ppxscal/embedding_network huggingface dataset:
Proceedings of the eleventh ACM SIGPLAN international conference on Functional programming
Venue: International Conference on Functional Programming (2006)

This volume contains the papers presented at ICFP 2006, the Eleventh ACM SIGPLAN International Conference on Functional Programming, which took place on September 18-20, 2006 in Portland, Oregon.ICFP 2006 provides a forum for researchers and developers to hear about the latest work on the design, implementations, principles, and uses of functional programming. The conference covers the entire spectrum of work, from practice to theory, including its peripheries. Its scope includes all languages that encourage programming with functions, including both purely applicative and imperative languages, as well as languages with objects and concurrency.There were 74 submissions, with 43 from North America (USA and Canada), 26 from Europe (Belgium, Denmark, France, Germany, Ireland, Italy, the Netherlands, Sweden, and the United Kingdom), 11 from Asia (Japan, Singapore, South Korea, and Taiwan), and 4 from Australia. There were 8 submissions from authors on multiple continents (these papers are counted twice in the breakdown above), and there was 1 submission from authors from multiple countries within a single continent. 11 papers had at least one author from industry. From the 74 submissions, the program committee selected 24 for presentation at the conference.At least three program committee members were responsible for obtaining reviews for each paper. Program committee members were assigned two thirds of their papers for which they were required to write reviews themselves, and one third for which they were allowed to rely on reviews from outside experts. All papers received at least three reviews, and a third of the papers received four or more reviews. Prior to the program committee meeting, the reviews were sent to the authors, who had the opportunity to write a reply of up to 500 words. Based on all of this informaton, papers were selected at a one-and-a-half day program committee meeting that took place at the University of Copenhagen. All program committee members were present at this meeting.
A Data Model and an Algebra for Querying XML Documents
Authors: Giacomo Buratti, Danilo Montesi
Venue: Proceedings of the 17th International Conference on Database and Expert Systems Applications (2006)

In this paper we address the important issue of establishing a formal background for the management of semistructured data. We define a data model and propose an algebra for XML. The algebra, clearly inspired by relational algebra, is quite intuitive; nevertheless it is able to represent most of XQuery expressions.
Analysis of Content Availability Optimization in BitTorrent
Authors: WU Chehai, LU Xianliang, DUAN Hancong
Venue: Proceedings of the 2006 International Conference on Hybrid Information Technology - Volume 01 (2006)

BitTorrent now is one of the most popular Peer-to-Peer content distribution networks. In BitTorrent, peers exchange content pieces with their neighbors on an overlay to distribute content. In order to accelerate the distribution, content availability , the overall disjointness of content pieces among peers on the overlay, should be optimized. In this paper, we investigate the problem of maximizing content availability in BitTorrent. Using computing theory we first prove that this optimization problem is an NP-Optimization problem and so no polynomial time algorithm is available unless P=NP. Further, we employ probability theory to analyze BitTorrent's solution to the problem, which is just a simple random overlay. We find that BitTorrent's random overlay has near-optimal content availability with high probability as well as good adaptivity to the real Internet environment, so we infer that the random overlay is enough for content availability optimization. Finally, a simulation-based comparison with an existing heuristic algorithm is presented to validate our analysis and findings.
An Evolutionary Computational Method for N-Connection Subgraph Discovery
Authors: Enhong Chen, Xujia Chen, Phillip C-Y Sheu, Tieyun Qian
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

The Problem of n-connection subgraph discovery (n-CSDP for short) is to find a small sized subgraph that can well capture the relationship among the n given nodes in a large graph. However there have been very few researches directly addressing the CSDP problem. Furthermore the currently available methods, for example, the electricity analogues based algorithm can only be suitable for tackling the 2-Keynodes CSDP and does not work anymore when n is greater than two. To deal with this problem, we propose an effective approach to discover the subgraph in two stages. In the first stage we propose a neighbor-growth based method to extract a relatively bigger candidate subgraph compared with that of result subgraph. In the second stage an evolutionary algorithm for optimizing the result subgraph is proposed. For this purpose, UTM code, a transformed representation of the adjacent matrix of graphs is designed to encode the topology of subgraph as individuals. Then corresponding evolutionary operators able to be directly performed on UTM code are given. Thus the efficiency of the algorithm is largely improved. The experimental results obtained on two real large scale graphs with different topology characteristics demonstrate that our method solves n-connection subgraph discovery problems effectively.
Modeling and analysis of a generic cross-docking facility
Authors: Ghazi M. Magableh, Manuel D. Rossetti, Scott Mason
Venue: Proceedings of the 37th conference on Winter simulation (2005)

Many companies utilize a number of complex product distribution networks to transport various types of goods. Most of their distribution networks consist of transporting goods from their suppliers to their Distribution Centers (DCs). In the process of transporting goods from suppliers to DCs, cross docking facilities are utilized to consolidate loads in order to minimize costs. A simulation model of a generic cross-docking facility (CF) was developed to examine the operational risks associated with individual CFs within a company's distribution network under a dynamic environment. The model was tested and validated on a large cross-docking facility. In addition, it was used to examine the effect of increasing demand through the cross-docking facility. The model is generic and can easily be expanded to model other cross-docking facilities.
A new subclass of complex-valued S-transform windows
Authors: C. R. Pinnegar
Venue: Signal Processing (2006)

The S-transform is a time-frequency representation whose analyzing function is the product of a fixed Fourier sinusoid with a scalable, translatable window. Thus the S-transform combines elements of wavelet transforms and windowed Fourier transforms. The S-transform can also be generalized to include windows that have frequency-dependent functional form, and frequency-dependent complex phase modulation, essentially giving phase-shifted wavelets which have no semblance at different scales. However, their frequency-dependent shapes can become so complicated that the resulting time-frequency S-spectra are impossible to interpret. In this paper, I define a subclass of complex-valued S-transform windows whose amplitude and phase modulations are connected to each other, making the resulting S-spectra easier to interpret.
Bio-Soft Computing Approach to Elevator Dispatching Problem
Authors: Don Jyh-Fu Jeng, Ikno Kim, Junzo Watada
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

A bio-soft computing method with fixed-length DNA to solve a elevator dispatching problem is presented in this paper. The fixed-length DNA strands are used in representing the nodes and costs, where the costs are varied by the melting temperature of DNA strands. The optimal solution to a 6-story 2-elevator dispatching problem is searched by biochemical techniques based on the thermodynamic properties of designed DNA strands. This research has shown that bio-soft computing has the potential to solve the engineering applications, and could be implemented in the future bio-systems
Serial Position Effects in Auditory Event-related Potentials during Working Memory Retrieval
Authors: Edward J. Golob, Arnold Starr
Venue: Journal of Cognitive Neuroscience (2004)

It is established that recall of an item from a list of sequentially presented items is sensitive to the item's position in the memorized list. However, little is known about the brain mechanisms that mediate these serial position effects. Studies of working memory retrieval using event-related potentials report amplitude reductions during retrieval (auditory cortical N100, neocortical late positive wave [LPW]) as memory load increases. We tested the hypothesis that N100 and LPW amplitudes to probes are also affected by serial position. Event-related potentials were recorded from subjects performing an auditory working memory task. A set of one or five digits was memorized, then subjects classified a probe digit as either present or absent from the memory set. A control task was also given. Amplitudes of the N100 and LPW were reduced in the 5-item versus the 1-item set. In the 5-item set N100 amplitude was significantly larger for the initial (1st) serial position, relative to Positions 2-5, while linear increases in LPW amplitude were seen across serial positions (5th > 1st position). A control task without memorization showed no N100 or LPW amplitude changes with set size or serial position. The findings reveal that the N100 and LPW are influenced differently by serial position during working memory retrieval: N100 shows a primacy effect and LPW demonstrates a recency effect. The results suggest that primacy and recency effects may be mediated by different brain regions at different times during memory retrieval.
An Empirical Study of Using Planning Poker for User Story Estimation
Authors: Nils C. Haugen
Venue: Proceedings of the conference on AGILE 2006 (2006)

Group estimation of user stories is an important part of Extreme Programming (XP), used for both planning releases and iterations. Research has shown that although group estimation in many cases is superior to individual estimation, there is still room for improvement. For instance, group estimation performance can be reduced by dominant personalities and anchoring effects. Through the analysis of 101 user story estimates, made by an XP team for release planning, we investigate whether the introduction of the planning poker estimation process improved the estimation ability of the team. The results show that planning poker improved the team's estimation performance in most cases, but that it increased estimation error in the extreme cases
An Electrophysiological Test of Directed Forgetting: The Role of Retrieval Inhibition
Authors: Markus Ullsperger, Axel Mecklinger, Ulrich Müller
Venue: Journal of Cognitive Neuroscience (2000)

A central issue in the research of directed forgetting is whether the differential memory performance for to-be-remembered (TBR) and to-be-forgotten (TBF) items is solely due to differential encoding or whether retrieval inhibition of TBF items plays an additional role. In this study, recognition-related event-related brain potentials (ERPs) were used to examine this issue. The spatio-temporal distributions of the old/new ERP effects obtained in Experiment 1 that employed a directed forgetting paradigm were compared with those recorded in Experiment 2 in which the level of processing was manipulated. In Experiment 1, participants were instructed to remember or to forget words by means of a cue presented after each word. ERPs recorded in the recognition test revealed early phasic frontal and parietal old/new effects for TBR items, whereas TBF items elicited only a frontal old/new effect. Moreover, a late right-frontal positive slow wave was more pronounced for TBF items, suggesting that those items were associated with a larger amount of post-retrieval processing. In Experiment 2, the same cueing method and the same stimulus materials were used, and memory encoding was manipulated by cueing participants to process the words either deeply or shallowly. Both deeply and shallowly encoded items elicited phasic frontal and parietal old/new effects followed by a late right-frontal positive slow wave. However, in contrast to TBR and TBF items, these effects differed only quantitatively. The results suggest that differential encoding alone cannot account for the effects of directed forgetting. They are more consistent with the view that items followed by an instruction to forget become inhibited and less accessible, and, therefore, more difficult to retrieve.
Exponential stability in p-th mean of solutions, and of convergent Euler-type solutions, of stochastic delay differential equations
Authors: Christopher T. H. Baker, Evelyn Buckwar
Venue: Journal of Computational and Applied Mathematics (2005)

One concept of the stability of a solution of an evolutionary equation relates to the sensitivity of the solution to perturbations in the initial data; there are other stability concepts, notably those concerned with persistent perturbations. Results are presented on the stability in p-th mean of solutions of stochastic delay differential equations with multiplicative noise, and of stochastic delay difference equations. The difference equations are of a type found in numerical analysis and we employ our results to obtain mean-square stability criteria for the solution of the Euler-Maruyama discretization of stochastic delay differential equations.The analysis proceeds as follows: We show that an inequality of Halanay type (derivable via comparison theory) can be employed to derive conditions for p-th mean stability of a solution. We then produce a discrete analogue of the Halanay-type theory, that permits us to develop a p-th mean stability analysis of analogous stochastic difference equations. The application of the theoretical results is illustrated by deriving mean-square stability conditions for solutions and numerical solutions of a constant-coefficient linear test equation.
Development of a Web-based Composition Support System --- Using Graph Clustering Methodologies Applied to an Associative Concepts Dictionary
Authors: Jaeyoung Jung, Maki Miyake, Nobuyasu Makoshi, Hiroyuki Akama
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

In this study, we propose a new web-based language learning system, called Associative Composition Support System (ACSS). Whereas most existing composition systems intend a grammatical, syntactical support, the new system focuses on helping learners write more fluently. This semantic support is contrived on the assumption that different types of lexical association could encourage the vocabulary building and learning in a newly interesting way. ACSS provides associative information on/between words, based on a semantic network built by graph clustering methodologies (MCL by Van Dongen and RMCL by Jung) that are applied to an associative concepts dictionary.
Large-Scale Service Composition in Semantic Service Discovery
Authors: Steffen Bleul, Thomas Weise, Kurt Geihs
Venue: Proceedings of the The 8th IEEE International Conference on E-Commerce Technology and The 3rd IEEE International Conference on Enterprise Computing, E-Commerce, and E-Services (2006)

Self-Healing and self-optimizing service based applications are important steps towards the self-organizing Service Oriented Architectures (SOA). Self-Organizing SOAs replace services by functional equivalent services in the case of faults or in respect of quality of service. These features depend on automatic service discovery which provides service alternatives. We enter the WSC'06 contest to present a semantic service discovery system for large sets of services. A recursive algorithm builds service compositions by adding services in each iteration. The search works backwards, since we add services that produce a certain output regardless of its input parameters. A valid service composition produces a set of queried output parameters and input parameters necessary for the composed services. The algorithm is improved by using efficient data structures in our service composition system.
Postmodern prospects for conceptual modelling
Authors: James Noble, Robert Biddle
Venue: Proceedings of the 3rd Asia-Pacific conference on Conceptual modelling - Volume 53 (2006)

A number of recent developments in software engineering -- from agile methods to aspect-oriented programming to design patterns to good enough software -- share a number of common attributes. These developments avoid a unifying theme or plan, focus on negotiation between different concerns, and exhibit a high level of context sensitivity. We argue that these developments are evidence of a postmodern turn in software engineering. In this paper, we survey a number of these developments and describe their potential implications for the practice of conceptual modelling.
Acceleration Strategies for Gaussian Mean-Shift Image Segmentation
Authors: Miguel A. Carreira-Perpinan
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1 (2006)

Gaussian mean-shift (GMS) is a clustering algorithm that has been shown to produce good image segmentations (where each pixel is represented as a feature vector with spatial and range components). GMS operates by defining a Gaussian kernel density estimate for the data and clustering together points that converge to the same mode under a fixed-point iterative scheme. However, the algorithm is slow, since its complexity is O(kN2), where N is the number of pixels and k the average number of iterations per pixel. We study four acceleration strategies for GMS based on the spatial structure of images and on the fact that GMS is an expectation-maximisation (EM) algorithm: spatial discretisation, spatial neighbourhood, sparse EM and EM-Newton algorithm. We show that the spatial discretisation strategy can accelerate GMS by one to two orders of magnitude while achieving essentially the same segmentation; and that the other strategies attain speedups of less than an order of magnitude.
Image Feature Detection and Matching Based on SUSAN Method
Authors: Weng Muyun, He Mingyi
Venue: Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 1 (2006)

A new approach to image edge detection and feature matching is proposed. In which.. the edge detection employs SUSAN (Small Univalue Segment Assimilating Nucleus) method at low level image processing. And the integration feature matching is based on NMI (Normalized Moment of Inertia) in combination with intensity and configuration information, achieving object recognition and tracking. The feature detection with SUSAN method locates precisely and not sensibly for local noise. NMI of image has translation invariability, rotation invariability and scale invariability. The simulated experimental results show that the algorithm is efficient for intensity variety, geometry aberration and some noise images.
Forming Successful eXtreme Programming Teams
Authors: Alan Gray, Andrew Jackson, Ioanna Stamouli, Shiu Lun Tsang
Venue: Proceedings of the conference on AGILE 2006 (2006)

XP is a lightweight process that provides principles for guiding projects and relies on the participants for its success. However, despite these guidelines, projects can be unsuccessful. We have previously identified that participants' levels of "buy-in" into the ethos of the methodology is a significant determinant of project success. This paper investigates how team formation influences buy-in and how buy-in, in turn, effects success, learning and working attitude within an academic environment. The empirical study shows that teams comprised of people with similar understandings of, and attitudes towards, the process tend to have a higher level of buy-in and, as a result, exceed their expected performance potential.
A scalable method for online learning of non-linear preferences based on anonymous negotiation data
Authors: D. J. A. Somefun, J. A. La Poutré
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

We consider the problem of a shop agent negotiating bilaterally with many customers about a bundle of goods or services together with a price. To facilitate the shop agent's search for mutually beneficial alternative bundles, we develop a method for online learning customers' preferences, while respecting their privacy. By introducing extra parameters, we represent customers' highly nonlinear preferences as a linear model. We develop a method for learning the underlying stochastic process of these parameters online.
Nonlinear system identification with pseudorandom multilevel excitation sequences
Authors: R. D. Nowak, B. D. Van Veen
Venue: Proceedings of the Acoustics, Speech, and Signal Processing, 1993. ICASSP-93 Vol 4., 1993 IEEE International Conference on - Volume 04 (1993)

The authors consider pseudorandom multilevel sequences (PRMS) for the identification of nonlinear systems modeled via a truncated Volterra series with a finite degree of nonlinearity and finite memory length. It is shown that PRMS are persistently exciting (PE) for a Volterra series model with nonlinearities of polynomial degree N if and only if the sequence takes on N+1 or more distinct levels. A computationally efficient least squares identification algorithm based on PRMS inputs is developed that avoids forming the inverse of the data matrix. Simulation results comparing identification accuracy using PRMS and Gaussian white noise are given.
Adaptive Appearance Based Face Recognition
Authors: Qi Li, Jieping Ye, Min Li, Chandra Kambhamettu
Venue: Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence (2006)

In this paper, we present an adaptive appearance based face recognition framework that combines the efficiency of global approaches and the robustness of local approaches together. The framework uses a novel eye locator to select an appropriate scheme for appearance based recognition. The eye locator first locates eye candidates via a new strength assignment, determined by the dissimilarity between the local appearance of an image point and the appearance of its neighboring points. Then the eye locator applies a simple but flexible model (half-circle snake) to the local context of the eye candidates in order to either refine the location of an eye candidate or discard non-eye candidates. We show the performance of our framework by testing on challenging face datasets containing extreme expressions, severe occlusions, and varied lighting conditions.
Random Weighting Estimation of White Noise Error Characteristic in Integrated INS/GPS/SAR System
Authors: Shesheng Gao, Zhihua Feng, Huaxing Li
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

Random weighting estimation is an emergent algorithmic method in statistic, which is firstly applied to the research of navigation system in this paper. An algorithm is presented for on-line random weighting estimating the white noise errors of integrated INS/GPS/SAR navigation system. Secondly, The Random weighting estimation is presented in terms of variance and drift rate parameters, and the method applied to estimate the white noise errors of INS/GPS/SAR navigation system is proved to be effective. Research result shows that the random weighting estimation has a better performance than other statistic estimation methods.