There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential to provide significant benefits to robots and automation systems. We consider robots and automation systems that rely on data or code from a network to support their operation, i.e., where not all sensing, computation, and memory is integrated into a standalone system. This survey is organized around four potential benefits of the Cloud: 1) Big Data: access to libraries of images, maps, trajectories, and descriptive data; 2) Cloud Computing: access to parallel grid computing on demand for statistical analysis, learning, and motion planning; 3) Collective Robot Learning: robots sharing trajectories, control policies, and outcomes; and 4) Human Computation: use of crowdsourcing to tap human skills for analyzing images and video, classification, learning, and error recovery. The Cloud can also improve robots and automation systems by providing access to: a) datasets, publications, models, benchmarks, and simulation tools; b) open competitions for designs and systems; and c) open-source software. This survey includes over 150 references on results and open challenges. A website with new developments and updates is available at: http://goldberg.berkeley.edu/cloud-robotics/

/pdf/a-survey-of-research-on-cloud-robotics-and-automation-1tjsiehd9n.pdf

A Survey of Research on Cloud Robotics and Automation

http://www.parkjonghyuk.net/lecture/2015-1st-lecture/networksecurity/Paper2.pdf

Security in cloud computing

We present a new algorithm for manifold learning and nonlinear dimensionality reduction. Based on a set of unorganized da-ta points sampled with noise from a parameterized manifold, the local geometry of the manifold is learned by constructing an approxi-mation for the tangent space at each point, and those tangent spaces are then aligned to give the global coordinates of the data pointswith respect to the underlying manifold. We also present an error analysis of our algorithm showing that reconstruction errors can bequite small in some cases. We illustrate our algorithm using curves and surfaces both in 2D/3D Euclidean spaces and higher dimension-al Euclidean spaces. We also address several theoretical and algorithmic issues for further research and improvements.

Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

A typical method to obtain valuable information is to extract the sentiment or opinion from a message. Machine learning technologies are widely used in sentiment classification because of their ability to “learn” from the training dataset to predict or support decision making with relatively high accuracy. However, when the dataset is large, some algorithms might not scale up well. In this paper, we aim to evaluate the scalability of Naive Bayes classifier (NBC) in large datasets. Instead of using a standard library (e.g., Mahout), we implemented NBC to achieve fine-grain control of the analysis procedure. A Big Data analyzing system is also design for this study. The result is encouraging in that the accuracy of NBC is improved and approaches 82% when the dataset size increases. We have demonstrated that NBC is able to scale up to analyze the sentiment of millions movie reviews with increasing throughput.

Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier

The strategy of data fusion has been applied in threat prediction and situation awareness and the terminology has been standardized by the Joint Directors of Laboratories (JDL) in the form of a so-called JDL Data Fusion Model, which currently called DFIG model. Higher levels of the DFIG model call for prediction of future development and awareness of the development of a situation. It is known that Bayesian Network is an insightful approach to determine optimal strategies against asymmetric adversarial opponent. However, it lacks the essential adversarial decision processes perspective. In this paper, a highly innovative data-fusion framework for asymmetric-threat detection and prediction based on advanced knowledge infrastructure and stochastic (Markov) game theory is proposed. In particular, asymmetric and adaptive threats are detected and grouped by intelligent agent and Hierarchical Entity Aggregation in Level 2 and their intents are predicted by a decentralized Markov (stochastic) game model with deception in Level 3. We have verified that our proposed algorithms are scalable, stable, and perform satisfactorily according to the situation awareness performance metric.

http://www2.ece.ohio-state.edu/~cruz/Papers/C127-245-Informationfusion2006.pdf

Game Theoretic Approach to Threat Prediction and Situation Awareness

Cooperative tracking plays a key role in space situation awareness for scenarios with a limited number of observations or poor performance of a single sensor or both. To use the information from multiple networked sensors, both centralized and decentralized fusion algorithms can be used. Compared with centralized fusion algorithms, decentralized fusion algorithms are more robust in terms of communication failure and computational burden. One popular distributed estimation approach is based on the average consensus that asymptotically converges to the estimate by multiple exchanges of neighborhood information. Consensus-based algorithms have become popular in recent years due to the fact that they do not require global knowledge of the network or routing protocols. The main contributions of this paper are 1) an effective space-based object (SBO) measurement model that considers the geometric relation of the Sun, the space object, the SBO sensor, and the Earth; 2) two consensus-based filters, the information-weighted consensus filter (ICF) and the Kalman consensus filter (KCF), are used to track space objects by using multiple SBO sensors; and 3) the cubature rule-embedded ICF (Cub-ICF) and KCF (Cub-KCF) are proposed to improve the accuracy of the ICF and KCF. Three scenarios that contain one or two space objects and four SBOs are used to test proposed algorithms. We also compare the consensus-based space object tracking algorithms with the centralized extended information filter (centralized EIF) and the centralized cubature information filter (centralized Cub-IF). The simulation results indicate that 1) cooperative space object tracking algorithms provide better results than algorithms using a single sensor, 2) the consensus-based tracking algorithms can achieve performance close to that of the centralized algorithms, and 3) the Cub-ICF and Cub-KCF outperform the conventional ICF and KCF for a challenging space object tracking case shown in the paper. The proposed Cub-ICF and Cub-KCF algorithms should facilitate the application of using consensus-based filters for cooperative space object tracking.

Cooperative space object tracking using space-based optical sensors via consensus-based filters

Information fusion utilizes a collection of data sources for uncertainty reduction, coverage extension, and situation awareness. Future information fusion solutions require systems design [1], coordination with users [2], metrics of performance [3], and methods of multilevel security [4]. A current trend that can enable all of these services is cloud computing. Cloud computing as defined by NIST is: Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. [5] Cloud computing provides capabilities (on-demand self service, broad network access, resource pooling, rapid elasticity, and measured service) over different types of clouds (private, community, public, and hybrid).

http://ieeexplore.ieee.org/iel7/62/6966955/06966964.pdf

Information fusion in a cloud computing era: A systems-level perspective

Information Fusion consists of organizing a set of data for correlation in time, association over multimodal collections, and estimation in space. There exist many methods for object tracking and classification; however video tracking suffers from exact methods in object labeling, the ability to correlate tracks through dropouts, and determination of intent. Our novel solution is to fuse video data with text data for better simultaneous tracking and identification. The need for such solutions resides in answering user queries, linking information over different collections, and providing meaningful product reports. For example, text data can establish that a certain person should be visible in a video. Together, video-to-text (V2T) enhances situation awareness, provides situation understanding, and affords situation assessment. V2T is an example of hard (e.g., video) and soft (i.e., text) data fusion that links Level 5 User Refinement to Level 1 object tracking and characterization. A demonstrated example for multimodal text and video sensing is shown where context provides the means for associating the multimode data aligned in space and time.

Dan Shen

Papers

Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier

Game Theoretic Approach to Threat Prediction and Situation Awareness

Cooperative space object tracking using space-based optical sensors via consensus-based filters

Information fusion in a cloud computing era: A systems-level perspective

Context aided video-to-text information fusion