waashk/dblp huggingface dataset:
cognitive agents - a procedural perspective relying on the predictability of object-action-complexes (oacs). embodied cognition suggests that complex cognitive traits can only arise when agents have a body situated in the world. the aspects of embodiment and situatedness are being discussed here from the perspective of linear systems theory. this perspective treats bodies as dynamic, temporally variable entities, which can be extended (or curtailed) at their boundaries. we show how acting agents can, for example, actively extend their body for some time by incorporating predictably behaving parts of the world and how this affects the transfer functions. we suggest that primates have mastered this to a large degree increasingly splitting their world into predictable and unpredictable entities. we argue that temporary body extension may have been instrumental in paving the way for the development of higher cognitive complexity as it is reliably widening the cause-effect horizon about the actions of the agent. a first robot experiment is sketched to support these ideas. we continue discussing the concept of object-action complexes (oacs) introduced by the european paco-plus consortium to emphasize the notion that, for a cognitive agent, objects and actions are inseparably intertwined. in another robot experiment we devise a semi-supervised procedure using the oac-concept to demonstrate how an agent can acquire knowledge about its world. here the notion of predicting changes fundamentally underlies the implemented procedure and we try to show how this concept can be used to improve the robot's inner model and behaviour. hence, in this article we have tried to show how predictability can be used to augment the agent's body and to acquire knowledge about the external world, possibly leading to more advanced cognitive traits.
generating human-like soccer primitives from human data. recently, interest in analysis and generation of human and human-like motion has increased in various areas. in robotics, in order to operate a humanoid robot, it is necessary to generate motions that have strictly dynamic consistency. furthermore, human-like motion for robots will bring advantages such as energy optimization. this paper presents a mechanism to generate two human-like motions, walking and kicking, for a biped robot using a simple model based on observation and analysis of human motion. our ultimate goal is to establish a design principle of a controller in order to achieve natural human-like motions. the approach presented here rests on the principle that in most biological motor learning scenarios some form of optimization with respect to a physical criterion is taking place. in a similar way, the equations of motion for the humanoid robot systems are formulated in such a way that the resulting optimization problems can be solved reliably and efficiently. the simulation results show that faster and more accurate searching can be achieved to generate an efficient human-like gait. comparison is made with methods that do not include observation of human gait. the gait has been successfully used to control robo-erectus, a soccer-playing humanoid robot, which is one of the foremost leading soccer-playing humanoid robots in the robocup humanoid league.
non-iterative nonlinear model predictive approach applied to the control of helicopters' group formation. two geometrical formation schemes that allow the definition of any desired three-dimensional formation mesh for a group of helicopters are presented. each formation scheme, which defines the leader-follower geometry of the formation mesh, has four parameters. these formation parameters are directly used as the output of decentralized controllers that independently control each helicopter in the group. the decentralized controllers are designed using a non-iterative nonlinear model predictive control (nmpc) method. the continuation method is used for solving, in real-time, for future control actions that minimize a nmpc cost function. it is shown by analyzing the number of floating point operations per calculation cycle that the calculation load of the nmpc method for this application is quite manageable for today's industrial embedded computers. simulations show that the formation schemes along with the nmpc controller can initialize and keep the formation of a group of helicopters even in the presence of bounded parameter uncertainty and environmental disturbance.
contour reconstruction using recursive smoothing splines - algorithms and experimental validation. in this paper, a recursive smoothing spline approach for contour reconstruction is studied and evaluated. periodic smoothing splines are used by a robot to approximate the contour of encountered obstacles in the environment. the splines are generated through minimizing a cost function subject to constraints imposed by a linear control system and accuracy is improved iteratively using a recursive spline algorithm. the filtering effect of the smoothing splines allows for usage of noisy sensor data and the method is robust to odometry drift. the algorithm is extensively evaluated in simulations for various contours and in experiments using a sick laser scanner mounted on a powerbot from activmedia robotics.
incremental pattern discovery on streams, graphs and tensors. incremental pattern discovery targets streaming applications where the data continuously arrive incrementally. the questions are how to find patterns (main trends) incrementally; or how to efficiently update the old patterns when new data arrive; or how to utilize the patterns to solve other problems such as anomaly detection? we first investigate a powerful data model, tensor stream (ts), where there is one tensor per timestamp. to capture diverse data formats, we have a zero-order ts for a single time-series (e.g., the stock price over time), a first-order ts for multiple time-series (sensor measurement streams), a second-order ts for matrices (graphs), and a high-order ts for multi-arrays (internet communication network, source-destination-port). second, we develop different online algorithms on ts: 1) the centralized and distributed spirit [7] for mining a 1st-order ts, as well as its extensions for local correlation function and privacy preservation; 2) the compact matrix decomposition (cmd) [5] and graphscope [4] for a 2nd-order ts; 3) the dynamic tensor analysis (dta) [2], streaming tensor analysis (sta) and window-based tensor analysis (wta) for a high-order ts. all the techniques are extensively evaluated for real applications such as network forensics, cluster monitoring.
mmis07, 08: mining multiple information sources workshop report. in this report, we summarize the research issues, contents, and outcomes of the two recent workshops on mining multiple information sources (mmis-07, 08) collocated with the 13th and the 14th acm sigkdd international conference on knowledge discovery and data mining (kdd-07 and kdd-08). we first summarize the research issues and topics which in workshop co-chairs' view are the major challenges for mining multiple information sources. then we briefiy introduce the content of the contributed papers in two years' program, along with the introduction to three keynote talks given by the invited speakers.
knowledge discovery from sensor data (sensorkdd). extracting knowledge and emerging patterns from sensor data is a nontrivial task. the challenges for the knowledge discovery community are expected to be immense. on one hand, dynamic data streams or events require real-time analysis methodologies and systems, while on the other hand centralized processing through high end computing is also required for generating offline predictive insights, which in turn can facilitate real-time analysis. in addition, emerging societal problems require knowledge discovery solutions that are designed to investigate anomalies, changes, extremes and nonlinear processes, and departures from the normal. keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of sensor-kdd series of workshops in conjunction with the prestigious acm sigkdd international conference of knowledge discovery and data mining. in this report, we summarize the events of the second acm-sigkdd international workshop on knowledge discovery form sensor data (sensor-kdd 2008).
learning preferences of new users in recommender systems: an information theoretic approach. recommender systems are an effective tool to help find items of interest from an overwhelming number of available items. collaborative filtering (cf), the best known technology for recommender systems, is based on the idea that a set of like-minded users can help each other find useful information. a new user poses a challenge to cf recommenders, since the system has no knowledge about the preferences of the new user, and therefore cannot provide personalized recommendations. a new user preference elicitation strategy needs to ensure that the user does not a) abandon a lengthy signup process, and b) lose interest in returning to the site due to the low quality of initial recommendations. we extend the work of [23] in this paper by incrementally developing a set of information theoretic strategies for the new user problem. we propose an offline simulation framework, and evaluate the strategies through extensive offline simulations and an online experiment with real users of a live recommender system.
kdd2008 workshop report dmmt'08: data mining using matrices and tensors. we provide a summary of theworkshop on data mining using matrices and tensors (dmmt'08) held in conjunction with acm sigkdd 2008, on august 24th in las vegas, usa. about 100 people attended the workshop. we report in detail about the research issues addressed in the talks at the workshop. more information about the workshop can be found at http://www.cs.fiu.edu/~taoli/kdd08-workshop.
report on the second kdd workshop on data mining for advertising. following the success of our first workshop, we organized adkdd 2008 1 - the second international workshop on data mining and audience intelligence for advertising, in conjunction with kdd 2008 at las vegas, nevada, usa. this report is a summary of the workshop, including brief descriptions of the accepted papers.
webkdd 2008: 10 years of knowledge discovery on the web post-workshop report. webkdd was held in conjunction with the 14th acm sigkdd international conference on knowledge discovery and data mining (kdd 2008), august 24, 2008, in henderson/las vegas, nevada. in 2008, webkdd was held for the tenth time. we report on the contents and outcomes of this anniversary workshop and include a small retrospection on 10 years of knowledge discovery on the web.
breast cancer identification: kdd cup winner's report. we describe the ideas and methodologies that we developed in addressing the kdd cup 2008 on early breast cancer detection, and discuss how they contributed to our success. the most important components of our solution were 1) the identification of predictive information in the patient identifier, 2) a linear svm on the 117 provided features, and 3) a heuristic post-processing approach to optimize the evaluation criteria.
kdd cup 2008 and the workshop on mining medical data. in this report we summarize the kdd cup 2008 task, which addressed a problem of early breast cancer detection. we describe the data and the challenges, the results and summarize the algorithms used by the winning teams. we also summarize the workshop on mining medical data held in conjunction with sigkdd on august 24, 2008 in las vegas, nv that brought together researchers working on various aspects of applying machine learning and data mining to challenging tasks in medical and health care domains.
on exploiting the power of time in data mining. we introduce the new paradigm of change mining as data mining over a volatile, evolving world with the objective of understanding change. while there is much work on incremental mining and stream mining, both focussing on the adaptation of patterns to a changing data distribution, change mining concentrates on understanding the changes themselves. this includes detecting when change occurs in the population under observation, describing the change, predicting change and pro-acting towards it. we identify the main tasks of change mining and discuss to what extent they are already present in related research areas. we elaborate on research results that can contribute to these tasks, giving a brief overview of the current state of the art and identifying open areas and challenges for the new research area.
snakdd 2008 social network mining and analysis postworkshop report. in this report, we summarize the contents and outcomes of the recent snakdd 2008 workshop on social network mining and analysis that was held in conjunction with the 14th acm sigkdd international conference on knowledge discovery and data mining (kdd 2008), august 24-27, 2008, in las vegas, nevada. the snakdd 2008 is the second in a successful series of workshops on social network mining and analysis. it solicited papers on broad overview subject "social network mining and analysis". the workshop drew a large number of participants from academia and industry. in this report we reflect on the results of the workshop, as captured in the regular paper session as well as the poster session.
learning to improve area-under-froc for imbalanced medical data classification using an ensemble method. this paper presents our solution for kdd cup 2008 competition that aims at optimizing the area under roc for breast cancer detection. we exploited weighted-based classification mechanism to improve the accuracy of patient classification (each patient is represented by a collection of data points). final predictions for challenge 1 are generated by combining outputs from weighted svm and adaboost; whereas we integrate svm, adaboost, and ga to produce the results for challenge 2. we have also tried location-based classification and model adaptation to add the testing data into training. our results outperform other participants given the same set of features, and was selected as the joint winner in kdd cup 2008.
pinkdd'08: privacy, security, and trust in kdd post workshop report. this report summarizes the events of the 2nd international workshop on privacy, security, and trust in kdd, at the 14th acm sigkdd international conference on knowledge discovery and data mining. the workshop was held on august 24, 2008 in las vegas, nevada and brought together computer scientists working on how data protection issues factor into the context of data mining.
retail sales prediction and item recommendations using customer demographics at store level. this paper outlines a retail sales prediction and product recommendation system that was implemented for a chain of retail stores. the relative importance of consumer demographic characteristics for accurately modeling the sales of each customer type are derived and implemented in the model. data consisted of daily sales information for 600 products at the store level, broken out over a set of non-overlapping customer types. a recommender system was built based on a fast online thin singular value decomposition. it is shown that modeling data at a finer level of detail by clustering across customer types and demographics yields improved performance compared to a single aggregate model built for the entire dataset. details of the system implementation are described and practical issues that arise in such real-world applications are discussed. preliminary results from test stores over a one-year period indicate that the system resulted in significantly increased sales and improved efficiencies. a brief overview of how the primary methods discussed here were extended to a much larger data set is given to confirm and illustrate the scalability of this approach.
algorithmic and statistical challenges in modern largescale data analysis are the focus of mmds 2008. we provide a report for the acm sigkdd community about the 2008 workshop on algorithms for modern massive data sets (mmds 2008), its origin in mmds 2006, and future directions for this interdisciplinary research area.
web data mining: exploring hyperlinks, contents, and usage data. this paper presents a review of the book "web data mining - exploring hyperlinks, contents, and usage data" by bing liu. the review concludes that the breadth and depth of this book makes it a required staple for every web mining researcher, student, or practitioner.
a brief survey on anonymization techniques for privacy preserving publishing of social network data. nowadays, partly driven by many web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. privacy preserving publishing of social network data becomes a more and more important concern. in this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. we identify the new challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case, and examine the possible problem formulation in three important dimensions: privacy, background knowledge, and data utility. we survey the existing anonymization methods for privacy preservation in two categories: clustering-based approaches and graph modification approaches.
an ( log ) approximation scheme for steiner tree in planar graphs. we give a polynomial-time approximation scheme (ptas) for the steiner tree problem in planar graphs. the running time is o(n log n).
the impact of parametrization in memetic evolutionary algorithms. memetic (evolutionary) algorithms integrate local search into the search process of evolutionary algorithms. as computational resources have to be spread adequately among local and evolutionary search, one has to care about when to apply local search and how much computational effort to devote to local search. often local search is called with a fixed frequency and run for a fixed number of iterations, the local search depth. there is empirical evidence that these parameters have a significant impact on performance, but a theoretical understanding as well as concrete design guidelines are missing. we initiate the rigorous theoretical analysis of memetic algorithms. to this end, we consider a simple memetic algorithm for pseudo-boolean optimization that captures basic working principles of memetic algorithms-the interplay of genetic operators like mutation and selection with local search. we present function classes where even small changes of the parametrization have a strong impact on performance. for almost every reasonable parameter setting we construct a function that, with high probability, can be optimized in polynomial time. however, changing the local search depth by a small additive term in any direction yields a superpolynomial optimization time, with high probability. for another class of functions altering the local search frequency by a factor of 2 even yields exponential optimization times. our results show exemplarily that parametrizing memetic evolutionary algorithms can be extremely hard. moreover, this work yields insights into the dynamic behavior of memetic algorithms and contributes to a theoretical foundation of hybrid metaheuristics.
the parallel complexity of signed graphs: decidability results and an improved algorithm. we consider a graph-based model for the process of gene assembly in ciliates, as proposed in [a. ehrenfeucht, t. harju, i. petre, d. m. prescott, g. rozenberg, computation in living cells: gene assembly in ciliates, springer, 2003]. the model consists of three operations, each reducing the order of the signed graph. reducing the graph to the empty graph through a sequence of operations corresponds to assembling a gene. we investigate parallel reductions of a given signed graph, where the graph is reduced through a sequence of parallel steps. a parallel step consists of operations such that any of their sequential compositions are applicable to the current graph. we improve the basic exhaustive search algorithm reported in [a. alhazov, c. li, i. petre, computing the graph-based parallel complexity of gene assembly, theoretical computer science, 2008 (in press)] to compute the parallel complexity of signed graphs. on the one hand, we reduce the number of sets of operations which should be checked for parallel applicability. on the other hand, we speed up the parallel applicability check procedure. we prove also that deciding whether a given parallel composition of operations is applicable to a given signed graph is a conp problem. deciding whether the parallel complexity (the length of a shortest parallel reduction) of a signed graph is bounded by a given constant is in np^n^p.
binary sequences with optimal autocorrelation. sequences have important applications in ranging systems, spread spectrum communication systems, multi-terminal system identification, code-division multiple access communication systems, global positioning systems, software testing, circuit testing, computer simulation, and stream ciphers. sequences and error-correcting codes are also closely related. in this paper, we give a well rounded treatment of binary sequences with optimal autocorrelation. we survey known ones and construct new ones.
energy efficient randomised communication in unknown adhoc networks. this paper studies broadcasting and gossiping algorithms in random and general adhoc networks. our goal is not only to minimise the broadcasting and gossiping time, but also to minimise the energy consumption, which is measured in terms of the total number of messages (or transmissions) sent. we assume that the nodes of the network do not know the network, and that they can only send with a fixed power, meaning they can not adjust the area sizes that their messages cover. we believe that under these circumstances the number of transmissions is a very good measure for the overall energy consumption. for random networks, we present a broadcasting algorithm where every node transmits at most once. we show that our algorithm broadcasts in o(logn) steps, w.h.p., where n is the number of nodes. we then present a o(dlogn) (d is the expected degree) gossiping algorithm using o(logn) messages per node. for general networks with known diameter d, we present a randomised broadcasting algorithm with optimal broadcasting time o(dlog(n/d)+log^2n) that uses an expected number of o(log^2n/log(n/d)) transmissions per node. we also show a tradeoff result between the broadcasting time and the number of transmissions: we construct a network such that any oblivious algorithm using a time-invariant distribution requires @w(log^2n/log(n/d)) messages per node in order to finish broadcasting in optimal time. this demonstrates the tightness of our upper bound. we also show that no oblivious algorithm can complete broadcasting w.h.p. using o(logn) messages per node.
lattices of local two-dimensional languages. the aim of this paper is to study local two-dimensional languages from an algebraic point of view. we show that local two-dimensional languages over a finite alphabet, with the usual relation of set inclusion, form a lattice. the simplest case loc"1 of local languages defined over the alphabet consisting of one element yields a distributive lattice, which can be easily described. in the general case of the lattice loc"n of local languages over an alphabet of n>=2 symbols, we show that loc"n is not semimodular, and we exhibit sublattices isomorphic to m"5 and n"5. we characterize the meet-irreducible elements, the coatoms, and the join-irreducible elements of loc"n. we point out some undecidable problems which arise in studying the lattices loc"n, n>=2. we study in some detail atoms and chains of loc"2. finally we examine the lattice loc"2^h of local string languages, i.e. the local languages over the binary alphabet consisting of objects of only one row. loc"2^h is an ideal of loc"2. as a lattice, it is not semimodular but satisfies the jordan-dedekind condition.