ppxscal/embedding_network huggingface dataset:
Hierarchical Shape Decomposition via Level Sets
Authors: Sibel Tari
Venue: Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing (2009)

A new tool for shape decomposition is presented. It is a function defined on the shape domain and computed using a linear system of equations. It is demonstrated that the level curves of the new function provide a hierarchical partitioning of the shape domain into visual parts, without requiring any features to be estimated. The new tool is an unconventional distance transform where the minimum distance to the union of the shape boundary and an unknown critical curve is computed. This curve divides the shape domain into two parts, one corresponding to the coarse scale structure and the other one corresponding to the fine scale structure.The connection of the new function to a variety of morphological concepts (Skeleton by Influence Zone, Aslan Skeleton, and Weighted Distance Transforms) is discussed.
Brief paper: New results on stabilization of Markovian jump systems with time delay
Authors: Zhongyang Fei, Huijun Gao, Peng Shi
Venue: Automatica (Journal of IFAC) (2009)

This paper studies the problem of stochastic stabilization for a class of Markovian jump systems with time delay. A new delay-dependent stochastic stability criterion on the stochastic stability of the system is derived based on a novel Lyapunov-Krasovskii functional (LKF) approach. The equivalence and superiority to existing results are demonstrated. Then a state feedback controller, which guarantees the stochastic stability of the closed-loop system, is designed. Illustrative examples are provided to show the reduced conservatism and effectiveness of the proposed techniques.
An evaluation study of clustering algorithms in the scope of user communities assessment
Authors: Pantelis N. Karamolegkos, Charalampos Z. Patrikakis, Nikolaos D. Doulamis, Panagiotis T. Vlacheas, Ioannis G. Nikolakopoulos
Venue: Computers Mathematics with Applications (2009)

In this paper, we provide the results of ongoing work in Magnet Beyond project, regarding social networking services. We introduce an integrated social networking framework through the definition or the appropriate notions and metrics. This allows one to run an evaluation study of three widely used clustering methods (k-means, hierarchical and spectral clustering) in the scope of social groups assessment and in regard to the cardinality of the profile used to assess users' preferences. Such an evaluation study is performed in the context of our service requirements (i.e. on the basis of equal-sized group formation and of maximization of interests' commonalities between users within each social group). The experimental results indicate that spectral clustering, due to the optimization it offers in terms of normalized cut minimization, is applicable within the context of Magnet Beyond socialization services. Regarding profile's cardinality impact on the system performance, this is shown to be highly dependent on the underlying distribution that characterizes the frequency of user preferences appearance. Our work also incorporates the introduction of a heuristic algorithm that assigns new users that join the service into appropriate social groups, once the service has been initialized and the groups have been assessed using spectral clustering. The results clearly show that our approach is able to adhere to the service requirements as new users join the system, without the need of an iterative spectral clustering application that is computationally demanding.
Harnessing a Refinement Theory to Compute Loop Functions
Authors: Ali Mili, Rahma Ben Ayed, Shir Aharon, Chaitanya Nadkarni
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

We consider a while loop on some space S and we are interested in deriving the function that this loop defines between its initial states and its final states (when it terminates). Such a capability is useful in a wide range of applications, including reverse engineering, software maintenance, program comprehension, and program verification. In the absence of a general theoretical solution to the problem of deriving the function of a loop, we explore engineering solutions. In this paper we use a relational refinement calculus to approach this complex problem in a systematic manner. Our approach has many drawbacks, some surmountable and some not (being inherent to the approach); nevertheless, it offers a way to automatically derive the function of loops or an approximation thereof, under some conditions.
Compressed Domain Motion Analysis for Video Semantic Events Detection
Authors: Kun Tao, Shouxun Lin, Yongdong Zhang
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

In this paper, a novel approach is proposed to estimate camera motion and segment moving objects from compressed video streams, aiming to detect semantic events in video clips. Simultaneously using the motion vectors and DC components of MPEG macroblocks (MB), the camera motion type and motion parameters of each frame are estimated with simplified models. Then the segmentation of moving objects is done at macroblock level. Exploring the variation of motion information in consecutive frame sequence, a composite feature can be formed to detect semantic events. The experiment results on TRECVID video corpus show that our approach is very effective and efficient.
Multi-objective Intelligent Optimization Model on Dynamic Error Measurement and Fault Diagnosis for Roll Grinder NC
Authors: Xiaoyan Ding, Lilan Liu, Zhengxiao Hua, Tao Yu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 03 (2009)

The error measurement and diagnosis process of roll grinder NC has dynamic complexity, non-linearity, and comprehensive characteristics. However, presently roll error measurement examination mostly uses the manual examination or single parameter optimization, and the efficiency of fault diagnosis is also inefficient. In this study, the multi-objective intelligence optimization model (MIOM) is applied to the roller error measurement and diagnosis. The algorithms are hybrid with modern intelligent ones, such as Artificial Neural Network, Fuzzy Logic Inference and Genetic Algorithm, etc. Fuzzy control rules are created base on expert knowledge. Multi-objective parameters can be simultaneously optimized in the same process. Meantime, by analyzing the optimized results of each error parameter, the state space observation equation model can be established, and the stability of the system can be calculated by NN. Therefore, the fault spot can be inferred out. Finally, according to the error diagnosis results, the diagram of curves is drawn by the 840D HMI. Through the experimental simulation tests, the application of MIOM can simplify roll error measuring and diagnosing processes, and the operations for roll grinder NC are more intellectualized.
QoS aware routing for end-to-end reliability of delay sensitive events in wireless sensor networks
Authors: R. Sumathi, R. Srinivasan
Venue: Proceedings of the Fourth International ICST Conference on COMmunication System softWAre and middlewaRE (2009)

Most of the applications developed for wireless sensor networks demand the QoS aware routing protocol in order to deliver the high critical data more reliably with minimum number of message exchanges. This will lead to achieve significant energy conservation and low delivery latency under congested network conditions. In this paper, a novel Energy Reservation based QoS (ERes-QoS) aware routing protocol has been proposed. The ERes-QoS ensures end-to-end performance guarantee for high critical data on hop-by-hop basis in two domains: timeliness and reliability. An angle based router set is constructed at each node, to achieve on-time reachability of the data. For QoS provisioning in a reliability domain, we use local decision approach at each intermediate node, which exploits Multipaths, Energy Reservation and hop-by-hop unicasting with acknowledgement, without end-to-end path discovery and maintenance. The Proposed protocol provides a differentiated routing service by assigning priorities to data packets according to their criticality. This is done by reserving the required energy to relay the traffic at the routers along the dynamically explored path, and by employing packet classifier and multi-level queue priority scheduling modules at each intermediate node. As a result, the ERes-QoS achieves high data delivery ratio that meets both timeliness and reliability requirement. Simulation results demonstrate that, the ERes-QoS routing protocol provides significant improvements in data delivery latency, path establishment latency and data delivery ratio.
Contracts for Mobile Processes
Authors: Giuseppe Castagna, Luca Padovani
Venue: Proceedings of the 20th International Conference on Concurrency Theory (2009)

Theories identifying well-formed systems of processes--those that are free of communication errors and enjoy strong properties such as deadlock freedom--are based either on session types, which are inhabited by channels, or on contracts, which are inhabited by processes. Current session type theories impose overly restrictive disciplines while contract theories only work for networks with fixed topology. Here we fill the gap between the two approaches by defining a theory of contracts for so-called mobile processes, those whose communications may include delegations and channel references.
Research of Knowledge-based Automatic Model Selection Strategy
Authors: Yanghe Feng, Chaofan Dai
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

It is necessary for the Intelligent Decision Support System to automatic select models, but there is less the available strategy. The knowledge-based automatic model selection strategy is proposed in the paper. The model is represented by knowledge frame and the reasoning trees are established according with the fact base and knowledge base, and then the model selection scheme is provided by integrating the experience and expert knowledge. The experiment shows that the strategy has high hit rate.
Safety and Privacy in Vehicular Communications
Authors: Josep Domingo-Ferrer, Qianhong Wu
Venue: Privacy in Location-Based Applications: Research Issues and Emerging Trends (2009)

Vehicular ad hoc networks (VANETs) will improve traffic safety and efficiency provided that car-to-car communication stays trustworthy. Therefore, it is crucial to ensure that the information conveyed by vehicle-generated messages is reliable. A sensible option is to request that the content of a message originated by a certain vehicle be endorsed by nearby peer vehicles. However, neither message generation nor message endorsement should entail any privacy loss on the part of vehicles co-operating in it. This chapter surveys the available solutions to this security-privacy tension and analyzes their limitations. A new privacy-preserving system is sketched which guarantees message authentication through both a priori and a posteriori countermeasures.
The marchitecture: a cognitive architecture for a robot baby
Authors: Marc Pickett, Tim Oates
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

The Marchitecture is a cognitive architecture for autonomous development of representations. The goals of The Marchitecture are domain independence, operating in the absence of knowledge engineering, learning an ontology of parameterized relational concepts, and elegance of design. To this end, The Marchitecture integrates classification, parsing, reasoning, and explanation. The Marchitecture assumes an ample amount of raw data to develop its representations, and it is therefore appropriate for long lived agents.
The surprising almost everywhere convergence of Fourier-Neumann series
Authors: íscar Ciaurri, Juan Luis Varona
Venue: Journal of Computational and Applied Mathematics (2009)

For most orthogonal systems and their corresponding Fourier series, the study of the almost everywhere convergence for functions in L^p requires very complicated research, harder than in the case of the mean convergence. For instance, for trigonometric series, the almost everywhere convergence for functions in L^2 is the celebrated Carleson theorem, proved in 1966 (and extended to L^p by Hunt in 1967). In this paper, we take the system j"n^@a(x)=2(@a+2n+1)J"@a"+"2"n"+"1(x)x^-^@a^-^1,n=0,1,2,... (with J"@m being the Bessel function of the first kind and of the order @m), which is orthonormal in L^2((0,~),x^2^@a^+^1dx), and whose Fourier series are the so-called Fourier-Neumann series. We study the almost everywhere convergence of Fourier-Neumann series for functions in L^p((0,~),x^2^@a^+^1dx) and we show that, surprisingly, the proof is relatively simple (inasmuch as the mean convergence has already been established).
Impact of unknown covariance structures in semiparametric models for longitudinal data: An application to Wisconsin diabetes data
Authors: Jialiang Li, Yingcun Xia, Mari Palta, Anoop Shankar
Venue: Computational Statistics Data Analysis (2009)

Semiparametric models are becoming increasingly attractive for longitudinal data analysis. Often there is lack of knowledge of the covariance structure of the response variable. Although it is still possible to obtain consistent estimators for both parametric and nonparametric components of a semipatrametric model by assuming an identity structure for the covariance matrix, the resulting estimators may not be efficient. We conducted extensive simulation studies to investigate the impact of an unknown covariance structure on estimators in semiparametric models for longitudinal data. In some situations the loss of efficiency could be substantial. A two-step estimator is thus proposed to improve the efficiency. Our study was motivated by a population based data analysis to examine the temporal relationship between systolic blood pressure and urinary albumin excretion.
Properties of Higher Order Nonlinear Diffusion Filtering
Authors: S. Didas, J. Weickert, B. Burgeth
Venue: Journal of Mathematical Imaging and Vision (2009)

This paper provides a mathematical analysis of higher order variational methods and nonlinear diffusion filtering for image denoising. Besides the average grey value, it is shown that higher order diffusion filters preserve higher moments of the initial data. While a maximum-minimum principle in general does not hold for higher order filters, we derive stability in the 2-norm in the continuous and discrete setting. Considering the filters in terms of forward and backward diffusion, one can explain how not only the preservation, but also the enhancement of certain features in the given data is possible. Numerical results show the improved denoising capabilities of higher order filtering compared to the classical methods.
Fast decoding for open vocabulary spoken term detection
Authors: B. Ramabhadran, A. Sethy, J. Mamou, B. Kingsbury, U. Chaudhari
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers (2009)

Information retrieval and spoken-term detection from audio such as broadcast news, telephone conversations, conference calls, and meetings are of great interest to the academic, government, and business communities. Motivated by the requirement for high-quality indexes, this study explores the effect of using both word and sub-word information to find in-vocabulary and OOV query terms. It also explores the trade-off between search accuracy and the speed of audio transcription. We present a novel, vocabulary independent, hybrid LVCSR approach to audio indexing and search and show that using phonetic confusions derived from posterior probabilities estimated by a neural network in the retrieval of OOV queries can help in reducing misses. These methods are evaluated on data sets from the 2006 NIST STD task.
On the Efficiency of a Prefix Path Holistic Algorithm
Authors: Radim Bača, Michal Krátký
Venue: Proceedings of the 6th International XML Database Symposium on Database and XML Technologies (2009)

In recent years, many approaches to XML twig pattern searching have been developed. Holistic approaches such as TwigStack are particularly significant in that they provide a powerful theoretical model for optimal processing of some query types. Holistic algorithms use various partitionings of an XML document called streaming schemes and they prove algorithm optimality depending on query characteristics.In this article, we introduce a variant of the TwigStack algorithm which can work with various streaming schemes. Its efficiency does not deteriorate when the number of streams per query node is increased, as it does in the case of the iTwigJoin algorithm. Since the indices utilized by the iTwigJoin and our algorithm are exactly the same, we can use heuristics to select the appropriate algorithm. The aim of this paper is to show that the prefix path streaming scheme algorithms can be efficient even for documents with many labeled paths.
A Groovy Virtual Drumming Agent
Authors: Axel Tidemann, Pinar Öztürk, Yiannis Demiris
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

This paper presents an architecture for an intelligent virtual agent that imitates human drumming behaviour. Through imitation, the agent models the user-specific variations that constitute the "groove" of the drummer. The architecture comprises a motor system that imitates arm movements of a human drummer, and a sound system that produces the sound of the human playing style. The presence of a sound system alleviates the need to use physical models that will create sound when a drum is struck, instead focusing on creating an imitative agent that booth looks and sounds similar to its teacher. Such a virtual agent can be used in a musical setting, where its visualization and sound system would allow it to be regarded as an artificial musician. The architecture is implemented using Echo State Networks, and relies on self-organization and a bottom-up approach when learning human drum patterns.
Canonical Reduction Systems in Symbolic Mathematics
Authors: Franz Winkler
Venue: Proceedings of the 3rd International Conference on Algebraic Informatics (2009)

Many algorithmic methods in mathematics can be seen as constructing canonical reduction systems for deciding membership problems. Important examples are the Gauss elimination method for linear systems, Euclid's algorithm for computing greatest common divisors, Buchberger's algorithm for constructing Gröbner bases, or the Knuth-Bendix procedure for equational theories. We explain the basic concept of a canonical reduction system and investigate the close connections between these algorithms.
FES Control Design for Paraplegia Using Indoor Rrowing Machine
Authors: Manabu Kosaka, Duncan Wood, Ian Swain
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

Andrews et. al. developed an indoor rowing system for paraplegia using functional electrical stimulation (FES), and presented that the system was safe and healthy for paraplegia. However, the system would be better if the following three terms are improved: Firstly, only quadriceps and hamstrings in both thighs are activated by FES. Secondly, in order to switch FES, it is required to push periodically manual on-off FES switches set on a handle bar at effective timing in every rowing cycle. That might lead to fail to operate switches. Thirdly, constant FES intensity is used, which causes early muscles fatigues and strong pains. This paper describes the following improvements for the system: Firstly, by using gastrocnemius in calf and anterior tibialis in shank in addition, rowing speed is raised. Secondly, switching FES on and off is automated, which avoid failure to operate switches. Thirdly, the intensity for FES is adjusted automatically, which lead to relieve early fatigues and pains.
Three BioNLP tools powered by a biological lexicon
Authors: Yutaka Sasaki, Paul Thompson, John McNaught, Sophia Ananiadou
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics: Demonstrations Session (2009)

In this paper, we demonstrate three NLP applications of the BioLexicon, which is a lexical resource tailored to the biology domain. The applications consist of a dictionary-based POS tagger, a syntactic parser, and query processing for biomedical information retrieval. Biological terminology is a major barrier to the accurate processing of literature within biology domain. In order to address this problem, we have constructed the BioLexicon using both manual and semiautomatic methods. We demonstrate the utility of the biology-oriented lexicon within three separate NLP applications.
UNT-Yahoo: SuperSenseLearner: combining SenseLearner with supersense and other coarse semantic features
Authors: Rada Mihalcea, Andras Csomai, Massimiliano Ciaramita
Venue: Proceedings of the 4th International Workshop on Semantic Evaluations (2007)

We describe the SuperSenseLearner system that participated in the English all-words disambiguation task. The system relies on automatically-learned semantic models using collocational features coupled with features extracted from the annotations of coarse-grained semantic categories generated by an HMM tagger.
Prediction Model of Alga's Growth Based on Support Vector Regression
Authors: Yan Qisheng, Wang Guohua
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

Support vector machines are a kind of novel machine learning methods, which have become the hotspot of machine learning because of their excellent learning performance. A alga’s growth prediction model is established by using support vector regression (SVR) method. The method is illustrated through examples, the results obtained by using SVR method are compared with that from neural network method and the results show that the prediction model based on support vector regression is more accurate and simple than neural network method.
Guest Editors' Introduction: Collaborative Visualization
Authors: Frank van Ham, Fernanda B. Viégas
Venue: IEEE Computer Graphics and Applications (2009)

This article introduces a special issue on collaborative visualization. The articles in this issue present ongoing research, covering topics ranging from prototype systems to the fundamental technical challenges of creating successful collaborative systems.
Tactic-based motion modeling and multi-sensor tracking
Authors: Yang Gu
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

Tracking in essence consists of using sensory information combined with a motion model to estimate the position of a moving object. Tracking efficiency completely depends on the accuracy of the motion model and of the sensory information. For a vision sensor like a camera, the estimation is translated into a command to guide the camera where to look. In this paper, we contribute a method to achieve efficient tracking through using a tactic-based motion model, combined vision and infrared sensory information. We use a supervised learning technique to map the state being tracked to the commands that lead the camera to consistently track the object. We present the probabilistic algorithms in detail and present empirical results both in simulation experiment and from their effective execution in a Segway RMP robot.