Abstract

Utility mining has been emerging as an important area in data mining. While existing works on utility mining have primarily focused on the problem of finding high-utility itemsets from transactional databases, they implicitly assume that each item occupies only one slot. However, in many real-world scenarios, the number of slots consumed by different items typically varies. Hence, this pa-per considers that a given item may physically occupy any fixed (integer) number of slots. Thus, we address the problem of efficiently determining the top-utility itemsets when a given number of slots is specified as input. The key contributions of our work are three-fold. First, we present an efficient framework to determine the top-utility itemsets for different user-specified number of slots that need to be filled. Second, we propose a novel flexible and efficient index, designated as the STUI index, for facilitating quick retrieval of the top-utility itemsets for a given number of slots. Third, we conducted an extensive performance evaluation using real datasets to demonstrate the overall effectiveness of the proposed indexing scheme in terms of execution time and utility (net revenue) as compared to a recent existing scheme.

Abstract

Periodic Frequent patterns (PFPs) are an important class of regularities that exist in a transactional database. In the literature,pattern growth-based approaches to mine PFPs have be proposed by considering a single machine. In this paper, we propose a Map-Reduce framework to mine PFPs by considering multiple machines. We have proposed a parallel algorithm by including the step of distributing transactional identifiers among the machines. Further, the notion of partition summary has been proposed to reduce the amount of data shuffled among the machines. Experiments on Apache Spark’s distributed environment show that the proposed approach speeds up with the increase in number of machines and the notion of partition summary significantly reduces the amount of data shuffled among the machines.

Abstract

A temporal database is a collection of transactions, ordered by their timestamps. Discovering periodic patterns in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered mining periodic patterns in temporal databases where items have dissimilar support and periodicity, despite that this type of data is very common in real-life. Discovering periodic patterns in such non-uniform temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness of patterns, and (ii) a method to efficiently find all periodic patterns. While a pattern-growth approach can be employed for the second sub-task, the first sub-task has to the best of our knowledge not been addressed. Moreover, how these two tasks are combined has significant implications. In this paper, we address this challenge. We introduce a model to assess the periodic interestingness of patterns in databases having a non-uniform item distribution, which considers that periodic patterns may have different period and minimum number of cyclic repetitions. Moreover, the paper introduces a pattern-growth algorithm to efficiently discover all periodic patterns. Experimental results demonstrate that the proposed algorithm is efficient and the proposed model may be utilized to find prior knowledge about event keywords and their associations in Twitter data.

Abstract

Time and frequency are two important dimensions to determine the interestingness of a pattern in a database. Periodic-frequent patterns are an important class of regularities that exist in a database with respect to these two dimensions. Current studies on periodic-frequent pattern mining have focused on discovering full periodic-frequent patterns, i.e., finding all frequent patterns that have exhibited complete cyclic repetitions in a database. However, partial periodic-frequent patterns are more common due to the imperfect nature of real-world. This paper proposes a flexible and generic model to find partial periodic-frequent patterns. A new interesting measure, periodic-ratio, has been introduced to determine the periodic interestingness of a frequent pattern by taking into account its proportion of cyclic repetitions in a database. The proposed patterns do not satisfy the anti-monotonic property. A novel pruning technique has been introduced to reduce the search space effectively. A pattern-growth algorithm to find all partial periodic-frequent patterns has also been presented in this paper. Experimental results demonstrate that the proposed model can discover useful information, and the algorithm is efficient.

Abstract

Field Programmable Gate Arrays(FPGA) exceed the computing power of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle by enabling hardware level parallelization at an architectural level. As has been proved in already published research works, introducing parallel architectures for a computationally intensive algorithm like Rapidly Exploring Random Trees(RRT) will result in an exploration that is fast, dense and uniform. FPGA based combinatorial architecture, which is one of the already published research works, delivers superlative speed-up but consumes very high power. The second of the already published research works, FPGA based hierarchical architecture, delivers relatively lower speed-up with acceptable power consumption levels. To combine the qualities of both, a hybrid architecture, that encompasses both combinatorial and hierarchical architecture, is designed. To determine the design parameters of the hybrid architecture, a cost function comprised of fundamentally inversely related speed-up and power parameters, as mentioned above, is formulated. This maximization of cost function, with its associated constraints, is then mathematically solved using a modified branch and bound, that leads to optimal deduction of the design parameters of the hybrid architecture. Via empirical experiments, it is observed that this hybrid architecture delivers the highest performance-per-watt out of the three architectures for differential, quad-copter and fixed wing kinematics, in environments of varying geometric complexity. The empirical experiments also confirmed that the hybrid architecture is scalable with 1.) Increase in the environment's geometric complexity and 2.) Increase in kinematic complexity of the robot.

Abstract

We study the binary Ehrenfeucht Mycielski sequence seeking a balance between the number of occurrences of different binary strings. There have been numerous attempts to prove the balance conjecture of the sequence, which roughly states that 1 and 0 occur equally often in it. Our contribution is twofold. First, we study weaker forms of the conjecture proved in the past and lay out detailed proofs for many lemmas which were stated without proofs. Secondly, we extend the claim of balance to that of normality and prove a weaker form of simple normality to word length 2.

Abstract

This paper presents a novel design of an Omnidirectional bendable Omnicrawler module-CObRaSO. Along with the longitudinal crawling and sideways rolling motion, the performance of the OmniCrawler is further enhanced by the introduction of Omnidirectional bending within the module, which is the key contribution of this paper. The Omnidirectional bending is achieved by an arrangement of two independent 1-DOF joints aligned at 90? wrt each other. The unique characteristic of this module is its ability to crawl in Omnidirectionally bent configuration which is achieved by a novel design of a 2-DOF roller chain and a backbone of a hybrid structure of a soft-rigid material. This hybrid structure provides compliant pathways for the lug-chain assembly to passively conform with the orientation of the module and crawl in Omnidirectional bent configuration, which makes this module one of its kind. Furthermore, we show that the unique modular design of CObRaSO unveils its versatility by achieving active compliance on an uneven surface, demonstrating its applications in different robotic platforms (an in-pipeline robot, Quadruped and snake robot) and exhibiting hybrid locomotion modes in various configurations of the robots. The mechanism and mobility characteristics of the proposed module have been verified with the aid of simulations and experiments on real robot prototype.

Abstract

Present visual servoing approaches are instance specific i.e. they control camera motion between two views of the same object. However, in practical scenarios where a robot is required to handle various instances of a category, classical visual servoing techniques are less suitable. We formulate across instance visual servoing as a pose induction and pose alignment problem. Initially, the desired pose given for any known instance is transferred to the novel instance through pose induction. Then the pose alignment problem is solved by estimating the current pose using the part aware keypoints reconstruction followed by a pose based visual servoing (PBVS) iteration. To tackle large variation in appearance across object instances in a category, we employ visual features that uniquely correspond to locations of object's parts in images. These part-aware keypoints are learned from annotated images using a convolutional neural network (CNN). Advantages of using such part-aware semantics are two-fold. Firstly, it conceals the illumination and textural variations from the visual servoing algorithm. Secondly, semantic keypoints enables us to match descriptors across instances accurately. We validate the efficacy of our approach through experiments in simulation as well as on a quadcopter. Our approach results in acceptable desired camera pose and smooth velocity profile. We also show results for large camera transformations with no overlap between current and desired pose for 3D objects, which is desirable in servoing context.

Abstract

We present PRVO, a probabilistic variant of Reciprocal Velocity Obstacle (RVO) for decentralized multi-robot navigation under uncertainty. PRVO characterizes the space of velocities that would allow each robot to fulfill its share in collision avoidance with a specified probability. PRVO is modeled as chance constraints over the velocity level constraints defined by RVO and takes into account the uncertainty associated with both state estimation as well as the actuation of each robot. Since chance constraints are in general computationally intractable, we propose a series of reformulations which when combined with time scaling based concepts leads to a closed form characterization of solution space of PRVO for a given probability of collision avoidance. We validate our formulation through numerical simulations in which we highlight the advantages of PRVO over the related existing formulations

Abstract

This paper presents a modular in-pipeline climbing robot with a novel compliant foldable OmniCrawler mechanism. The robot has a series of 3 compliant foldable OmniCrawler modules interconnected by links via passive joints. The circular cross-section of the module enables a holonomic motion to facilitate the alignment of the robot in the direction of bends. Additionally, the crawler mechanism provides a fair amount of traction, even on slippery pipe surfaces. These advantages of crawler modules have been further augmented by incorporating active compliance in the module, which helps to negotiate sharp bends in small diameter pipes. Introducing compliance in the crawler module with a single chain-lugs assembly is the the key novelty of this design. For the desirable pipe diameter and curvature of the bends, the spring stiffness value for each passive joint is determined by formulating a constrained optimization problem using the quasi-static model of the robot. Moreover, a minimum friction coefficient value between the module-pipe surface which can be vertically climbed by the robot without slipping is estimated. The numerical simulation results have further been validated by experiments on real robot prototype.