ppxscal/embedding_network huggingface dataset:
Comparing Linguistic Features for Modeling Learning in Computer Tutoring
Authors: Kate Forbes-Riley, Diane Litman, Amruta Purandare, Mihai Rotaru, Joel Tetreault
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

We compare the relative utility of different automatically computable linguistic feature sets for modeling student learning in computer dialogue tutoring. We use the PARADISE framework (multiple linear regression) to build a learning model from each of 6 linguistic feature sets: 1) surface features, 2) semantic features, 3) pragmatic features, 4) discourse structure features, 5) local dialogue context features, and 6) all feature sets combined. We hypothesize that although more sophisticated linguistic features are harder to obtain, they will yield stronger learning models. We train and test our models on 3 different train/test dataset pairs derived from our 3 spoken dialogue tutoring system corpora. Our results show that more sophisticated linguistic features usually perform better than either a baseline model containing only pretest score or a model containing only surface features, and that semantic features generalize better than other linguistic feature sets.
Decentralized Bootstrapping of P2P Systems: A Practical View
Authors: Jochen Dinger, Oliver P. Waldhorst
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

So far, bootstrapping constitutes the only centralized task in otherwise decentralized peer-to-peer (P2P) systems. As a contribution to the development of generally applicable decentralized bootstrap mechanisms, in this paper we analyze two decentralized approaches from a practical point of view. We consider local host caches and random address probing for bootstrapping into the BitTorrent DHT as an example for a widely deployed P2P system. Based on the results of an extensive measurement study we show that local host caches allow rejoining the P2P system quickly after short times of disconnection, but are impracticable for infrequent or first-time users. Furthermore, random address probing is feasible using a direct Internet connection with high bandwidth, but is subject to practical issues raised by typical NAT routers and the distribution of ports used by BitTorrent clients. We propose two mechanisms for increasing the performance of random address probing: (1) probing multiple ports per host and (2) hash-based filter-resistant port selection, making distributed bootstrapping feasible even from a practical point of view.
Intelligent Agent Based Workforce Empowerment
Authors: Nazaraf Shah, Edward Tsang, Yossi Borenstein, Raphael Dorne, Anne Liret, Chris Voudouris
Venue: Proceedings of the Third KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications (2009)

The fast moving and competitive global economy requires that organisations manage their human resources in an efficient and flexible manner in order to tap their full potentials. The management of human resources has crucial role in success or failure of an organisation. Today's services organisations strive to tailor their human re-source management policies that improve their performance by empowering employees. Employee empowerment is a management style that helps organisations to utilise their employee's potential to maximum and be able to react and adapt quickly and efficiently to changes in service provision. This paper explores the possibility of empowering British Telecommunications (BT) field workforce and its potential benefits by using simulator. We investigate the application of a `pull-based strategy' for empowering BT field workforce and its relation with motivation and productivity of BT workforce. Currently jobs are allocated to BT field engineers by a central scheduler; we refer to it as a `push strategy'. In this simulation we attempt to assess the impact of empowerment on BT FE motivation and towards overall increase in productivity.
An Optimization Method for the Data Space Partition Obtained by Classification Techniques for the Monitoring of Dynamic Processes
Authors: C. Isaza, J. Aguilar-Martin, M. V. Le Lann, J. Aguilar, A. Rios-Bolivar
Venue: Proceeding of the 2006 conference on Artificial Intelligence Research and Development (2006)

In this paper, a new method for the automatic optimization of the classes obtained by application of fuzzy classification techniques is presented. We propose the automatic validation and adjustment of the partition obtained. The new approach is independent of the type of fuzzy classification technique and can be applied in the supervision of complex processes.
Kepstrum analysis and real-time application to noise cancellation
Authors: J. Jeong
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

This paper provides analysis of kepstrum (known elsewhere as complex cepstrum) and its real-time application to noise cancellation. The method uses kepstrum estimation for identification of the ratio of acoustic path transfer functions between two microphones and efficient processing technique for real-time processing. It is applied to the front-end of speech enhancement method, the Griffiths and Jim beamformer. The experiments are processed in real-time by software implementation in LabVIEW. It will be shown that the front-end kepstrum approach effectively cancels echoes and hence reverberation in a noisy environment. For a real-time application, the computational complexity on kepstrum processing has also been investigated and compared with NLMS algorithm of adaptive filter.
Using Machine Learning Techniques to Analyze and Support Mediation of Student E-Discussions
Authors: Bruce M. McLaren, Oliver Scheuer, Maarten De Laat, Rakheli Hever, Reuma De Groot, Carolyn P. Rosé
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Students are starting to use networked visual argumentation tools to discuss, debate, and argue with one another about topics presented by a teacher. However, this development gives rise to an emergent issue for teachers: how do they support students during these e-discussions? The ARGUNAUT system aims to provide the teacher (or moderator) with tools that will facilitate effective moderation of several simultaneous e-discussions. Awareness Indicators, provided as part of a moderator's user interface, help monitor the progress of discussions on several dimensions (e.g., critical reasoning). In this paper we discuss preliminary steps taken in using machine learning techniques to support the Awareness Indicators. Focusing on individual contributions (single objects containing textual content, contributed in the visual workspace by students) and sequences of two linked contributions (two objects, the connection between them, and the students' textual contributions), we have run a series of machine learning experiments in an attempt to train classifiers to recognize important student actions, such as using critical reasoning and raising and answering questions. The initial results presented in this paper are encouraging, but we are only at the beginning of our analysis.
Plenary lecture 3: the efficiency of parallel metaheuristics for combinatorial optimization - paradigms, models and implementations
Authors: Plamenka Borovska
Venue: Proceedings of the 8th WSEAS International Conference on Software engineering, parallel and distributed systems (2009)

Parallel metaheuristics have proved to provide efficient and powerful tools for combinatorial optimization of grand challenge scientific and engineering problems. Metaheuristics offer the opportunity to find out optimal or suboptimal solution of NP-hard problems in reasonable time. Combinatorial optimization based on metaheuristics implies tree major aspects - the search space, the neighborhood relations and the guiding function, the specific forms of which determine the metaphor of the computation. The search strategies for the optimum implied may be trajectory-based or population-based, the latter simulating biological or cultural evolution. The major goal of parallelizing metaheuristics is not only to reduce significantly the computational time but to improve the quality of solutions obtained as well. The motives to utilize parallel metaheuristics are diversification and intensification. The paper focuses on the specifics of designing parallel computational models based on metaheuristics, implementing various parallel algorithmic paradigms and optimizing the correlations architectural space - target parallel computer architecture. Classifications of parallel computational models in respect to the granularity are presented. The aspects of tuning algorithmic parameters to the specifics of the problem being solved are considered. The problems of building up metaheuristics class libraries are under consideration. Parallel performance evaluation and quality of solution estimation on the basis of parallel program implementations are treated. Case studies are presented for trajectory-based and population-based parallel metaheuristics implementations on compact computer cluster of multi-core servers (super-server).
Covering Steps Graphs of Time Petri Nets
Authors: Hanifa Boucheneb, Kamel Barkaoui
Venue: Electronic Notes in Theoretical Computer Science (ENTCS) (2009)

We consider here time Petri nets and the covering steps graph technique proposed by Vernadat et al. for untimed Petri nets. In this technique, some transitions are put together to be fired in a single transition step. This paper investigates how this technique can be extended to be applied to time Petri nets.
Functional analysis of finger contact locations during grasping
Authors: Matei Ciocarlie, Hao Dang, Jamie Lukos, Marco Santello, Peter Allen
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

In this paper we present a method for studying human selection of fingertip contact point locations during grasping and manipulation. Our aim is to perform a functional analysis, looking at how a particular choice of contact point distribution affects the subjects' ability to resist external forces applied to the grasped object. We rely on grasp quality metrics derived from the Grasp Wrench Space, a method traditionally used for quantifying robotic grasps. We illustrate this approach by studying human grasps of a spherical object under a number of different disturbances. Our results indicate that the quality metrics we use can help explain the subjects' choice of contact points, although other possible grasping strategies may exist.
A Proposal to Extend the OpenMP Tasking Model for Heterogeneous Architectures
Authors: Eduard Ayguade, Rosa M. Badia, Daniel Cabrera, Alejandro Duran, Marc Gonzalez, Francisco Igual, Daniel Jimenez, Jesus Labarta, Xavier Martorell, Rafael Mayo, Josep M. Perez, Enrique S. Quintana-Ortí
Venue: Proceedings of the 5th International Workshop on OpenMP: Evolving OpenMP in an Age of Extreme Parallelism (2009)

OpenMP has evolved recently towards expressing unstructured parallelism, targeting the parallelization of a broader range of applications in the current multicore era. Homogeneous multicore architectures from major vendors have become mainstream, but with clear indications that a better performance/power ratio can be achieved using more specialized hardware (accelerators), such as SSE-based units or GPUs, clearly deviating from the easy-to-understand shared-memory homogeneous architectures. This paper investigates if OpenMP could still survive in this new scenario and proposes a possible way to extend the current specification to reasonably integrate heterogeneity while preserving simplicity and portability. The paper leverages on a previous proposal that extended tasking with dependencies. The runtime is in charge of data movement, tasks scheduling based on these data dependencies and the appropriate selection of the target accelerator depending on system configuration and resource availability.
Univariate Algebraic Kernel and Application to Arrangements
Authors: Sylvain Lazard, Luis Peñaranda, Elias Tsigaridas
Venue: Proceedings of the 8th International Symposium on Experimental Algorithms (2009)

We present a cgal-based univariate algebraic kernel, which provides certified real-root isolation of univariate polynomials with integer coefficients and standard functionalities such as basic arithmetic operations, greatest common divisor (gcd) and square-free factorization, as well as comparison and sign evaluations of real algebraic numbers.We compare our kernel with other comparable kernels, demonstrating the efficiency of our approach. Our experiments are performed on large data sets including polynomials of high degree (up to 2 000) and with very large coefficients (up to 25 000 bits per coefficient).We also address the problem of computing arrangements of x-monotone polynomial curves. We apply our kernel to this problem and demonstrate its efficiency compared to previous solutions available in cgal.
Surprising results on task assignment in server farms with high-variability workloads
Authors: Mor Harchol-Balter, Alan Scheller-Wolf, Andrew R. Young
Venue: Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems (2009)

This paper investigates the performance of task assignment policies for server farms, as the variability of job sizes (service demands) approaches infinity. Our results reveal that some common wisdoms regarding task assignment are flawed. The Size-Interval-Task-Assignment policy (SITA), which assigns each server a unique size range, was heretofore thought of by some as the panacea for dealing with high-variability job-size distributions. We show SITA to be inferior to the much simpler greedy policy, Least-Work-Left (LWL), for certain common job-size distributions, including many modal, hyperexponential, and Pareto distributions. We also define regimes where SITA's performance is superior, and prove simple closed-form bounds on its performance for the above-mentioned distributions.
Brief paper: Separator-type robust stability theorem of sampled-data systems allowing noncausal LPTV scaling
Authors: Tomomichi Hagiwara
Venue: Automatica (Journal of IFAC) (2009)

This paper derives a necessary and sufficient condition for robust stability of sampled-data systems, which is stated by using the notion of separators that are dealt with in an operator-theoretic framework. Such operator-theoretic treatment of separators provides a new perspective, which we call noncausal linear periodically time varying scaling and leads to reducing conservativeness in robust stability analysis. A numerical example is given to demonstrate the results.
Model-based software design tools for the cell processor
Authors: Nicholas Lowell
Venue: Proceedings of the 47th Annual Southeast Regional Conference (2009)

This paper introduces the larger features of the Cell Processor that allow this specialized hardware architecture to provide a significant amount of increased performance. Specialized configurations call for specialized programming in order to harness the available performance increase. Such high computation configurations are prime targets for signal processing applications. There exists a tool set for modeling the dataflow of a signal processing application. A major goal exists to allow for generation of code to be used on the Cell. The first step involves learning the required techniques for programming by way of porting an example application to the Cell. This paper shows the first steps of utilizing the multi-core architecture which yields a significant increase in performance with room for further improvement in the future.
Searching for interacting features in subset selection
Authors: Zheng Zhao, Huan Liu
Venue: Intelligent Data Analysis (2009)

The evolving and adapting capabilities of robust intelligence are best manifested in its ability to learn. Machine learning enables computer systems to learn, and improve performance. Feature selection facilitates machine learning (e.g., classification) by aiming to remove irrelevant features. Feature (attribute) interaction presents a challenge to feature subset selection for classification. This is because a feature by itself might have little correlation with the target concept, but when it is combined with some other features, they can be strongly correlated with the target concept. Thus, the unintentional removal of these features may result in poor classification performance. It is computationally intractable to handle feature interactions in general. However, the presence of feature interaction in a wide range of real-world applications demands practical solutions that can reduce high-dimensional data while preserving feature interactions. In this paper, we take up the challenge to design a special data structure for feature quality evaluation, and to employ an information-theoretic feature ranking mechanism to efficiently handle feature interaction in subset selection. We conduct experiments to evaluate our approach by comparing with some representative methods, perform a lesion study to examine the critical components of the proposed algorithm to gain insights, and investigate related issues such as data structure, ranking, time complexity, and scalability in search of interacting features.
Choosing the best set of variables in regression analysis using integer programming
Authors: Hiroshi Konno, Rei Yamamoto
Venue: Journal of Global Optimization (2009)

This paper is concerned with an algorithm for selecting the best set of s variables out of k(> s) candidate variables in a multiple linear regression model. We employ absolute deviation as the measure of deviation and solve the resulting optimization problem by using 0-1 integer programming methodologies. In addition, we will propose a heuristic algorithm to obtain a close to optimal set of variables in terms of squared deviation. Computational results show that this method is practical and reliable for determining the best set of variables.
Theoretical and Practical Aspects of Mutual Information Based Side Channel Analysis
Authors: Emmanuel Prouff, Matthieu Rivain
Venue: Proceedings of the 7th International Conference on Applied Cryptography and Network Security (2009)

A large variety of side channel analyses performed on embedded devices involve the linear correlation coefficient as wrong-key distinguisher. This coefficient is actually a sound statistical tool to quantify linear dependencies between univariate variables. However, when those dependencies are non-linear, the correlation coefficient stops being pertinent so that another statistical tool must be investigated. Recent works showed that the Mutual Information measure is a promising candidate, since it detects any kind of statistical dependency. Substituting it for the correlation coefficient may therefore be considered as a natural extension of the existing attacks. Nevertheless, the first applications published at CHES 2008 have revealed several limitations of the approach and have raised several questions. In this paper, an in-depth analysis of side channel attacks involving the mutual information is conducted. We expose their theoretical foundations and we assess their limitations and assets. Also, we generalize them to higher orders where they seem to be an efficient alternative to the existing attacks. Eventually, we provide simulations and practical experiments that validate our theoretical analyses.
Stability of Si impurity in high-κ oxides
Authors: Naoto Umezawa, Kenji Shiraishi, Toyohiro Chikyow
Venue: Microelectronic Engineering (2009)

The interface structure of a high permittivity (high-@k) oxide with Si substrate affects the electrical properties of the high-@k based transistors. Our theoretical analysis suggests that the formation of a SiO"2 layer at the high-@k/Si interface originates from the instability of a Si impurity in the high-@k oxide. Our computational results revealed that the Si impurity is much more stable in La"2O"3 than in HfO"2, indicating La"2O"3 is a silicate former, while SiO"2 is likely to precipitate at the HfO"2/Si interface.
ICA, kernel methods and nonnegativity: New paradigms for dynamical component analysis of fMRI data
Authors: Peter Gruber, Anke Meyer-Bäse, Simon Foo, Fabian J. Theis
Venue: Engineering Applications of Artificial Intelligence (2009)

In the last decades, functional magnetic resonance imaging (fMRI) has been introduced into clinical practice. As a consequence of this advanced noninvasive medical imaging technique, the analysis and visualization of medical image time-series data poses a new challenge to both research and medical application. But often, the model data for a regression or generalized linear model-based analysis are not available. Hence exploratory data-driven techniques, i.e. blind source separation (BSS) methods are very popular in functional nuclear magnetic resonance imaging (fMRI) data analysis since they are neither based on explicit signal models nor on a priori knowledge of the underlying physiological process. The independent component analysis (ICA) represents a main BSS method which searches for stochastically independent signals from the multivariate observations. In this paper, we introduce a new kernel-based nonlinear ICA method and compare it to standard BSS techniques. This kernel nonlinear ICA (kICA) overcomes the restrictions of linearity of the mixing process usually encountered with ICA. Dimension reduction is an important preprocessing step for this nonlinear technique and is performed in a novel way: a genetic algorithm is designed which determines the optimal number of basis vectors for a reduced-order feature space representation as an optimization problem of the condition number of the resulting basis. For the fMRI data, a comparative quantitative evaluation is performed between kICA with different kernels, nonnegative matrix factorization (NMF) and other BSS algorithms. The comparative results are evaluated by task-related activation maps, associated time courses and ROC study. The comparison is performed on fMRI data from experiments with 10 subjects. The external stimulus was a visual pattern presentation in a block design. The most important obtained results in this paper represent that kICA and sparse NMF (sNMF) are able to identify signal components with high correlation to the fMRI stimulus, and kICA with a Gaussian kernel is comparable to standard ICA algorithms and even more, it yields spatially focused results.