ppxscal/embedding_network huggingface dataset:
A new perspective on implementation by voting trees
Authors: Felix Fischer, Ariel D. Procaccia, Alex Samorodnitsky
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

Voting trees provide an abstract model of decision-making among a group of individuals in terms of an iterative procedure for selecting a single vertex from a tournament. A family of voting trees is said to implement a given voting rule if for every tournament it chooses according to the rule. While partial results concerning implementable rules and necessary conditions for implementability have been obtained, a complete characterization of voting rules implementable by trees has proven surprisingly hard to find. A prominent rule that cannot be implemented by trees is the Copeland rule, which singles out vertices with maximum degree. In this paper, we suggest a new angle of attack and re-examine the implementability of the Copeland solution using paradigms and techniques at the core of theoretical computer science. We study the extent to which voting trees can approximate the maximum degree, and give upper and lower bounds on the worst-case ratio between the degree of the vertex chosen by a tree and the maximum degree, both for the deterministic model concerned with a single fixed tree, and for randomizations over arbitrary sets of trees. Our main positive result is a randomization over surjective trees of polynomial size that provides an approximation ratio of at least 1/2. The proof is based on a connection between a randomization over caterpillar trees and a rapidly mixing Markov chain.
A Study of Message Reading Efficiency of Color Screen Mobile Phones
Authors: Xuemin Zhang, Bo Wang, Lilin Rao, Bin Yang, Yongna Li, Xueming Lu
Venue: Proceeding of the 2006 conference on Learning by Effective Utilization of Technologies: Facilitating Intercultural Understanding (2006)

This study explored the message reading efficiency of color screen mobile phones from the aspects of screen background and Chinese text message presenting style. Our results showed that although both background and TIPS affect message reading efficiency respectively, these two factors intervene in each other. A suggested optimal combination to expedite message reading is to present text message in a scrolling-by-page style matched with a blank white background. This study may provide value to the design of mobile phone message reading, mobile phone net play and other small screen HCI, such as palmtop computer, electronic dictionary and etc.
Comparing bibliometric statistics obtained from the Web of Science and Scopus
Authors: Éric Archambault, David Campbell, Yves Gingras, Vincent Larivière
Venue: Journal of the American Society for Information Science and Technology (2009)

For more than 40 years, the Institute for Scientific Information (ISI, now part of Thomson Reuters) produced the only available bibliographic databases from which bibliometricians could compile large-scale bibliometric indicators. ISI's citation indexes, now regrouped under the Web of Science (WoS), were the major sources of bibliometric data until 2004, when Scopus was launched by the publisher Reed Elsevier. For those who perform bibliometric analyses and comparisons of countries or institutions, the existence of these two major databases raises the important question of the comparability and stability of statistics obtained from different data sources. This paper uses macrolevel bibliometric indicators to compare results obtained from the WoS and Scopus. It shows that the correlations between the measures obtained with both databases for the number of papers and the number of citations received by countries, as well as for their ranks, are extremely high (R2 &ap; .99). There is also a very high correlation when countries' papers are broken down by field. The paper thus provides evidence that indicators of scientific production and citations at the country level are stable and largely independent of the database. &copy; 2009 Wiley Periodicals, Inc.
Modified Mixture of Experts for Diabetes Diagnosis
Authors: Elif Derya Übeyli
Venue: Journal of Medical Systems (2009)

Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. The major objective of the paper is to be a guide for the readers, who want to develop an automated decision support system for detection of diabetics and subjects having risk factors of diabetes. The purpose was to determine an optimum classification scheme with high diagnostic accuracy for this problem. Several different classification algorithms were tested and their performances in detection of diabetics were compared. The performance of the classification algorithms was illustrated on the Pima Indians diabetes data set. The present research demonstrated that the modified mixture of experts (MME) achieved diagnostic accuracies which were higher than that of the other automated diagnostic systems.
PR-OWL: A Framework for Probabilistic Ontologies
Authors: Paulo C. G. Costa, Kathryn B. Laskey
Venue: Proceeding of the 2006 conference on Formal Ontology in Information Systems: Proceedings of the Fourth International Conference (FOIS 2006) (2006)

Across a wide range of domains, there is an urgent need for a well-founded approach to incorporating uncertain and incomplete knowledge into formal domain ontologies. Although this subject is receiving increasing attention from ontology researchers, there is as yet no broad consensus on the definition of a probabilistic ontology and on the most suitable approach to extending current ontology languages to support uncertainty. This paper presents two contributions to developing a coherent framework for probabilistic ontologies: (1) a formal definition of a probabilistic ontology, and (2) an extension of the OWL Web Ontology Language that is consistent with our formal definition. This extension, PR-OWL, is based on Multi-Entity Bayesian Networks (MEBN), a first-order Bayesian logic that unifies Bayesian probability with First-Order Logic. As such, PR-OWL combines the full representation power of OWL with the flexibility and inferential power of Bayesian logic.
Smoothing curve by orthogonal polynomials
Authors: Sukpak Pansomboon, Pongpan Rattanathanawan, Maitree Podisuk
Venue: Proceedings of the 15th american conference on Applied mathematics (2009)

The interpolation by polynomial of degree n, pn(x), may smooth any curve y = f(x) with the condition that this polynomial must passes through n + 1 points of the curve y = f(x). This condition may make the shape of the curve of y = pn(x) different from the exact shape of curve of y = f(x) especially in the case that the points of f(x) are obtained from an experiment which may not be the exact point of the curve y = f(x). But the least square approximation by orthogonal polynomial, qn(x), may give the better shape than the interpolation by polynomial, pn(x). However the curve of y = qn(x) may not pass through any exact point of the curve y = f(x). M. Podisuk, P. Rattanathanawan and P. Phataranavik in [1] introduced the sequences of orthogonal polynomials with step functions as their weight functions but they did not use them for least square approximation. In this paper, the sequences of orthogonal polynomials with step functions as their weight functions will be used for least square approximation.
Automated tools within workflows for 3D structural construction from surface and subsurface data
Authors: O. Fernandez, S. Jones, N. Armstrong, G. Johnson, A. Ravaglia, J. A. Muñoz
Venue: Geoinformatica (2009)

Mainstream techniques for the construction of 3D geological models frequently rely on high-density subsurface datasets, such as 3D seismic surveys or dense 2D seismic grids. In settings where the available geological and/or geophysical data are relatively sparse, the construction of a consistent 3D structural framework is seldom straightforward and is strongly dependent on the optimization of the use of surface data. To deal with this challenge, workflows for constructing 3D models from sparse surface data have been proposed by multiple authors. This paper presents three automated techniques which optimize the use of data and enhance the time-efficiency of 3D model construction: 3D geological contact analysis, 3D dip-domain construction, and 3D isopach measurement and construction.
Understanding epilepsy seizure structure using tensor analysis
Authors: Bulent Yener
Venue:  (2008)

Data in many disciplines are arranged as two-way datasets; in other words, matrices. However, matrices may not be enough to fully represent the information content of the data and two-way analysis techniques may fail in terms of capturing and interpreting the underlying structure in a dataset. Tensors, on the other hand, represent datasets by preserving their multi-modal structures and tensor decomposition methods, which are mostly based on generalizations of two-way factor models to higher-order datasets, can extract the true underlying structures of the data. In this thesis, we introduce mathematical models based on multi-modal data construction and analysis with a goal of understanding epilepsy seizure dynamics and developing automated and objective approaches for the analysis of large amounts of scalp electroencephalogram (EEG) data. In the first part of this study, we address the problem of identification of a seizure origin through an analysis of ictal EEG, which is proven to be an effective standard in epileptic focus localization. We rearrange multi-channel ictal EEG data as a third-order Epilepsy Tensor with modes: time samples, scales and channels, through continuous wavelet transform. Then we demonstrate that multiway analysis techniques, in particular Parallel Factor Analysis, can successfully model the complex structure of an epilepsy seizure, localize an epileptic seizure origin and extract artifacts. Furthermore, we introduce an approach for removing artifacts using multilinear subspace analysis. In the second part, we focus on seizure recognition and aim to automatically differentiate between seizure and non-seizure periods. We represent multi-channel EEG data using a set of features. These features expected to have distinct trends during seizure and non-seizure periods include features from both time and frequency domains. First, we rearrange multi-channel EEG signals as a third-order tensor called an Epilepsy Feature Tensor with modes: time epochs, features and channels. Second, we model the epilepsy feature tensor using a multilinear regression model, i.e., Multilinear Partial Least Squares, which is the generalization of Partial Least Squares regression to higher-order datasets. This two-step approach facilitates EEG data analysis from multiple channels represented by several features from different domains. We develop patient-specific as well as patient non-specific seizure detection models and obtain promising performance in both approaches.In summary, this thesis demonstrates how multi-channel epileptic EEG signals can be rearranged as multi-modal datasets and how tensor decompositions can be used to mark the seizure period or localize the seizure origin. Nevertheless, in order for these methodologies to be clinically applicable, the performance of the proposed techniques should be tested and enhanced on large datasets containing heterogeneous epileptic patterns and patients.
A Ratio-Based Update Scheme for Mobile Agent Location Management
Authors: Ting Yuan Yeh, Tzong I. Wang
Venue: Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (2009)

A location management scheme is critical for a mobile agent system to trace mobile agents and support on-the-go communications between peer agents. For the time being, most mobile agent location management schemes consist of both a tracking phase and a message delivery phase, and dutifully create or incur plenty of update messages and interaction messages in order to guarantee the effectiveness of the schemes. Excessive transmission of messages not only downgrades the overall performance of mobile agent location management scheme, but also causes network overhead. This paper proposes a ratio-based update scheme that can minimize the overall transmission costs of produced messages by intelligently adjusting the ratio between update messages and forwarding pointers. Experiments are conducted to evaluate the proposed scheme, which reveal that this scheme is general, adaptable, and possessed of higher granularity in the network latency. Moreover, the results also indicate that this scheme is better than previous schemes in optimizing the overall performance.
Proceedings of the 2009 workshop on Resiliency in high performance
Venue: High Performance Distributed Computing (2009)

Recent trends in high-performance computing (HPC) systems have clearly indicated that future increases in performance, in excess of those resulting from improvements in single-processor performance, will be achieved through corresponding increases in system scale, i.e., using a significantly larger component count. As the raw computational performance of the world's fastest HPC systems increases from today's current tera-scale to next-generation peta-scale capability and beyond, their number of computational, networking, and storage components will grow from the ten-to-one-hundred thousand compute nodes of today's systems to several hundreds of thousands of compute nodes and more in the foreseeable future. This substantial growth in system scale, and the resulting component count, poses a challenge for HPC system and application software with respect to fault tolerance and resilience. Furthermore, recent experiences on extreme-scale HPC systems with non-recoverable soft errors, i.e., bit flips in memory, cache, registers, and logic added another major source of concern. The probability of such errors not only grows with system size, but also with increasing architectural vulnerability caused by employing accelerators, such as FPGAs and GPUs, and by shrinking nanometer technology. Reactive fault tolerance technologies, such as checkpoint/restart, are unable to handle high failure rates due to associated overheads, while proactive resiliency technologies, such as preemptive migration, simply fail as random soft errors can't be predicted. Moreover, soft errors may even remain undetected resulting in silent data corruption. The goal of this Workshop is to bring together experts in the area of fault tolerance and resiliency for HPC to present the latest achievements and to discuss the challenges ahead.
Modeling distance and bandwidth dependency of TOA-based UWB ranging error for positioning
Authors: Giovanni Bellusci, Gerard J. M. Janssen, Junlin Yan, Christian C. J. M. Tiberius
Venue: Research Letters in Communications (2009)

A statistical model for the range error provided by TOA estimation using UWB signals is given, based on UWB channel measurements between 3.1 and 10.6 GHz. The range error has been modeled as a Gaussian random variable for LOS and as a combination of a Gaussian and an exponential random variable for NLOS. The distance and bandwidth dependency of both the mean and the standard deviation of the range error has been analyzed, and insight is given in the different phenomena which affect the estimation accuracy. A possible application of themodel to weighted least squares positioning is finally investigated. Noticeable improvements compared to the traditional least squares method have been obtained.
Time-space lower bounds for satisfiability and related problems on randomized machines
Authors: Dieter Van Melkebeek
Venue:  (2008)

Computational complexity studies the resources required for computers to solve certain problems; deciding satisfiability of Boolean formulas is one of the most fundamental such problems. This thesis proves lower bounds on the time and memory space required to solve satisfiability and related problems on randomized machines. We establish the first randomized time-space lower bounds for the problem &Sigma; &ell;SAT of deciding the validity of quantified Boolean formulas with at most &ell;-1 quantifier alternations. We show that for any integer &ell; &ge; 2 and constant c < &ell;, there exists a positive constant d such that &Sigma;&ell;SAT cannot be computed by randomized machines with two-sided error in time nc and space nd. This result also applies to other natural complete problems in the polynomial-time hierarchy, such as maximizing the number of literals that can be removed from a disjunctive normal form formula without changing its meaning. As for &ell; = 1, we argue that similar results for satisfiability or tautologies would follow from a simulation that swaps Arthur and Merlin in two-round Merlin-Arthur games at subquadratic cost. We prove that the latter requires non-black-box techniques: any Arthur-Merlin game requires time &Omega;( t2) to black-box simulate Merlin-Arthur games running in time t. This proves that known simulations of the latter class by the former with quadratic overhead are tight within the black-box setting. In the case of the tautologies problem, we obtain nontrivial time-space lower bounds for randomized machines with one-sided error , i.e., randomized machines that can only err on tautological formulas. In fact, we prove new results for nondeterministic machines solving tautologies, which relates to proof complexity and the NP versus coNP problem: for every c < 34 &ap; 1.587 there exists a positive d such that tautologies cannot be decided by nondeterministic machines in time nc and space nd. We can do better in the one-sided error setting: we prove that for every constant c < 2 cos(&pi;/7) &ap; 1.801, there exists a positive constant d such that tautologies cannot be decided by randomized machines with one-sided error in time nc and space nd.
Building theory in the sciences of the artificial
Authors: Shirley Gregor
Venue: Proceedings of the 4th International Conference on Design Science Research in Information Systems and Technology (2009)

This essay extends Simon's arguments in the Sciences of the Artificial to a critical examination of how theorizing in Information Technology disciplines should occur. The essay is framed around a number of fundamental questions that relate theorizing in the artificial sciences to the traditions of the philosophy of science. Theorizing in the artificial sciences is contrasted with theorizing in other branches of science and the applicability of the scientific method is questioned. The paper argues that theorizing should be considered in a holistic manner that links two modes of theorizing: an interior mode with the how of artifact construction studied and an exterior mode with the what of existing artifacts studied. Unlike some representations in the design science movement the paper argues that the study of artifacts once constructed can not be passed back uncritically to the methods of traditional science. Seven principles for creating knowledge in IT disciplines are derived: (i) artifact system centrality; (ii) artifact purposefulness; (iii) need for design theory; (iv) induction and abduction in theory building; (v) artifact construction as theory building; (vi) interior and exterior modes for theorizing; and (viii) issues with generality. The implicit claim is that consideration of these principles will improve knowledge creation and theorizing in design disciplines, for both design science researchers and also for researchers using more traditional methods. Further, attention to these principles should lead to the creation of more useful and relevant knowledge.
Discrete logarithms for finite groups
Authors: Lee C. Klingler, Spyros S. Magliveras, Fred Richman, Michal Sramka
Venue: Computing (2009)

We present group-theoretic and cryptographic properties of a generalization of the traditional discrete logarithm problem from cyclic to arbitrary finite groups. Questions related to properties which contribute to cryptographic security are investigated, such as distributional, coverage and complexity properties. We show that the distribution of elements in a certain multiset tends to uniform. In particular we consider the family of finite non-abelian groups , p a prime, as possible candidates in the design of new cryptographic primitives, based on our new discrete logarithm.
Image compression via textual substitution
Authors: Bruno Carpentieri
Venue: WSEAS Transactions on Information Science and Applications (2009)

Textual substitution methods, often called dictionary methods or Lempel-Ziv methods, after the important work of Lempel and Ziv, are one-dimensional compression methods that maintain a constantly changing dictionary of strings to adaptively compress a stream of characters by replacing common substrings with indices (pointers) into a dictionary. Lempel and Ziv proved that the proposed schemes were practical as well as asymptotically optimal for a general source model. Two-dimensional (i.e. images) applications of textual substitution methods have been widely studied in the past. Those applications involve first the application of a linearization strategy to the input data, and then the encoding of the resulting monodimensional vector using LZ type one-dimensional methods. More recent strategies blend textual substitution methods with Vector Quantization. In this paper we discuss the textual substitution methods for image compression, with particular attention to the AVQ class of algorithms, and review recent advances in the field.
A behavior based approach to virus detection
Authors: Peter J. Clarke, Yi Deng
Venue:  (2008)

Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown viruses quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replication called self-reference replication, (SR-replication), has been formalized as one main type of replication which specifically replicates by modifying or creating other files on a system to include the virus itself. This replication type was used to detect viruses attempting replication by referencing themselves which is a necessary step to successfully replicate files. The approach does not require a priori knowledge about known viruses. Detection was accomplished at runtime by monitoring currently executing processes attempting to replicate. Two implementation prototypes of the detection approach called SRRAT were created and tested on the Microsoft Windows operating systems focusing on the tracking of user mode Win32 API system calls and Kernel mode system services. The research results showed SR-replication capable of distinguishing between file infecting viruses and benign processes with little or no false positives and false negatives.