of the learned codebook, thus decreasing the computational complexity due to the nearest neighbor search.

Using Human–Computer Interfaces to Investigate ‘Mind-As-It-Could-Be’ from the First-Person Perspective

Abstract: There is a growing community of researchers who are interested in establishing a science of the experiential or ‘lived’ aspects of the human mind. This shift from cognitive science to consciousness science presents a profound challenge to synthetic approaches. To be sure, symbolic artificial intelligence constituted the original foundation of cognitive science; subsequent progress in robotics has helped to pioneer a new understanding of the mind as essentially embodied, situated, and dynamical, while artificial life has informed the concept of biological self-organization. However, with regard to the development of a science of the experienced mind, the relevance of these synthetic approaches still remains uncertain. We propose to address the challenge of first-person experience by designing new human–computer interfaces, which aim to artificially mediate a participant’s sensorimotor loop such that novel kinds of experience can emerge for the user. The advantage of this synthetic approach is that computer interface technology enables us to systematically vary the ways in which participants experience the world and thereby allows us to systematically investigate ‘mind-as-it-could-be’ from the first-person perspective. We illustrate the basic principles of this method by drawing on examples from our research in sensory substitution, virtual reality, and interactive installation.

The Mind’s Chorus: Creativity Before Consciousness

Abstract: I present a theoretical, hypothetical model of creative cognition, broadly framed within a massively parallel view of mental computation, and based on statistical simulation of aspects of memory and perception, particularly sequence. The theory is located at a level of abstraction substantively above that of neural substrate; it models function and not detailed mechanism and works over symbolic representations of percepts. I support the proposal with evidence from a range of computational work concerning learning of and generation from statistical models and argue that the perceptual grounding of the mechanisms presented here may be generalised away, to account, ultimately, for original thought itself.

The Drive for Creativity and the Escape from Creativity: Neurocognitive Mechanisms

Abstract: We discuss cognitive bases for creative versus non-creative knowledge acquisition and suggest neural substrates for these processes. Cognitive mechanisms driving the human mind both toward and away from creativity are related to ancient mechanisms of adaptive behavior. A paradoxical role of language is discussed: on the one hand, language makes higher cognition possible; on the other, language enables heuristic thinking, using millennial truths instead of original creative thinking. Creativity requires overcoming cognitive dissonances and choosing task relevance over salience. Functions of conceptual, emotional, conscious, and unconscious mechanisms are analyzed and related to various brain regions. Future research directions are discussed.

Computational Algorithms Derived from Multiple Scales of Neocortical Processing

Abstract: A statistical mechanics of neocortical interactions of columnar activity and the vector potential of minicolumnar electromagnetic activity provide a context to explore neocortical information processes and influences on cognitive processing at multiple scales, i.e., mesoscopic (columnar scales), macroscopic (mesoscopic influences at regional scales), and microscopic (mesoscopic influences of ions affecting interactions between and among neurons and astrocytes). Even within this confined context, a case has been made that it should not be expected that the proposed Holy Grail of neuroscience, i.e., to ultimately explain all brain processing in terms of a nonlinear science at molecular scales, is at all realistic. As with many Crusades for some truths, other truths can be trampled.

On the Way to Large-Scale and High-Resolution Brain-Chip Interfacing

Abstract: Brain-chip-interfaces (BCHIs) are hybrid entities where chips and nerve cells establish a close physical interaction allowing the transfer of information in one or both directions. Typical examples are represented by multi-site-recording chips interfaced to cultured neurons, cultured/acute brain slices, or implanted “in vivo”. This paper provides an overview on recent achievements in our laboratory in the field of BCHIs leading to enhancement of signals transmission from nerve cells to chip or from chip to nerve cells with an emphasis on in vivo interfacing, either in terms of signal-to-noise ratio or of spatiotemporal resolution. Oxide-insulated chips featuring large-scale and high-resolution arrays of stimulation and recording elements are presented as a promising technology for high spatiotemporal resolution interfacing, as recently demonstrated by recordings obtained from hippocampal slices and brain cortex in implanted animals. Finally, we report on an automated tool for processing and analysis of acquired signals by BCHIs.

Can Quantum Entanglement Between Ion Transition States Effect Action Potential Initiation

Abstract: The involvement of atomic determinants in molecular models underlying ion-conducting proteins suggests a revisitation of classical concepts that are based on rate-theory models (e.g. ‘gating’ particles) and bulk solvation concepts. Here, we investigate possible effects of a quantum correlation regime within ion-conducting molecules (voltage gated ion channels) on the onset dynamics of propagating voltage pulses (action potentials, APs). In particular, we focus on the initiation characteristics of action potentials, (API). We model the classical onset parameters of the sodium current in the Hodgkin–Huxley equation as three similar but independent probabilistic mechanisms that can become quantum correlated. The underlying physics is general and can involve entanglement between various degrees of freedom underlying ion transition states or ‘gating states’ during conduction, for example, Na+ ions in different channel locations, or different coordination states of ions with atoms lining sub-regions of the protein (‘filter-states’). We find that the resulting semi-classical version of the Hodgkin–Huxley equation, incorporating entangled sodium channel system states, can either enhance or slow down the rise in membrane potentials at the time of signal initiation. As in principle a single sodium channel can drive the membrane to an AP threshold, we suggest that the observed effects of a semi quantum-classical signal description point to a self-amplification of Na+ channels and may be due to quantum interferences within the atomic environment of channel atoms. If inserted into canonical generators of AP signals, the suggested quantum term can further enhance signal onset-rapidness, an aspect that has recently been observed in real cortical neurons and that seems to be inevitable for the encoding of high-frequency input.

A Time-Dependent Saliency Model Combining Center and Depth Biases for 2D and 3D Viewing Conditions

Abstract: The role of the binocular disparity depth cue in the deployment of visual attention is examined in this paper. To address this point, we compared eye tracking data recorded while observers viewed natural images in 2D and 3D conditions. The influence of disparity on saliency, center and depth biases is first studied. Results show that visual exploration is affected by the binocular disparity. In particular, participants tend to look first at closer areas in 3D condition, and then direct their gaze to more widespread locations. Beside this behavioral analysis, we assessed the extent to which state-of-the-art models of bottom-up visual attention predict where observers looked at in both viewing conditions. To improve their ability to predict salient regions, low-level features as well as higher level foreground/background cues are examined. Results indicate that, consecutively to initial centering response, the foreground feature plays an active role in the early but also middle instants of attention deployments. Importantly, this influence is more pronounced in stereoscopic conditions. It supports the notion of a quasi-instantaneous bottom-up saliency modulated by higher figure/ground processing. Beyond depth information itself, the foreground cue might constitute an early process of "selection for action". Finally, we proposed a time-dependent computational model to predict saliency on still pictures. The proposed approach combines low-level visual features, center and depth biases. Its performance outperforms state-of-the-art models of bottom-up attention.

Advances in Brain-Inspired Cognitive Systems

Abstract: This special issue focuses on recent advancements in the field of brain-inspired cognitive systems. It comprises 18 articles which are carefully selected, significantly revised versions of papers presented at the seventh International Conference on Brain-Inspired Cognitive Systems (BICS 2015) held at Hefei, China, on December 11–13, 2015. The aim of BICS 2015 was to bring together leading scientists, engineers, and educators who use analytic, syntactic, and computational methods both to understand the prodigious processing properties of biological systems and, specifically, of the brain, and to exploit such knowledge to advance computational methods toward higher levels of cognitive competence. The conference featured plenary speeches given by renowned scholars and a range of technical sessions focusing on timely topics of interest to the scientific community. Based on the recommendation of symposium organizers and reviewers, a number of authors were invited to submit revised versions of their contributions to this special issue. All articles went through a rigorous review procedure involving at least three independent experts before being accepted for publication. Chen et al. propose an efficient algorithm for actionbased pedestrian identification using hierarchical matching pursuit and order-preserving sparse coding to identify and classify features. Gepperth et al. present a novel system for performing multi-sensor fusion with experience-based learning. The authors demonstrate that near-optimal fusion can be learned and is a resource-efficient alternative to empirical estimation of joint probability distributions and Bayesian inference. They also show that their generative learning approach outperforms Bayesian optimum as it is capable of rejecting outliers through detection of systematic changes in input statistics. Mi et al. describe a novel system for occluded face recognition. They propose a new similarity matrix called ‘averaged degree of aggregation of matched pixels’ and show that their system is very robust against occlusion and competitive in terms of recognition accuracy and computation time. Jiang et al. present a cognitively distributed simultaneous localization and tracking algorithm, with applications in Gaussian distributed wireless sensor networks, based on adaptive distributed filtering for target tracking and sensor localization. Their proposed system shows higher accuracy in parameter estimation, coupled with low computational complexity. Zhao et al. propose a new method called ‘elastic matching’ for common visual pattern discovery, through inducing sparse solutions and conducting detection more robustly. Zhang et al. describe a discriminative Lasso in which sparsity and correlation are jointly considered. The method & Amir Hussain ahu@cs.stir.ac.uk

From Neuroelectrodynamics to Thinking Machines

Abstract: Natural systems can provide excellent solutions to build artificial intelligent systems. The brain represents the best model of computation that leads to general intelligent action. However, current mainstream models reflect a weak understanding of computations performed in the brain that is translated in a failure of building powerful thinking machines. Specifically, temporal reductionist neural models elude the complexity of information processing since spike timing models reinforce the idea of neurons that compress temporal information and that computation can be reduced to a communication of information between neurons. The active brain dynamics and neuronal data analyses reveal multiple computational levels where information is intracellularly processed in neurons. New experimental findings and theoretical approach of neuroelectrodynamics challenge current models as they now stand and advocate for a change in paradigm for bio-inspired computing machines.

Autonomous cognitive systems in real-world environments: Less control, more flexibility and better interaction

Abstract: In October 2011, the “2nd European Network for Cognitive Systems, Robotics and Interaction”, EUCogII, held its meeting in Groningen on “Autonomous activity in real-world environments”, organized by Tjeerd Andringa and myself This is a brief personal report on why we thought autonomy in real-world environments is central for cognitive systems research and what I think I learned about it The theses that crystallized are that (a) autonomy is a relative property and a matter of degree, (b) increasing autonomy of an artificial system from its makers and users is a necessary feature of increasingly intelligent systems that can deal with the real world and (c) more such autonomy means less control but at the same time improved interaction with the system

Blame the Opponent! Effects of Multimodal Discrediting Moves in Public Debates

Abstract: In political persuasion, the persuader, besides bearing logical arguments and triggering emotions, must present one’s own image (one’s ethos) of a credible and reliable person, by enhancing three dimensions of it: competence, benevolence, and dominance. In a parallel way, she/he may cast discredit on the opponent by criticizing, accusing or insulting, on the same three dimensions. The work provides a description and a typology of multimodal discrediting moves focusing on the discrediter’s multimodal behavior. Based on an Italian corpus of political debates, the analysis points out which facial expressions, gaze behavior, gestures, postures, and prosodic features are used to convey discredit concerning the three target features of competence, benevolence, and dominance. Finally, an experimental study is presented assessing the effects of the different types of discrediting moves on potential electors. Results show that casting discredit on the other’s competence while also performing gestures, and casting discredit on the other’s dominance without gesturing, makes arguments more shareable and convincing.

Affect Sensing Using Linguistic, Semantic and Cognitive Cues in Multi-threaded Improvisational Dialogue

Abstract: In this paper, a context-based affect detection component embedded in an improvisational virtual platform is implemented. The software allows up to five human characters and one intelligent agent to be engaged in one session to conduct creative improvisation within loose scenarios. The transcripts produced showed several conversations being conducted in parallel. Some of these conversations reveal personal subjective opinions or feelings about situations, while others are caused by social interactions and show opinions and emotional responses to other participant characters. These two types of conversations serve to inform the descriptions of the personal and the social contexts, respectively. In order to detect affect from such contexts, first of all a naive Bayes classifier is used to categorize these two types of conversations based on linguistic cues. A semantic-based analysis is also used to further derive the discussion themes and identify the target audiences for the social interaction inputs. Then, two statistical approaches have been developed to provide affect detection, respectively, in the social and personal emotion contexts. The emotional history of each individual character is used in interpreting affect relating to the personal contexts, while the social context affect detection takes account of interpersonal relationships, sentence types, emotions implied by the potential target audiences in their most recent interactions and discussion themes. The new development of context-based affect detection is integrated with the intelligent agent. The work addresses one challenging cognitive topic in the affective computing field, the detection and revealing of the relevant “context” to inform affect detection. The work addresses the journal’s themes on human emotion behavior analysis and understanding.

Creativity through autonomy and interaction

Abstract: In this paper, we have sought to bring together several strands of our work, on motivation, autonomous agents and interaction between agents, to show how creativity can have a central place within what might be considered rather straightforward aspects of the design of modern computing systems. We review our previous work on the SMART agent framework and re-interpret it in the light of considerations of creativity arising from autonomy, motivation and contributing to the process of autonomous interaction. Here, behaviour is not prescribed but is determined in relation to motivation, leading to different, potentially creative outcomes for different individuals, especially during the process of interaction. Moreover, considering interaction as discovery imbues it with the same creative aspect as in scientific discovery, in which it can be argued that creativity plays a significant role in theory formation and revision. In fact, these are two sides of the same coin: in our view, the creativity in discovery arises from the motivation and autonomy of the individual involved.

Extended Sparse Distributed Memory and Sequence Storage

Abstract: Sparse distributed memory (SDM) is an auto-associative memory system that stores high-dimensional Boolean vectors. SDM uses the same vector for the data (word) and the location where it is stored (address). Here, we present an extension of the original SDM that uses word vectors of larger size than address vectors. This extension preserves many of the desirable properties of the original SDM: auto-associability, content addressability, distributed storage and robustness over noisy inputs. In addition, it adds new functionality, enabling an efficient auto-associative storage of sequences of vectors, as well as of other data structures such as trees. Simulations testing this new memory are described.

Fault Self-repairing Flight Control of a Small Helicopter via Fuzzy Feedforward and Quantum Control Techniques

Abstract: In this paper, the longitudinal-lateral attitude control and fault self-repairing of a small helicopter is investigated using fuzzy feedforward and quantum control techniques. The Lagrange-Euler equation is used to derive a mathematical model of the helicopter flight dynamics. To handle the complex faults of the helicopter flight system, a model reference-based self-repairing control law is proposed using quantum control techniques, which can improve the helicopter’s self-repairing and control precision. In addition, a fuzzy feedforward compensation controller is designed to improve the anti-disturbance performance. Finally, simulation results are given to illustrate the effectiveness of the developed intelligent self-repairing controller.

Qualitative Information Processing in Tripartite Synapses: A Hypothetical Model

Abstract: A new model of synaptic information processing is proposed. It focuses on tripartite synapses and the glial network, called syncytium. A tripartite synapse consists not only of the presynapse and postsynapse as neuronal components, but also of the glial components the astrocyte and its syncytium. It is hypothesized that in the astrocytic syncytium, intentional programs may be generated that determine the expression of astrocytic receptors. Intentional programing is formalized as so-called negative language, which can be transformed into a place structure integrated as astrocytic receptors. Based on the formalism of tritogrammatics, astrocytic receptors embody places of the same or different qualities for the occupancy with cognate neurotransmitters. Dependent on the pattern of astrocytic receptors, astrocytes may be capable of qualitatively modifying synaptic information processing. Although the model presented is experimentally supported, there are methodical limits in experimental biological brain research. Hence, the technical implementation may represent a real and promising alternative.

A Collaborative Video Annotation System Based on Semantic Web Technologies

Abstract: In recent years, videos have become more and more a familiar multimedia format for common users. In particular, the advent of Web 2.0 and the spreading of video-sharing services over the Web have led to an explosion of online video content. The capability to provide broader support in accessing and exploring video content, and in general other kind of multimedia formats as images and documents, is becoming more and more important. In this context, the value of semantically structured data and metadata is recognized as a key factor both to improve search efficiency and to guarantee data interoperability. This latter aspect is critical to connect different, heterogeneous content coming from a variety of data sources. On the other hand, the annotation of video resources has been increasingly understood as a medium factor to enable deep analysis of contents and collaborative study of online digital objects. However, as existing annotation tools provide poor support for semantically structured content or in some cases express the semantics in proprietary and non-interoperable formats, such knowledge that users build by carefully annotating contents hardly crosses the boundaries of a single system and often cannot be reused by different communities (e.g., to classify content or to discover new relations among resources). In this paper, a novel Semantic Web-based annotation system is presented that enables user annotations to form semantically structured knowledge at different levels of granularity and complexity. Annotation can be reused by external applications and mixed with Web of Data sources to enable “serendipity,” the reuse of data produced for a specific task (annotation) by different people and in different contexts from the one data originated from. The main ideas behind the approach are to build on ontologies and support linking, at data level, to precise thesauri and vocabularies, as well as to the Linked Open Data cloud. By describing the software model, developed in the context of SemLib EU project, and by providing an implementation of an online video annotation tool, the main aim of this paper is to demonstrate how such technologies can enable a scenario where users annotations are created while browsing the Web, naturally shared among users, stored in machine readable format and then possibly recombined with external data and ontologies to enhance end-user experience.

Sentence-Level Emotion and Valence Tagging

Abstract: The paper proposes the tagging of sentence-level emotion and valence based on the word-level constituents on the SemEval 2007 affect sensing news corpus. The baseline system for each emotion class assigns the class label to each word, while the WordNet Affect lists updated using the SentiWordNet were also used as the lexicon-based system. Though the inclusion of morphology into the lexicon-based system improves the performance of the word-level emotion tagging, the Conditional Random Field-based machine-learning framework was employed for the word-level emotion-tagging system, and it outperforms both the baseline- and lexicon-based systems. Six separate sense scores for six emotion types are calculated from the SentiWordNet and applied to word-level emotion tagged constituents for identifying sentential emotion scores. Three emotion scoring methods followed by a post-processing technique were employed for identifying the sentence-level emotion tags. In addition to that, the best two emotion tags corresponding to the maximum obtained sense scores are assigned to the sentences, whereas the sentence-level valence is identified based on the total sense scores of the word-level emotion tags along with their polarity. Evaluation was carried out with respect to the best two emotion tags on 250 gold standard test sentences and achieved satisfactory results for sentence-level emotion and valence tagging.

Perceptual Information Integration: Hypothetical Role of Astrocytes

Abstract: In perceptual processes, signals carrying information about a stimulus are transmitted through multiple processing lines to populations of receptive neurons and thalamocortical circuits, leading to the formation of a spatial ensemble of local field potentials. This paper addresses the problem of how the brain integrates patterns embodied in local fields to (re)construct the stimulus in a conscious episode. Four examples of human perception are given to illustrate the requirements of the integrative process. Considering the strategic position of astrocytes, mediating somatic signals carried by blood flow and information carried by the neuronal network, as well as their intrinsic information processing capabilities, these cells seem ideally placed to integrate spatially distributed information. The amplitude-modulated calcium waveform in astrocytes is a multiscale phenomenon, simultaneously operating on temporal scales of milliseconds and seconds, as well as in micro and macro spatial scales. Oscillatory synchrony, constructive wave interference and communication by means of ionic antennae are proposed to constitute a neuro-astroglial self-organizing mechanism capable of perceptual integration and adding a feeling-like quality to information content. A proposal for constructing an artificial astrocyte is described, which would be capable of testing the hypothesis of astrocytic information integration made earlier.

Optimal Path Computation for Autonomous Aerial Vehicles

Abstract: In this paper, a path planning approach is developed and demonstrated for an unmanned aerial vehicle (UAV); the algorithm is applicable for autonomous robot path planning also. The main contribution of the paper is the development of an extension to the Bellman–Ford algorithm that enables incorporation of constraints directly into the algorithm during run-time. This, therefore, provides a framework for path planning, which does not cause violation of the dynamical constraints of the vehicle (or robot), such as its angle of turn. Furthermore, a procedure for computing a number of sub-optimal paths is developed so that a range of options is available for selection; the optimality of the paths is also proved. These sub-optimal paths are generated in an order of priority (optimality). An objective function is developed that models different conflicting objectives in a unified framework; these objectives can be assigned different weights. The objectives may include minimizing the length of the path, keeping the path as straight as possible, visiting areas of interest, avoiding obstacles, approaching the terminal point from a given direction, etc. The algorithm is tested for complex mission objectives, and results are discussed.

Multi-Robot Exploration in Wireless Environments

Abstract: This paper presents a multi-robot exploration approach for application in wireless environments. The challenges generally faced by a robot team are to maintain network connectivity among themselves, in order to have an accurate map of the environment at each instant and have an efficient navigation plan for moving toward the unexplored area. To address these issues, we focus on the integration of such connectivity constraints and take navigation plan problems into account. A modified A* based algorithm is proposed for planning the navigation of the robots. A communication protocol based on the concept of leader-follower is developed for maintaining network connectivity. Mobile robots typically use a wireless connection to communicate with the other team members and establishes a Mobile Ad Hoc NETwork among themselves. A communication route is established between each robot pair for exchanging local map data, in order to achieve consistent global map of the environment at each instant. If the routes have multiple hops, this raises the problem of message delaying because time delay accumulates per hop traveled. The purpose of the proposed Leader Follower Interaction Protocol is to reduce the total number of hop counts required for all transmissions between robot pairs. This is different from the centralized approach where the leader is a fixed base station. The role of leader in the proposed approach switches from one robot to others as network’s wireless topology changes as robots move. Simulation results show the effectiveness of communication protocol, as well as the navigation mechanism.

Physiological LQR Design for Postural Control Coordination of Sit-to-Stand Movement

Abstract: The neurophysiological mechanisms involved in postural stabilization are not well understood. Active and passive mechanisms at muscle and spinal levels as well as visual and vestibular processes are known to contribute toward postural stabilization and coordination of voluntary movement. The motivation for this research is to use a modeling–simulation framework to achieve two aims: (a) to ascertain viability of a physiologically motivated optimal controller design in the maintenance of posture and coordination of voluntary movement and (b) to study the relative contribution from active (feedforward) and passive (feedback) mechanisms in the execution of said movement. We employ a multi-segment sagittal model built on anatomical proportions with three degrees of freedom, including rotation at the ankle, knee, and hip joints. The behavior of the biomechanical model is controlled by an optimal linear quadratic regulator whose state and control weights are derived from physiological considerations. Representative postural and voluntary movements are simulated to illustrate the analysis–synthesis framework of biomechanical movement. Our analytical and simulation results support an active–passive model of postural stabilization and movement coordination. Besides expanding our understanding of the physiological stabilization processes in the body, the insight gained from this study promotes awareness of the existence of optimizing controllers in the central nervous system.

Speaker-Characterized Emotion Recognition using Online and Iterative Speaker Adaptation

Abstract: This paper proposes a novel speech emotion recognition (SER) framework for affective interaction between human and personal devices Most of the conventional SER techniques adopt a speaker-independent model framework because of the sparseness of individual speech data However, a large amount of individual data can be accumulated on a personal device, making it possible to construct speaker-characterized emotion models in accordance with a speaker adaptation procedure In this study, to address problems associated with conventional adaptation approaches in SER tasks, we modified a representative adaptation technique, maximum likelihood linear regression (MLLR), on the basis of selective label refinement We subsequently carried out the modified MLLR procedure in an online and iterative manner, using accumulated individual data, to further enhance the speaker-characterized emotion models In the SER experiments based on an emotional corpus, our approach exhibited performance superior to that of conventional adaptation techniques as well as the speaker-independent model framework

What Languages Tell Us About the Structure of the Human Mind

Abstract: Languages seem to fall into three communicative types. They all talk about reality, but they do not understand it in the same way: (1) some (Russian, Chinese and Hindi) talk about the situation common to the speaker and the hearer, (2) others (Georgian, Turkish and Bulgarian) about the speaker’s experience of that situation and (3) still others (Danish, Swedish and English) also involve the hearer’s experience of it. The choice among a third-person, a first-person or a second-person perspective is a semiotic choice, but it appears that the same kind of choice is made at other areas relating to perception and cognition. If one collects the three linguistic descriptions of ‘our world’, one gets access to the way in which our mind is organized and how it functions: people seem to have two different kinds of vision, and visual stimuli are processed in three stages corresponding to input (experience), intake (understanding) and outcome (a combination of what was experienced and what was understood).