waashk/dblp huggingface dataset:
semi-supervised density-based clustering. most of the effort in the semi-supervised clustering literature was devoted to variations of the k-means algorithm. in this paper we show how background knowledge can be used to bias a partitional density-based clustering algorithm. our work describes how labeled objects can be used to help the algorithm detecting suitable density parameters for the algorithm to extract density-based clusters in specific parts of the feature space. considering the set of constraints estabilished by the labeled dataset we show that our algorithm, called ssdbscan, automatically finds density parameters for each natural cluster in a dataset. four of the most interesting characteristics of ssdbscan are that (1) it only requires a single, robust input parameter, (2) it does not need any user intervention, (3) it automaticaly finds the noise objects according to the density of the natural clusters and (4) it is able to find the natural cluster structure even when the density among clusters vary widely. the algorithm presented in this paper is evaluated with artificial and real-world datasets, demonstrating better results when compared to other unsupervised and semi-supervised density-based approaches.
a new mca-based divisive hierarchical algorithm for clustering categorical data. clustering categorical data faces two challenges, one is lacking of inherent similarity measure, and the other is that the clusters are prone to being embedded in different subspace. in this paper, we propose the first divisive hierarchical clustering algorithm for categorical data. the algorithm, which is based on multiple correspondence analysis (mca), is systematic, efficient and effective. in our algorithm, mca plays an important role in analyzing the data globally. the proposed algorithm has five merits. first, our algorithm yields a dendrogram representing nested groupings of patterns and similarity levels at different granularities. second, it is parameter-free, fully automatic and, most importantly, requires no assumption regarding the number of clusters. third, it is independent of the order in which the data are processed. forth, it is scalable to large data sets; and finally, using the novel data representation and chi-square distance measures makes our algorithm capable of seamlessly discovering the clusters embedded in the subspaces. experiments on both synthetic and real data demonstrate the superior performance of our algorithm.
semi-supervised multi-task learning with task regularizations. multi-task learning refers to the learning problem of performing inference by jointly considering multiple related tasks. there have already been many research efforts on supervised multi-task learning. however, collecting sufficient labeled data for each task is usually time consuming and expensive. in this paper, we consider the semi-supervised multitask learning (ssmtl) problem, where we are given a small portion of labeled points together with a large pool of unlabeled data within each task. we assume that the different tasks can form some task clusters and the task in the same cluster share similar classifier parameters. the final learning problem is relaxed to a convex one and an efficient gradient descent strategy is proposed. finally the experimental results on both synthetic and real world data sets are presented to show the effectiveness of our method.
efficient discovery of frequent correlated subgraph pairs. the recent proliferation of graph data in a wide spectrum of applications has led to an increasing demand for advanced data analysis techniques. in view of this, many graph mining techniques, such as frequent subgraph mining and correlated subgraph mining, have been proposed. in many applications, both frequency and correlation play an important role. thus, this paper studies a new problem of mining the set of frequent correlated subgraph pairs. a simple algorithm that combines existing algorithms for mining frequent subgraphs and correlated subgraphs results in a multiplication of the mining operations, the majority of which are redundant. we discover that most of the graphs correlated to a common graph are also highly correlated. we establish theoretical foundations for this finding and derive a tight lower bound on the correlation of any two graphs that are correlated to a common graph. this theoretical result leads to the design of a very effective skipping mechanism, by which we skip the processing of a majority of graphs in the mining process. our algorithm, fcp-miner, is a fast approximate algorithm, but we show that the missing pairs are only a small set of marginally correlated pairs. extensive experiments verify both the efficiency and effectiveness of fcp-miner.
temporal neighborhood discovery using markov models. temporal data, which is a sequence of data tuples measured at successive time instances, is typically very large. hence instead of mining the entire data, we are interested in dividing the huge data into several smaller intervals of interest which we call temporal neighborhoods. in this paper we propose an approach to generate temporal neighborhoods through unequal depth discretization. we describe two novel algorithms (a) similarity based merging (smerg) and, (b) stationary distribution based merging (stmerg). these algorithms are based on the robust framework of markov models and the markov stationary distribution respectively. we identify temporal neighborhoods with distinct demarcations based on unequal depth discretization of the data. we discuss detailed experimental results in both synthetic and real world data. specifically we show (i) the efficacy of our approach through precision and recall of labeled bins, (ii) the ground truth validation in real world datasets and, (iii) knowledge discovery in the temporal neighborhoods such as global anomalies. our results indicate that we are able to identify valuable knowledge based on our ground truth validation from real world traffic data.
bi-relational network analysis using a fast random walk with restart. identification of nodes relevant to a given node in a relational network is a basic problem in network analysis with great practical importance. most existing network analysis algorithms utilize one single relation to define relevancy among nodes. however, in real world applications multiple relationships exist between nodes in a network. therefore, network analysis algorithms that can make use of more than one relation to identify the relevance set for a node are needed. in this paper, we show how the random walk with restart (rwr) approach can be used to study relevancy in a bi-relational network from the bibliographic domain, and show that making use of two relations results in better results as compared to approaches that use a single relation. as relational networks can be very large, we also propose a fast implementation for rwr by adapting an existing iterative aggregation and disaggregation (iad) approach. the iad-based rwr exploits the block-wise structure of real world networks. experimental results show significant increase in running time for the iad-based rwr compared to the traditional power method based rwr.
pub: a class description technique based on partial coverage of subspace. a good description of a class should be accurate and interpretable. previous works describe classes either by analyzing the correlation of each attribute with the class, or by producing rules as in building a classifier. these solutions suffer from issues in accuracy and interpretability. a description naturally consists of sentences, where each sentence consists of a set of terms. normally, a sentence is defined as a disjunction or conjunction of several terms, each of which specifies a constraint (range/set of values) on an attribute. from the data analysis point of view, a sentence specifies a subspace in the database. in this paper, we create a richer yet interpretable form of a sentence, i.e., a sentence describes an object if any $k$ attributes of that object satisfy the specified constraints. to that end, we design \textsc{pub}, an algorithm that produces descriptions with our form of sentences. while constructing a sentence (within the description), \textsc{pub} finds the optimal range/set of values for each attribute in linear time. we also empirically show that \textsc{pub} is efficient, and able to produce more accurate, concise and interpretable descriptions than current approaches on various real datasets.
non-negative laplacian embedding. laplacian embedding provides a low dimensional representation for a matrix of pairwise similarity data using the eigenvectors of the laplacian matrix. the true power of laplacian embedding is that it provides an approximation of the ratio cut clustering. however, ratio cut clustering requires the solution to be {\it nonnegative}. in this paper, we propose a new approach, nonnegative laplacian embedding, which approximates ratio cut clustering in a more direct way than traditional approaches. from the solution of our approach, clustering structures can be read off directly. we also propose an efficient algorithm to optimize the objective function utilized in our approach. empirical studies on many real world datasets show that our approach leads to more accurate ratio cut solution and improves clustering accuracy at the same time.
synthesizing novel dimension reduction algorithms in matrix trace oriented optimization framework. dimension reduction (dr) algorithms are generally categorized into feature extraction and feature selection algorithms. in the past, few works have been done to contrast and unify the two algorithm categories. in this work, we introduce a matrix trace oriented optimization framework to provide a unifying view for both feature extraction and selection algorithms. we show that the unified view of dr algorithms allows us to discover some essential relationships among many state-of- the-art dr algorithms. inspired by these essential insights, we propose to synthesize unlimited number of novel dr algorithms by combining, mapping and integrating the state- of-the-art algorithms. we present examples of newly synthesized dr algorithms with experimental results to show the effectiveness of our automatically synthesized algorithms.
kernel conditional quantile estimation via reduction revisited. quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. in this paper, we show how to estimate these conditional quantile functions within a bayes risk minimization framework using a gaussian process prior. the resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. moreover, it can directly be used in combination with tools and extensions of standard gaussian processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. no existing approach enjoys all of these desirable properties. experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches."
automatic formation deployment of decentralized heterogeneous multi-robot networks with limited sensing capabilities. heterogeneous multi-robot networks require novel tools for applications that require achieving and maintaining formations. this is the case for distributing sensing devices with heterogeneous mobile sensor networks. here, we consider a heterogeneous multi-robot network of mobile robots. the robots have a limited range in which they can estimate the relative position of other network members. the network is also heterogeneous in that only a subset of robots have localization ability. we develop a method for automatically configuring the heterogeneous network to deploy a desired formation at a desired location. this method guarantees that network members without localization are deployed to the correct location in the environment for the sensor placement.
tracked vehicle with circular cross-section to realize sideways motion. in this video, a novel tracked mechanism for sideways motion is presented. the tracked mechanism is of circular cross-section and has active rolling axes at the center of the circles. conventional tracked mechanisms can support massive loads, but cannot produce sideways motion. additionally, previous crawler edges sink undesirably on soft ground, particularly when the vehicle body is subject to a sideways tilt. the proposed design solves these drawbacks by adopting a circular cross-section crawler. a prototype has been developed to illustrate the concept. motion experiments confirm the novel properties of this mechanism: sideways motion and robustness against edge-sink. motion experiments, with a test vehicle are also presented.
rollin' justin - mobile platform with variable base. research on humanoid robots for use in servicing tasks, e.g. fetching and delivery, attracts steadily more interest. with "rollin' justin" a mobile robotic system and research platform is presented that allows sophisticated control algorithms and dexterous manipulation. this video gives an overview of the mobile humanoid robotic system "rollin' justin" with special emphasis on mechanical design features, control issues and high-level system capabilities such as human robot interaction.
three dimensional hybrid microassembly combining robotic microhandling and self-assembly. hybrid microassembly combining robotic microhandling and self-assembly aims at having the best of the both: good efficiency, reliability, accuracy and capability in creating complex structures. in this paper, a microassembly technique combining robotic tweezer-type microgripper and droplet self-alignment is discussed. the assembly method is evaluated by applying it in different 3d assembly cases, which would pose problems to a solution based on robotics or self-assembly alone. in the first, part rotation is realized by capillary forces. in the second, hierarchical structures are realized. finally, cantilever structures are created using the method.
development of leg-robot for simulation of spastic movement with compact mr fluid clutch. in this study, we propose a leg-robot with a compact mr fluid clutch (cmrfc) to realize haptic control of abnormal spastic movements of brain-injured patients. this system can be used in the practical training for trainees of physical therapy. additionally, we will study to figure out the physiological mechanism of spastic movements of human with the process to simulate patient-like spastic motion by this robot. in this paper, basic structure and mechanism of the leg-robot with cmrfc are explained. finally, experimental results of some kinds of haptic control for spastic movements are described.
a flexible robot skin for safe physical human robot interaction. providing contact sensing on the whole body of a robot is a key feature to increase the safety level of physical human-robot interaction. in this paper, a new robot skin capable of sensing multiple contact locations is presented. the motivation behind the proposed design is to produce a relatively inexpensive skin having the capability to provide the spatial location of collisions and also to add compliance to the robot's external cover. the resulting device is a thin flexible sensor sheet made of polyimide films with electrically conductive ink and a pressure sensitive conductive rubber sheet. the problem of internal wire routing is circumvented by the use of conductive ink and a circuit is proposed to minimize the number of output wires. to provide collision absorption and mechanical robustness, the sensor is embedded in different layers of polyurethane using shape deposition manufacturing (sdm). the paper presents the design and the fabrication process of the skin but also some experimental results on the determination of the mechanical properties of the resulting sensor as well as its potential for increasing human safety during human robot interaction.
bouncing an unconstrained ball in three dimensions with a blind juggling robot. we describe the design of a juggling robot that is able to vertically bounce a completely unconstrained ball without any sensing. the robot consists of a linear motor actuating a machined aluminum paddle. the curvature of this paddle keeps the ball from falling off while the apex height of the ball is stabilized by decelerating the paddle at impact. we analyze the mapping of perturbations of the nominal trajectory over a single bounce to determine the design parameters that stabilize the system. the first robot prototype confirms the results from the stability analysis and exhibits substantial robustness to perturbations in the horizontal degree of freedoms. we then measure the performance of the robot and characterize the noise introduced into the system as white noise. this allows us to refine the design parameters by minimizing the h2 norm of an input-output representation of the system. finally, we design an h2 optimal controller for the apex height using impact time measurements as feedback and show that the closed-loop performance is only marginally better than what is achieved with open-loop control.
a novel method for learning policies from constrained motion. many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the environment. constraints are usually unobservable and frequently change between contexts. in this paper, we present a novel approach for learning (unconstrained) control policies from movement data, where observations come from movements under different constraints. as a key ingredient, we introduce a small but highly effective modification to the standard risk functional, allowing us to make a meaningful comparison between the estimated policy and constrained observations. we demonstrate our approach on systems of varying complexity, including kinematic data from the asimo humanoid robot with 27 degrees of freedom.
a real-time helicopter testbed for insect-inspired visual flight control. the paper describes an indoor helicopter testbed that allows implementing and testing of bio-inspired control algorithms developed from scientific studies on insects. the helicopter receives and is controlled by simulated sensory inputs (e.g. visual stimuli) generated in a virtual 3d environment, where the connection between the physical world and the virtual world is provided by a video camera tracking system. the virtual environment is specified by a 3d computer model and is relatively simple to modify compared to realistic scenes. this enables rapid examinations of whether a certain control law is robust under various environments, an important feature of insect behavior. as a first attempt, flight stabilization and yaw rate control near hover are demonstrated, utilizing biologically realistic visual stimuli as in the fruit fly drosophila melanogaster.
behavioral control for multi-robot perimeter patrol: a finite state automata approach. this paper proposes a multiple robot control algorithm to approach the problem of patrolling an open or closed line. the algorithm is fully decentralized, i.e., no communication occurs between robots or with a central station. robots behave according only to their sensing and computing capabilities to ensure high scalability and robustness towards robots' fault. the patrolling algorithm is designed in the framework of behavioral control and it is based on the concept of action: an higher level of abstraction with respect to the behaviors. each action is obtained by combining more elementary behaviors in the null-space-behavioral framework. a finite-state-automata is designed as supervisor in charge of selecting the appropriate action. the approach has been validated in simulation as well as experimentally with a patrol of 3 pioneer robots available at the distributed intelligence laboratory of the university of tennessee.
development of underwater robots using piezoelectric fiber composite. underwater robot using piezoelectric fiber composite with the merits of being soft and capable of large displacement, is proposed in this paper. by pasting the thin and flexible composite on to a structure, meandering motion observed in underwater creatures such as fish can be realized by generating propagation wave for propulsive force. hence, a compact, effective and powerful robot capable of underwater movement can be realized. this paper describes the mechanism design and control method of the robot using piezoelectric fiber composite based on the principle of underwater creatures and shows the feasibility of the proposal by the experimental results of two developed prototypes.
on-line time-optimal path tracking for robots. this paper focuses on time-optimal path tracking, which involves planning of robot motions along prescribed geometric paths. starting from a discretized convex reformulation of time-optimal path tracking problems, a log-barrier based batch solution method is presented which allows to rapidly obtain an approximate solution with smooth actuator torques. based on this batch method, a recursive variant is derived for on-line path tracking. by means of an experimental test case in which the path data is generated on-line by human demonstration, the results and trade-offs in calculation time, delay and path duration are compared for the batch and recursive variant of the log-barrier method as well as for an exact solution method.
chomp: gradient optimization techniques for efficient motion planning. existing high-dimensional motion planning algorithms are simultaneously overpowered and underpowered. in domains sparsely populated by obstacles, the heuristics used by sampling-based planners to navigate "narrow passages" can be needlessly complex; furthermore, additional post-processing is required to remove the jerky or extraneous motions from the paths that such planners generate. in this paper, we present chomp, a novel method for continuous path refinement that uses covariant gradient techniques to improve the quality of sampled trajectories. our optimization technique both optimizes higher-order dynamics and is able to converge over a wider range of input paths relative to previous path optimization strategies. in particular, we relax the collision-free feasibility prerequisite on input paths required by those strategies. as a result, chomp can be used as a standalone motion planner in many real-world planning queries. we demonstrate the effectiveness of our proposed method in manipulation planning for a 6-dof robotic arm as well as in trajectory generation for a walking quadruped robot.
multimodal telepresent control of dlr's rollin' justin. this video presents a telepresence system which enables a human operator to explore a remote environment by means of a multimodal man machine interface and rollin' justin as teleoperator. the man machine interface allows for bimanual, dexterous manipulation and, through two different operating modi of the man machine interface, wide area movement as well. a bimanual assembly task, consisting of grasping a connector, opening and closing it again, is shown in this video.
articulated object tracking by rendering consistent appearance parts. we describe a general methodology for tracking 3-dimensional objects in monocular and stereo video that makes use of gpu-accelerated filtering and rendering in combination with machine learning techniques. the method operates on targets consisting of kinematic chains with known geometry. the tracked target is divided into one or more areas of consistent appearance. the appearance of each area is represented by a classifier trained to assign a class-conditional probability to image feature vectors. a search is then performed on the configuration space of the target to find the maximum likelihood configuration. in the search, candidate hypotheses are evaluated by rendering a 3d model of the target object and measuring its consistency with the class probability map. the method is demonstrated for tool tracking on videos from two surgical domains, as well as in a human hand-tracking task.