Community Trolling: An Active Learning Approach for Topic Based Community Detection in Big Data

Community detection plays an important role in creation and transfer of information. Active learning has been employed recently to improve the performance of community detection techniques. Active learning provides a semi-automatic approach in a selective sampling of data. Based on this, a community trolling approach for topic based community detection in big data is proposed. Community trolling selectively samples the data relevant to the current context from polluted big data using active learning. Fine-tuned data is then used to study community and its sub-communities. Community trolling as a precursor to community detection leads to a reduction of the huge unreliable dataset into a reliable dataset and results in the better prediction of community elements such as important topics and important entities. Finally, the effectiveness of approach was evaluated by implementing it on a real world Tumbler dataset. The results illustrate that community trolling provides a richer dataset resulting in more appropriate communities.     

A generalized scheme for mapping parallel algorithms

A generalized mapping strategy that uses a combination of graph theory, mathematical programming, and heuristics is proposed. The authors use the knowledge from the given algorithm and the architecture to guide the mapping. The approach begins with a graphical representation of the parallel algorithm (problem graph) and the parallel computer (host graph). Using these representations, the authors generate a new graphical representation (extended host graph) on which the problem graph is mapped. An accurate characterization of the communication overhead is used in the objective functions to evaluate the optimality of the mapping. An efficient mapping scheme is developed which uses two levels of optimization procedures. The objective functions include minimizing the communication overhead and minimizing the total execution time which includes both computation and communication times. The mapping scheme is tested by simulation and further confirmed by mapping a real world application onto actual distributed environments     

MODEEP: a motion-based object detection and pose estimation method for airborne FLIR sequences

In this paper, we present a method called MODEEP (Motion-based Object DEtection and Estimation of Pose) to detect independently moving objects (IMOs) in forward-looking infrared (FLIR) image sequences taken from an airborne, moving platform. Ego-motion effects are removed through a robust multi-scale affine image registration process. Thereafter, areas with residual motion indicate potential object activity. These areas are detected, refined and selected using a Bayesian classifier. The resulting regions are clustered into pairs such that each pair represents one object's front and rear end. Using motion and scene knowledge, we estimate object pose and establish a region of interest (ROI) for each pair. Edge elements within each ROI are used to segment the convex cover containing the IMO. We show detailed results on real, complex, cluttered and noisy sequences. Moreover, we outline the integration of our fast and robust system into a comprehensive automatic target recognition (ATR) and action classification system.     

High-fidelity bilateral teleoperation systems and the effect of multimodal haptics.

In master-slave teleoperation applications that deal with a delicate and sensitive environment, it is important to provide haptic feedback of slave/environment interactions to the user's hand as it improves task performance and teleoperation transparency (fidelity), which is the extent of telepresence of the remote environment available to the user through the master-slave system. For haptic teleoperation, in addition to a haptics-capable master interface, often one or more force sensors are also used, which warrant new bilateral control architectures while increasing the cost and the complexity of the teleoperation system. In this paper, we investigate the added benefits of using force sensors that measure hand/master and slave/environment interactions and of utilizing local feedback loops on the teleoperation transparency. We compare the two-channel and the four-channel bilateral control systems in terms of stability and transparency, and study the stability and performance robustness of the four-channel method against nonidealities that arise during bilateral control implementation, which include master-slave communication latency and changes in the environment dynamics. The next issue addressed in the paper deals with the case where the master interface is not haptics capable, but the slave is equipped with a force sensor. In the context of robotics-assisted soft-tissue surgical applications, we explore through human factors experiments whether slave/environment force measurements can be of any help with regard to improving task performance. The last problem we study is whether slave/environment force information, with and without haptic capability in the master interface, can help improve outcomes under degraded visual conditions.     

Experimental characterization of piezoelectrically actuated micromachined silicon valveless micropump

In this paper, performance of piezoelectrically actuated pyramidal valveless micropumps is studied experimentally in detail. Valveless micropumps based on silicon and glass substrate are fabricated using MEMS technology. Two different sizes of micropumps having overall dimensions of 5 mm × 5 mm × 1 mm and 10 mm × 10 mm × 1 mm are fabricated and characterized. In the fabricated micropumps, the thickness of silicon diaphragm is <20 µm which gives the advantage of operating pump at low voltage with excellent stability and consistency. The performance of micropumps in terms of flowrate and backpressure is evaluated for a wide range of driving frequency and actuating voltages. The maximum flowrate of water in the 10-mm micropump is 355 µl/min and backpressure of 3.1 kPa at zero flowrate for an applied voltage of 80 V at frequency 1.05 kHz. The reported micropumps have low footprint, high flowrate and backpressure. Thus, these micropumps are especially suited for biological applications as these can withstand adequate amount of backpressure. Comparative study of the performance of these micropumps with those available in the literature brings out the efficacy of these micropumps.     

Acoustic modeling problem for automatic speech recognition system: conventional methods (Part I)

In automatic speech recognition (ASR) systems, the speech signal is captured and parameterized at front end and evaluated at back end using the statistical framework of hidden Markov model (HMM). The performance of these systems depend critically on both the type of models used and the methods adopted for signal analysis. Researchers have proposed a variety of modifications and extensions for HMM based acoustic models to overcome their limitations. In this review, we summarize most of the research work related to HMM-ASR which has been carried out during the last three decades. We present all these approaches under three categories, namely conventional methods, refinements and advancements of HMM. The review is presented in two parts (papers): (i) An overview of conventional methods for acoustic phonetic modeling, (ii) Refinements and advancements of acoustic models. Part I explores the architecture and working of the standard HMM with its limitations. It also covers different modeling units, language models and decoders. Part II presents a review on the advances and refinements of the conventional HMM techniques along with the current challenges and performance issues related to ASR.     

Attitudinal choice models with applications in human decision making

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world.     

Soft information set for multicriteria decision making

An information source value is perceived differently by different agents. In this paper, we present a new knowledge representation structure, termed as soft information set (SIS), to provide a parameterized representation of the information values, as perceived by an agent. The properties of SIS are investigated and the notion of relations in SIS is devised. The significance of the proposed SIS in aiding multi criteria decision making is illustrated through real case-studies.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

CONTOUR: an efficient algorithm for discovering discriminating subsequences

In recent years we have witnessed several applications of frequent sequence mining, such as feature selection for protein sequence classification and mining block correlations in storage systems. In typical applications such as clustering, it is not the complete set but only a subset of discriminating frequent subsequences which is of interest. One approach to discovering the subset of useful frequent subsequences is to apply any existing frequent sequence mining algorithm to find the complete set of frequent subsequences. Then, a subset of interesting subsequences can be further identified. Unfortunately, it is very time consuming to mine the complete set of frequent subsequences for large sequence databases. In this paper, we propose a new algorithm, CONTOUR, which efficiently mines a subset of high-quality subsequences directly in order to cluster the input sequences. We mainly focus on how to design some effective search space pruning methods to accelerate the mining process and discuss how to construct an accurate clustering algorithm based on the result of CONTOUR. We conducted an extensive performance study to evaluate the efficiency and scalability of CONTOUR, and the accuracy of the frequent subsequence-based clustering algorithm.