ppxscal/embedding_network huggingface dataset:
Speculative improvements to verifiable bounds check elimination
Authors: Andreas Gampe, Jeffery von Ronne, David Niedzielski, Kleanthis Psarris
Venue: Proceedings of the 6th international symposium on Principles and practice of programming in Java (2008)

As a safety measure, the Java programming language requires bounds checking of array accesses. This usually translates to dynamic checks each time an array element is accessed. Static analysis can help eliminate some of those checks by proving them to be redundant, reducing the runtime overhead. Compilation of Java programs is usually method-based, and dynamic dispatch complicates interprocedural analysis. The result is a severely restricted static analysis. This paper presents a novel combination of two techniques to alleviate this problem. By assuming constraints that cannot safely be inferred from the program, the amount of provable safe bounds can be greatly extended. These constraints, called speculations, can be derived automatically from the program code by an analyzer, which assumes that there will be no violation of the array bounds. To ensure that the speculations hold at runtime, additional checks have to be injected into the code. Finding good speculations that benefit the runtime performance can be expensive. This paper shows that the speculation technique can be combined with a verifiable annotation framework, allowing most of the work to be shifted to compile-time. Experimental results show that this combination of techniques increases the number of eliminated bounds checks and can result in speedups that approach unconditional bounds check removal.
An immersed-boundary method for flow-structure interaction in biological systems with application to phonation
Authors: Haoxiang Luo, Rajat Mittal, Xudong Zheng, Steven A. Bielamowicz, Raymond J. Walsh, James K. Hahn
Venue: Journal of Computational Physics (2008)

A new numerical approach for modeling a class of flow-structure interaction problems typically encountered in biological systems is presented. In this approach, a previously developed, sharp-interface, immersed-boundary method for incompressible flows is used to model the fluid flow and a new, sharp-interface Cartesian grid, immersed-boundary method is devised to solve the equations of linear viscoelasticity that governs the solid. The two solvers are coupled to model flow-structure interaction. This coupled solver has the advantage of simple grid generation and efficient computation on simple, single-block structured grids. The accuracy of the solid-mechanics solver is examined by applying it to a canonical problem. The solution methodology is then applied to the problem of laryngeal aerodynamics and vocal fold vibration during human phonation. This includes a three-dimensional eigen analysis for a multi-layered vocal fold prototype as well as two-dimensional, flow-induced vocal fold vibration in a modeled larynx. Several salient features of the aerodynamics as well as vocal fold dynamics are presented.
Pattern Classification in No-Limit Poker: A Head-Start Evolutionary Approach
Authors: Brien Beattie, Garrett Nicolai, David Gerhard, Robert J. Hilderman
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

We have constructed a poker classification system which makes informed betting decisions based upon three defining features extracted while playing poker: hand value, risk, and aggressiveness. The system is implemented as a player-agent, therefore the goals of the classifier are not only to correctly determine whether each hand should be folded, called, or raised, but to win as many chips as possible from the other players. The decision space is found by evolutionary methods, starting from a data-driven initial state. Our results showed that evolving an agent from a data-driven "head-start" position resulted in the best performance over agents evolved from scratch, data-driven agents, random agents, and "always fold" agents.
A Random Effects Sensitivity Analysis for Patient Pathways Model
Authors: Shola Adeyemi, Thierry J. Chaussalet
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

In this paper, we present a random effects approach to modelling of patient pathways with an application to the neonatal unit of a large metropolitan hospital. This approach could be used to identify pathways such as those resulting in high probabilities of death/survival, and to estimate cost of care or length of stay. Patient-specific discharge probabilities could also be predicted as a function of the random effect. We also investigate the sensitivity of our modelling results to random effects distribution assumptions.
Classificators of radio holographic images of airplanes with application of probabilistic neural networks and fuzzy logic
Authors: Milena Kostova, Valerij Djurov
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

Two methods of distinguishing radio holographic images of geometrical signs are presented: Probability Neural Network and Fuzzy Logic. For the purpose we have presented 30 types of airplanes which are separated in two categories - military and transport airplanes. A comparative analysis of the two "intelligent" classificators has also been conducted based on criteria such as accuracy of identification.
A Research on Local Mean in Empirical Mode Decomposition
Authors: Yong-Ping Huang, Xue-Yao Li, Ru-Bo Zhang
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

The conventional Empirical mode decomposition (EMD) method uses envelope mean interpolated by cubic spline fitting, which is sensitive to extrema. An efficient method for finding the local mean is generated by using support vector regression machines. The analysis results indicate that the proposed algorithm has higher performance in the ability of frequency separation, insensitive to the sampling frequency and can eliminate mode mixing in small-amplitude sine waves intermittence.
Empirical results from using custom-made software project control centers in industrial environments
Authors: Marcus Ciolkowski, Jens Heidrich, Frank Simon, Mathias Radicke
Venue: Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement (2008)

One means for institutionalizing project control, systematic quality assurance, and management support on the basis of measurement and explicit models is the establishment of so-called Software Project Control Centers. Nowadays many companies develop their own dashboards for project control or use off-the-shelf tools that provide a predefined functionality. It is not clear how to tailor an existing tool to the specific needs and goals. An engineering-like approach providing the methodological foundation is needed for systematically defining and applying project control mechanisms. "Soft-Pit" is a research project focusing on an improvement-oriented approach for setting up and applying project control mechanisms in a goal-oriented way and evaluating the practical benefits of such a control center. This article describes the results of industrial case studies conducted in the context of the project. Moreover, lessons learned are discussed, related work is described, and future work is presented.
Multi-label Hierarchical Classification of Protein Functions with Artificial Immune Systems
Authors: Roberto T. Alves, Myriam R. Delgado, Alex A. Freitas
Venue: Proceedings of the 3rd Brazilian symposium on Bioinformatics: Advances in Bioinformatics and Computational Biology (2008)

This work proposes two versions of an Artificial Immune System (AIS) - a relatively recent computational intelligence paradigm --- for predicting protein functions described in the Gene Ontology (GO). The GO has functional classes (GO terms) specified in the form of a directed acyclic graph, which leads to a very challenging multi-label hierarchical classification problem where a protein can be assigned multiple classes (functions, GO terms) across several levels of the GO's term hierarchy. Hence, the proposed approach, called MHC-AIS (Multi-label Hierarchical Classification with an Artificial Immune System), is a sophisticated classification algorithm tailored to both multi-label and hierarchical classification. The first version of the MHC-AIS builds a global classifier to predict all classes in the application domain, whilst the second version builds a local classifier to predict each class. In both versions of the MHC-AIS the classifier is expressed as a set of IF-THEN classification rules, which have the advantage of representing comprehensible knowledge to biologist users. The two MHC-AIS versions are evaluated on a dataset of DNA-binding and ATPase proteins.
Linear Equation on Polynomial Single Cycle T-Functions
Authors: Jin-Song Wang, Wen-Feng Qi
Venue: Information Security and Cryptology: Third SKLOIS Conference, Inscrypt 2007, Xining, China, August 31 - September 5, 2007, Revised Selected Papers (2007)

Polynomial functions are widely used in the design of cryptographic transformations such as block ciphers, hash functions and stream ciphers, which belong to the category of T-functions. When a polynomial function is used as state transition function in a pseudorandom generator, it is usually required that the polynomial function generates a single cycle. In this paper, we first present another proof of the sufficient and necessary condition on a polynomial function $f(\mathbf{x})=c_0+c_1\mathbf{x}+c_2\mathbf{x}^2+\cdots+c_m\mathbf{x}^m \bmod 2^n(n \geq 3)$ being a single cycle T-function. Then we give a general linear equation on the sequences {x i } generated by these T-functions, that is, $$ \mathbf{x}_{i+2^{j-1},j}=\mathbf{x}_{i,j}+\mathbf{x}_{i,j-1} +ajA_{i,2}+a(j-1)+b\bmod 2,3\leq j \leq n-1, $$ where A i,2 is a sequence of period 4, a and b are constants determined by the coefficients c i . This equation shows that the sequences generated by polynomial single cycle T-functions have potential secure problems.
Sociocultural influences on computer anxiety among preservice teachers: an exploratory study
Authors: Susan Olson
Venue:  (2008)

Computer anxiety remains a significant problem despite the widespread use of technology. Computer anxiety among preservice teachers is especially problematic because it reduces the effectiveness of computer education and leads to insufficient computer skills and confidence for teachers to integrate computer technology. This study focuses on the influence of sociocultural factors on computer anxiety in a select group of preservice teachers. Literature describing the sociocultural factors influencing computer anxiety using qualitative research methodology is limited. Sociocultural theory, first advanced by Vygotsky (1978), provided a framework for developing questions that focus on how sociocultural factors influence computer anxiety. Using case study research methodology, one main research question and three sub questions were examined: How are typical preservice teachers in a Midwestern metropolitan university with computer anxiety different from those with no computer anxiety and why? (1) How do preservice teachers with computer anxiety compare to preservice teachers with no computer anxiety in life experiences and background in relation to computer use? (2) What is the pattern in the background and experiences of computer use between students with and without computer anxiety? and (3) What sociocultural factors influence computer anxiety? Starting with a group of 115 preservice teachers and using criteria sampling, four cases were selected for further study, two with computer anxiety, and two with no computer anxiety. The cases were studied using interviews, field observations, and student journals. The results were reviewed for each using a map of computer learning experiences within sociocultural environment. The experiences were grouped into four clusters of sociocultural environments: home, school, peers, and others. A cross-case analysis was completed, comparing the two cases with no computer anxiety to the two cases with computer anxiety. Distinctions between the cases with no computer anxiety and the cases with computer anxiety were detailed and possible explanations provided. The study presents evidence that cases with no computer anxiety and those with computer anxiety have differences that appear to be related to differences in social resources within the sociocultural environment of computing. Further study is needed to understand how such social factors are created and maintained and their long range effect.
Mapping the UML2 Semantics of Associations to a Java Code Generation Model
Authors: Dominik Gessenharter
Venue: Proceedings of the 11th international conference on Model Driven Engineering Languages and Systems (2008)

It is state of the art to provide UML modeling by means of class diagrams and code generation from there. But whereas drawing diagrams is most often well supported, code generation is limited in scope. Association classes, multiplicities, aggregation and composition are not correctly or not at all processed by most code generators. One reason may be that the UML semantics is not formally defined in the UML specification. As a result of that, associations are usually transformed into code by using properties of the same type as the associated classes or corresponding typed sets. This approach must fail although the UML2 Superstructure Specification considers association ends owned by a class to be equal to a property of the owning class. In this paper, we describe why associations should be implemented as classes when generating code from class diagrams.
Spectral Clustering and Embedding with Hidden Markov Models
Authors: Tony Jebara, Yingbo Song, Kapil Thadani
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Clustering has recently enjoyed progress via spectral methods which group data using only pairwise affinities and avoid parametric assumptions. While spectral clustering of vector inputs is straightforward, extensions to structured data or time-series data remain less explored. This paper proposes a clustering method for time-series data that couples non-parametric spectral clustering with parametric hidden Markov models (HMMs). HMMs add some beneficial structural and parametric assumptions such as Markov properties and hidden state variables which are useful for clustering. This article shows that using probabilistic pairwise kernel estimates between parametric models provides improved experimental results for unsupervised clustering and visualization of real and synthetic datasets. Results are compared with a fully parametric baseline method (a mixture of hidden Markov models) and a non-parametric baseline method (spectral clustering with non-parametric time-series kernels).
Process Discovery Using Integer Linear Programming
Authors: J. M. Werf, B. F. Dongen, C. A. Hurkens, A. Serebrenik
Venue: Proceedings of the 29th international conference on Applications and Theory of Petri Nets (2008)

The research domain of process discovery aims at constructing a process model (e.g. a Petri net) which is an abstract representation of an execution log. Such a Petri net should (1) be able to reproduce the log under consideration and (2) be independent of the number of cases in the log. In this paper, we present a process discovery algorithm where we use concepts taken from the language-based theory of regions, a well-known Petri net research area. We identify a number of shortcomings of this theory from the process discovery perspective, and we provide solutions based on integer linear programming.
Towards Constructive Homological Algebra in Type Theory
Authors: Thierry Coquand, Arnaud Spiwack
Venue: Proceedings of the 14th symposium on Towards Mechanized Mathematical Assistants: 6th International Conference (2007)

This paper reports on ongoing work on the project of representing the Kenzo system [15] in type theory [11].
Program Schemes with Deep Pushdown Storage
Authors: Argimiro Arratia, Iain A. Stewart
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

Inspired by recent work of Meduna on deep pushdown automata, we consider the computational power of a class of basic program schemes, $\mbox{NPSDS}_s$, based around assignments, while-loops and non- deterministic guessing but with access to a deep pushdown stack which, apart from having the usual push and pop instructions, also has deep-push instructions which allow elements to be pushed to stack locations deep within the stack. We syntactically define sub-classes of $\mbox{NPSDS}_s$ by restricting the occurrences of pops, pushes and deep-pushes and capture the complexity classes NP and PSPACE. Furthermore, we show that all problems accepted by program schemes of $\mbox{NPSDS}_s$ are in EXPTIME.
Minimal Representations for Majority Games
Authors: Josep Freixas, Xavier Molinero, Salvador Roura
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

This paper presents some new results about majority games. Isbell (1959) was the first to find a majority game without a minimum normalized integer representation; he needed 12 voters to construct such a game. Since then, it has been an open problem to find the minimum number of voters of a majority game without a minimum normalized integer representation. Our main new results are:1. All majority games with less than 9 voters have a minimum integer representation.2. For 9 voters, there are 14 majority games without a minimum integer representation, but all these games admit a minimum normalized integer representation.3. For 10 voters, there exist majority games with neither a minimum integer representation nor a minimum normalized integer representation.
Optimal Random Matchings on Trees and Applications
Authors: Jeff Abrahamson, Béla Csaba, Ali Shokoufandeh
Venue: Proceedings of the 11th international workshop, APPROX 2008, and 12th international workshop, RANDOM 2008 on Approximation, Randomization and Combinatorial Optimization: Algorithms and Techniques (2008)

In this paper we will consider tight upper and lower bounds on the weight of the optimal matching for random point sets distributed among the leaves of a tree, as a function of its cardinality. Specifically, given two n sets of points R = {r 1,...,r n } and B = {b 1,...,b n } distributed uniformly and randomly on the m leaves of ¿-Hierarchically Separated Trees with branching factor b such that each of its leaves is at depth ¿, we will prove that the expected weight of optimal matching between R and B is $\Theta(\sqrt{nb}\sum_{k=1}^h(\sqrt{b}\l)^k)$, for h = min (¿,log b n). Using a simple embedding algorithm from ¿ d to HSTs, we are able to reproduce the results concerning the expected optimal transportation cost in [0,1] d , except for d = 2. We also show that giving random weights to the points does not affect the expected matching weight by more than a constant factor. Finally, we prove upper bounds on several sets for which showing reasonable matching results would previously have been intractable, e.g., the Cantor set, and various fractals.
A Note about Total Stability of a Class of Hybrid Systems
Authors: Manuel De la Sen
Venue: Informatica (2006)

Robust stability results for nominally linear hybrid systems are obtained from total stability theorems for purely continuous-time and discrete-time systems. The class of hybrid systems dealt with consists of, in general, coupled continuous-time and digital systems subject to state perturbations whose nominal (i.e., unperturbed) parts are linear and time-varying, in general. The obtained sufficient conditions on robust stability are dependent on the values of the parameters defining the over-bounding functions of the uncertainties and the weakness of the coupling between the analog and digital sub-states provided that the corresponding uncoupled nominal subsystems are both exponentially stable.
Expanding the Plausible Solution Space for Robustness in an Intelligent Tutoring System
Authors: Hameedullah Kazi, Peter Haddawy, Siriwan Suebnukarn
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59 % when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p < 0.05.
Enlightenment? the next generation of Linux desktops
Authors: Jay Kruizenga
Venue: Linux Journal (2008)

Discover E, and unlock the secrets of Enlightenment.
Unidimensional Multiscale Local Features for Object Detection Under Rotation and Mild Occlusions
Authors: Michael Villamizar, Alberto Sanfeliu, Juan Andrade Cetto
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the present one uses a local feature description that allows small changes in the histogram signature, giving robustness to partial occlusions. Local features over the object histogram are extracted during a Boosting learning phase, selecting the most discriminant features within a training histogram image set. The Integral Histogram has been used to compute local histograms in constant time.
Optimizing one-shot learning with binary synapses
Authors: Sandro Romani, Daniel J. Amit, Yali Amit
Venue: Neural Computation (2008)

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.