ppxscal/embedding_network huggingface dataset:
Using abstraction in Two-Player Games
Authors: Mehdi Samadi, Jonathan Schaeffer, Fatemeh Torabi Asr, Majid Samar, Zohreh Azimifar
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

For most high-performance two-player game programs, a significant amount of time is devoted to developing the evaluation function. An important issue in this regard is how to take advantage of a large memory. For some two-player games, endgame databases have been an effective way of reducing search effort and introducing accurate values into the search. For some one-player games (puzzles), pattern databases have been effective at improving the quality of the heuristic values used in a search. This paper presents a new approach to using endgame and pattern databases to assist in constructing an evaluation function for two-player games. Via abstraction, single-agent pattern databases are applied to two-player games. Positions in endgame databases are viewed as an abstraction of more complicated positions; database lookups are used as evaluation function features. These ideas are illustrated using Chinese checkers and chess. For each domain, even small databases can be used to produce strong game play. This research has relevance to the recent interest in building general game-playing programs. For two-player applications where pattern and/or endgame databases can be built, abstraction can be used to automatically construct an evaluation function.
Ævol: A tool for defining and planning architecture evolution
Authors: David Garlan, Bradley Schmerl
Venue: Proceedings of the 2009 IEEE 31st International Conference on Software Engineering (2009)

Architecture evolution is a key feature of most software systems. There are few tools that help architects plan and execute these evolutionary paths. We demonstrate a tool to enable architects to describe evolution paths, associate properties with elements of the paths, and perform tradeoff analysis over these paths.
Continuous time domain properties of causal cubic splines
Authors: D. Petrinovic
Venue: Signal Processing (2009)

The paper studies the continuous time domain properties of causal cubic splines with equidistant knots. Causality is achieved by truncating the impulse response of the ideal interpolator to a chosen length. The introduced error affects the final interpolation differently depending on the considered output filter formulation. For the first formulation it is the regularity that is sacrificed while for the second it is the interpolation and splitting of unit delay properties. The analysis of the casual cardinal splines' spectra also demonstrates different manifestation of the causality induced error that affects either the pass-band region only or both the pass-band and the stop-band region. The frequency kernel is used for objective comparison of the causal and the non-causal interpolators including the causal piecewise cubic MOMS interpolator. Their relative interpolation performance was determined both for wide-band as well as for oversampled narrow-band input signals. The transparent delay is identified for which the causal and the non-causal interpolators achieve identical band-limited reconstruction accuracy.
Learning Kernel Matrix from Gene Ontology and Annotation Data for Protein Function Prediction
Authors: Yiming Chen, Zhoujun Li, Junwan Liu
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III (2009)

During the last few years, Kernel methods have gained considerable attention for analyzing biological data for protein function prediction. Based on biological processes annotation of Yeast and GO(gene ontology), we constructed a kernel matrix to predict protein functions. We used measurement method about semantic similarity on GO and adaptive Hausdorff distance to successfully obtain protein similarity matrix, and furthermore, transformed protein similarity matrix to a undirected graph. Then, We developed a novel method that can learn optimal diffusion kernel from graph by maximizing kernel-target alignment. Experimental results illustrate that the kernel matrix generated by our formula has larger AUC value than ordinary diffusion kernel and those proposed before. Our method can even learn a common optimal kernel matrix for multiple predict tasks at one run. Furthermore, it can also be directly used to learn from various biolobical networks.
Photo stream segmentation using context information
Authors: Ramesh Jain
Venue:  (2008)

This dissertation defines the problem of photo stream segmentation and proposes solutions to address two technical challenges faced by photo stream segmentation. The first problem is hierarchical photo segmentation. Photo stream segmentation is to group photos into clusters, each of which corresponds to an event. Existing works were focused on flat partitions of photo streams for single user. However, hierarchical structure is preferred. One of the objectives of photo stream segmentation is to help understand and recognize the event that is captured by a photo stream. Thus it is ideal to segment the photo stream into a structure that matches the event's structure. Event is of hierarchical structure. It is therefore natural to segment photo streams in hierarchical structure. Another problem is photo stream segmentation for multi-user. When photos taken by different people are of the same event, this enables opportunities to another interesting research problem, i.e., how to segment photos from multi-user according to events. As photo on-line sharing becomes popular, it is very common to segment photo streams from multiple users. However, this problem has also been ignored. This dissertation addresses the two problems aforementioned. There are two types of information that can be used for photo stream segmentation: content information and context information. This dissertation is focused on investigation of context information usage in photo stream segmentation. Based on context information, hierarchical mixture of Gaussian model is used as a unified framework for photo stream segmentation. It is assumed that all the photos from a photo stream are known i.i.d. samples from a mixture of underlying Gaussian distributions where each component represents an event. EM algorithm is used to learn the model parameters and the Bayesian Information Criterion is used to determine the number of component. We compare the performance when different context information is used which includes not only time, but also location and camera parameters.
Presenting identity in a virtual world through avatar appearances
Authors: Carman Neustaedter, Elena Fedorovskaya
Venue: Proceedings of Graphics Interface 2009 (2009)

One of the first tasks that people must do when entering a virtual world (VW) is create a virtual representation for themselves. In many VWs, this means creating an avatar that represents some desired appearance, whether a reflection of one's real life self, or a different identity. We investigate the variety of ways in which people create and evolve avatar appearances in the VW of Second Life&reg; (SL) through contextual interviews. Our findings reveal that users balance pressures from the societal norms of SL with the need to create an appearance that matches a desired virtual identity. These identity needs differ based on four types of users---Realistics, Ideals, Fantasies, and Roleplayers---where each presents unique challenges for avatar design. Current research tends to focus on the needs of only one of these user types.
Evaluating the Financial Performance of Business Method Patent Owners
Authors: Nancy L. Martin, Peter P. Mykytyn,  Jr.
Venue: Information Systems Management (2009)

Business method patents provide legal protection for the patent owner as well as other possible advantages, but for the most part IS academicians have not examined their effects. In an effort to understand the potential for business method patents to create competitive advantage, this study compares the financial performance of business method patent owners to the performance of their nearest competitors and industries. Although limited in its findings, this study helps to demarcate important issues for future research.
Trust and Electronic Government Success: An Empirical Study
Authors: Thompson Teo, Shirish Srivastava, Li Jiang
Venue: Journal of Management Information Systems (2008)

Electronic government is being increasingly recognized as a means for transforming public governance. Despite this increasing interest, information systems (IS) literature is mostly silent on what really contributes to the success of e-government Web sites. To fill this gap, this study examines the role of trust in e-government success using the updated DeLone and McLean IS success model as the theoretical framework. The model is tested via a survey of 214 Singapore e-government Web site users. The results show that trust in government, but not trust in technology, is positively related to trust in e-government Web sites. Further, trust in e-government Web sites is positively related to information quality, system quality, and service quality. The quality constructs have different effects on "intention to continue" using the Web site and "satisfaction" with the Web site. Post hoc analysis indicates that the nature of usage (active versus passive users) may help us better understand the interrelationships among success variables examined in this study. This result suggests that the DeLone and McLean model can be further extended by examining the nature of IS use. In addition, it is important to consider the role of trust as well as various Web site quality attributes in understanding e-government success.
Interactive Portrait Art
Authors: Ye Ning, Terence Sim
Venue: Proceedings of the 2008 IEEE Workshop on Applications of Computer Vision (2008)

Traditionally, enjoying a portrait art, e.g. the Mona Lisa, is a passive activity. The spectator merely views the painting and admires the brush strokes, composition, etc. But now, with real-time computer vision and graphics algorithms, we can inject interactivity into portrait art, thereby bringing these art works back to life and giving a new dimension to art enjoyment. Specifically, in our art installation, a spectator is allowed to animate the face in a portrait art work to produce any expression she/he likes. The system consists of one personal computer and one camera. Given one frontal portrait picture as input, it generates an animationready avatar with minimum user intervention. The spectator then can perform before the camera any expression. And the system will capture the facial motion of the spectator and retarget the motion to the avatar. The animation of the avatar is rendered back to the original portrait picture. The motion retargetting is done in real-time.
Solution of continuous problems of optimal covering with spheres using optimal set-partition theory
Authors: E. M. Kiseleva, L. I. Lozovskaya, E. V. Timoshenko
Venue: Cybernetics and Systems Analysis (2009)

The paper considers a continuous problem of optimal c-sphere covering of a compact set from ¿ from E n with a given number of spheres of minimum radius and a problem of covering a set with the minimum number of spheres of given radius. Algorithms are proposed and substantiated to solve the problems using optimal set-partition theory and Shor's r-algorithm.
Genetic programming for credit scoring: The case of Egyptian public sector banks
Authors: Hussein A. Abdou
Venue: Expert Systems with Applications: An International Journal (2009)

Credit scoring has been widely investigated in the area of finance, in general, and banking sectors, in particular. Recently, genetic programming (GP) has attracted attention in both academic and empirical fields, especially for credit problems. The primary aim of this paper is to investigate the ability of GP, which was proposed as an extension of genetic algorithms and was inspired by the Darwinian evolution theory, in the analysis of credit scoring models in Egyptian public sector banks. The secondary aim is to compare GP with probit analysis (PA), a successful alternative to logistic regression, and weight of evidence (WOE) measure, the later a neglected technique in published research. Two evaluation criteria are used in this paper, namely, average correct classification (ACC) rate criterion and estimated misclassification cost (EMC) criterion with different misclassification cost (MC) ratios, in order to evaluate the capabilities of the credit scoring models. Results so far revealed that GP has the highest ACC rate and the lowest EMC. However, surprisingly, there is a clear rule for the WOE measure under EMC with higher MC ratios. In addition, an analysis of the dataset using Kohonen maps is undertaken to provide additional visual insights into cluster groupings.
A framework and implementation of information content reasoning in a database
Authors: Xiaochuan Wu, Junkang Feng
Venue: WSEAS Transactions on Information Science and Applications (2009)

Databases' capability is limited in terms of inference. Especially, when users explore information beyond the scope of data within databases, the databases normally cannot provide the information. The underlying reason of the problem is that queries are answered based on a direct match between a query and data (up to aggregations of the data). We observe that it is possible to find information from a database beyond that. To this end, we propose a framework for information content reasoning in a database. A number of basic concepts are defined first. Then we present the framework and explain how it works. Moreover, we describe how such a framework is implemented by means of a prototype including a test with sample queries.
Letters: Adaptive binary tree for fast SVM multiclass classification
Authors: Jin Chen, Cheng Wang, Runsheng Wang
Venue: Neurocomputing (2009)

This paper presents an adaptive binary tree (ABT) to reduce the test computational complexity of multiclass support vector machine (SVM). It achieves a fast classification by: (1) reducing the number of binary SVMs for one classification by using separating planes of some binary SVMs to discriminate other binary problems; (2) selecting the binary SVMs with the fewest average number of support vectors (SVs). The average number of SVs is proposed to denote the computational complexity to exclude one class. Compared with five well-known methods, experiments on many benchmark data sets demonstrate our method can speed up the test phase while remain the high accuracy of SVMs.
Monitoring Online Tests through Data Visualization
Authors: Gennaro Costagliola, Vittorio Fuccella, Massimiliano Giordano, Giuseppe Polese
Venue: IEEE Transactions on Knowledge and Data Engineering (2009)

We present an approach and a system to let tutors monitor several important aspects related to online tests, such as learner behavior and test quality. The approach includes the logging of important data related to learner interaction with the system during the execution of online tests and exploits data visualization to highlight information useful to let tutors review and improve the whole assessment process. We have focused on the discovery of behavioral patterns of learners and conceptual relationships among test items. Furthermore, we have led several experiments in our faculty in order to assess the whole approach. In particular, by analyzing the data visualization charts, we have detected several previously unknown test strategies used by the learners. Last, we have detected several correlations among questions, which gave us useful feedbacks on the test quality.
STARPro --- A new multithreaded direct execution platform for Esterel
Authors: Simon Yuan, Sidharta Andalam, Li Hsien Yoong, Partha S. Roop, Zoran Salcic
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

Esterel programs have traditionally been compiled to software code for general purpose processors or to hardware netlists. This paper, instead, proposes a reactive processor for the direct execution of Esterel. This intermediate approach offers the same flexibility as software compilation, while at the same time, providing much better code size and execution time. The proposed architecture, called STARPro, is a pipelined, multithreaded, reactive processor that provides native support for the direct execution of Esterel. STARPro manages Esterel threads and their scheduling, and also features a hardware preemption unit to assist the handling of the abort constructs in Esterel. In addition to the proposed architecture, we have also developed a new intermediate format called UCCFG"s"d (unrolled concurrent control-flow graph with surface and depth) to represent the structure of an Esterel program in our compiler. UCCFG"s"d closely resembles the Esterel source, and it has also been designed with Esterel hardware support in mind, allowing a straight forward translation into STARPro assembly instructions. We have compared the performance of STARPro against a recent reactive architecture and found an average of 37% speed-up in worst-case reaction times, and 38% in average-case reaction times.
Plenary lecture 5: reduction methods for approximate solution of the singular integro-differential equations in lebesgue spaces
Authors: Iurie Caraus
Venue: Proceedings of the 15th american conference on Applied mathematics (2009)

We obtain the numerical schemes of reduction methods for the approximate solution of the singular integro-differential equations. The equations are defined on the arbitrary smooth closed contour of complex plane. Theoretical background for these methods is proven in Lebesgue spaces.
Teach Yourself VISUALLY Flash CS4 Professional
Authors: Keith Butters
Venue:  (2009)

As the industry standard for creating lively Web animations, Flash allows you to develop animation content or turn existing graphics into dynamic Web page animationsthe CS4 release guarantees a world of new and thrilling possibilities If you are a beginner-to-intermediate level Flash user, this book will get you up and running fast as you dive into the tasks of building animation sequences and using ActionScript to create interactive Web page components Contains 150 useful and interesting Flash tasks presented in full color that demonstrates how easy it can be to design rich and dynamic content for any Web site
Number theoretic algorithms for elliptic curves
Authors: Lawrence Washington
Venue:  (2008)

We present new algorithms related to both theoretical and practical questions in the area of elliptic curves and class field theory. The dissertation has two main parts, as described below. Let O be an imaginary quadratic order of discriminant D < 0, and let K = QD . The class polynomial HD of O is the polynomial whose roots are precisely the j-invariants of elliptic curves with complex multiplication by O . Computing this polynomial is useful in constructing elliptic curves suitable for cryptography, as well as in the context of explicit class field theory. In the first part of the dissertation, we present an algorithm to compute HD p-adically where p is a prime inert in K and not dividing D. This involves computing the canonical lift E&tilde; of a pair (E, f) where E is a supersingular elliptic curve and f is an embedding of O into the endomorphism ring of E. We also present an algorithm to compute HD modulo p for p inert which is used in the Chinese remainder theorem algorithm to compute HD. For an elliptic curve E over any field K, the Weil pairing en is a bilinear map on the points of order n of E. The Weil pairing is a useful tool in both the theory of elliptic curves and the application of elliptic curves to cryptography. However, for K of characteristic p, the classical Weil pairing on the points of order p is trivial. In the second part of the dissertation, we consider E over the dual numbers K[&epsi;] and define a non-degenerate &ldquo;Weil pairing on p-torsion.&rdquo; We show that this pairing satisfies many of the same properties of the classical pairing. Moreover, we show that it directly relates to recent attacks on the discrete logarithm problem on the p-torsion subgroup of an elliptic curve over the finite field Fq . We also present a new attack on the discrete logarithm problem on anomalous curves using a lift of E over Fp [&epsi;].
Chaotic bee swarm optimization algorithm for path planning of mobile robots
Authors: Jiann-Horng Lin, Li-Ren Huang
Venue: Proceedings of the 10th WSEAS international conference on evolutionary computing (2009)

This paper is based on swarm intelligence and chaotic dynamics for learning. We address this issue by considering the problem of path planning for mobile robots. Autonomous systems assume intelligent behavior with ability of dealing in complex and changing environments. Path planning problem, which can be studied as an optimization problem, seems to be of high importance for arising of intelligent behavior for different real-world problem domains. In recent years, swarm intelligence has gained increasingly high interest among the researchers from different domains such as commerce, science and engineering. Bees' warming about their hive is an example of swarm intelligence. It's particularly fitting apply methods inspired by swarm intelligence to sundry optimization problems, and chiefly if the space to be explored is large and complex. In this paper, we propose a new approach to the problem of path planning for mobile robots based on an improved artificial bee colony optimization combined with chaos. In artificial bee colony optimization, chaos is hybridized to form a chaotic bee swarm optimization, which reasonably combines the population-based evolutionary searching ability of artificial bee colony optimization and chaotic searching behavior. The track of chaotic variable can travel ergodically over the whole search space. In general, the chaotic variable has special characters, i.e., ergodicity, pseudo-randomness and irregularity. Generally, the parameters of the artificial bee colony optimization are the key factors to affect the convergence of the artificial bee colony optimization. In fact, however, it cannot ensure the optimization's ergodicity entirely in phase space because they are absolutely random in the traditional artificial bee colony optimization. Therefore, this paper provides a new method that introduces chaotic mapping with certainty, ergodicity and the stochastic property into artificial bee colony optimization so as to improve the global convergence.
Evaluating Inquiry Learning Through Recognition-Based Tasks
Authors: Tom Murray, Kenneth Rath, Beverly Woolf, David Marshall, Merle Bruno, Toby Dragon, Kevin Kohler, Matthew Mattingly
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

The Rashi inquiry learning environment for human biology was evaluated using a new instrument for assessing gains in scientific inquiry skills. The instrument was designed to be sensitive to the small pre-post skill gains that are hypothesized for short learning interventions. It is also designed to be scored with less effort than the verbal protocol analysis methods most often used to asses higher order skills. To achieve these ends the instrument is “item-based”, “recognition-based” and “difference-based.” We describe our assessment design method and results of its first use.