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In the real world, a large amount of systems can be described by networks where nodes represent entities and edges the interconnections between them. Community structure in networks is one of the interesting properties revealed in the study of networks. Many methods have been developed to extract communities from networks using the generative models which give the probability of generating networks based on some assumption about the communities. However, many generative models require setting the number of communities in the network. The methods based on such models are lack of practicality, because the number of communities is unknown before determining the communities. In this paper, the Bayesian nonparametric method is used to develop a new community detection method. First, a generative model is built to give the probability of generating the network and its communities. Next, the model parameters and the number of communities are calculated by fitting the model to the actual network. Finally, the communities in the network can be determined using the model parameters. In the experiments, we apply the proposed method to the synthetic and real-world networks, comparing with some other community detection methods. The experimental results show that the proposed method is efficient to detect communities in networks.

Modeling and analyzing the dynamic shape of human motion is a challenging task owing to temporal variations in the shape and multiple sources of observed shape variations such as viewpoint, motion speed, clothing, etc. We present a new framework for dynamic shape analysis based on temporal normalization and factorized shape style analysis. Using a nonlinear generative model with motion manifold embedding in a low-dimensional space, we detect cycles of periodic motion like gait in different views and synthesize temporally-aligned shape sequences from the same type of motion at different speeds. The bilinear analysis of temporally-aligned shape sequences decomposes dynamic motion into time-invariant shape style factors and time-dependent motion factors. We extend the bilinear model into a tensor shape model, a multilinear decomposition of dynamic shape sequences for view-invariant shape style representations. The shape style is a view-invariant, time-invariant, and speed-invariant shape signature and is used as a feature vector for human identification. The shape style can be adapted to new environmental conditions by iterative estimation of style and content factors to reflect new observation conditions. We present the experimental results of gait recognition using the CMU Mobo gait database and the USF gait challenging database.

We present a systematic and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. Instead of using a generic classifier off-the-shelf, like a neural network or support vector machine, our classifier design is guided by prior knowledge about the problem and statistical properties found in the data. In particular, we exploit the well-known fact that event-related drifts in EEG potentials, albeit hard to detect in a single trial, can well be observed if averaged over a sufficiently large number of trials. We propose to use the average signal and its variance as a generative model for each event class and use Bayes' decision rule for the classification of new and unlabeled data. The method is successfully applied to a data set from the NIPS*2001 Brain–Computer Interface post-workshop competition. Our result turned out to be competitive with the best result of the competition.

We present a methodology that applies a machine learning technique — genetic programming — to the problem of finding plausible generative models for complex networks. We specifically apply this method to the analysis of alliance networks, a type of kinship network used by social anthropologists where nodes are groups and directed edges represent a group giving a wife to another group. Network generators are represented as computer programs. Evolutionary search is used to find programs that generate networks that best approximate real networks. The quality evaluation of a model is based on a set of network metrics with anthropological meaning. We evolve generators for seventeen real alliance networks and find that our approach is capable of generating high quality results both in terms of network similarity and human readability of the programs. We present and discuss a subset of the experimental results that highlights several interesting aspects of our findings. We believe in the applicability of the methodology to complex networks in general and propose that these are the first steps towards an artificial network scientist.

Concepts in a certain domain of science are linked via intrinsic connections reflecting the structure of knowledge. To get a qualitative insight and a quantitative description of this structure, we perform empirical analysis and modeling of the network of scientific concepts in the domain of physics. To this end, we use a collection of manuscripts submitted to the e-print repository arXiv and the vocabulary of scientific concepts collected via the ScienceWISE.info platform and construct a network of scientific concepts based on their co-occurrences in publications. The resulting complex network possesses a number of specific features (high node density, dissortativity, structural correlations, skewed node degree distribution) that cannot be understood as a result of simple growth by several commonly used network models. We show that the model based on a simultaneous account of two factors, growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network.

This paper summarizes our recent attempts to integrate action and perception within a single optimization framework. We start with a statistical formulation of Helmholtz's ideas about neural energy to furnish a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts. Using constructs from statistical physics it can be shown that the problems of inferring the causes of our sensory inputs and learning regularities in the sensorium can be resolved using exactly the same principles. Furthermore, inference and learning can proceed in a biologically plausible fashion. The ensuing scheme rests on Empirical Bayes and hierarchical models of how sensory information is generated. The use of hierarchical models enables the brain to construct prior expectations in a dynamic and context-sensitive fashion. This scheme provides a principled way to understand many aspects of the brain's organization and responses. We will demonstrate the brain-like dynamics that this scheme entails by using models of birdsongs that are based on chaotic attractors with autonomous dynamics. This provides a nice example of how non-linear dynamics can be exploited by the brain to represent and predict dynamics in the environment.

This paper aims to comprehensively explore the pivotal role of simulation and modeling in the field of Artificial Intelligence (AI). It focuses on elucidating the diverse applications of simulation and modeling in training AI systems, optimizing algorithms, and enhancing decision-making processes. To achieve this objective, we conducted an extensive review of the literature from the Scopus database, employing a well-defined selection process. We utilized keywords such as “simulation,” “modeling,” “Artificial Intelligence,” and related terms to identify relevant papers published within the last 10 years. The selection criteria included assessing the relevance, quality, contribution, and recent citations of the papers. After a rigorous screening process, we selected 40 papers with the highest overall scores for inclusion in our review. The selected papers encompass a wide range of domains where simulation and modeling play a vital role in advancing AI applications. These domains include manufacturing, healthcare, energy consumption prediction, public sector decision-making, education, environmental modeling, and more. Our review highlights how AI leverages simulation and modeling to improve predictive accuracy, optimize resource allocation, and enhance decision-making processes across diverse sectors. We also discuss the potential future directions in the integration of simulation and modeling with AI, emphasizing its significance in various fields.