ppxscal/embedding_network huggingface dataset:
Physical self-motion facilitates object recognition, but does not enable view-independence
Authors: Wataru Teramoto, Bernhard E. Riecke
Venue: Proceedings of the 4th symposium on Applied perception in graphics and visualization (2007)

It is well known that people have difficulties in recognizing an object from novel views as compared to learned views, resulting in increased response times and/or errors. This so-called view-dependency has been confirmed by many studies. In the natural environment, however, there are two ways of changing views of an object: one is to rotate an object in front of a stationary observer (object-movement), the other is for the observer to move around a stationary object (observer-movement). Note that almost all previous studies are based on the former procedure. Simons et al. [2002] criticized previous studies in this regard and examined the difference between object- and observer-movement directly. As a result, Simons et al. [2002] reported the elimination of this view-dependency when novel views resulted from observer-movement, instead of object-movement. They suggest the contribution of extra-retinal (vestibular and proprioceptive) information to object recognition. Recently, however, Zhao et al. [2007] reported that the observer's movement from one view to another only decreased view-dependency without fully eliminating it. Furthermore, even this effect vanished for rotations of 90&deg; instead of 50&deg;. Larger rotations were not tested. The aim of the present study was to clarify the underlying mechanism of this phenomenon and to investigate larger angles of view change (45-180&deg;, in 45&deg; steps).
Variance estimation over sliding windows
Authors: Linfeng Zhang, Yong Guan
Venue: Proceedings of the twenty-sixth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2007)

Capturing characteristics of large data streams has received considerable attention. The constraints in space and time restrict the data stream processing to only one pass (or a small number of passes). Processing data streams over sliding windows make the problem more difficult and challenging. In this paper, we address the problem of maintaining ∈-approximate variance of data streams over sliding windows. To our knowledge, the best existing algorithm requires O(1/∈2 log N) space, though the lower bound for this problem is Ω(1/∈ log N). We propose the first ∈-approximation algorithm to this problem that is optimal in both space and worst case time. Our algorithm requires O(1/∈ log N) space. Furthermore, its running time is O(1) in worst case.
Mobile Phone as an Inspections Tool--Experiences with a Service for Field Force
Authors: Teemu Jalava, Jukka K. Nurminen
Venue: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02 (2007)

In this paper we investigate the use of mobile phone as an inspection tool for field force workers. The paper presents the architecture and our experiences with the development and use of a pilot solution for Nokia as a point of sale (POS) review tool. The main focus is on dealing with the special issues faced in the mobile environment and on providing the solution as a service. XForms and the underlying messaging framework provided a good interface for the users and transparent connectivity with the server side. The main lessons learned was that mobile phone can be a good inspection tool but needs carefully planned user interface, the system has to have offline functionality in cases where is no network coverage and XForms provided a good solution for making quick changes into the forms when needed.
An unconstrained single stage criterion for blind source separation
Authors: A. Dapena
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 05 (1996)

This paper addresses the problem of separating an unknown linear mixture of signals. A new algorithm is presented to adapt the coefficients of the linear combiners that perform the signal separation. The method is based on the well-known Shalvi and Weinstein (1990) criteria for blind equalization to which a cross-term has been added to prevent the same signal be extracted by several linear combiners simultaneously. The unconstrained criterion stationary points are analysed, the particular choices of the criteria are shown, a stochastic gradient algorithm is derived to compute the optimum separating parameters, and simulation results are presented.
MDL, A Domain-Specific Language for Molecular Dynamics
Authors: Trevor Cickovski, Chris Sweet, Jesus A. Izaguirre
Venue: Proceedings of the 40th Annual Simulation Symposium (2007)

Molecular Dynamics (MD) involves solving Newton's equations of motion for a molecular system and propagating the system by time-dependent updates of atomic positions and velocities. As a severe limitation of molecular dynamics is the size of the timestep used for propagation, a key area of research is the development of efficient propagation algorithms which can maintain accuracy and stability with larger timesteps. We present MDL, an MD domain-specific language with the goals of allowing prototyping, testing and debugging of these algorithms. We illustrate the use of parallelism within MDL to implement the Finite Temperature String Method, and interfacing to visualization and graphical tools.
Discover behavior of Turkish people in Orkut
Authors: Alberto Ochoa-Zezzatti, Javier Martinez, Alberto Hernandez, Jaime Munoz
Venue: Proceedings of the 17th International Conference on Electronics, Communications and Computers (2007)

Orkut is an online community of friends, which hosts over 150000 Turkeys, as the seventh most common nationality on this community. This research analyzes the trends of Orkut memberships by Turkeys and examines the their characteristics as a window into learning more about the youth population of Turkey and their connection with the Internet. This research suggest that Turkeys membership in Orkut rose sharply during the second half of year 2004, but stopped its fase growth after the filtering of network inside Turkey and emergence of competing social networks in early 2005. The demographic data gathered through random sampling of Turkish members of Orkut suggests that the average age of this population is 26 years old, and only 20% of which are married. This population is comprised of 68% male and 32% female. About half (52%) of the population declare themselves as muslim and 8% declare to have other religions, including being spiritual but not religious. Also the data suggests that 19% of the population smoke and 43% drink alcohol with different frequencies. The statistics on the usage and linkage on the Orkut network suggests that individuals have an average of 34 friends on the network and have about 10 fans and 19 scraps on their file. The regression analysis provides a window into smoking, drinking, popularity, and friendship patterns of individuals. Beyond expected relationships, such as lower drinking among muslims, one can see that older and married people have more friends in Orkut, women and those who don't smoke have more fans, people with children tend to drink more, drinking and smoking are closely related, and finally, people of other religions tend to smoke and drink more than non-religious people.
Improved heuristics for the bounded-diameter minimum spanning tree problem
Authors: Alok Singh, Ashok K. Gupta
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2007)

Given an undirected, connected, weighted graph and a positive integer k, the bounded-diameter minimum spanning tree (BDMST) problem seeks a spanning tree of the graph with smallest weight, among all spanning trees of the graph, which contain no path with more than k edges. In general, this problem is NP-Hard for 4 &#x2264;&#x00a0; k <&#x00a0; n &#x2212; 1, where n is the number of vertices in the graph. This work is an improvement over two existing greedy heuristics, called randomized greedy heuristic (RGH) and centre-based tree construction heuristic (CBTC), and a permutation-coded evolutionary algorithm for the BDMST problem. We have proposed two improvements in RGH/CBTC. The first improvement iteratively tries to modify the bounded-diameter spanning tree obtained by RGH/CBTC so as to reduce its cost, whereas the second improves the speed. We have modified the crossover and mutation operators and the decoder used in permutation-coded evolutionary algorithm so as to improve its performance. Computational results show the effectiveness of our approaches. Our approaches obtained better quality solutions in a much shorter time on all test problem instances considered.
The Bio-inspired Service Life-Cycle: An Overview
Authors: Heiko Pfeffer, David Linner, Ilja Radusch, Stephan Steglich
Venue: Proceedings of the Third International Conference on Autonomic and Autonomous Systems (2007)

The increase in mobility and heterogeneity of networked devices raised the need for autonomous service oriented computing environments promising a significant reduction in development and management complexity. Current services underlie a traditional service life cycle, leaving little room for autonomous service behavior, causing that service autonomy is commonly realized through rule-based adaptation mechanisms. Within this paper, a biologically inspired life-cycle is proposed, enabling service evolution in addition to service adaptation. This concept is then extended to the services' design phase, providing the autonomous creation and transformation of services in order to respond to changes in the user's requirements or surrounding and the ability to generate new functionalities potentially not yet requested by to the user1.
Convergence analysis of the delayless subband adaptive filter based on the frequency domain expression
Authors: S. Miyagi
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 01 (2000)

The upper limit of the step size and the variance of error signal for a two-band delayless subband adaptive filter are theoretically evaluated in the frequency domain. The averaging method and the ordinary differential equation (ODE) method are used for this purpose. It is shown that the calculated result of upper limit of the step size has a similar form of that of the fullband LMS adaptive filter. In the ODE analysis, not only the convergence condition for the adaptive filter but also the variance of the error signal can be derived. The proposed method is also applied to a half-band delayless subband adaptive filter. We demonstrate that these theoretical results give good approximations for simulation results.
Accurate numerical derivatives in MATLAB
Authors: L. F. Shampine
Venue: ACM Transactions on Mathematical Software (TOMS) (2007)

Complex step differentiation (CSD) is a technique for computing very accurate numerical derivatives in languages that support complex arithmetic. We describe here the development of a CSD package in MATLAB called PMAD. We have extended work done in other languages for scalars to the arrays that are fundamental to MATLAB. This extension raises questions that we have been able to resolve in a satisfactory way. Our goal has been to make it as easy as possible to compute approximate Jacobians in MATLAB that are all but exact. Although PMAD has a fast option for the expert that implements CSD as in previous work, the default is an object-oriented implementation that asks very little of the user.
Learning nonparametric kernel matrices from pairwise constraints
Authors: Steven C. H. Hoi, Rong Jin, Michael R. Lyu
Venue: Proceedings of the 24th international conference on Machine learning (2007)

Many kernel learning methods have to assume parametric forms for the target kernel functions, which significantly limits the capability of kernels in fitting diverse patterns. Some kernel learning methods assume the target kernel matrix to be a linear combination of parametric kernel matrices. This assumption again importantly limits the flexibility of the target kernel matrices. The key challenge with nonparametric kernel learning arises from the difficulty in linking the nonparametric kernels to the input patterns. In this paper, we resolve this problem by introducing the graph Laplacian of the observed data as a regularizer when optimizing the kernel matrix with respect to the pairwise constraints. We formulate the problem into Semi-Definite Programs (SDP), and propose an efficient algorithm to solve the SDP problem. The extensive evaluation on clustering with pairwise constraints shows that the proposed nonparametric kernel learning method is more effective than other state-of-the-art kernel learning techniques.
An attractor space approach to blind image deconvolution
Authors: Kim-Hui Yap
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03 (2001)

We present a new approach to adaptive blind image deconvolution based on computational reinforced learning in attractor-embedded solution space. A new subspace optimization technique is developed to restore the image and identify the blur. Conjugate gradient optimization is employed to provide an adaptive image restoration while a new evolutionary scheme is devised to generate the high-performance blur estimates. The new technique is flexible as it does not suffer from various image or blur constraints imposed by most traditional blind methods. Experimental results show that the new algorithm is effective in blind deconvolution of images degraded under different blur structures and noise levels.
Stochastic Metrics for Debugging the Timing Behaviour of Real-Time Systems
Authors: Joaquin Entrialgo, Javier Garcia, Jose Luis Diaz, Daniel Fernando Garcia
Venue: Proceedings of the 13th IEEE Real Time and Embedded Technology and Applications Symposium (2007)

Stochastic analysis techniques for real-time systems model the execution time of tasks as random variables. These techniques constitute a very powerful tool to study the behaviour of real-time systems. However, as they can not avoid all the timing bugs in the implementation, they must be combined with measurement techniques in order to gain more confidence in the implemented system. In this paper, a set of tools to measure, analyze and visualize traces of real-time systems is presented. These tools are driven by stochastic models. In order to find bugs in the timing behaviour of the system, two metrics, called "pessimism" and "optimism", are proposed. They are based on two random variables, the optimistic and the pessimistic execution time, which are also introduced in this paper. These metrics are used in the debugging tools to compare the model and the measured system in order to find errors. The metrics are examined in three case studies.
Neighbor search with global geometry: a minimax message passing algorithm
Authors: Kye-Hyeon Kim, Seungjin Choi
Venue: Proceedings of the 24th international conference on Machine learning (2007)

Neighbor search is a fundamental task in machine learning, especially in classification and retrieval. Efficient nearest neighbor search methods have been widely studied, with their emphasis on data structures but most of them did not consider the underlying global geometry of a data set. Recent graph-based semi-supervised learning methods capture the global geometry, but suffer from scalability and parameter tuning problems. In this paper we present a (nearest) neighbor search method where the underlying global geometry is incorporated and the parameter tuning is not required. To this end, we introduce deterministic walks as a deterministic counterpart of Markov random walks, leading us to use the minimax distance as a global dissimilarity measure. Then we develop a message passing algorithm for efficient minimax distance calculation, which scales linearly in both time and space. Empirical study reveals the useful behavior of the method in image retrieval and semi-supervised learning.
Cost-Effectiveness of Storage Grids and Storage Clusters
Authors: A. Brinkmann, S. Effert
Venue: Proceedings of the 15th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (2007)

Grid computing has become a driving force of scientific computing. By combining the resources of distributed computing centers under a unified management and offering their computing power (nearly) as seamless as electrical power, grid computing is seen as an important step to solve large scale-out problems. Storage grids and storage clusters try to transfer this idea into the storage domain: By combining the capacity, computing and communication power of storage appliances under a single middleware, storage grids promise to scale in capacity and bandwidth while keeping the administration overhead low. Futhermore, they claim to be more cost effective than standard storage architectures, because they can be built up from standard components. Inside this paper, we analyze the costs related to the least expensive storage grid architecture, which is build solely from standard server components and we show that it is possible to build large-scale architectures with enterprise functionality from standard hardware.
Computing the Gamma Function Using Contour Integrals and Rational Approximations
Authors: Thomas Schmelzer, Lloyd N. Trefethen
Venue: SIAM Journal on Numerical Analysis (2007)

Some of the best methods for computing the gamma function are based on numerical evaluation of Hankel&rsquo;s contour integral. For example, Temme evaluates this integral based on steepest descent contours by the trapezoid rule. Here we investigate a different approach to the integral: the application of the trapezoid rule on Talbot-type contours using optimal parameters recently derived by Weideman for computing inverse Laplace transforms. Relatedly, we also investigate quadrature formulas derived from best approximations to $\exp(z)$ on the negative real axis, following Cody, Meinardus, and Varga. The two methods are closely related, and both converge geometrically. We find that the new methods are competitive with existing ones, even though they are based on generic tools rather than on specific analysis of the gamma function.
A Network IDS with low false positive rate
Authors: Yan Qiao, Xie Weixin
Venue: Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02 (2002)

An intrusion detection model AINIDS (an artificial immunological network intrusion detection system) based on the biological immune mechanism is given, which consists of two types of components: detectors and monitor agents. The detectors derive from LISYS (a network-based IDS given by Hofmeyr) and have the same advantages as LISYS has such as: distributability, diversity, error tolerant, dynamic defensive, adaptability, and perfectly integrating the anomaly detection techniques with misuse detection techniques, and so on. Three monitor agents in AINIDS provide the co-stimulation signal to the detectors in order to effectively reduce the false positive alarm. These agents monitor whether the integrity, confidentiality, or availability of a crucial computer system is compromised respectively. Since AINIDS adopts a more objective and reasonable co-stimulation mechanism based on the definition of intrusion and the principle of biological immune than LISYS does, it has very low false positive rate. The preliminary experiment results show the effectiveness of our system.
Evolving dependability
Authors: Andy M. Tyrrell, Andrew J. Greensted
Venue: ACM Journal on Emerging Technologies in Computing Systems (JETC) (2007)

Evolvable hardware offers much for the future of complex systems design. Evolutionary techniques not only have the potential for larger solution space coverage, but when implemented on hardware, also allow system designs to adapt to changes in the environment, including failures in system components. This article reviews a number of novel techniques, all based in the field of bio-inspired systems, that provide varying degrees of dependability over and above standard designs. In particular, three different techniques are considered: using FPGAs and ideas from developmental biology to create designs that possess emergent fault-tolerant properties, using FPGAs and continuous evolution to circumvent faults as and when they occur, and, finally, we consider a novel ASIC designed and built with bio-inspired systems in mind.
A Schedule-based Pathfinding Algorithm for Transit Networks Using Pattern First Search
Authors: Ruihong Huang
Venue: Geoinformatica (2007)

The lack of effective and efficient schedule-based pathfinding algorithms for transit networks has limited the application of GIS in transit trip planning services. This paper introduces a schedule-based path finding algorithm for transit networks. Based on a pattern-centered spatiotemporal transit network model, the algorithm searches the network by following route patterns. A pattern is a spatial layout of a route in transit terminology. A route usually has many patterns to serve various locations at different times. This path search algorithm is significantly different from traditional shortest path algorithms that are based on adjacent node search. By establishing a set of lemmas and theorems the paper proves that paths generated by the PFS algorithm are schedule-coordinated fastest paths for trips with given constraints. After analyzing computation and database query complexities of the algorithm the paper indicates that the PFS is efficient in computation and database query. Finally, effectiveness and efficiency of the algorithm are demonstrated by implementations in GIS-based online transit trip planners in Wisconsin, US.
Statistical Equivalent Models for Computer Simulators with an Application to the Random Waypoint Mobility Model
Authors: Kumar Viswanath, Katia Obraczka, Athanasios Kottas, Bruno Sansó
Venue: Simulation (2007)

Statistical Equivalent Models, or SEMs, have recently been proposed as a general approach to study computer simulators. By fitting a statistical model to the simulator's output, SEMs provide an efficient way to quickly explore the simulator's result. In this paper, we develop a SEM for random waypoint mobility, one of the most widely used mobility models employed by network simulators in the evaluation of communication protocols for wireless multi-hop ad hoc networks (MANETs). We chose the random waypoint mobility model as a case study of SEMs due to recent results pointing out some serious drawbacks of the model (e.g., [1]). In particular, these studies show that, under the random waypoint mobility regime, average node speed tends to zero in steady state. They also show that average node speed varies considerably from the expected average value for the time scales under consideration in most simulation analysis. In order to investigate further the behavior of the random waypoint model, we developed a SEM that captured speed decay over time under random waypoint mobility using maximum speed and terrain size as input parameters. A Bayesian approach to model fitting was employed to capture the uncertainty due to unknown parameters of the statistical model. The SEM is given by the posterior predictive distributions of the average node speed as a function of time. A direct result from our model is that, by characterizing average node speed as a function of time, our approach provides an accurate estimate of the &#x201C;warm-up&#x201D; period required by simulations using the random waypoint mobility model. Simulation data from the &#x201C;warmup&#x201D; period can then be discarded to obtain accurate protocol performance results. Given that random waypoint mobility is still, by far, the most widely used mobility model in the evaluation of MANETs, the contribution of this work is potentially significant as it allows network protocol designers to continue to use the original random waypoint mobility model and yet obtain accurate results characterizing MANET protocol performance.