ppxscal/embedding_network huggingface dataset:
Early Prediction of Student Frustration
Authors: Scott W. Mcquiggan, Sunyoung Lee, James C. Lester
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

Affective reasoning has been the subject of increasing attention in recent years. Because negative affective states such as frustration and anxiety can impede progress toward learning goals, intelligent tutoring systems should be able to detect when a student is anxious or frustrated. Being able to detect negative affective states early, i.e., before they lead students to abandon learning tasks, could permit intelligent tutoring systems sufficient time to adequately prepare for, plan, and enact affective tutorial support strategies. A first step toward this objective is to develop predictive models of student frustration. This paper describes an inductive approach to student frustration detection and reports on an experiment whose results suggest that frustration models can make predictions early and accurately.
Parallelizing Record Linkage for Disclosure Risk Assessment
Authors: Joan Guisado-Gámez, Arnau Prat-Pérez, Jordi Nin, Victor Muntés-Mulero, Josep Ll. Larriba-Pey
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

Handling very large volumes of confidential data is becoming a common practice in many organizations such as statistical agencies. This calls for the use of protection methods that have to be validated in terms of the quality they provide. With the use of Record Linkage (RL) it is possible to compute the disclosure risk, which gives a measure of the quality of a data protection method. However, the RL methods proposed in the literature are computationally costly, which poses difficulties when frequent RL processes have to be executed on large data.Here, we propose a distributed computing technique to improve the performance of a RL process. We show that our technique not only improves the computing time of a RL process significantly, but it is also scalable in a distributed environment. Also, we show that distributed computation can be complemented with SMP based parallelization in each node increasing the final speedup.
A multi-layered security architecture for modelling complex systems
Authors: Clive Blackwell
Venue: Proceedings of the 4th annual workshop on Cyber security and information intelligence research: developing strategies to meet the cyber security and information intelligence challenges ahead (2008)

Existing practical architectural models have been proposed with a hierarchy of layers such as Neumann's 8-layered security model. These models cannot reason about complex systems convincingly, so we need new models for systematic and faithful analysis. We have simplified Neumann's model to create a three-layer security model that can be used for understanding and reasoning about the security of complex systems and formalised to automate analysis. The three layers are the semantic (involving people and organisations), logical (computers and networks) and physical layers including the relationships and interactions between them. Our model can be used to analyse systems more systematically and holistically including human and physical factors, rather than as technical systems alone. The model is applied to examine the security of the electricity grid, which is very difficult to analyse because of its complexity. It can also model other aspects of critical infrastructure and other complex systems such as financial networks.
Supporting e-Learning with Language Technology for Portuguese
Authors: Mariana Avelãs, António Branco, Rosa Gaudio, Pedro Martins
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

In this paper we report on the development and adaptation of language technology tools for Portuguese aimed at supporting e-Learning via the extension of a Learning Management System with new functionalities. We also describe how these tools were integrated into this Learning Management System and present results of both their intrinsic and extrinsic evaluation.
General iterative algorithms for solving mixed quasi-variational-like inclusions
Authors: Lu-Chuan Zeng, Qamrul Hasan Ansari, Jen-Chih Yao
Venue: Computers Mathematics with Applications (2008)

In this paper, we extend the auxiliary variational inequality technique due to Ding and Yao [X.P. Ding, J.C. Yao, Existence and algorithm of solutions for mixed quasi-variational-like inclusions in Banach spaces, Comput. Math. Appl. 49 (2005) 857-869] to develop iterative algorithms for finding the approximate solutions of a mixed quasi-variational-like inclusion problem (in short, MQVLIP) in the setting of Banach spaces. We first establish a result on the existence of a solution of the equilibrium problem by virtue of the Fan-KKM lemma. Then by using this result and a result by Ding and Tan [X.P. Ding, K.K. Tan, A minimax inequality with applications to existence of equilibrium point and fixed point theorems, Colloq. Math. 63 (2) (1992) 233-247], we derive the existence of a unique solution of MQVLIP and the existence of approximate solutions generated by the proposed algorithms. Moreover, we also provide the new criteria for convergence of approximate solutions to the exact solution of MQVLIP.
Enhancing the Performance of Maximum---Likelihood Gaussian EDAs Using Anticipated Mean Shift
Authors: Peter A. Bosman, Jörn Grahl, Dirk Thierens
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Many Estimation---of---Distribution Algorithms use maximum-likelihood (ML) estimates. For discrete variables this has met with great success. For continuous variables the use of ML estimates for the normal distribution does not directly lead to successful optimization in most landscapes. It was previously found that an important reason for this is the premature shrinking of the variance at an exponential rate. Remedies were subsequently successfully formulated (i.e. Adaptive Variance Scaling (AVS) and Standard---Deviation Ratio triggering (SDR)). Here we focus on a second source of inefficiency that is not removed by existing remedies. We then provide a simple, but effective technique called Anticipated Mean Shift (AMS) that removes this inefficiency.
A Connectionist Thematic Grid Predictor for Pre-parsed Natural Language Sentences
Authors: João Luís Rosa
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Inspired on psycholinguistics and neuroscience, a symbolic-connectionist hybrid system called ¿-Pred (Thematic Pred ictor for natural language) is proposed, designed to reveal the thematic grid assigned to a sentence. Through a symbolic module, which includes anaphor resolution and relative clause processing, a parsing of the input sentence is performed, generating logical formulae based on events and thematic roles for Portuguese language sentences. Previously, a morphological analysis is carried out. The parsing displays, for grammatical sentences, the existing readings and their thematic grids. In order to disambiguate among possible interpretations, there is a connectionist module, comprising, as input, a featural representation of the words (based on verb/noun WordNet classification and on classical semantic microfeature representation), and, as output, the thematic grid assigned to the sentence. ¿-Pred employs biologically inspired training algorithm and architecture, adopting a psycholinguistic view of thematic theory. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Crossed lines
Authors: Sarah Atkinson
Venue: Proceedings of the 3rd international conference on Digital Interactive Media in Entertainment and Arts (2008)

This paper provides a brief description of the interactive film Crossed Lines.
Granulation of Knowledge in Decision Systems: The Approach Based on Rough Inclusions. The Method and Its Applications
Authors: Lech Polkowski
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

Rough set approach to knowledge entails its granulation: knowledge represented as a collection of classifications by means of indiscernibility of objects consists of indiscernibility classes that form elementary granules of knowledge. Granules of knowledge that emerge as unions of elementary granules are also characterized as exact concepts that are described with certainty. Relaxing of indiscernibility relations has led to various forms of similarity relations. In this lecture, we discuss the approach to similarity rooted in mereological theory of concepts, whose primitive notion is that of a rough inclusion. Rough inclusions are predicates/relations of a part to a degree. Partial containment is the basic underlying phenomenon related to uncertainty, therefore rough inclusions allow for a formalization of a wide spectrum of contexts in which reasoning under uncertainty is effected.Granules are formed by means of rough inclusions as classes of objects close to a specified center of the granule to a given degree; formally, they resemble neighborhoods formed with respect to a certain metric. Classes of objects in turn are defined by the class operator borrowed from mereology. The usage of mereological techniques based on the notion of a part is justified by its greater elegance and transparency in comparison to the naive theory of concepts based on the notion of an element.At IEEE GrC 2005, 2006 the Author put forth the idea of a granular information/decision system whose objects are granules formed from the original information/decision system; the idea was issued along with the hypothesis that granular systems at sufficiently large radii of granulation, should preserve information about objects coded in the attribute---value language to a sufficiently high degree. This idea is here discussed along with results of some tests that bear it out.The second application that is reflected in the lecture is about missing values; the approach discussed here is also based on granulation and the idea is to absorb objects with missing values into granules of knowledge in order to replace in a sense the missing value with a defined one decided by the granule.
Adaptive hybrid control for noise rejection
Authors: Aman Ganesh, Jaswinder Singh
Venue: Proceedings of the 9th WSEAS International Conference on Neural Networks (2008)

System modeling and identification is an important tool for analysis, evaluation and prediction in classification, decision analysis and automatic control. Many models have been suggested for noise rejection. The identification of adaptive controllers for practical system is desired for better control and for improved system response even in the presence of the noise. In this paper, a hybrid controller is developed, using Artificial Neural Network and Genetic algorithm, which can provide adaptation. The neuro-controller translates the data (input, output and noise data pertaining to the system) into a control action. A genetic algorithm in integration with neuro-controller is used to determine the weights and biases of the neural network for better response. The performance of these two approaches has been evaluated using data of different plants on a common set of performance indices. The simulations results show that identified GA based Adaptive neuro-controller along with PID controller provides a much better system response under noise condition as compared to using PID controller. The hybrid controller provides a better system response and also helps in maintaining the optimal set point.
Oblivious Randomized Direct Search for Real-Parameter Optimization
Authors: Jens Jägersküpper
Venue: Proceedings of the 16th annual European symposium on Algorithms (2008)

The focus is on black-box optimization of a function given as a black box, i.e. an oracle for f-evaluations. This is commonly called direct search, and in fact, most methods for direct search are heuristics. Theoretical results on the performance/behavior of such heuristics are still rare. One reason: Like classical optimization algorithms, also direct-search methods face the challenge of step-size control, and usually, the more sophisticated the step-size control, the harder the analysis. Obviously, when we want the search to actually converge to a stationary point (i.e., the distance from this point tends to zero) at a nearly constant rate, then step sizes must be adapted. In practice, however, obtaining an ¿-approximation for a given ¿> 0 is often sufficient, and usually all N parameters are bounded, so that the maximum distance from the optimum is bounded. Thus, in such cases reasonable step sizes lie in a predetermined bounded interval. Considering the minimization of the distance from a fixed point as the objective, we address the question, for randomized heuristics that use isotropic sampling to generate new candidate solutions, whether we might get rid of step-size control --- namely of the problems connected to it, like so-called premature convergence --- by choosing step sizes randomly according to some properly predefined distribution over this interval. As this choice of step sizes is oblivious to the course of the optimization, we gain robustness against a loss of step-size control. Naturally, the question is: What is the price w.r.t. local convergence speed? As we shall see, merely a factor of order ln (d/¿), where d is the diameter of the the decision space, an N-dimensional interval region.
Coevolutionary bid-based genetic programming for problem decomposition in classification
Authors: Peter Lichodzijewski, Malcolm I. Heywood
Venue: Genetic Programming and Evolvable Machines (2008)

In this work a cooperative, bid-based, model for problem decomposition is proposed with application to discrete action domains such as classification. This represents a significant departure from models where each individual constructs a direct input-outcome map, for example, from the set of exemplars to the set of class labels as is typical under the classification domain. In contrast, the proposed model focuses on learning a bidding strategy based on the exemplar feature vectors; each individual is associated with a single discrete action and the individual with the maximum bid `wins' the right to suggest its action. Thus, the number of individuals associated with each action is a function of the intra-action bidding behaviour. Credit assignment is designed to reward correct but unique bidding strategies relative to the target actions. An advantage of the model over other teaming methods is its ability to automatically determine the number of and interaction between cooperative team members. The resulting model shares several traits with learning classifier systems and as such both approaches are benchmarked on nine large classification problems. Moreover, both of the evolutionary models are compared against the deterministic Support Vector Machine classification algorithm. Performance assessment considers the computational, classification, and complexity characteristics of the resulting solutions. The bid-based model is found to provide simple yet effective solutions that are robust to wide variations in the class representation. Support Vector Machines and classifier systems tend to perform better under balanced datasets albeit resulting in black-box solutions.
Coevolution of a Fuzzy Rule Base for Classification Problems
Authors: Barbara Fusińska, Marek Kisiel-Dorohinicki, Edward Nawarecki
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

In the paper a certain class of classification systems based on hybrid fuzzy-genetic approach is considered. The particular solution proposed allows for coevolution of two different populations, that search for a rule base structure and linguistic variables definitions respectively. The search is organised according to the principles of evolutionary multi-agent systems, which results in high generalisation capabilities of the system, as illustrated by the preliminary experimental results.
Minimum Distortion Embeddings into a Path of Bipartite Permutation and Threshold Graphs
Authors: Pinar Heggernes, Daniel Meister, Andrzej Proskurowski
Venue: Proceedings of the 11th Scandinavian workshop on Algorithm Theory (2008)

The problem of computing minimum distortion embeddings of a given graph into a line (path) was introduced in 2004 and has quickly attracted significant attention with subsequent results appearing in recent stoc and soda conferences. So far all such results concern approximation algorithms or exponential-time exact algorithms. We give the first polynomial-time algorithms for computing minimum distortion embeddings of graphs into a path when the input graphs belong to specific graph classes. In particular, we solve this problem in polynomial time for bipartite permutation graphs and threshold graphs.
Providing Fault-Tolerance in Unreliable Grid Systems Through Adaptive Checkpointing and Replication
Authors: Maria Chtepen, Filip H. Claeys, Bart Dhoedt, Filip Turck, Peter A. Vanrolleghem, Piet Demeester
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

As grids typically consist of autonomously managed subsystems with strongly varying resources, fault-tolerance forms an important aspect of the scheduling process of applications. Two well-known techniques for providing fault-tolerance in grids are periodic task checkpointing and replication. Both techniques mitigate the amount of work lost due to changing system availability but can introduce significant run-time overhead. The latter largely depends on the length of checkpointing interval and the chosen number of replicas, respectively. This paper presents a dynamic scheduling algorithm that switches between periodic checkpointing and replication to exploit the advantages of both techniques and to reduce the overhead. Furthermore, several novel heuristics are discussed that perform on-line adaptive tuning of the checkpointing period based on historical information on resource behavior. Simulation-based comparison of the proposed combined algorithm versus traditional strategies based on checkpointing and replication only, suggests significant reduction of average task makespan for systems with varying load.
A Vague Sets Based Hierarchical Synthetic Evaluation Algorithm for Health Condition Assessment
Authors: Hua Bai, Lixin Gao
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

The hierarchical synthetic evaluation algorithm for health condition assessment of buried water pipeline is studied. Usually, the deterioration of buried pipeline is influenced by many uncertainty factors, such as material, surrounding environment, etc. It is essential to properly represent and use multi-source information to conduct rational health condition evaluation analysis. By the use of a hierarchical framework, a vague sets based synthetic evaluation algorithm has been developed for supporting such evaluation analysis, the kernel of which organizes data, measures similarity and evaluates the result on the basis of the vague sets. Compared with the existing fuzzy comprehensive evaluation method, this method is more powerful and efficient to deal with conflicting information as well as partial ignorance.
Joint Topology Control and Power Conservation for Wireless Sensor Networks Using Transmit Power Adjustment
Authors: Deying Li, Hongwei Du, Lin Liu, Scott C. Huang
Venue: Proceedings of the 14th annual international conference on Computing and Combinatorics (2008)

Topology control with individual node transmission power adjustment in wireless sensor networks has shown to be effective in increasing network capacity and prolonging network lifetime. In this paper, we focus on the strong minimum power restricted topology control problem, which to adjust the limited transmission power for each sensor node to find a power assignment to reserve strong connectivity and achieve minimum energy cost in the wireless sensor network. We proposed three heuristics based on the problem. Simulation results have demonstrated the efficiency of three heuristics.
CoChemEx: Supporting Conceptual Chemistry Learning Via Computer-Mediated Collaboration Scripts
Authors: Dimitra Tsovaltzi, Nikol Rummel, Niels Pinkwart, Andreas Harrer, Oliver Scheuer, Isabel Braun, Bruce M. Mclaren
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Chemistry students, like students in other disciplines, often learn to solve problems by applying well-practiced procedures. Such an approach, however, may hinder conceptual understanding. We propose to promote conceptual learning by having pairs of students collaborate on problems in a virtual laboratory (VLab), assisted by a computer-mediated collaboration script that guides the students through the stages of scientific experimentation and adapts to their needs for support. We used the results from a small-scale study comparing how singles and dyads solve chemistry problems with the VLab with and without scripts to develop a scripted collaborative experimentation environment. A subsequent small-scale study compared an adaptive and a non-adaptive version of the system. Qualitative data analyses revealed a tendency for the dyads in the adaptive feedback condition to improve their collaboration and be more motivated than the non-adaptive dyads. In this paper, we present our research framework and report on preliminary results from the two small-scale studies.
Magnetic and microstructure properties for the Ni-Zn thin films electrodeposited in ultrasounds field
Authors: Cristian Pirghie, Petru Stiuca
Venue: Proceedings of the 9th WSEAS International Conference on Acoustics & Music: Theory & Applications (2008)

In this work we show some results about some correlations between microstructure and magnetic properties for the Ni-Zn thin films obtained use electrodeposition in ultrasounds field. We modified ultrasounds power start 1 to 10 kW/m2 one by one. Ultrasounds were applied perpendicular to the direction of electrical field between electrodes. The thickness of studied samples was in the range of 100-120 nm. We studied microstructure used Transmission Electron Microscopy (TEM), High Resolution Transmission Electron Microscopy (HRTEM), Selected Area Electron Diffraction (SAED) and X Ray Diffraction (XRD) respectively. Morphology of thin films were studied used Atomic Force Microscopy. The use of ultrasounds with different power on electrolytic bath leads to formation of polycrystalline thin films with greater uniformity of grain diameter.
A Thermodynamical Search Algorithm for Feature Subset Selection
Authors: Félix F. González, Lluís A. Belanche
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

This work tackles the problem of selecting a subset of features in an inductive learning setting, by introducing a novel Thermodynamic Feature Selection algorithm (TFS). Given a suitable objective function, the algorithm makes uses of a specially designed form of simulated annealing to find a subset of attributes that maximizes the objective function. The new algorithm is evaluated against one of the most widespread and reliable algorithms, the Sequential Forward Floating Search (SFFS). Our experimental results in classification tasks show that TFS achieves significant improvements over SFFS in the objective function with a notable reduction in subset size.