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Cognitive Informatics & Cognitive Computing (ICCI*CC ), 2011 10th IEEE International Conference on

Date 18-20 Aug. 2011

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Displaying Results 1 - 25 of 72
  • Songs to syntax: Cognition, combinatorial computation, and the origin of language

    Page(s): 1
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    Language comprises a central component of what the co-founder of modern evolutionary theory, Alfred Russell Wallace, called “man's intellectual and moral nature” - the human capacities for creative imagination, language and symbolism generally, a complex that is sometimes simply called “the human capacity.” This complex seems to have crystallized fairly recently among a small group in East Africa of whom we are all descendants, distinguishing contemporary humans sharply from all other animals, with enormous consequences for the whole of the biological world, as well as for the study of computational cognition. How can we explain this evolutionary leap? On the one hand, common descent has been important in the evolution of the brain, such that avian and mammalian brains may be largely homologous, particularly in the case of brain regions involved in auditory perception, vocalization and auditory memory. On the other hand, there has been convergent evolution of the capacity for auditory-vocal learning, and possibly for structuring of external vocalizations, such that apes lack the abilities that are shared between songbirds and humans. Language's recent evolutionary origin suggests that the computational machinery underlying syntax arose via the introduction of a single, simple, combinatorial operation. Further, the relation of a simple combinatorial syntax to the sensory-motor and thought systems reveals language to be asymmetric in design: while it precisely matches the representations required for inner mental thought, acting as the “glue” that binds together other internal cognitive and sensory modalities, at the same time it poses computational difficulties for externalization, that is, parsing and speech or signed production. Despite this mismatch, language syntax leads directly to the rich cognitive array that marks us as a symbolic species, including mathematics, music, and much more. View full abstract»

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  • Cognitive Dynamic Systems: An integrative field that will be a hallmark of the 21st century

    Page(s): 2
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    The integrative field of Cognitive Dynamic Systems is inspired by the human brain, hence the four defining principles of what we mean be cognition: 1. The perception-action cycle, which provides for information gain attained by processing environmental observables from one cycle to the next. 2. Memory for predicting the consequences of actions taken by the system on the environment. 3. Attention for the allocation of available resources. 4. Intelligence, which builds on the previous three principles for decision-making aimed at intelligent choices in the face of unavoidable environmental uncertainties. In this lecture, I will elaborate on each one of these four principles. In particular, I will briefly describe Cognitive Radio and Cognitive Radar that have established themselves as two fast-emerging example applications of cognitive dynamic systems. I will conclude the lecture by describing the vision for a new generation of Cognitive Energy Systems inspired by the human brain. View full abstract»

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  • Human centricity and perception-based perspective of architectures of Granular Computing

    Page(s): 3
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (261 KB) |  | HTML iconHTML  

    In spite of their striking diversity, numerous tasks and architectures of intelligent systems such as those permeating multivariable data analysis (e.g., time series, spatio-temporal, and spatial dependencies), decision-making processes along with their models, recommender systems and others exhibit two evident commonalities. They promote human centricity and vigorously engage perceptions (rather than plain numeric entities) in the realization of the systems and their usage. Information granules play a pivotal role in such settings. In the sequel, Granular Computing delivers a cohesive framework supporting a formation of information granules and facilitating their processing. We exploit two essential concepts of Granular Computing. The first one, formed with the aid of a principle of justifiable granularity, deals with the construction of information granules. The second one, based on an idea of an optimal allocation of information granularity, helps endow constructs of intelligent systems with a very much required conceptual and modeling flexibility. The talk covers in detail two representative studies. The first one is concerned with a granular interpretation of temporal data where the role of information granularity is profoundly visible when effectively supporting human centric description of relationships existing in data. In the second study being focused on the Analytic Hierarchy Process (AHP) used in decision-making, we show how an optimal allocation of granularity helps facilitate collaborative activities (e.g., consensus building) in group decision-making. View full abstract»

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  • On inference algebra: A formal means for machine reasoning and cognitive computing

    Page(s): 4 - 6
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    Inference as a fundamental mechanism of thought is one of the gifted abilities of human beings. Inference can be described as a cognitive process that creates rational causations between a pair of cause and effect based on empirical arguments, formal reasoning, and/or statistical regulations [2, 15, 24, 26]. Conventional logic inferences may be classified as logical arguments, deductive, inductive, abductive, and analogical inferences [4, 9, 10, 11, 15]. View full abstract»

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  • It's time for multiscale analysis and synthesis in cognitive systems

    Page(s): 7 - 10
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    Much of the past modelling, analysis and synthesis of autonomous intelligent systems, autonomic systems, cognitive systems, and natural cognitive processes have been conducted using single-scale approaches. This talk presents fundamentals of multiscale analysis and synthesis, with emphasis on monofractals and multifractals as they apply to cognitive informatics and cognitive computing. Examples of many natural self-affine objects and phenomena are provided, and several analytical results are presented to demonstrate the advantages of multiscale analysis. Examples of multiscale synthesis of noise are also provided to illustrate how to generate processes for research of signals contaminated by natural noise. View full abstract»

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  • Cognitive Informatics in Year 10 and Beyond: summary of the plenary panel

    Page(s): 11 - 22
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    The contemporary wonder of sciences and engineering has recently refocused on the starting point of them: How the brain processes internal and external information autonomously and cognitively rather than imperatively as those of conventional computers do? This leads to the advances in the field of Cognitive Informatics (CI) as a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science. CI investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC'11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world's renowned researchers and scholars in the field of cognitive informatics and cognitive computing. View full abstract»

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  • A formal knowledge representation system for the cognitive learning engine

    Page(s): 23 - 32
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    Knowledge representation is one of the central problems in the design and implementation of a cognitive learning engine (CLE). A formal knowledge representation system (FKRS) is developed for autonomous concept formation based on concept algebra. The object-attribute-relation (OAR) model for knowledge representation is adopted in the design of FKRS. The conceptual model, architectural model, and behavioral models of the FKRS system is formally designed and specified in real-time process algebra (RTPA). The FKRS system is implemented in Java as a major component towards the development of the CLE and other knowledge-based systems in cognitive computing and computational intelligence. View full abstract»

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  • Brain architecture for visual object identification

    Page(s): 33 - 40
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (923 KB) |  | HTML iconHTML  

    Visual objects identification is a key cognitive process for intelligent virtual agents that evolve in virtual environments. This process allows the elaboration of intern representation of the environment for cognitive manipulation and posterior intelligent response production. There exists many architectures based on memory modules for visual elements identification of environment as they were invariant, this seems to be different as real humans process visual scene. This document presents a description of visual object identification task based on current neuroscience state of the art. This work is part of a proposal of a cognitive architecture that lend us bring virtual agents with more human behaviors. Finally, we realized an implementation that shows afferent/efferent flow and processing of information of our proposed architecture. View full abstract»

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  • Extraction of geospatial information on the Web for GIS applications

    Page(s): 41 - 48
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (882 KB) |  | HTML iconHTML  

    Many Web pages contain textual descriptions about locations such as addresses, phone numbers, landmarks, and names. These location-related descriptions are valuable geospatial information for business applications. Therefore, the Web can be perceived as a large geospatial database that could provide up-to-date data for Geographic Information Systems (GIS). Currently this rich and frequently updated Web geospatial information is underutilized. Most related work has been focused on identifying and extracting location names from Web pages for the purpose of page indexing. Little research has been found on extracting and using various types of Web geospatial information for enterprise GIS applications. This paper tries to fill the gap by (1) proposing new algorithms for retrieving different types of geospatial information on Web pages and resolving location name ambiguity issues; (2) exploring how GIS can leverage extracted Web geospatial data for enterprise applications. View full abstract»

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  • The operational semantics of Concept Algebra for cognitive computing and machine learning

    Page(s): 49 - 58
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    Concept Algebra (CA) is a denotational mathematical structure for formal knowledge representation and manipulations in cognitive computing and machine learning. CA provides a rigorous and dynamic knowledge modeling and processing tool, which extends the informal, static, and application-specific ontological technologies. An operational semantics for the calculus of CA is formally elaborated using a set of computational processes in real-time process algebra (RTPA). A case study is presented on how machines and agents may mimic the key ability of human beings to autonomously manipulate knowledge using CA. This work demonstrates the expressive power and a wide range of applications of CA for both humans and machines in cognitive computing, semantic computing, machine learning, and computational intelligence. View full abstract»

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  • Inconsistency-induced learning: A step toward perpetual learners

    Page(s): 59 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1121 KB) |  | HTML iconHTML  

    One of the long-term research questions in machine learning is how to build never-ending learners. The state-of-the-practice in the field of machine learning thus far is still dominated by the one-time learner paradigm: some learning algorithms are utilized on data sets to produce certain results, and then the learner is put away and the results are put to work. Such a learn-once-apply-next (or LOAN) approach may not be adequate in dealing with many real world problems and is in sharp contrast with human's life-long learning process. On the other hand, learning is often brought on through some stimulus. In this paper, we describe a framework for inconsistency-induced learning. The framework relies on utilizing inconsistency as learning stimulus and inconsistency resolution as impetus for continuous learning. The framework hinges on recognizing inconsistency in information or knowledge, identifying the cause of inconsistency, revising beliefs to explain, resolve, or accommodate inconsistency. The perpetual learning process is triggered by an agent encountering some antagonistic circumstance, and is embodied in the continuous inconsistency-induced belief revisions. Though there can be other stimuli to learning, we believe that inconsistency-induced learning can be an important step toward building perpetual learning agents. View full abstract»

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  • Applying qualitative reasoning to a driver's cognitive mental load

    Page(s): 67 - 74
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    In this paper, we explore applying qualitative reasoning to a driver's mental state in real driving situations so as to develop a working load for intelligent transportation systems. We identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the driver's cognitive state, we will measure eye movements during car-driving situations. We can acquire data for the various actions of a car driver, in particular braking, acceleration, and steering angles from our experiment car. We constructed a driver cognitive mental load using the framework of qualitative reasoning. We checked the response of our model by qualitative simulation. We also verified the model using real data collected by driving an actual car. The results indicated that our model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations. View full abstract»

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  • Cognitive computational models of emotions

    Page(s): 75 - 84
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    Emotions are one of the important unconscious mechanisms that influence human behaviors, attentions, and decision making. The emotion process helps to determine how humans perceive their internal status and needs in order to form consciousness of an individual. Emotions have been studied from multidisciplinary perspectives and covered a wide range of empirical and psychological topics, such as understanding the emotional processes, creating cognitive and computational models of emotions, and applications in computational intelligence. This paper presents a comprehensive survey of cognitive and computational models of emotions resulted from multidisciplinary studies. We concentrate on exploring how cognitive models have served as the theoretical basis of the computational models of emotions. A comparative analysis of current approaches is elaborated with discussions of how recent advances in this area may improve the development of a coherent Cognitive Computational Model of Emotions (C2MEs), which leads to the machine simulated emotions for cognitive robots and autonomous agent systems in cognitive informatics and cognitive computing. View full abstract»

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  • Cognitive weave pattern prioritization: An application-oriented approach

    Page(s): 85 - 95
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    Very often, the recognition of a pattern is accompanied by a cognitive process of interpretation and understanding. In the arts and sciences, as well as in our daily lives, we learned patterns from nature and create new patterns for various applications. Weave pattern is one of the most important artificial patterns in our daily lives and there are numerous applications. To manipulate the weave patterns, texton indexing and prioritization are needed to perform, which is associated with a cognitive process of interpretation and understanding of pattern. In this regard, we use an interdisciplinary approach to help selecting weave texture patterns using tailored features and algorithms, taking into account essential features or rules of pattern design. The features and algorithms are designed based on the object-attribute-relation (OAR) model and cognitive informatics model. Three essential features of weave pattern are proposed, i.e. the complexity of patterns in production process, visual structural appearance and cognitive features to track for weave pattern. Our experiments on a wide variety of weave patterns show that the proposed approach is capable of effectively prioritizing weave texture patterns. View full abstract»

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  • Does empathy between artificial agents improve agent teamwork?

    Page(s): 96 - 102
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    Both everyday experience and scientific studies indicate that emotional intelligence in general, and empathy in particular, improve the effectiveness of human teamwork. Research in affective computing confirms their significance in systems where humans and artificial agents interact. This paper explores the notion of empathy between artificial agents that has so far received little attention, and argues that it could have significant impact on the design of robust and resilient agent teams. Combining the formal framework of Emotional BDI agents with the principles underlying the leading model of empathy in psychology and neuroscience, we define a hierarchy of affective and behavioral responses, integrated into an algorithm that formalizes the interactions between the subject and object of empathy in the domain of artificial practical reasoning agents. View full abstract»

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  • Learning in proximity

    Page(s): 103 - 111
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    In most articles about machine learning, particularly in reinforcement learning, a learning system interacts with an unknown environment and tries to improve its performance (receiving fewer penalties) according to feedback from environment as reinforcement signals. In this paper we show how a learning system can learn to interact with environment from an experienced agent (experienced learning system). Simulation results show that when a learning system learns from an experienced agent before exposure, it can interact better than a same raw learning system and receive fewer penalties from the environment. View full abstract»

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  • A novel fuzzy multimodal information fusion technology for human biometric traits identification

    Page(s): 112 - 119
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    In recent years, biometric based security systems achieved more attention due to continuous terrorism threats around the world. However, a security system comprised of a single form of biometric information cannot fulfill users' expectations and may suffer from noisy sensor data, intra and inter class variations and continuous spoof attacks. To overcome some of these problems, multimodal biometric systems with multiple physiological, behavioral, and soft biometric information are becoming more popular due to increased recognition accuracy. In order to take full advantage of the multimodal approaches, one of the main issues is to implement the fusion mechanism for different biometric information. In this research, we utilize the physiological attributes (face, ear and iris) along with soft biometric information (gender, ethnicity and eye color). A fuzzy fusion mechanism for robust and reliable multimodal biometric based security systems is developed. The proposed fuzzy fusion scheme adopts rank, match score and soft biometrics information as the input and produces final identification decision via a fuzzy rule-based inference system. The experimental results show that the fuzzy fusion method can provide us faster, higher and more reliable recognition performance than conventional unimodal methods. The system can be effectively used at any security critical applications. View full abstract»

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  • Sequential three-way decisions with probabilistic rough sets

    Page(s): 120 - 125
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    When approximating a concept, probabilistic rough set models use probabilistic positive, boundary and negative regions. Rules obtained from the three regions are recently interpreted as making three-way decisions, consisting of acceptance, deferment, and rejection. A particular decision is made by minimizing the cost of correct and incorrect classifications. This framework is further extended into sequential three-way decision-making, in which the cost of obtaining required evidence or information is also considered. View full abstract»

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  • PLC fuzzy intelligent device based traffic signal intersection

    Page(s): 126 - 129
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    Based on a single intersection traffic signal for research object, by traffic detection sensor, the fuzzy control theory and PLC, developed a traffic fuzzy control system. It can solve the traffic flow is balanced and stable, have traffic real-time adjustment according to the traffic information, in order to achieve real-time control ability, to adjust local traffic is effective. View full abstract»

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  • Fuzzy PID control of induction generators

    Page(s): 130 - 134
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    Based on wind-turbine character analyzing, maximum wind energy tracking is selected as active power control target. A method of using fuzzy logic principles to deduce optimum turbine speed was proposed, to keep optimum tip speed rate operating without the wind velocity measurement. A dual-passage excitation fuzzy control strategy based on dynamic synchronous reference frame was applied to control the proposed optimal stator active and reactive power. The operational performances of the wind turbine system with DFIG with wind speed variation were analyzed and compared by using Matlab/Simulink. The results show the correctness and feasibility of the proposed control strategy. View full abstract»

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  • An intelligent fault recognizer for rotating machinery via remote characteristic vibration signal detection

    Page(s): 135 - 143
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    Monitoring industrial machine health in real-time is not only highly demanded but also significantly complicated and difficult. Possible reasons for this include: (a) Access to the machines on site is sometimes impracticable; and (b) The environment in which they operate is usually not human-friendly due to pollution, noise, hazardous wastes, etc. Despite the theoretically sound findings on developing intelligent solutions for machine condition based monitoring, there are few commercial tools in the market that can readily be used. This paper reports on the development of an intelligent fault recognition and monitoring system (Melvin I), which detects and diagnoses rotating machine conditions according to changes in fault frequency indicators. The signals and data are remotely collected from designated sections of machines via data acquisition cards. They are processed by a signal processor in order to extract characteristic vibration signals of ten key performance indicators (KPIs). A 3-layer neural network is designed to recognize and classify faults based on the set of KPIs. The system implemented in our laboratory and applied in the field can also incorporate new experiences into the knowledge base without overwriting previous training. Preliminary results have demonstrated that Melvin I is a smart tool for both system vibration analysts and industrial machine operators. View full abstract»

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  • An interactive visualization of Genetic Algorithm on 2-D graph

    Page(s): 144 - 151
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    The visualization of search space makes it easy to understand the behavior of the Genetic Algorithms (GAs). We proposed a novel way for representation of multidimensional search space of the GA on 2-D graph. This visualization is carried out based on the gene values of the current generation, and user interruption is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and to improve the searching process of the GA with user's intention in different generations. Besides the selection of best individual or parents for the next generation, interference of user is required to propose a new individual in the search space. Hence, the active user intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA). View full abstract»

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  • A method for using BP neural network to monitor running state of a steam turbine gearbox

    Page(s): 152 - 155
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    The relationship between the gearbox's running state and the characteristic parameters is complex and nonlinear. In this paper, a diagnostic method for BP neural network gear box's running state based on principal component analysis is proposed. The method is mainly extracted from 8 main characteristic parameters and 10 groups of training samples. On this basis, the BP neural network classifier is designed, and use the network to identify steam turbine gearbox's running state identify the operational status, so as to facilitate timely maintenance, reduce production costs and create some economic benefits. View full abstract»

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  • Stability conditions for uncertain BAM neural networks of neutral-type with time-varying delays

    Page(s): 156 - 160
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    This paper deals with the robust stability for uncertain bidirectional associative memory (BAM) neural networks of neutral-type with time-varying delays. The parameter uncertainties are assumed to be norm bounded, the discrete delays and neutral delays are time-varying delays. The combined method, which is based on the Lyapunov-Krasovskii functional (LKF) combined with some inequality techniques, is used to investigate this problem. Then, by constructing a new LKF, using the Newton-Leibniz formula and introducing some free weighting matrices, some sufficient conditions are proposed to guarantee global asymptotic robust stability for the considered systems. It is also shown that the obtained results can be established in terms of linear matrix inequality (LMI). View full abstract»

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  • Semi-Supervised Discriminative Mutual Subspace Method

    Page(s): 161 - 166
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    Subspace recognition has recently attracted more attention for vector set or image set matching in machine learning and computer vision. In this paper, we firstly give a more simple proof of Procrustes metric (Theorem 2) than literature [1,7]. Then, a novel Semi-Supervised Discriminative Mutual Subspace Method (SS-DMSM) is proposed based on Procrustes metric. For finding a better discriminative subspace, our SS-DMSM algorithm sufficiently considers the intrinsic geometric information on Grassmann manifold that is the set of all subspaces, and effectively uses the label information of those training subspaces. Experimental results on Cambridge gesture database and ETH-80 database show that our SS-DMSM algorithm outperforms the classical MSM and CMSM algorithms. View full abstract»

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