ppxscal/embedding_network huggingface dataset:
Fairness in dead-reckoning based distributed multi-player games
Authors: Sudhir Aggarwal, Hemant Banavar, Sarit Mukherjee, Sampath Rangarajan
Venue: Proceedings of 4th ACM SIGCOMM workshop on Network and system support for games (2005)

In a distributed multi-player game that uses dead-reckoning vectors to exchange movement information among players, there is inaccuracy in rendering the objects at the receiver due to network delay between the sender and the receiver. The object is placed at the receiver at the position indicated by the dead-reckoning vector, but by that time, the real position could have changed considerably at the sender. This inaccuracy would be tolerable if it is consistent among all players; that is, at the same physical time, all players see inaccurate (with respect to the real position of the object) but the same position and trajectory for an object. But due to varying network delays between the sender and different receivers, the inaccuracy is different at different players as well. This leads to unfairness in game playing. In this paper, we first introduce an "error" measure for estimating this inaccuracy. Then we develop an algorithm for scheduling the sending of dead-reckoning vectors at a sender that strives to make this error equal at different receivers over time. This algorithm makes the game very fair at the expense of increasing the overall mean error of all players. To mitigate this effect, we propose a budget based algorithm that provides improved fairness without increasing the mean error thereby maintaining the accuracy of game playing. We have implemented both the scheduling algorithm and the budget based algorithm as part of BZFlag, a popular distributed multi-player game. We show through experiments that these algorithms provide fairness among players in spite of widely varying network delays. An additional property of the proposed algorithms is that they require less number of DRs to be exchanged (compared to the current implementation of BZflag) to achieve the same level of accuracy in game playing.
TTTC Newsletter
Venue: IEEE Design Test (2006)

This newsletter provides information on past and upcoming events related to the IEEE Computer Society's Test Technology Technical Council and the test community.
Opportunities and Obligations for Physical Computing Systems
Authors: John A. Stankovic, Insup Lee, Aloysius Mok, Raj Rajkumar
Venue: Computer (2005)

Seamlessly integrating computing with the physical world via sensors and actuators, physical computing systems promise to give society an improved living standard, greater security, and unparalleled convenience and efficiency
Ontology Extraction from Tables on the Web
Authors: Masahiro Tanaka, Toru Ishida
Venue: Proceedings of the International Symposium on Applications on Internet (2006)

Previous works on information extraction from tables make use of prior knowledge such as a cognition model of tables or lexical knowledge bases for specific domains. However, we often need to interpret table structures in each table differently and to treat lexicons in various domains to more fully utilize the broad range of tables available on the Web. The method proposed in this paper uses relations represented by structures to extract an ontology from a table. Once the interpretations of table structures are given by humans, the table structures are automatically generalized to extract relations from the whole table. We define a formal representation of generalized table structure based on the adjacency of cells and iterative structures. As the result of the comparison with a method proposed in a previous work, it was shown that our method is suited to extraction of various relations which are needed for descriptions in RDF/OWL.
An Autonomous Sewer Robots Navigation Based on Stereo Camera Information
Authors: Alireza Ahrary, Li Tian, Sei-ichiro Kamata, Masumi Ishikawa
Venue: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence (2005)

In this paper, we propose a method for an autonomous sewer robots to navigate through a sewer pipe system based on stereo camera information. In this method, a local features such as manholes and pipe joints are extracting as a feature pixels in the Region of Interest (ROI) of left image. Then, an accurate and fast stereo matching measure named Linear Computation is implemented in this ROI image to compute the distance between the robots and local features. Finally, the distance data can be used for navigation map in sewer pipe system. The experimental results show that our method can provide sufficient information for an autonomous sewer robots navigation.
Feature Selection for Building Cost-Effective Data Stream Classifiers
Authors: Like Gao, X. Sean Wang
Venue: Proceedings of the Fifth IEEE International Conference on Data Mining (2005)

A stream classifier is a decision model that assigns a class label to a data stream, based on its arriving data. Various features of the stream can be used in the classifier, each of which may have different relevance to the classification task and different cost in obtaining its value. As time passes by, some less costly features may become more relevant, but the time needed for decision may be considered as a cost. A challenge is how to balance the different costs when building a cost-effective classifier. This paper proposes a new feature selection strategy that extends the traditional Relief algorithm in two aspects: (1) estimate the classification cost associated with each feature, and (2) order all the features with a score that combines both cost estimation and classification relevance. A classifier is then built with the selected features using a traditional classification method. Experimental results show that classifiers constructed with this strategy are indeed cost effective.
Two-Path Limited Speculation Method for Static/Dynamic Optimization in Multithreaded Systems
Authors: Takashi Yokota, Moriyuki Saito, Fumihito Furukawa, Kanemitsu Ootsu, Takanobu Baba
Venue: Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (2005)

Extracting thread-level parallelism, as well as instruction-level one, is greatly expected for modern microprocessors. Speculative multithreading is one of the most hopeful candidates, although, its performance improvement depends on prediction accuracy. On the other hand, profiling techniques can accelerate programs since it can reflect the programs' actual behavior. This paper focuses our discussion on utilizing path-based profiling results in improving speculative multithreaded codes. Programs have many conditional branches thus many possible paths arise, however, actually executed paths are very limited. Thus we show that most frequent two paths are sufficient candidates for prediction and speculation, and we present a novel speculation method, two-path limited speculation. Furthermore, we present a simple path predictor based on two level branch predictor. Analytical and trace-driven evaluation results show the effectiveness of the proposed method.
Applying Text Mining and Machine Learning Techniques to Gene Clusters Analysis
Authors: Debora Maria Rossi de Medeiros, Andre Carlos Ponce de Leon Ferreira de Carvalho
Venue: Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications (2005)

Genomic data clustering has been receiving a growing attention during last years. However, finding the biological meaning of the clusters is still a manual work, which becomes very difficult as the amount of data grows. In this paper, the authors present a few experiments applying Text Mining and Machine Learning techniques to help associating meaning to genes clusters. These experiments were applied to papers abstracts and interaction database data related to Saccaromyces cerevisiae genes both for identifying texts content and for explaining the biological meaning of the genes clusters found. The results were compared to information published by experts in molecular biology and a number of relevant equivalences were found.
Unsupervised learning of arbitrarily shaped clusters using ensembles of Gaussian models
Authors: Hichem Frigui
Venue: Pattern Analysis Applications (2005)

We propose a new clustering algorithm, called SyMP, which is based on synchronization of pulse-coupled oscillators. SyMP represents each data point by an Integrate-and-Fire oscillator and uses the relative similarity between the points to model the interaction between the oscillators. SyMP is robust to noise and outliers, determines the number of clusters in an unsupervised manner, and identifies clusters of arbitrary shapes. The robustness of SyMP is an intrinsic property of the synchronization mechanism. To determine the optimum number of clusters, SyMP uses a dynamic and cluster dependent resolution parameter. To identify clusters of various shapes, SyMP models each cluster by an ensemble of Gaussian components. SyMP does not require the specification of the number of components for each cluster. This number is automatically determined using a dynamic intra-cluster resolution parameter. Clusters with simple shapes would be modeled by few components while clusters with more complex shapes would require a larger number of components. The proposed clustering approach is empirically evaluated with several synthetic data sets, and its performance is compared with GK and CURE. To illustrate the performance of SyMP on real and high-dimensional data sets, we use it to categorize two image databases.
Adaptive Scoring Method based on Freshness for Fresh Information Retrieval
Authors: Minoru UEHARA, Nobuyoshi SATO, Yoshifumi SAKAI
Venue: Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration (2005)

Fresh information is important for real business. In order to realize fresh information retrieval, we need not only to collect documents in a short time, but also to rank the results in the suitable order. However, conventional ranking methods are not suited for fresh information retrieval because they ignore temporal value of information. So, we have proposed the novel ranking method FTF IDF for fresh information retrieval. FTF IDF extends TF IDF by means of using FTF(fresh term frequency) instead of TF(term frequency). FTF differs from TF because FTF decreases as time goes. The speed of decreasing FTF is determined by the dumping factor. The dumping factor is sensitive against small changes of documents. So, we use a threshold to ignore such small changes. In some papers we published, we detect the optimal threshold manually. In this paper, we proposed an adaptive calculating method in order to detect threshold automatically. In this method, the optimal value is determined by iterating to test generated thresholds. In this paper, we describe our method and its evaluation.
Implementing Temporal-Difference Learning with the Scaled Conjugate Gradient Algorithm
Authors: Tasos Falas, Andreas Stafylopatis
Venue: Neural Processing Letters (2005)

This paper investigates the use of the scaled conjugate gradient (SCG) algorithm in temporal-difference (TD) learning for time series prediction. Special emphasis is given on the implementation details, after examining the theoretical background of the algorithm and the learning methodology and how these could be combined. Simple time series (linear, sinusoidal, etc.) as well as more complex ones, coming from real data, are used to examine the behavior of this novel combination of learning algorithm and methodology. Preliminary experimental results indicate that the implementation as presented in this paper indeed works, but the performance (in terms of learning speed and generalization ability) of TD learning using the SCG algorithm is not as good as expected, at least on the representative problems examined. An attempt to rationalize these results is presented.
Al2O3 with metal-nitride nanocrystals as a charge trapping layer of MONOS-type nonvolatile memory devices
Authors: S. Choi, S. S. Kim, H. Yang, M. Chang, S. Jeon, C. Kim, D. Y. Kim, H. Hwang
Venue: Microelectronic Engineering (2005)

The charge trapping characteristics were evaluated for Al2O3 with metal-nitride (TiN) nanocrystals, which was simply prepared by rf magnetron co-sputtering method, as a new charge trapping layer of a metal-oxide-nitride-oxide-silicon-type nonvolatile memory device. Compared with the control sample without TiN nanocrystals, Al2O3 with TiN nanocrystals exhibits larger width of capacitance-voltage hysteresis and higher programming speed. The formation of highly thermally stable TiN nanocrystals embedded in Al2O3 was confirmed by transmission electron microscopy, x-ray photoelectron spectroscopy, and x-ray diffraction analyses.
Detecting Intra-enterprise Scanning Worms based on Address Resolution
Authors: David Whyte, Paul C. van Oorschot, Evangelos Kranakis
Venue: Proceedings of the 21st Annual Computer Security Applications Conference (2005)

Signature-based schemes for detecting Internet worms often fail on zero-day worms, and their ability to rapidly react to new threats is typically limited by the requirement of some form of human involvement to formulate updated attack signatures. We propose an anomaly-based detection technique detailing a method to detect propagation of scanning worms within individual network cells, thus protecting internal networks from infection by internal clients. Our software implementation indicates that this technique is both accurate and rapid enough to enable automatic containment and suppression of worm propagation within a network cell. Our approach relies on an aggregate anomaly score, derived from the correlation of Address Resolution Protocol (ARP) activity from individual network attached devices. Our preliminary analysis and prototype indicate that this technique can be used to rapidly detect zero-day worms within a very small number of scans.
A discrete system simulation package for a mini computer
Authors: C. S. Wallace, D. Harper, R. Hagan
Venue: ACM SIGSIM Simulation Digest (1975)

Although the breadth and quality of the software available for 16-bit mini computers has improved dramatically in recent years, there are few if any mini computers for which a general purpose discrete simulation language or package is available. As many modern minis have sufficient computation power and storage to attack non-trivial simulation problems, this lack is surprising and unfortunate. In this paper, we describe a general purpose discrete simulation package which has been implemented on a Hewlett-Packard 2100A mini computer at Monash University, using Fortran as a host language.
Software usando reconhecimento e s&iacute;ntese de voz: o estado da arte para o Portugu&ecirc;s brasileiro
Authors: Nelson Neto, Ênio Silva, Erick Sousa
Venue: Proceedings of the 2005 Latin American conference on Human-computer interaction (2005)

Speech is a natural interface for human-computer interaction. Speech (or voice) technology is a well-developed field when one considers the international community. There is a wide variety of academic and industrial software. The majority of them assumes a recognizer or synthesizer is available, and can be programmed through an API. In contrast, there are few resources in public domain for Brazilian Portuguese. This work discusses some of these issues and compares SAPI and JSAPI, which are APIs promoted by Microsoft and Sun, respectively. We also present two examples: a tic-tac-toe JSAPI-based game using Portuguese digits recognition and a computer-aided language learning (CALL) application using SAPI-based speech recognition in English and synthesis in Portuguese.
A New Feature Ranking Method in a HMM-Based Handwriting Recognition System
Authors: Sijun Kang, Venu Govindaraju
Venue: Proceedings of the Eighth International Conference on Document Analysis and Recognition (2005)

In this paper we propose a new feature ranking method in a recognition system, by introducing the concept of the effectiveness of the distinguishing power of features and considering the correlation among features. To find the subset of most important features, first the best feature can be identified by its effective distinguishing power and put in an empty feature set. Then each of the remaining features is ranked based on their effective distinguishing capacity contribution and the highest-ranked feature is added to the selected subset. This process is repeated till the performance of the system reaches its peak or the effective distinguishing contribution falls below a certain value. The application of this method to an existing handwriting recognition system showed strong support for our methodology of feature ranking.
Modeling Low Mach Number Reacting Flow with Detailed Chemistry and Transport
Authors: H. N. Najm, O. M. Knio
Venue: Journal of Scientific Computing (2005)

An efficient projection scheme is developed for the simulation of reacting flow with detailed kinetics and transport. The scheme is based on a zero-Mach-number formulation of the compressible conservation equations for an ideal gas mixture. It relies on Strang splitting of the discrete evolution equations, where diffusion is integrated in two half steps that are symmetrically distributed around a single stiff step for the reaction source terms. The diffusive half-step is integrated using an explicit single-step, multistage, Runge---Kutta---Chebyshev (RKC) method. The resulting construction is second-order convergent, and has superior efficiency due to the extended real-stability region of the RKC scheme. Two additional efficiency-enhancements are also explored, based on an extrapolation procedure for the transport coefficients and on the use of approximate Jacobian data evaluated on a coarse mesh. We demonstrate the construction in 1D and 2D flames, and examine consequences of splitting errors. By including the above enhancements, performance tests using 2D computations with a detailed C1C2 methane-air mechanism and a mixture-averaged transport model indicate that speedup factors of about 15 are achieved over the starting split-stiff scheme
A new approach for estimating null value in relational database
Authors: Ching-Hsue Cheng, Jia-Wen Wang
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2006)

In general, a database system will not operate properly if it exist some null values of attributes in the system. In this paper, we propose a new approach to estimate null values in relational database, which utilize other clustering algorithm to cluster data, and use fuzzy correlation and distance similarity to calculate the correlation of different attribute. For verifying our method, this paper utilize mean of absolute error rate (MAER) as evaluation criterion to compare with other methods; it is shown that our proposed method proves importance than the existing methods for estimating null values in relational database systems.
Sprinkler: A Reliable and Energy Efficient Data Dissemination Service for Wireless Embedded Devices
Authors: Vinayak Naik, Anish Arora, Prasun Sinha, Hongwei Zhang
Venue: Proceedings of the 26th IEEE International Real-Time Systems Symposium (2005)

We present Sprinkler, a reliable data dissemination service for wireless embedded devices which are constrained in energy, processing speed, and memory. Sprinkler embeds a virtual grid over the network whereby it can locally compute a connected dominating set of the devices to avoid redundant transmissions ,and a transmission schedule to avoid collisions. Sprinkler transmits O(1) times the optimum number of packets in O(1) of the optimum latency; its time complexity is O(1). Thus, Sprinkler is suitable for resource-constrained wireless embedded devices. We evaluate the performance of Sprinkler in terms of the number of packet transmissions and the latency, both in an outdoor and an indoor environment. Our indoor evaluation is based on an implementation in the Kansei testbed, that houses 210 XSSs whose transmission power is controllable to even low ranges. We compare Sprinkler with the existing reliable data dissemination services, analytically or using simulations also. Our evaluationsshow that Sprinkler is not only energy efficientas compared to existing schemes but also have less latency. Further, the energy consumption of nodes and the latency grows linearly as a function of newly added nodes as network grows larger.
A Rule Reasoning Method and Its Application
Authors: Liu Fuyan, Lv Shaoyi, Chen Chouyong
Venue: Proceedings of the The Fifth International Conference on Computer and Information Technology (2005)

In this paper we present a rule reasoning method, which is based on rough sets theory, for inducing rules from examples. The key idea of the method is that it combines a criterion of the dependency degree of attributes with decision makers' priori knowledge to select attributes of objects. Especially, it uses a compound weights algorithm to perform a proper reduction owing to several reductions that each rule can have and select the most effective attribute subset. As a result, a practical and effective reduced knowledge rule set can be acquired.
A quantitative trust establishment framework for reliable data packet delivery in MANETs
Authors: C. Zouridaki, B. L. Mark, M. Hejmo, R. K. Thomas
Venue: Proceedings of the 3rd ACM workshop on Security of ad hoc and sensor networks (2005)

In mobile ad hoc networks (MANETs), a source node must rely on other nodes to forward its packets on multi-hop routes to the destination. Secure and reliable handling of packets by the intermediate nodes is difficult to ensure in an ad hoc environment. We propose a trust establishment scheme for MANETs which aims to improve the reliability of packet forwarding over multi-hop routes in the presence of potentially malicious nodes. Each node forms an "opinion" about each of the other nodes based on both first and second-hand observation data collected from the network. The opinion metric can be incorporated into ad hoc routing protocols to achieve reliable packet delivery even when a portion of the network exhibits malicious behavior. We present numerical results which demonstrate the effectiveness of the proposed trust establishment scheme.
Graph based multi-modality learning
Authors: Hanghang Tong, Jingrui He, Mingjing Li, Changshui Zhang, Wei-Ying Ma
Venue: Proceedings of the 13th annual ACM international conference on Multimedia (2005)

To better understand the content of multimedia, a lot of research efforts have been made on how to learn from multi-modal feature. In this paper, it is studied from a graph point of view: each kind of feature from one modality is represented as one independent graph; and the learning task is formulated as inferring from the constraints in every graph as well as supervision information (if available). For semi-supervised learning, two different fusion schemes, namely linear form and sequential form, are proposed. For each scheme, it is derived from optimization point of view; and further justified from two sides: similarity propagation and Bayesian interpretation. By doing so, we reveal the regular optimization nature, transductive learning nature as well as prior fusion nature of the proposed schemes, respectively. Moreover, the proposed method can be easily extended to unsupervised learning, including clustering and embedding. Systematic experimental results validate the effectiveness of the proposed method.