ppxscal/embedding_network huggingface dataset:
The Application of Hidden Markov Model Based on Semantic Case Amelioration in Chinese Word Sense Tagging
Authors: Hao Fang, Yimin Ding, Min Yang
Venue: Proceedings of the 2008 Second International Conference on Genetic and Evolutionary Computing (2008)

Word sense tagging is one of the difficult points in the field of natural language processing. This paper has studied Chinese word sense tagging with the hidden Markov model (HMM) based on semantic case amelioration in order to make use of statistical methods. Firstly,word sense tagging to the real text for application was carried on the HowNet, which is a kind of repository and regards the concept, which represented by words and expressions as the description object to reveal the relations between concepts and the relations between the attributes of the concepts. Secnodly, the semantic standard concept was introduced to make the improvement to one step HMM. Lastly, the linear interpolation algorithm was used to compute the parameters of hidden Markov model based on semantic case amelioration. Finally pretty good experimental results have been achieved.
WhiteWater: Distributed Processing of Fast Streams
Authors: Ioana Stanoi, George Mihaila, Themis Palpanas, Christian Lang
Venue: IEEE Transactions on Knowledge and Data Engineering (2007)

Monitoring systems today often involve continuous queries over streaming data, in a distributed collaborative fashion. The distribution of query operators over a network of processors, and their processing sequence, form a query configuration with inherent constraints on the throughput it can support. In this paper we discuss the implications of measuring and optimizing for output throughput, and its limitations. We propose to use instead the more granular input throughput and a version of throughput measure, the profiled input throughput, that is focused on matching the expected behavior of the input streams. We show how to evaluate a query configuration based on profiled input throughput, and that the problem of finding the optimal configuration is NP-hard. Furthermore, we describe how to overcome the complexity limitation by adapting hill-climbing heuristics to reduce the search space of configurations. We show experimentally that the approach used is not only efficient but also effective.
Finding Minimal Unsatisfiable Cores of Declarative Specifications
Authors: Emina Torlak, Felix Sheng-Ho Chang, Daniel Jackson
Venue: Proceedings of the 15th international symposium on Formal Methods (2008)

Declarative specifications exhibit a variety of problems, such as inadvertently overconstrained axioms and underconstrained conjectures, that are hard to diagnose with model checking and theorem proving alone. Recycling core extraction is a new coverage analysis that pinpoints an irreducible unsatisfiable core of a declarative specification. It is based on resolution refutation proofs generated by resolution engines, such as SAT solvers and resolution theorem provers. The extraction algorithm is described, and proved correct, for a generalized specification language with a regular translation to the input logic of a resolution engine. It has been implemented for the Alloy language and evaluated on a variety of specifications, with promising results.
DOOM: A Decentralized Object-Oriented Machine
Authors: Wim Bronnenberg, Loek Nijman, Eddy Odijk, Rob Van Twist
Venue: IEEE Micro (1987)

Research into the potentials and problems of parallelism led to a new programming language and supportive parallel operating system and architecture. Studies continue on a 100-node prototype.
Advanced Scheme to Reduce IPTV Channel Zapping Time
Authors: Jieun Lee, Geonbok Lee, Seunghak Seok, Byungdeok Chung
Venue: Proceedings of the 10th Asia-Pacific Network Operations and Management Symposium on Managing Next Generation Networks and Services (2007)

IPTV just has been spread throughout the world. For the success of IPTV service, it is a key feature to satisfy QoS (Quality of Services) of total IPTV solution, such as tolerable channel zapping time.A related work reduces IPTV channel zapping time using a home gateway to take a role of IGMP-proxy. However, the related work considers the only case that a subscriber selects an adjacent channel of the current one. Additionally the related work requires a home gateway for each subscriber. In this paper, we propose a solution for the two problems using rating server without home gateway.Settop boxes instead of home gateways join or leave the adjacent channel groups for fast changes to the adjacent channels. Additionally settop boxes obtain an expected channel list by sending a query message to rating server and join the expected multicast groups in advance.
Comparison of Different Integral Performance Criteria for Optimal Hydro Generator Governor Tuning with a Particle Swarm Optimization Algorithm
Authors: Hongqing Fang, Long Chen, Zuyi Shen
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

In this paper, the particle swarm optimization (PSO) algorithm with constriction factor approach (CFA) is proposed to optimal hydro generators governor Proportional-Integral-Derivative (PID) gains for small hydraulic transients. And four different integral performance criteria of turbine speed deviation such as integrated absolute error (IAE), integral of time weighted absolute value of error (ITAE), integral of squared error (ISE) and integral of time weighted squared error (ITSE) have been taken as fitness function respectively, and the differences have been investigated and compared. A step speed disturbance test on no-load operation has been performed. The digital simulation results show that the proposed PSO method has stable convergence characteristic and good computational ability, and it can effectively optimal hydro generators governor PID gains. And the dynamic performance of hydro generators governor system for small hydraulic transients is much better if ITAE criterion is applied as fitness function.
Hadamard Transform Based Fast Partial Distortion Elimination Algorithm for Lossless and Lossy Motion Estimation
Authors: Soonjong Jin, Hyuk Lee, Jechang Jeong
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

In this paper, based on ordered Hadamard transform and its probability model, a fast partial distortion elimination algorithm is proposed. For lossless motion estimation, we propose DC and AC constraints to model local block complexities using ordered Hadamard transform. Using sum of these constraints, we obtain the optimized search order in the matching error calculation by descending order of local constraints. And for the fast search in lossy motion estimation, using probability model which is approximated by the cumulative distribution function of constraints, we also propose the accurate total matching error prediction algorithm without all matching error calculations. The proposed algorithm reduces the motion estimation complexity up to 92.4% for lossy motion estimation and 89.4% for lossless motion estimation on average, compared with the conventional full search algorithm.
Chinese Patent Mining Based on Sememe Statistics and Key-Phrase Extraction
Authors: Bo Jin, Hong-Fei Teng, Yan-Jun Shi, Fu-Zheng Qu
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Recently, key-phrase extraction from patent document has received considerable attention. However, the current statistical approaches of Chinese key-phrase extraction did not realize the semantic comprehension, thereby resulting in inaccurate and partial extraction. In this study, a Chinese patent mining approach based on sememe statistics and key-phrase extraction has been proposed to extract key-phrases from patent document. The key-phrase extraction algorithm is based on semantic knowledge structure of HowNet, and statistical approach is adopted to calculate the chosen value of the phrase in the patent document. With an experimental data set, the results showed that the proposed algorithm had improvements in recall from 62% to 73% and in precision from 72% to 81% compared with term frequency statistics algorithm.
A Stochastic Multi-agent Model of Stem Cell Proliferation
Authors: Richard C. Wath, Pietro Liò
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

In this paper we describe a Multi-Agent framework to model the dynamics of self-renewing and differentiating stem cells. Our model is extended to an effective stochastic simulator of 5-bromo-2-deoxyuridine (BrdU) uptake and loss on different detection thresholds. Matching the results of an existing BrdU model validates our effort. Additionally our Multi-Agent approach enables much more statistically rich analysis and provides a modelling platform stem cell scientists can easily understand and relate to.
Theoretical Analysis of Initial Particle Swarm Behavior
Authors: Sabine Helwig, Rolf Wanka
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

In this paper, particle trajectories of PSO algorithms in the first iteration are studied. We will prove that many particles leave the search space at the beginning of the optimization process when solving problems with boundary constraints in high-dimensional search spaces. Three different velocity initialization strategies will be investigated, but even initializing velocities to zero cannot prevent this particle swarm explosion. The theoretical analysis gives valuable insight into PSO in high-dimensional bounded spaces, and highlights the importance of bound handling for PSO: As many particles leave the search space in the beginning, bound handling strongly influences particle swarm behavior. Experimental investigations confirm the theoretical results.
Ontological Vulnerability Assessment
Authors: Aaron Steele
Venue: Proceedings of the 2008 international workshops on Web Information Systems Engineering (2008)

Vulnerability assessment is a vital part of the risk management process. The accuracy and reliability of calculated risk depends on comprehensive and correct assessment of system vulnerabilities. Current vulnerability assessment techniques fail to consider systems in their entirety and consequently are unable to identify complex vulnerabilities (i.e. those vulnerabilities that are due to configuration settings and unique system environments). Complex vulnerabilities can exist for example when a unique combination of system components are present in a system and configured in such a way that they can be collectively misused to compromise a system.Ontologies have emerged as a useful means for modeling domains of interest. This research shows that taking an ontological approach to vulnerability assessment results in improved identification of complex vulnerabilities. By ontologically modeling the domain of vulnerability assessment, the resulting ontology can be instantiated with a system of interest. The process of instantiating the ontology doubles as a technique for methodically discovering complex vulnerabilities present in the given system. Furthermore, it is suggested that the instantiated ontology will also be able to be queried in order to discover additional complex vulnerabilities present in the system by reasoning through implicit knowledge captured by the instantiated ontology. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Interpolation of the Double Discrete Logarithm
Authors: Gerasimos C. Meletiou, Arne Winterhof
Venue: Proceedings of the 2nd international workshop on Arithmetic of Finite Fields (2008)

The double discrete logarithm has attracted interest as a one-way function in cryptography, in particular in group signature schemes and publicly verifiable secret sharing schemes. We obtain lower bounds on the degrees of polynomials interpolating the double discrete logarithm in multiplicative subgroups of a finite field and in the group of points on an elliptic curve over a finite field, respectively. These results support the assumption of hardness of the double discrete logarithm if the parameters are properly chosen. Similar results for other cryptographic one-way functions including the discrete logarithm, the Diffie-Hellmann mapping and related functions as well as functions related to the integer factoring problem have already been known to the literature. The investigations on the double discrete logarithm in this paper are motivated by these results on other cryptographic functions.
Neuromorphic Quantum-Based Adaptive Support Vector Regression for Tuning BWGC/NGARCH Forecast Model
Authors: Bao Rong Chang, Hsiu Fen Tsai
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

A prediction model, called BPNN-weighted grey model and cumulated 3-point least square polynomial (BWGC), is used for resolving the overshoot effect; however, it may encounter volatility clustering due to the lack of localization property. Thus, we incorporate the non-linear generalized autoregressive conditional heteroscedasticity (NGARCH) into BWGC to compensate for the time-varying variance of residual errors when volatility clustering occurs. Furthermore, in order for adapting both models optimally, a neuromorphic quantum-based adaptive support vector regression (NQASVR) is schemed to regularize the coefficients for both BWGC and NGARCH linearly to improve the generalization and the localization at the same time effectively.
Uses of Contextual Knowledge in Mobile Robots
Authors: D. Calisi, A. Farinelli, G. Grisetti, L. Iocchi, D. Nardi, S. Pellegrini, D. Tipaldi, V. A. Ziparo
Venue: Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing (2007)

In this paper, we analyze work on mobile robotics with the goal of highlighting the uses of contextual knowledge aiming at a flexible and robust performance of the system. In particular, we analyze different robotic tasks, ranging from robot behavior to perception, and then propose to characterize "contextualization" as a design pattern. As a result, we argue that many different tasks indeed can exploit contextual information and, therefore, a single explicit representation of knowledge about context may lead to significant advantages both in the design and in the performance of mobile robots.
Research on Technologies of Spatial Configuration Information Retrieval
Authors: Haibin Sun
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

The problem of spatial configuration information retrieval is a Constraint Satisfaction Problem (CSP), which can be solved using traditional CSP algorithms. But the spatial data can be reorganized using index techniques like R-tree and the spatial data are approximated by their Minimum Bounding Rectangles (MBRs), so the spatial configuration information retrieval is actually based on the MBRs and some special techniques can be studied. This paper studies the mapping relationships among the spatial relations for real spatial objects, the corresponding spatial relations for their MBRs and the corresponding spatial relations between the intermediate nodes and the MBRs in R-tree.
MSSML: A Molecular Spectroscopic Simulations Markup Language for Rovibrational Studies
Authors: Javier Díaz, Sebastián Reyes, Camelia Muñoz-Caro, Alfonso Niño
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

This work presents the development of an XML based language for the standardization of the information needed to build molecular rovibrational hamiltonians. This Molecular Spectroscopic Simulations Markup Language (MSSML) allows to collect and represent in a unified way the information obtained from electronic structure calculations performed in heterogeneous distributed systems, such as Internet-based Grids of computers. This standard language will permit the use of a web (Grid) services approach to automate the simulation of rovibrational spectra.
Improved Adaptive Fingerprint Binarization
Authors: Josef Ström Bartunek, Mikael Nilsson, Jörgen Nordberg, Ingvar Claesson
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

In this paper improvements to a previous work are presented. Removing the redundant artifacts in the fingerprint mask is introduced enhancing the final result. The proposed method is entirely adaptive process adjusting to each fingerprint without any further supervision of the user. Hence, the algorithm is insensitive to the characteristics of the fingerprint sensor and the various physical appearances of the fingerprints. Further, a detailed description of fingerprint mask generation not fully described in the previous work is presented. The improved experimental results are presented.
Zero-Automatic Networks
Authors: Thu-Ha Dao-Thi, Jean Mairesse
Venue: Discrete Event Dynamic Systems (2008)

We continue the study of zero-automatic queues first introduced in Dao-Thi and Mairesse (Adv Appl Probab 39(2):429---461, 2007). These queues are characterized by a special buffering mechanism evolving like a random walk on some infinite group or monoid. The simple M/M/1 queue and Gelenbe's G-queue with positive and negative customers are the two simplest 0-automatic queues. All stable 0-automatic queues have an explicit "multiplicative" stationary distribution and a Poisson departure process (Dao-Thi and Mairesse, Adv Appl Probab 39(2):429---461, 2007). In this paper, we introduce and study networks of 0-automatic queues. We consider two types of networks, with either a Jackson-like or a Kelly-like routing mechanism. In both cases, and under the stability condition, we prove that the stationary distribution of the buffer contents has a "product-form" and can be explicitly determined. Furthermore, the departure process out of the network is Poisson.
UMD_VDT, an Integration of Detection and Tracking Methods for Multiple Human Tracking
Authors: Son Tran, Zhe Lin, David Harwood, Larry Davis
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

We integrate human detection and regional affine invariant feature tracking into a robust human tracking system. First, foreground blobs are detected using background subtraction. The background model is built with a local predictive model to cope with large illumination changes. Detected foreground blobs are then used by a box tracker to establish stable tracks of moving objects. Human detection hypotheses are detected using a combination of both shape and region information through a hierarchical part-template matching method. Human detection results are then used to refine tracks for moving people. Track refinement, extension and merging are carried out with a robust tracker that is based on regional affine invariant features. We show experimental results for the separate components as well as the entire system.
Non-contact Method for Producing Tactile Sensation Using Airborne Ultrasound
Authors: Takayuki Iwamoto, Mari Tatezono, Hiroyuki Shinoda
Venue: Proceedings of the 6th international conference on Haptics: Perception, Devices and Scenarios (2008)

This paper describes a new tactile device which produces stress fields in 3D space. Combined with 3D stereoscopic displays, this device is expected to provide high-fidelity tactile feedback for the interaction with 3D visual objects. The principle is based on a non-linear phenomenon of ultrasound, acoustic radiation pressure. We fabricated a prototype device to confirm the feasibility as a tactile display. The prototype consists of 91 airborne ultrasound transducers packed in the hexagonal arrangement, a 12 channel driving circuit, and a PC. The transducers which were in the same distance from the center of the transducer array were connected to form a 12 channel annular array. The measured total output force within the focal region was 0.8 gf. The spatial resolution was 20 mm. The prototype could produce sufficient vibrations up to 1 kHz.
Postural Control of Two-Stage Inverted Pendulum Using Reinforcement Learning and Self-organizing Map
Authors: Jae-Kang Lee, Tae-Seok Oh, Yun-Su Shin, Tae-Jun Yoon, Il-Hwan Kim
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

This paper considers reinforcement learning control with the self-organizing map. Reinforcement learning uses the observable states of objective system and signals from interaction of the system and environment as input data. For fast learning in neural network training, it is necessary to reduce learning data. In this paper, we use the self-organizing map to partition the observable states. Partitioning states reduces the number of learning data which is used for training neural networks. And neural dynamic programming design method is used for the controller. For evaluating the designed reinforcement learning controller, a double linked inverted pendulum on the cart system is simulated. The designed controller is composed of serial connection of self-organizing map and two Multi-layer Feed-Forward Neural Networks.
Sparse Bayes Machines for Binary Classification
Authors: Daniel Hernández-Lobato
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

In this paper we propose a sparse representation for the Bayes Machine based on the approach followed by the Informative Vector Machine (IVM). However, some extra modifications are included to guarantee a better approximation to the posterior distribution. That is, we introduce additional refining stages over the set of active patterns included in the model. These refining stages can be thought as a backfitting algorithm that tries to fix some of the mistakes that result from the greedy approach followed by the IVM. Experimental comparison of the proposed method with a full Bayes Machine and a Support Vector Machine seems to confirm that the method is competitive with these two techniques. Statistical tests are also carried out to support these results.
Adapting word prediction to subject matter without topic-labeled data
Authors: Keith Trnka
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

Word prediction helps to increase communication rate when using Augmentative and Alternative Communication devices. Basic prediction systems offer topically inappropriate predictions for the context, thus we adapt the predictions to the topic of discourse. However, previous work has relied on texts that are grouped into topics by humans. In contrast, we avoid this restriction by treating each document as a topic. The results are comparable to human-labeled topics and also the method is applicable to unlabeled text.
Model-Driven Simulation of a Maritime Surveillance System
Authors: Martin Monperrus, Fabre Jaozafy, Gabriel Marchalot, Joel Champeau, Brigitte Hoeltzener, Jean-Marc Jézéquel
Venue: Proceedings of the 4th European conference on Model Driven Architecture: Foundations and Applications (2008)

This paper reports an industrial experiment made at Thales to use Model Driven Architecture (MDA) for system engineering. System engineering processes are currently mainly document-centric. The main experiment goal was to study the applicability of MDA at the system engineering level. The experiment consisted of setting up a model-driven simulation environment for a maritime surveillance system. The simulation is achieved thanks to 3 models conform to 3 metamodels. The implementation uses the Eclipse Modeling Framework and is written in the Java Programming language. This pilot project met the deadline, the budget and the threshold of desired functionalities. We report the main advances given by the MDA approach in the context of simulation for system engineering.