ppxscal/embedding_network huggingface dataset:
The mean reply depth algorithm: a correlational study of quantitative versus qualitative measures of threaded discourse
Authors: David A. Wiley
Venue:  (2008)

In the world of online learning, instructors look for the keys that open the door to student learning, satisfaction, retention, and ability to transfer newly acquired knowledge to real-world situations. One of the most common communication tools in online courses is the threaded discussion. With a growing number of students participating in online education comes the potential for an increase in faculty load. Chapter 2 begins with a review of the literature including the topics of asynchronous computer-mediated communication, criticisms and advantages of utilizing asynchronous tools in online courses, threaded discourse, mean reply depth, and rubrics to evaluate online threaded discourse. This paper reviews the research correlating the mean reply depth and the mean qualitative measure and reports on statistically significant results found at the conclusion of the research. Chapter 3 discusses how research in the field of online learning has mushroomed in the last few years. Along with the increased interest in this research topic, the application of asynchronous computer-mediated communication, specifically threaded discourse, has also increased in volume of work bringing with it countless hours for instructors spent evaluating the additional discourse. Threaded discourse is chosen for its promise of involving students in engaging and cognitively challenging discussion. With the large number of messages to process comes an increase of faculty evaluation hours. In order to better facilitate this, many instructors utilize a rubric. Although most rubrics for discourse assessment are based on practical application, the researcher analyzed and synthesized many rubrics to develop a meta-rubric incorporating all major components of the reviewed rubrics in order to perform the mean qualitative measure. Chapter 4 reviews the practical side of the study conducted by the researcher in utilizing both quantitative and qualitative methods on threaded discourse while using a mixed methodology. The quantitative method incorporated the mean reply depth; the qualitative method incorporated the mean qualitative measure. This paper reviews the pros and cons of each approach used in the research and concludes with lessons learned during the process.
Cost-Sensitive Decision Trees with Pre-pruning
Authors: Jun Du, Zhihua Cai, Charles X. Ling
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

This paper explores two simple and efficient pre-pruning strategies for the cost-sensitive decision tree algorithm to avoid overfitting. One is to limit the cost-sensitive decision trees to a depth of two. The other is to prune the trees with a pre-specified threshold. Empirical study shows that, compared to the error-based tree algorithm C4.5 and several other cost-sensitive tree algorithms, the new cost-sensitive decision trees with pre-pruning are more efficient and perform well on most UCI data sets.
Extracting Personalised Ontology from Data-Intensive Web Application: an HTML Forms-Based Reverse Engineering Approach
Authors: Sidi Mohamed Benslimane, Mimoun Malki, Mustapha Kamal Rahmouni, Djamal Benslimane
Venue: Informatica (2007)

The advance of the Web has significantly and rapidly changed the way of information organization, sharing and distribution. The next generation of the web, the semantic web, seeks to make information more usable by machines by introducing a more rigorous structure based on ontologies. In this context we try to propose a novel and integrated approach for a semi-automated extraction of ontology-based semantic web from data-intensive web application and thus, make the web content machine-understandable. Our approach is based on the idea that semantics can be extracted by applying a reverse engineering technique on the structures and the instances of HTML-forms which are the most convenient interface to communicate with relational databases on the current data-intensive web application. This semantics is exploited to produce over several steps, a personalised ontology.
Ensembles of Multi-Objective Decision Trees
Authors: Dragi Kocev, Celine Vens, Jan Struyf, Sašo Džeroski
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Ensemble methods are able to improve the predictive performance of many base classifiers. Up till now, they have been applied to classifiers that predict a single target attribute. Given the non-trivial interactions that may occur among the different targets in multi-objective prediction tasks, it is unclear whether ensemble methods also improve the performance in this setting. In this paper, we consider two ensemble learning techniques, bagging and random forests, and apply them to multi-objective decision trees (MODTs), which are decision trees that predict multiple target attributes at once. We empirically investigate the performance of ensembles of MODTs. Our most important conclusions are: (1) ensembles of MODTs yield better predictive performance than MODTs, and (2) ensembles of MODTs are equally good, or better than ensembles of single-objective decision trees, i.e., a set of ensembles for each target. Moreover, ensembles of MODTs have smaller model size and are faster to learn than ensembles of single-objective decision trees.
Improved algorithms for feedback vertex set problems
Authors: Jianer Chen, Fedor V. Fomin, Yang Liu, Songjian Lu, Yngve Villanger
Venue: Journal of Computer and System Sciences (2008)

We present improved parameterized algorithms for the feedback vertex set problem on both unweighted and weighted graphs. Both algorithms run in time O(5^kkn^2). The algorithms construct a feedback vertex set of size at most k (in the weighted case this set is of minimum weight among the feedback vertex sets of size at most k) in a given graph G of n vertices, or report that no such feedback vertex set exists in G.
Collaborative Explorative Data Analysis Applied in HTML
Authors: Mikael Jern
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

This paper introduces a Visual Analytics framework for the support of collaborative explorative data analysis (EDA) based on task-relevant visualization components embedded in HTML documents. The goal of our research is to let the analyst visually explore and search the answers to various questions about data and simultaneously capture and save important discoveries and thus enable collaboration and sharing of gained insights and knowledge to remotely dispersed team members over the Internet. A team will benefit from an interactive collaborative instrument that can "coach" them in the understanding and testing of hypotheses leading to faster understanding and a higher confidence level in the visual information. The foundation for this approach is based on the publicly available GeoAnalytics visualization framework and class library (GAV).
Consistency Preservation and Crazy Formulas in BMS
Authors: Guillaume Aucher
Venue: Proceedings of the 11th European conference on Logics in Artificial Intelligence (2008)

We provide conditions under which seriality is preserved during an update in the BMS framework. We consider not only whether the entire updated model is serial but also whether its generated submodels are serial. We also introduce the notion of crazy formulas which are formulas such that after being publicly announced at least one of the agents' beliefs become inconsistent.
Neural Network Based Recognition by Using Genetic Algorithm for Feature Selection of Enhanced Fingerprints
Authors: Adem Alpaslan Altun, Novruz Allahverdi
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

In order to ensure that the performance of a fingerprint recognition system will be powerful with respect to the quality of input fingerprint images, the enhancement of fingerprints is essential. In this study wavelet transform and contourlet transform which is a new extension of the wavelet transform in two dimensions are applied for fingerprint enhancement. In addition, feature selection is a process that chooses a subset of features from the original fingerprint features so that the feature space is optimally reduced according to a certain criterion. In this study, a Genetic Algorithms (GAs) approach to fingerprint feature selection is proposed and selected features are input to Artificial Neural Networks (ANNs) for fingerprint recognition. The performance has been tested on fingerprint recognition.
A Web-Based Customer Support Knowledge Base System
Authors: S. Wood, R. J. Howlett
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

This paper discusses the design and implementation of a web-based customer support system. The support facility operates using an adaptable knowledge base capable of diagnosing user problems. The system was developed for use by a software reseller in order to provide 24 hour support to its customer base accessible via the World Wide Web (WWW). Through integration with the company's in-house systems the support facility is able to extract expert knowledge from company staff and tailor the information for customer use.
Effectiveness of Local Feature Selection in Ensemble Learning for Prediction of Antimicrobial Resistance
Authors: Seppo Puuronen, Mykola Pechenizkiy, Alexey Tsymbal
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

In the real world concepts are often not stable but change over time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift (CD), complicates the task of learning a robust model. Different Ensemble Learning (EL) approaches (that instead of learning a single classifier try to learn and maintain a set of classifiers over time) have been shown to perform reasonably well in the presence of concept drift. In this paper we study how much local feature selection (FS) can improve ensemble performance for data with concept drift. Our results show that FS may improve the performance of different EL strategies, yet being more important for EL with static integration of classifiers like (weighted) voting. Further, the improvement of EL due to FS can be explained by its effect on the accuracy and diversity of base classifiers. The results also provide some additional evidence that diversity can be better utilized with the dynamic integration of classifiers.
A Sparse Regression Mixture Model for Clustering Time-Series
Authors: K. Blekas, Nikolaos Galatsanos, A. Likas
Venue: Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications (2008)

In this study we present a new sparse polynomial regression mixture model for fitting time series. The contribution of this work is the introduction of a smoothing prior over component regression coefficients through a Bayesian framework. This is done by using an appropriate Student-t distribution. The advantages of the sparsity-favouring prior is to make model more robust, less independent on order p of polynomials and improve the clustering procedure. The whole framework is converted into a maximum a posteriori (MAP) approach, where the known EM algorithm can be applied offering update equations for the model parameters in closed forms. The efficiency of the proposed sparse mixture model is experimentally shown by applying it on various real benchmarks and by comparing it with the typical regression mixture and the K-means algorithm. The results are very promising.
FERMTOR: A Tunable Multiprocessor Architecture
Authors: Jonathan Rose, Wayne Loucks, Zvonko Vranesic
Venue: IEEE Micro (1985)

Multiprocessing can be cost-effective when a general-purpose system is adaptable to specific uses.
An Efficient Proxy Signature Scheme with Revocation
Authors: Manik Lal Das, Ashutosh Saxena, Ved P. Gulati
Venue: Informatica (2004)

A proxy signature allows a designated person, called a proxy signer, to sign the message on behalf of the original signer. Proxy signatures are very useful tools when one needs to delegate his/her signing capability to other party. A number of proxy signature schemes have been proposed and succeeded for proxy delegations, but the schemes are in defective in proxy revocations. In this paper, we propose two proxy signature schemes based on RSA cryptosystems. The proposed first scheme does not consider proxy revocation mechanism; however, it will help us to compare our protocol with the existing RSA-based schemes. The proposed second scheme provides an effective proxy revocation mechanism. The proposed schemes do not require any secure channel to proxy key delivery and support the necessary security requirements of proxy signature.
The Persuasive Power of Human-Machine Dialogue
Authors: Divya Ramachandran, John Canny
Venue: Proceedings of the 3rd international conference on Persuasive Technology (2008)

The persuasive power of live interaction is hard to match, yet technologies are increasingly taking on roles to promote behavioral change. We believe that speech-based interfaces offer a compelling mode of interaction for engaging users and are motivated to understand how to best present persuasive information using speech interaction. We present a study comparing the persuasive power of two speech-based information systems, one which uses a recorded message-based lecture presentation and another which uses an interactive dialogic presentation. We measure the persuasive power across both conditions using a survival task. We find that the dialogic system is significantly more persuasive than the lecture system. We also find that the dialogic system presents significantly (almost four times) less information than the lecture system. We analyze our results using three standard rank correlation methods. We point to limitations of these measures and propose a new metric which appears to be more sensitive for this task.
An equivalent circuit approach to organic solar cell modelling
Authors: Ali Cheknane, Hikmat S. Hilal, Fayçal Djeffal, Boumediène Benyoucef, Jean-Pierre Charles
Venue: Microelectronics Journal (2008)

In this paper, we present a simulation study for a newly prepared organic solar cell, based on a composite of poly (2-methoxy-5-(2'-ethylhexyloxy)-1, 4-phenylenevinylene (MEH-PPV) with [6, 6]-phenyl C"6"0 butyric acid methyl ester (PCBM). Photo-current density vs. voltage (J-V) characteristics, for the cell, which were experimentally studied earlier, have been revisited here. The results indicated that the conduction mechanism in the organic solar cell is strongly influenced by the excitonic diffusion. Sound correlation, between theoretical and experimental photo-current density vs. voltage (J-V) plots, has been achieved. Moreover, the simulation clearly demonstrates that the performance of the tested device can be described, with sound accuracy, by a two-diode-equivalent model.
Driving Cars by Means of Genetic Algorithms
Authors: Yago Saez, Diego Perez, Oscar Sanjuan, Pedro Isasi
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

The techniques and the technologies supporting Automatic Vehicle Guidance are an important issue. Automobile manufacturers view automatic driving as a very interesting product with motivating key features which allow improvement of the safety of the car, reducing emission or fuel consumption or optimizing driver comfort during long journeys. Car racing is an active research field where new advances in aerodynamics, consumption and engine power are critical each season. Our proposal is to research how evolutionary computation techniques can help in this field. As a first goal we want to automatically learn to drive, by means of genetic algorithms, optimizing lap times while driving through three different circuits.
Restricted Non-cooperative Games
Authors: Seth J. Chandler
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

Traditional non-cooperative game theory has been an extraordinarily powerful tool in modeling biological and economic behavior, as well as the effect of legal rules. And, although it contains plausible concepts of equilibrium behavior, it does not contain a theory of dynamics as to how equilibria are to be reached. This paper on Restricted Non-Cooperative Games inserts dynamic content into traditional game theory and thus permits modeling of more realistic settings by imposing topologies that restrict the strategies available to players. It uses Mathematica to show how the payoff array used in conventional game theory, coupled with these strategy topologies, can construct a $\texttt{"}$game network$\texttt{"}$, which can be further visualized, analyzed, and $\texttt{"}$scored$\texttt{"}$ for each of the players. The paper likewise uses Mathematica to analyze settings in which each player has the ability to engineer its own strategy topology and suggests other potential extensions of Restricted Non-Cooperative Games.
Note: Bounds on some van der Waerden numbers
Authors: Tom Brown, Bruce M. Landman, Aaron Robertson
Venue: Journal of Combinatorial Theory Series A (2008)

For positive integers s and k"1,k"2,...,k"s, the van der Waerden number w(k"1,k"2,...,k"s;s) is the minimum integer n such that for every s-coloring of set {1,2,...,n}, with colors 1,2,...,s, there is a k"i-term arithmetic progression of color i for some i. We give an asymptotic lower bound for w(k,m;2) for fixed m. We include a table of values of w(k,3;2) that are very close to this lower bound for m=3. We also give a lower bound for w(k,k,...,k;s) that slightly improves previously-known bounds. Upper bounds for w(k,4;2) and w(4,4,...,4;s) are also provided.
Performance Analysis of 802.11e Burst Transmissions with FEC Codes over Wireless Sensor Networks
Authors: Jong-Suk Ahn, Jong-Hyuk Yoon, Young-Im Cho
Venue: Proceedings of the 3rd international conference on Embedded Software and Systems (2007)

WSNs (Wireless Sensor Network) can employ a burst technique known as 802.11e TXOP (Transmission Opportunity) operation to send sensor data backlogged during the sleep period [1] and FEC (Forward Error Correction) techniques to resist against their high BER (Bit Error Rate) [2]. This paper extends the 802.11e evaluation model [3], [4] to accurately calculate the performance of two TXOP schemes such as BI (Basic Immediate ACK) and BB (Basic Block ACK) with FEC codes. The generalized model accounts for the effect of propagation errors negligible in wireless LANs, the impact of HOB (Head-Of-Block) collision, and the effect of adopting FEC codes. This paper evaluates this extended model over real WSN channels abstracted by Gilbert model. The numerical calculations over real WSN traces confirm that the inclusion of HOB collisions causes the throughput difference of BB by up-to 194% comparing to [3]. They also predicts a trade-off between the performance improvement and the overhead of FEC codes over WSN channels.
Approximately Bisimilar Symbolic Models for Incrementally Stable Switched Systems
Authors: Antoine Girard, Giordano Pola, Paulo Tabuada
Venue: Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control (2008)

Switched systems constitute an important modeling para- digm faithfully describing many engineering systems in which software interacts with the physical world. Despite considerable progress on stability and stabilization of switched systems, the constant evolution of technology demands that we make similar progress with respect to different, and perhaps more complex, objectives. This paper describes one particular approach to address these different objectives based on the construction of approximately equivalent (bisimilar) symbolic models for a switched system. The main contribution of this paper consists in showing that under standard assumptions ensuring incremental stability of a switched system (i.e. existence of common or multiple Lyapunov functions), it is possible to construct a symbolic model that is approximately bisimilar to the original switched system with a precision that can be chosen a priori. To support the computational merits of the proposed approach we present a realistic example of a boost dc-dc converter and show how to synthesize a switched controller that regulates the output voltage at a desired level.
Wavelet Based Spatial Scaling of Coupled Reaction Diffusion Fields
Authors: Sudib K. Mishra, Krishna Muralidharan, Pierre Deymier, George Frantziskonis, Srdjan Simunovic, Sreekanth Pannala
Venue: Proceedings of the 8th international conference on Computational Science, Part II (2008)

Multiscale schemes for transferring information from fine to coarse scales are typically based on some sort of averaging. Such schemes smooth the fine scale features of the underlying fields, thus altering the fine scale correlations. As a superior alternative to averaging, a wavelet based scheme for the exchange of information between a reactive and diffusive field in the context of multiscale reaction-diffusion problems is proposed and analyzed. The scheme is shown to be efficient in passing information along scales, from fine to coarse, i.e. up-scaling as well as from coarse to fine, i.e. down-scaling. In addition, it retains fine scale statistics, mainly due to the capability of wavelets to represent fields hierarchically. Critical to the success of the scheme is the identification of dominant scales containing the majority of useful information. The scheme is applied in detail to the analysis of a diffusive system with chemically reacting boundary. Reactions are simulated using kinetic Monte Carlo (KMC) and diffusion is solved by finite differences. Spatial scale differences are present at the interface of the KMC sites and the diffusion grid. The computational efficiency of the scheme is compared to results obtained by local averaging, and to results from a benchmark model. The spatial scaling scheme ties to wavelet based schemes for temporal scaling, presented elsewhere by the authors.
3D Multiscale Visualisation for Medical Datasets
Authors: N. J. B. McFarlane, G. J. Clapworthy, A. Agrawal, M. Viceconti, F. Taddei, E. Schileo, F. Baruffaldi
Venue: Proceedings of the 2008 Fifth International Conference BioMedical Visualization: Information Visualization in Medical and Biomedical Informatics (2008)

A 3D multiscale view has been developed for medical datasets that are composed of multiple images of very different spatial sizes. The example data consisted of 3 volume images of bone: a 400mm femur; a 20mm micro-CT image of trabeculae; and a 0.5mm nano-CT image of a single trabecula. The images were nested, the nano-CT being inside the micro-CT, which was within the head of the femur. The images were visualised using a volume slice view. The dataset was multiscale in that each image could not be usefully resolved at the scale of the next larger image. The multiscale interaction within the system is based on the replacement of sub-scale images by small click-and-zoom tokens. Issues relating to interaction with the tokens are discussed and early outcomes from this form representation are described.