Modularity-Based Clustering for Network-Constrained Trajectories

We present a novel clustering approach for moving object trajectories that are constrained by an underlying road network. The approach builds a similarity graph based on these trajectories then uses modularity-optimization hiearchical graph clustering to regroup trajectories with similar profiles. Our experimental study shows the superiority of the proposed approach over classic hierarchical clustering and gives a brief insight to visualization of the clustering results.Comment: 20-th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2012), Bruges : Belgium (2012

Developmental Learning: A Case Study in Understanding “Object Permanence”

The concepts of muddy environment and muddy tasks set the ground for us to understand the essence of intelligence, both artificial and natural, which further motivates the need of Developmental Learning for machines. In this paper, a biologically inspired computational model is proposed to study one of the fundamental and controversial issues in cognitive science – “Object Permanence.” This model is implemented on a robot, which enables us to examine the robot’s behavior based on perceptual development through realtime experiences. Our experimental result shows consistency with prior researches on human infants, which not only sheds light on the highly controversial issue of object permanence, but also demonstrates how biologically inspired developmental models can potentially develop intelligent machines and verify computationalmodeling that has been established in cognitive science

Towards a Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition

A key goal of computational neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how laminar neocortical circuits give rise to biological intelligence. These circuits embody two new and revolutionary computational paradigms: Complementary Computing and Laminar Computing. Circuit properties include a novel synthesis of feedforward and feedback processing, of digital and analog processing, and of pre-attentive and attentive processing. This synthesis clarifies the appeal of Bayesian approaches but has a far greater predictive range that naturally extends to self-organizing processes. Examples from vision and cognition are summarized. A LAMINART architecture unifies properties of visual development, learning, perceptual grouping, attention, and 3D vision. A key modeling theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. It is noted how higher-order attentional constraints can influence multiple cortical regions, and how spatial and object attention work together to learn view-invariant object categories. In particular, a form-fitting spatial attentional shroud can allow an emerging view-invariant object category to remain active while multiple view categories are associated with it during sequences of saccadic eye movements. Finally, the chapter summarizes recent work on the LIST PARSE model of cognitive information processing by the laminar circuits of prefrontal cortex. LIST PARSE models the short-term storage of event sequences in working memory, their unitization through learning into sequence, or list, chunks, and their read-out in planned sequential performance that is under volitional control. LIST PARSE provides a laminar embodiment of Item and Order working memories, also called Competitive Queuing models, that have been supported by both psychophysical and neurobiological data. These examples show how variations of a common laminar cortical design can embody properties of visual and cognitive intelligence that seem, at least on the surface, to be mechanistically unrelated.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Aprendizaje de representaciones desenredadas de escenas a partir de imágenes.

Artificial intelligence is at the forefront of a technological revolution, in particular as a key component to build autonomous agents. However, not only training such agents come at a great computational cost, but they also end up lacking human basic abilities like generalization, information extrapolation, knowledge transfer between contexts, or improvisation. To overcome current limitations, agents need a deeper understanding of their environment, and more efficiently learning it from data. There are very recent works that propose novel approaches to learn representations of the world: instead of learning invariant object encodings, they learn to isolate, or disentangle, the different variable properties which form an object. This would not only enable agents to understand object changes as modifications of one of their properties, but also to transfer such knowledge on the properties between different categories. This Master Thesis aims to develop a new machine learning model for disentangling object properties on monocular images of scenes. Our model is based on a state-of-the-art architecture for disentangled representations learning, and our goal is to reduce the computational complexity of the base model while also improving its performance. To achieve this, we will replace a recursive unsupervised segmentation network by an encoder-decoder segmentation network. Furthermore, before training such overparametrized neural model without supervision, we will profit from transfer learning of pre-trained weights from a supervised segmentation task. After developing a first vanilla model, we have tuned it to improve its performance and generalization capability. Then, an experimental validation has been performed on two commonly used synthetic datasets, evaluating both its disentanglement performance and computational efficiency, and on a more realistic dataset to analyze the model capability on real data. The results show that our model outperforms the state of the art, while reducing its computational footprint. Nevertheless, further research is needed to bridge the gap with real world applications.<br /

Design and Analysis of Smartphone Application Development Methodology

Abstract— The use of modern Smartphone encourages by recent powerful devices such as Apple’s iPhone, Samsung‘s Note, Google’s Android devices etc. In general Smartphone application usage is rapidly growing & expanding throughout the globe. There are need set of emerging guidelines for how to build the new best possible Smartphone applications. Intelligence of mobile has created a wide range of opportunities for researchers, academicians, scientists, engineers, and developers to create the new applications for end users and businesses. Information technology industry enormously concentrates on how to best build smart phone based applications widely. There are various issues in Cutting-edge research and applications development on computational intelligence in mobile environment The mobile-based application development industry is increasingly growing up due to the huge and intensive use of applications in mobile devices; most of them are running on Android based Operating System. As such to develop, analysis and design research model for remotely accessing and control smart phone devices, object oriented strategy is one of the powerful among various traditional software development models. The Various object oriented intelligent development approaches contributes in addressing these issues, as well as discover other potential elements in the mobile paradigm. There are several issues & emerging guidelines that developers follows when building new business or social Smartphone based model.. The combination of mobile computing and computational intelligence focuses on learning model and knowledge generated by mobile users and mobile technology. Mobile technology covers various applications of computational intelligence to mobile paradigm, including intelligence, mobile data, security, mobile agent, location-based mobile information services, intelligent networks, mobile multimedia data access and control