ppxscal/embedding_network huggingface dataset:
Evaluations of Automatic Speaker Classification Systems
Authors: Alvin F. Martin
Venue: Speaker Classification I: Fundamentals, Features, and Methods (2007)

The annual NIST Speaker Recognition Evaluations (SREs) from 1996 to 2006 have been internationally recognized as the leading source or performance evaluation of research systems in the speaker classification field. We discuss how these evaluations have developed and been conducted and the performance measures used. We consider the key factors that have been studied for their effect on performance, including training and test durations, channel variability, and speaker variability. We examine the extent to which progress has been observed in state-of-the-art performance. We also consider how the technology has changed over the past decade, other evaluations that have been conducted or planned, and where the field may be headed in the future.
A Bottom-Up Approach to Clausal Tableaux
Authors: Nicolas Peltier
Venue: Proceedings of the 16th international conference on Automated Reasoning with Analytic Tableaux and Related Methods (2007)

We present a new proof procedure for first-order logic. It is close in spirit to the usual tableaux-based procedures, but uses a more compact representation of the search space. Roughly speaking, it constructs the tableau from the leaves to the root, and tries to factorize common subtrees when possible.We study the complexity of our procedure for several propositional classes and we show that it is polynomial for all these classes.
Adding robustness to nominal output-feedback controllers for uncertain nonlinear systems: A nonlinear version of disturbance observer
Authors: Juhoon Back, Hyungbo Shim
Venue: Automatica (Journal of IFAC) (2008)

The linear disturbance observer (DOB) approach has been successfully employed as a tool for robust control and disturbance rejection in practice. In this paper, we present a nonlinear DOB which inherits all the benefits of the classical linear DOB approach. In particular, we prove that the proposed nonlinear DOB recovers not only the steady-state performance but also the transient performance of the nominal closed-loop system under plant uncertainties and input disturbances. One novel feature of the proposed controller is that it is an add-on type inner-loop output-feedback controller; thus any type of outer-loop controller can be combined to enhance the closed-loop system performance. Our result is a semi-global one and the stability proof is given when the nonlinear system has a well-defined relative degree with a stable zero dynamics.
ETA: Experience with an Intel Xeon Processor as a Packet Processing Engine
Authors: Greg Regnier, Dave Minturn, Gary McAlpine, Vikram A. Saletore, Annie Foong
Venue: IEEE Micro (2004)

As part of an effort to accelerate server TCP/IP networking, Intel R&D has developed a software prototype that uses one of the intel xeon processors in a multiprocessor server as a packet processing engine (PPE). This prototype serves as a vehicle for empirical measurement and analysis of a highly programmable PPE that is closely tied to the server's core CPU and memory complex.
English-to-French CLIR: A Knowledge-Light Approach through Character N-Grams Alignment
Authors: Jesús Vilares, Michael P. Oakes, Manuel Vilares
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

This paper describes an extension of our work presented in the robust English-to-French bilingual task of the CLEF 2007 workshop, a knowledge-light approach for query translation in Cross-Language Information Retrieval systems. Our work is based on the direct translation of character n-grams, avoiding the need for word normalization during indexing or translation, and also dealing with out-of-vocabulary words. Moreover, since such a solution does not rely on language-specific processing, it can be used with languages of very different nature even when linguistic information and resources are scarce or unavailable. The results obtained have been very positive, and support the findings from our previous English-to-Spanish experiments.
Alambic: a privacy-preserving recommender system for electronic commerce
Authors: Esma A&#x00ef;meur, Gilles Brassard, Jos&#x00e9; M. Fernandez, Flavien Serge Mani Onana
Venue: International Journal of Information Security (2008)

Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. Customers should be able to keep private their personal information, including their buying preferences, and they should not be tracked against their will. The commercial interests of merchants should also be protected by allowing them to make accurate recommendations without revealing legitimately compiled valuable information to third parties. We introduce a theoretical approach for a system called Alambic, which achieves the above privacy-protection objectives in a hybrid recommender system that combines content-based, demographic and collaborative filtering techniques. Our system splits customer data between the merchant and a semi-trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system could only be subverted by a coalition between these two parties.
Revelator's Complex Adaptive Reasoning Methodology for Resource Infrastructure Evolution
Authors: Mary Keeler, Arun Majumdar
Venue: Proceedings of the 16th international conference on Conceptual Structures: Knowledge Visualization and Reasoning (2008)

Countless archives around the world, including C.S. Peirce's manuscripts at Harvard, wait for an infrastructure that makes possible the collaborative work to re-create them as "e-resources." Meanwhile, the U.S. National Science Foundation's new Cyberinfrastructure Initiative, calls for deeper understanding of infrastructure, enabling the engagement of researchers as participants in sustained e-resource development. Revelator is a game conceived as a methodology for that collaborative participation, in an evolving infrastructure of Conceptual Structures technology. Our conception of Revelator's game context derives from C. S. Peirce's theory of knowledge evolution in semeosis and his pragmatic methodology for inquiry, and incorporates J.H. Holland's complex adaptive systems modeling. Revelator's role is to engage participants in "the game of inquiry," which reveals significant patterns and paths structured by complex logical relations among the conditional propositions that represent players' conjectures as plays in the game. Revelator offers a general methodology for building trustworthy collaborative research, in an evolving infrastructure of knowledge technology and e-resources.
Personalized Learning Using Ontologies and Semantic Web Technologies
Authors: Maria Vargas-Vera, Miltiadis Lytras
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

This paper presents an in-depth analysis of the semantic web contribution towards personalization in e-learning content exploitation. The aim of this research work is to develop a framework for analyzing the semantic web and ontological issues related with the design and implementation of high performance e-learning systems enabled by advanced semantic web and ontological engineering. Within this context the concept of personalization is linked to the state of the art of semantic web and Ontologies research. The main emphasis is paid to the management of personal profiles and identities.
On the importance of migration for fairness in online grid markets
Authors: Lior Amar, Ahuva Mu'alem, Jochen Stößer
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Computational grids offer users a simple access to tremendous computer resources for solving large scale computing problems. Traditional performance analysis of scheduling algorithms considers overall system performance while fairness analysis focuses on the individual performance each user receives. Until recently, only few grids and cluster systems provided preemptive migration (e.g. [2]), which is the ability of dynamically moving computational tasks across machines during runtime. The emergent technology of virtualization (e.g. [4]) provides off-the-shelf support for migration, thus making the use of this feature more accessible (even across different OS's). In this paper, we study the close relation between migration and fairness. We present fairness and quality of service properties for economic online scheduling algorithms. Under mild assumptions we show that it is impossible to achieve these properties without the use of migration. On the other hand, if zero cost migration is used, then these properties can be satisfied.
The Frobenius Problem and Its Generalizations
Authors: Jeffrey Shallit
Venue: Proceedings of the 12th international conference on Developments in Language Theory (2008)

Let x 1, x 2, ..., x n be positive integers. It is well-known that every sufficiently large integer can be represented as a non-negative integer linear combination of the x i if and only if $\gcd(x_1, x_2, \ldots, x_n) = 1$. The Frobenius problem is the following: given positive integers x 1, x 2, ..., x n with $\gcd(x_1, x_2, \ldots, x_n) = 1 $, compute the largest integer not representable as a non-negative integer linear combination of the x i . This largest integer is sometimes denoted g(x 1,..., x n ).
A Model for Trust Metrics Analysis
Authors: Isaac Agudo, Carmen Fernandez-Gago, Javier Lopez
Venue: Proceedings of the 5th international conference on Trust, Privacy and Security in Digital Business (2008)

Trust is an important factor in any kind of network essential, for example, in the decision-making process. As important as the definition of trust is the way to compute it. In this paper we propose a model for defining trust based on graph theory and show examples of some simple operators and functions that will allow us to compute trust.
Predicting Forensic Admission among the Mentally Ill: A Bayesian Approach
Authors: Erkki JO Soini, Tarja Rissanen, Jari Tiihonen, Markku Eronen, Sheilagh Hodgins, Olli-Pekka Ryynänen
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

Our objective was to explore protective and risk factors for a later forensic admission among the mentally ill in a multinational setting. Between 1998-2000, 308 forensic and general psychiatry patients were recruited for a case-control study in four countries. A greedy Bayesian algorithm was utilized to search for generalized factors from datasets and create a merger model (naive Bayesian fusion) using a new type of triangulation. The evidence were assessed with various measures and the data included independent training and test sets. The most influential risk factors included violent crime prior to the crime that lead to the index hospitalization (PO 11.8, 95% CrI 5.9 to 30.3), conviction of the biological father (9.4, 4.7 to 20.0), and no use of psychotropic medications before the age of 18 (7.7, 4.3 to 16.5). The merger model indicated high discriminative power, robustness and accuracy.
Type-2 implications on non-interactive fuzzy truth values
Authors: Zsolt Gera, József Dombi
Venue: Fuzzy Sets and Systems (2008)

In this paper we consider algebras of fuzzy truth values equipped with an implication operation. Generalizing the definition of type-1 fuzzy implications, we discuss extended fuzzy S-implications and coimplications and also extended residual implications and coimplications, in particular to the extended residual of minimum. We study the algebraic properties of these operations and their relationships to extended t-norms and t-conorms in particular to meet and join. These investigations are intended to provide a theoretical background for type-2 approximate reasoning applications.
Performance of the census UNIVAC system
Authors: J. L. McPherson, S. N. Alexander
Venue: Papers and discussions presented at the Dec. 10-12, 1951, joint AIEE-IRE computer conference: Review of electronic digital computers (1951)

In June of 1948 the National Bureau of Standards acting on behalf of the Bureau of the Census contracted with the Eckert-Mauchly Computer Corporation for a UNIVAC System. This UNIVAC System, now generally known as the Census UNIVAC System, was accepted on March 30, 1951, and since that time has been devoted almost exclusively to tabulating results of the 1950 Census of Population and Housing.
Zero-order TSK-type fuzzy system learning using a two-phase swarm intelligence algorithm
Authors: Chia-Feng Juang, Chiang Lo
Venue: Fuzzy Sets and Systems (2008)

This paper proposes zero-order Takagi-Sugeno-Kang (TSK)-type fuzzy system learning using a two-phase swarm intelligence algorithm (TPSIA). The first phase of TPSIA learns fuzzy system structure and parameters by on-line clustering-aided ant colony optimization (ACO). Phase two aims to further optimize all of the free parameters in the fuzzy system using particle swarm optimization (PSO). In clustering-aided ACO (CACO), fuzzy system structure is learned through on-line clustering. Once a new rule is generated by clustering, the consequent is selected from a discrete set of candidate values by ACO. In ACO, the path of an ant is regarded as a combination of consequent values selected from every rule. CACO helps to locate good initial fuzzy systems for subsequent phase learning. In Phase two, initial particles in PSO are randomly generated according to the best solution found by CACO. All free parameters in the designed fuzzy system are optimally tuned by PSO. Simulations on fuzzy control of three nonlinear plants are conducted to verify TPSIA performance. Comparisons with other learning algorithms demonstrate TPSIA performance.
Swarming in a Virtual World: A PSO Approach to Virtual Camera Composition
Authors: Luca Gaspero, Andrea Ermetici, Roberto Ranon
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

Camera placement in 3D scenes is a relevant issue in most 3D graphics interactive application, such as videogames, data visualization, and virtual tours. Virtual Camera Composition (VCC) consists in automatically positioning a camera in a virtual world, such that the resulting image satisfies a set of visual cinematographic properties [1]. We propose a Particle Swarm algorithm to solve the problem, which exhibits superior performances w.r.t. other approaches. The algorithm has been tested on a set of image descriptions on a complex 3D model.
The Complexity of Small Universal Turing Machines
Authors: Damien Woods, Turlough Neary
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

We survey some work concerned with small universal Turing machines, cellular automata, and other simple models of computation. For example it has been an open question for some time as to whether the smallest known universal Turing machines of Minsky, Rogozhin, Baiocchi and Kudlek are efficient (polynomial time) simulators of Turing machines. These are some of the most intuitively simple computational devices and previously the best known simulations were exponentially slow. We discuss recent work that shows that these machines are indeed efficient simulators. As a related result we also find that Rule 110, a well-known elementary cellular automaton, is also efficiently universal. We also mention some new universal program-size results, including new small universal Turing machines and new semi-weakly universal Turing machines. We then discuss some ideas for future work arising out of these, and other, results.
Learning Dynamic Bayesian Networks Structure Based on Bayesian Optimization Algorithm
Authors: Song Gao, Qinkun Xiao, Quan Pan, Qingguo Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
Providing Non-stop Service for Message-Passing Based Parallel Applications with RADIC
Authors: Guna Santos, Angelo Duarte, Dolores Rexachs, Emilio Luque
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

The current supercomputers are almost achieving the petaflop level. These machines present a high number of interruptions in a relatively short time interval. Fault tolerance and preventive maintenance are key issues in order to enlarge the MTTI (Mean Time To Interrupt). In this paper we present how RADIC, a architecture for fault tolerance, provides different protection levels able to avoid system interruptions and allows the performance of preventive maintenance tasks. Our experiments show the effectiveness of our solution in order to keep a high availability with a large MTTI.
A Novel Method for Prediction of Protein Domain Using Distance-Based Maximal Entropy
Authors: Shuxue Zou, Yanxin Huang, Yan Wang, Chengquan Hu, Yanchun Liang, Chunguang Zhou
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Detecting the boundaries of protein domains has been an important and challenging problem in experimental and computational structural biology. In this paper the domain detection is first taken as an imbalanced data learning problem. A novel undersampling method using distance-based maximal entropy in the feature space of SVMs is proposed. On multiple sequence alignments that are derived from a database search, multiple measures are defined to quantify the domain information content of each position along the sequence. The overall accuracy is about 87% together with high sensitivity and specificity. Simulation results demonstrate that the utility of the method can help not only in predicting the complete 3D structure of a protein but also in the machine learning system on general imbalanced datasets.
Automatic identification of cardiac health using modeling techniques: A comparative study
Authors: U. Rajendra Acharya, Meena Sankaranarayanan, Jagadish Nayak, Chen Xiang, Toshiyo Tamura
Venue: Information Sciences: an International Journal (2008)

Heart rate variability (HRV), a widely adopted quantitative marker of the autonomic nervous system can be used as a predictor of risk of cardiovascular diseases. Moreover, decreased heart rate variability (HRV) has been associated with an increased risk of cardiovascular diseases. Hence in this work HRV signal is used as the base signal for predicting the risk of cardiovascular diseases. The present study concerns nine cardiac classes that include normal sinus rhythm (NSR), congestive heart failure (CHF), atrial fibrillation (AF), ventricular fibrillation (VF), preventricular contraction (PVC), left bundle branch block (LBBB), complete heart block (CHB), ischemic/dilated cardiomyopathy (ISCH) and sick sinus syndrome (SSS). A total of 352 cardiac subjects belonging to the nine classes were analyzed in the frequency domain. The fast Fourier transforms (FFT) and three other modeling techniques namely, autoregressive (AR) model, moving average (MA) model and the autoregressive moving average (ARMA) model are used to estimate the power spectral densities of the RR interval variability. The spectral parameters obtained from the spectral analysis of the HRV signals are used as the input parameters to the artificial neural network (ANN) for classification of the different cardiac classes. Our findings reveal that the ARMA modeling technique seems to give better resolution and would be more promising for clinical diagnosis.
LogAnswer - A Deduction-Based Question Answering System (System Description)
Authors: Ulrich Furbach, Ingo Glöckner, Hermann Helbig, Björn Pelzer
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

LogAnswer is an open domain question answering system which employs an automated theorem prover to infer correct replies to natural language questions. For this purpose LogAnswer operates on a large axiom set in first-order logic, representing a formalized semantic network acquired from extensive textual knowledge bases. The logicbased approach allows the formalization of semantics and background knowledge, which play a vital role in deriving answers. We present the functional LogAnswer prototype, which consists of automated theorem provers for logical answer derivation as well as an environment for deep linguistic processing.
Temporal Variation of Urban Pattern Morphology in Southern Italy Explored by Using Landsat Data
Authors: Luciano Telesca, Rosa Coluzzi, Rosa Lasaponara
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

This paper deals with the spatial characterization of urban expansion by using spatial fractal analysis applied to multidate Multispectral Scanner (MSS) and Thematic Mapper (TM) satellite imagery. The investigation was focused on one small southern Italy town, for which the border was extracted from NASA Landsat images acquired in 1976 (MSS), in 1987 and 1998 (TM). The border was analysed using the box counting method, which is a well-know technique to estimate the spatial fractal dimension, that quantifies the shape irregularity of an object. The obtained results show that the fractal dimension of the border of the investigated town increases from 1976 to 1998, indicating a tendency toward a more irregular shape. This increase is also connected with the urban expansion and the population growth.
Fingerprint Alignment with Deformable Templates
Authors: Krzysztof Slot, Marek Gozdzik
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

The following paper introduces an extension to the deformable template paradigm and presents an application ofthe resulting image analysis method for fingerprint alignment. Instead of a scalar form of information that is to be stored at each of grid's nodes, we propose to use multidimensional vectors. To implement this idea, we formulate a novel rule for grid-object interactions that are necessary to execute grid-matching. The proposed concept enables an application of deformable templates to an analysis of vector fields, such as an orientation field, which can be derived for fingerprint images. We show that deformable templates with vector-valued nodes are a promising tool for compensation of nonlinear fingerprint deformations, so that they can appear useful in an image-alignment phase of fingerprint recognition procedures.