ppxscal/embedding_network huggingface dataset:
Found in Translation
Authors: Marco Turchi, Ilias Flaounas, Omar Ali, Tijl Bie, Tristan Snowsill, Nello Cristianini
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (2009)

We present a complete working system that gathers multilingual news items from the Web, translates them into English, categorises them by topic and geographic location and presents them to the final user in a uniform way. Currently, the system crawls 560 news outlets, in 22 different languages, from the 27 European Union countries. Data gathering is based on RSS crawlers, machine translation on Moses and the text categorisation on SVMs. The system also presents on a European map statistical information about the amount of attention devoted to the various topics in each of the 27 EU countries. The integration of Support Vector Machines, Statistical Machine Translation, Web Technologies and Computer Graphics delivers a complete system where modern Statistical Machine Learning is used at multiple levels and is a crucial enabling part of the resulting functionality.
A Dependable Threshold Broadcast Encryption System for Key Distribution in Mobile Ad Hoc Network
Authors: Piyi Yang, Zhenfu Cao, Xiaolei Dong
Venue: Proceedings of the 2009 Second International Conference on Dependability (2009)

The focus of this paper is to design a dependable ID-based threshold broadcast encryption (TBE) scheme, by which a center can distribute keys over a network, so that a privileged subset of users can collaborate to jointly compute a specific key. Then a conventional secret-key cryptosystem, such as AES, can be used to encrypt the subsequent broadcast with the distributed key. Because a key distribution can be done in an encrypted broadcast without any key pre-distribution, re-keying protocols for group membership operations can be simplified, a center can use the ID-based TBE scheme to distribute new and random session key. We show that the proposed scheme is robust and secure under chosen-ciphertext attacks based on Decisional Bilinear Diffie-Hellman (DBDH) assumption. Compared with the previously proposed TBE schemes, our scheme needs only one exponentiation computation, which makes it be more efficient than those schemes.
Cancer class prediction: Two stage clustering approach to identify informative genes
Authors: Mohammed Alshalalfah, Reda Alhajj
Venue: Intelligent Data Analysis (2009)

Cancer classification is an important research area that has attracted the attention of several research groups over the last decades. However, there has been no general agreed upon approach for assigning tumors to known classes (a.k.a. class prediction). One challenge in microarray analysis, especially in cancerous gene expression profiles, is to identify genes or group of genes that are highly expressed in tumor cells but not in normal cells and vice versa. All of the methods described in the literature deal with features obtained directly from the data. Further, several clustering techniques have been proposed for the analysis of genome expression data, such as k-means, Self organizing maps, etc. However, these methods do not provide information about the influence of a given gene on the overall shape of the clusters. In this paper, we try to generate informative data, which can be more powerful in the classification of genes. We identify a set of reduced features capable of distinguishing between two classes by two stage clustering of genes using fuzzy c-means. In the first stage, the proposed clustering method clusters the original data. In the second stage, it clusters genes in each of the clusters produced from the first stage. We decided on using fuzzy c-means because a fuzzy model fits better gene expression data analysis by having a gene belong to different classes with a degree of membership per class. However, fuzziness parameter m is a major problem in applying fuzzy c-means for clustering. In this approach, we try to better identify the value of the fuzziness parameter when applying fuzzy c-means for microarray data. Support vector machine combined with different kernel functions are used for classification. The results from the experiments conducted on three benchmark data sets (including one multi-class data set) demonstrate the applicability and effectiveness of the proposed approach as compared to the other approaches described in the literature.
Modeling Instability in a New Framework of Interval Valued Process Algebra
Authors: Jie Zhou, Yixiang Chen
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

We present a new setting of interval valued CCS ($ICCS^{is}$, for short) to model the probabilistic phenomenon and instability in concurrency and interactivesystems. This model is an extension of probabilistic CCS, which is deriving from Milner's standard CCS. In this model, an interval value $x=[n,p](n\leq p)$ is assigned to each components of a summation, where $n$ can be viewed as the necessity degree with which one action is performed, and $p$ the possibility degree. Then $u=1-\frac{n}{p}$ stands for the instability of the system with respect to a certain action. We introduce the syntax and operational semantics of $ICCS^{is}$, and give the notion of strong bisimulation and $\nu$-strong bisimulation over $ICCS^{is}$. Some important properties of these two bisimulations are investigated.
Analysis of Tactual Impression by Audio and Visual Stimulation for User Interface Design in Mixed Reality Environment
Authors: Mami Kagimoto, Asako Kimura, Fumihisa Shibata, Hideyuki Tamura
Venue: Proceedings of the 3rd International Conference on Virtual and Mixed Reality: Held as Part of HCI International 2009 (2009)

In a mixed-reality (MR) environment, a touchable object can be made to change its appearance when a computer-generated image (MR visual stimulation) is superimposed onto it. In this research, we conduct experiments to study the effects of MR visual and audio stimuli on the tactual impression of the "roughness" of an object. We show that MR visual stimulation alters a subject's tactual impression of the roughness of an object and that the addition of MR audio stimulation intensifies that effect.
Weighted one-against-all
Authors: Alina Beygelzimer, John Langford, Bianca Zadrozny
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 2 (2005)

The one-against-all reduction from multiclass classification to binary classification is a standard technique used to solve multiclass problems with binary classifiers. We show that modifying this technique in order to optimize its error transformation properties results in a superior technique, both experimentally and theoretically. This algorithm can also be used to solve a more general classification problem "multi-label classification," which is the same as multiclass classification except that it allows multiple correct labels for a given example.
Fault-Tolerant Tension Allocation of Ship Mobile Mooring System under Sea Wind
Authors: Wangqiang Niu, Xixia Huang, Wei Gu, Jianxin Chu
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 01 (2009)

A fault-tolerant tension allocation framework is suggested for ship mobile system consisting of eight anchor windlasses. The fault-tolerant performance is evaluated under sea wind disturbance. The simulation results indicate that ship mobile mooring systems have certain fault-tolerant ability. When one windlass failed, the ship is able to completely counteract the sea wind disturbance. When two windlasses failed, the ship fault-tolerant ability becomes worse, and the remaining six windlasses are able to roughly balance the wind disturbance. When three windlasses failed, the ship fault-tolerant ability becomes even worse, and the remaining five windlasses are unable to counteract the lateral force from the sea wind. In summary, more windlasses failed, more performance degradation of the mooring system is resulted.
Point Matching Estimation for Moving Object Tracking Based on Kalman Filter
Authors: Wei Zeng, Guibin Zhu, Yao Li
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

Video-based information collection has become an important research direction, and moving object tracking technique plays a key role nowadays. The classic corner tracking algorithm doesn’t meet the real-time requirement, and loses the object mostly due to occlusions, the change of geometrical scale or/and some similar objects approaching to the object. To solve the problems, a new algorithm based on Kalman filter and point matching estimation is proposed in the paper. Combined with predicting the target’s location based on Kalman filter, the extracted multi-scale corner points which are geometrically invariant are given different weights for the responsible function, and then divided the image into blocks, the location is tracked by its average vector. The experiments results show that the proposed method can perform well in on-line and robust tracking systems.
Emipirical Research on Term Structure of Buy-Back Rates Based on EGARCH Model
Authors: Li Ning, He Dong-ping
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 01 (2009)

There is a great significance to research the term structure of interest rate based on the background of China’s gradual marketization of interest rates. In the paper, the dynamic changing situations of buy-back rates are researched and time series theories, such as GARCH and EGARCH model, are respectively applied to estimate the term structure of buy-back rates. The empirical results show that (1) as to the fitting results, the EGARCH model is better than the GARCH model for fitting the term structure of 1 day, 7 days, 14 days, 21 days, 2 months, and 3 months buy-back rates;(2) the 1 day, 14 days, 1 month, 2 months, 3 months buy-back rates have very strong mean-reversion characteristic, but 7 days, 21 days buy-back rates have not; (3) diffusions of term structure of the 1 day, 7 days, 14 days, 21 days, three months buy-back rates have reverse lever effect, but 1 month and 2 months buy-back rates have symmetrical fluctuation.
Near-optimal observation selection using submodular functions
Authors: Andreas Krause, Carlos Guestrin
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

AI problems such as autonomous robotic exploration, automatic diagnosis and activity recognition have in common the need for choosing among a set of informative but possibly expensive observations. When monitoring spatial phenomena with sensor networks or mobile robots, for example, we need to decide which locations to observe in order to most effectively decrease the uncertainty, at minimum cost. These problems usually are NP-hard. Many observation selection objectives satisfy submodularity, an intuitive diminishing returns property - adding a sensor to a small deployment helps more than adding it to a large deployment. In this paper, we survey recent advances in systematically exploiting this submodularity property to efficiently achieve near-optimal observation selections, under complex constraints. We illustrate the effectiveness of our approaches on problems of monitoring environmental phenomena and water distribution networks.
Objective vs. Subjective Evaluation of Speakers with and without Complete Dentures
Authors: Tino Haderlein, Tobias Bocklet, Andreas Maier, Elmar Nöth, Christian Knipfer, Florian Stelzle
Venue: Proceedings of the 12th International Conference on Text, Speech and Dialogue (2009)

For dento-oral rehabilitation of edentulous (toothless) patients, speech intelligibility is an important criterion. 28 persons read a standardized text once with and once without wearing complete dentures. Six experienced raters evaluated the intelligibility subjectively on a 5-point scale and the voice on the 4-point Roughness-Breathiness-Hoarseness (RBH) scales. Objective evaluation was performed by Support Vector Regression (SVR) on the word accuracy (WA) and word recognition rate (WR) of a speech recognition system, and a set of 95 word based prosodic features. The word accuracy combined with selected prosodic features showed a correlation of up to r = 0.65 to the subjective ratings for patients with dentures and r = 0.72 for patients without dentures. For the RBH scales, however, the average correlation of the feature subsets to the subjective ratings for both types of recordings was r < 0.4.
A Genetic Algorithm Based Approach For Mixed-Media Advertising Budgeting
Authors: Sun Ying, Mao Zhizhong
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (2009)

Mixed-media advertising which has become more effective and popular attracts the attention of advertising agencies, media planners and advertisers. How to decide and allocate the budget for mixed-media advertising becomes a fundamental part of a company's advertising campaign.In this paper, an optimization approach based on Genetic Algorithm is proposed to deal with this advertising budget problem which is formulated as a constrained nonlinear programming problem. Then an experiment example is given to explain the process of our approach. The result of this example supports the feasibility and efficiency of our method.
Sensitivity analysis for Szeg&#x0151; polynomials
Authors: Sun-Mi Kim, Lothar Reichel
Venue: Numerische Mathematik (2009)

Szeg&#x0151; polynomials are orthogonal with respect to an inner product on the unit circle. Numerical methods for weighted least-squares approximation by trigonometric polynomials conveniently can be derived and expressed with the aid of Szeg&#x0151; polynomials. This paper discusses the conditioning of several mappings involving Szeg&#x0151; polynomials and, thereby, sheds light on the sensitivity of some approximation problems involving trigonometric polynomials.
A Comparison of Team Performance Measures for Global Software Development Student Teams
Authors: Kathleen Swigger, Fatma Cemile Serce, Ferda Nur Alpaslan, Robert Brazile, George Dafoulas, Victor Lopez
Venue: Proceedings of the 2009 Fourth IEEE International Conference on Global Software Engineering (2009)

One of the most difficult tasks for global software development researchers is quantifying the performance of groups and students who participate in these distributed projects.There has been much debate about which factors better correlate with team performance and which best describe a successful team. The purpose of this paper is to compare the different approaches that have been used to evaluate the performance of global software learners and show how these techniques can affect research results. Using data from student groups engaged in global software development projects for the past year, the authors apply a number of different assessment methods and show their effects on different performance indicators. Our study suggests that the selection of appropriate measures to evaluate team performance can dramatically affect how one identifies successful teams.
Programming Ruby 1.9: The Pragmatic Programmers' Guide, 3rd edition
Authors: Dave Thomas, Chad Fowler, Andy Hunt
Venue: Facets Of Ruby (2009)

Ruby is a fully object-oriented language, much like the classic object-oriented language, Smalltalk. Like Smalltalk, it is dynamically typed (as opposed to Java or C++), but unlike Smalltalk, Ruby features the same conveniences found in modern scripting languages, making Ruby a favorite tool of intelligent, forward-thinking programmers and the basis for the Rails web framework. This is the reference manual for Ruby, including a description of all the standard library modules, a complete reference to all built-in classes and modules (including all the new and changed methods introduced by Ruby 1.9). It also includes all the new and changed syntax and semantics introduced since Ruby 1.8. Learn about the new parameter passing rules, local variable scoping in blocks, fibers, multinationalization, and the new block declaration syntax, among other exciting new features.
Touch Is Everywhere: Floor Surfaces as Ambient Haptic Interfaces
Authors: Yon Visell, Alvin Law, Jeremy R. Cooperstock
Venue: IEEE Transactions on Haptics (2009)

Floor surfaces are notable for the diverse roles that they play in our negotiation of everyday environments. Haptic communication via floor surfaces could enhance or enable many computer-supported activities that involve movement on foot. In this paper, we discuss potential applications of such interfaces in everyday environments and present a haptically augmented floor component through which several interaction methods are being evaluated. We describe two approaches to the design of structured vibrotactile signals for this device. The first is centered on a musical phrase metaphor, as employed in prior work on tactile display. The second is based upon the synthesis of rhythmic patterns of virtual physical impact transients. We report on an experiment in which participants were able to identify communication units that were constructed from these signals and displayed via a floor interface at well above chance levels. The results support the feasibility of tactile information display via such interfaces and provide further indications as to how to effectively design vibrotactile signals for them.
Scatter Search algorithm for Protein Structure Prediction
Authors: Nashat Mansour, Christine Kehyayan, Hassan Khachfe
Venue: International Journal of Bioinformatics Research and Applications (2009)

In this paper, we present a Scatter Search (SS) algorithm for predicting 3D structures of proteins based on torsion angles representation. Given the protein's sequence of Amino Acids (AAs), our algorithm produces a 3D structure that aims to minimise the energy function associated with the structure. SS is an evolutionary approach that is based on a population of candidate solutions. These candidates undergo evolutionary operations that combine search intensification and diversification over a number of iterations. We evaluate our algorithm on three proteins taken from a Protein Data Bank (PDB). The results show that our algorithm is able to produce 3D structures with good sub-optimal energy values. Also, the Root Mean Square Deviations (RMSD) of these structures from the reference proteins are promising within limits imposed by the assumptions made.
The pyramid match: efficient learning with partial correspondences
Authors: Kristen Grauman
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 2 (2007)

It is often useful to represent a single example by a set of the local features that comprise it. However, this representation poses a challenge to many conventional learning techniques, since sets may vary in cardinality and the elements are unordered. To compare sets of features, researchers often resort to solving for the least-cost correspondences, but this is computationally expensive and becomes impractical for large set sizes. We have developed a general approximate matching technique called the pyramid match that measures partial match similarity in time linear in the number of feature vectors per set. The matching forms a Mercer kernel, making it valid for use in many existing kernel-based learning methods. We have demonstrated the approach for various learning tasks in vision and text processing, and find that it is accurate and significantly more efficient than previous approaches.
Efficient Symbolic Model Checking for Process Algebras
Authors: José Meulen, Charles Pecheur
Venue: Formal Methods for Industrial Critical Systems: 13th International Workshop, FMICS 2008, L'Aquila, Italy, September 15-16, 2008, Revised Selected Papers (2009)

Different approaches have been developed to mitigate the state space explosion of model checking techniques. Among them, symbolic verification techniques use efficient representations such as BDDs to reason over sets of states rather than over individual states. Unfortunately, past experience has shown that these techniques do not work well for loosely-synchronized models. This paper presents a new algorithm and a new tool that combines BDD-based model checking with partial order reduction (POR) to allow the verification of models featuring asynchronous processes, with significant performance improvements over currently available tools. We start from the ImProviso algorithm (Lerda et al.) for computing reachable states, which combines POR and symbolic verification. We merge it with the FwdUntil method (Iwashita et al.) that supports verification of a subset of CTL. Our algorithm has been implemented in a prototype that is applicable to action-based models and logics such as process algebras and ACTL. Experimental results on a model of an industrial application show that our method can verify properties of a large industrial model which cannot be handled by conventional model checkers.
Cournot Competition Under Yield Uncertainty: The Case of the U.S. Influenza Vaccine Market
Authors: Sarang Deo, Charles J. Corbett
Venue: Manufacturing Service Operations Management (2009)

This paper is inspired by the recurring mismatch between demand and supply in the U.S. influenza vaccine market. Economic theory predicts that an oligopolistic market with unregulated but costly entry will experience excess entry and oversupply, not the undersupply observed in the market for influenza vaccine in recent years. In this paper, we examine the interaction between yield uncertainty, a key characteristic of many production processes, including that for influenza vaccine, and firms' strategic behavior. We find that yield uncertainty can contribute to a high degree of concentration in an industry and a reduction in the industry output and the expected consumer surplus in equilibrium. We use parameter values loosely based on the U.S. influenza vaccine market to numerically illustrate the impact of yield uncertainty.
SAT, SMT and Applications
Authors: Armin Biere
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

SAT solving has gained tremendous interest. On the practical side there have been considerable performance improvements, due to new highly efficient algorithms, new heuristics, and optimized data structures. There are new applications and reformulations of important classical problems, mainly in the context of formal methods, where SAT solving is also applied successfully in an industrial setting. These applications range from equivalence checking, configuration, over model checking to test case generation. SAT is becoming one of the most important core technology in all these areas. Many applications actually use Satisfiability Modulo Theory (SMT), which can be seen as an extension of SAT solving. SMT has it roots in automated theorem proving. But it heavily relies on SAT technology. We discuss some key technologies in practical SAT solving, e.g. how to write a fast solver, some aspects in lifting propositional SAT technology to richer domains, how competitions can help to improve the state-of-the-art and finally touch on applications in model checking, hardware and software verification.