ppxscal/embedding_network huggingface dataset:
Improving the discrimination of vegetation and landform patterns in sandy rangelands: a synergistic approach
Authors: P. D. Blanco, G. I. Metternicht, H. F. Del Valle
Venue: International Journal of Remote Sensing (2009)

Soil erosion is a key factor in land degradation processes in the sandy rangelands of the Peninsula Valdes of Patagonia, Argentina. Mapping landform and vegetation patterns is important for improving prediction, monitoring and planning of areas threatened by sand and shrub encroachment. This paper investigates the contribution of optical sensors, such as the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and textural measures derived from microwave Radarsat Advanced Synthetic Aperture Radar (ASAR) to their discrimination. An evaluation is undertaken to compare the classification accuracy achieved by specific regions of the spectrum and their synergistic use in an object-oriented approach. Image segmentation and object-oriented classifications were applied to the datasets. This required defining appropriate fuzzy membership functions for characterizing active and stabilized lineal dunes and the main vegetation classes. Improvements in the discrimination of active and stabilized dunes (vegetated by either scrub or grass) were achieved by using an object-oriented classification that integrated microwave and visible near-infrared (NIR) data. Changes in surface roughness caused by different vegetation types stabilizing the dunes affected the radar backscattering. Whereas Radarsat enabled a clear separation of scrub-stabilized dunes, Terra-ASTER showed superior performance in the cartography of grass-stabilized dunes. The synergistic use of microwave and visible and near-infrared (VNIR) data yielded a substantial increase in the discrimination and mapping of landform/vegetation patterns.
Characterizing individual communication patterns
Authors: R. Dean Malmgren, Jake M. Hofman, Luis A.N. Amaral, Duncan J. Watts
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for characterizing individual behavior and, by extension, identifying latent structure in human populations. Here, we propose a model of individual e-mail communication that is sufficiently rich to capture meaningful variability across individuals, while remaining simple enough to be interpretable. We show that the model, a cascading non-homogeneous Poisson process, can be formulated as a double-chain hidden Markov model, allowing us to use an efficient inference algorithm to estimate the model parameters from observed data. We then apply this model to two e-mail data sets consisting of 404 and 6,164 users, respectively, that were collected from two universities in different countries and years. We find that the resulting best-estimate parameter distributions for both data sets are surprisingly similar, indicating that at least some features of communication dynamics generalize beyond specific contexts. We also find that variability of individual behavior over time is significantly less than variability across the population, suggesting that individuals can be classified into persistent "types". We conclude that communication patterns may prove useful as an additional class of attribute data, complementing demographic and network data, for user classification and outlier detection-a point that we illustrate with an interpretable clustering of users based on their inferred model parameters.
Design principles of adaptive cellular immunity for artificial immune systems
Authors: Hugo Antonius van den Berg
Venue: Soft Computing - A Fusion of Foundations, Methodologies and Applications (2009)

Artificial immune systems (AISs) have been proposed as a new computing paradigm. This paper reviews design principles of adaptive cellular immunity, based on the immunological literature rather than the simplified mathematical models which have thus far dominated the development of framework for design, interpretation, and application of AISs.
Ontology-Based mediation system
Authors: Nora Maiz, Omar Boussaid, Fadila Bentayeb
Venue: Proceeding of the 2006 conference on Leading the Web in Concurrent Engineering: Next Generation Concurrent Engineering (2006)

Data integration by mediation is more practical then centralized data warehouse when data is not static. The use of ontologies in the mediation allows semantic and structural integration. In this paper, we propose a new mediation system based on a hybrid architecture of ontologies modeled according to GLAV (Generalized Local As View) model. Thus, we propose an ascending method for building local and global ontologies, which facilitates the semantic reconciliation between data sources. Moreover, we use OWL (Ontology Web Language) for defining mappings between data sources, and ontologies. We also propose a language to formulate user queries. The query language handles global ontology concepts and local ontologies properties because we assume that the user is expert in its domain. Finally, we propose a query rewriting strategy: queries are decomposed to obtain a set of equivalent subqueries that are sent to the corresponding data sources for execution, and after that recomposed to obtain the final result.
A knowledge-based coach for reasoning about historical causation
Authors: Liz Masterman
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

The ability to explain the causes of historical events is a key skill for learners to acquire, but the ill-structured nature of the task means they cannot be guided through a problem-space of well-defined moves to reach a correct answer. This paper investigates whether a knowledge-based computer coach can provide effective guidance to learners as they construct diagrammatic explanations of the causes leading to a particular event. The design of the coach was based on a model of expert reasoning synthesised from the historiographical literature and on an analysis of teacher-learner interactions observed during classroom activities. Coaching was provided at two levels: a) generalised (decontextualised) guidance and b) guidance directly relevant to the topic of study. Where appropriate, learners could choose to disregard the coach's advice. The knowledge-base underlying the coach could also be made available as a scaffolding aid. An evaluation with three groups of students aged 12-13 showed that i) maximal scaffolding and content-specific coaching resulted in diagrammatic explanations of greater accuracy and superior structural quality to those produced either with generalised guidance or with no guidance at all, and ii) learners' appreciation of the subjective nature of historical explanations was not compromised by the coaching interventions.
Ensembles of Feature Subspaces for Object Detection
Authors: Shiliang Sun
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Detecting objects (e.g., people, faces) from images is an important problem in many applications of machine learning and computer vision. Most past work has concentrated on the issues of feature extraction and classification, where less attention has been paid to the selection and manipulation of features. In this paper, the method of ensembles of feature subspaces is applied to construct multiple classifiers for object detection. Specifically, random subspace with sum rule and principal component analysis (PCA) are taken as a case study. Individual support vector machine classifiers are constructed using randomly selected eigenvectors from PCA spaces. Then sum rule is adopted to combine the prediction of individual classifiers. The performance of the proposed method is tested on two applications: pedestrian detection and face detection, which show promising results. We also compare sum rule with majority voting in their performance for weighting individual classifiers, and find that sum rule is superior.
How Many Objects?: Determining the Number of Clusters with a Skewed Distribution
Authors: Satoshi Oyama, Katsumi Tanaka
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

We propose a supervised approach to enable accurate determination of the number of clusters in object identification. We use the aggregated attribute values of the data set to be clustered as explanatory variables in the prediction model. Attribute aggregation can be done in linear time with respect to the number of data items, so our method can be used to predict the number of clusters with a low computational burden. To deal with skewed target values, we introduce a two-stage method as well as a method using a higher-order combination of explanatory variables. Experiments demonstrate our methods enable more accurate prediction than existing methods.
Explaining Self-Explaining: A Contrast between Content and Generation
Authors: Robert G. M. Hausmann, Kurt Vanlehn
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Self-explaining has been repeatedly shown to result in positive learning outcomes for students in a wide variety of disciplines. However, there are two potential accounts for why self-explaining works. First, those who self-explain experience more content than those who do not. Second, there are differences in the activity of generating the explanations versus merely comprehending them. To compare these two accounts, the present in vivo classroom study, conducted in the PSLC physics LearnLab, attempted to contrast robust learning from generating explanations with the actively of studying instructional explanations. The students' learning activities (self-explaining vs. paraphrasing) were crossed with the completeness of the examples they studied (complete vs. incomplete). During a classroom period on electrodynamics, students alternated between solving problems and studying examples. On these problems, the self-explainers made fewer errors and asked for less help than paraphrasers. On homework problems done many days later, self-explainers displayed evidence of far transfer in a related, yet new domain (i.e., magnetism). In conclusion, prompting students to self-explain while studying examples, in an authentic classroom environment, can result in positive near-and long-term learning.
Interface feature prioritization for web services: Case of online flight reservations
Authors: Cagla Ozen Seneler, Nuri Basoglu, Tugrul Daim
Venue: Computers in Human Behavior (2009)

Acceptance, utility, and usability of system designs have become a focal interest in human-computer interaction (HCI) research, yet at present there is a lack detailed understandings of which system design features influence them. The purpose of the study is to examine the effects of five product design features; customization, adaptive behavior, memory load, content density, and speed on user preference through an experimental study by using conjoint analysis. In experimental study, instead of classical conjoint cards, prototypes were generated for products. Besides, desirability and market segments of product prototypes were identified. In line with the results, among the five product design features, speed is the most and customization is the least important features that affect user preference. Contrary to the expectations, customization has a relatively small importance value in this research. Subsequent design features that influence user preference after speed are minimal memory load, adaptive behavior, and content density, respectively. According to findings, interfaces that have high-speed, minimal memory load, adaptive behavior, low content density, and customization features are more preferable than those that do not.
Optimal Entry Timing in Markets with Social Influence
Authors: Yogesh V. Joshi, David J. Reibstein, Z. John Zhang
Venue: Management Science (2009)

Firms routinely face the challenging decision of whether to enter a new market where a firm's strong presence in an existing market has a positive influence (the leverage effect) on product adoption in the new market, but the reciprocal social influence on the existing market is negative (the backlash effect). In this paper, we show that a firm's optimal entry strategy in this situation cannot be characterized by the familiar “now or never” or “now or at maturity” strategies proposed in the literature. We show that a strong leverage effect does not necessarily provide the justification for a firm to enter a new market, and neither should a strong backlash effect necessarily deter a firm from embracing a new market. The optimal strategy is predicated on a judicious trade-off between the three factors of leverage, backlash, and patience. Thus, an astute manager can always find the opportune time to enter the new market if she takes into account the dynamic and recursive nature of cross-market interaction effects, where leverage enhances the backlash but backlash weakens the leverage in a nonlinear, dynamic fashion. We illustrate that firms stand to benefit from explicit considerations of these effects in deciding whether and when to enter a new market. Furthermore, we explore how the optimal time of entry into the new market relates to the time of peak sales for the existing market, demonstrating that depending on the interactive effects of leverage and backlash, entry could be optimal either before or after peak sales in the existing market.
Introduction to Genetic Algorithms, 1st edition
Authors: S. N. Sivanandam, S. N. Deepa
Venue:  (2007)

The book contains basic concepts, several applications of Genetic Algorithms and solved Genetic Problems using MATLAB software and C/C++. The salient features of the book include - detailed explanation of Genetic Algorithm concepts, - numerous Genetic Algorithm Optimization Problems, - study on various types of Genetic Algorithms, - implementation of Optimization problem using C and C++, - simulated solutions for Genetic Algorithm problems using MATLAB 7.0, - brief description on the basics of Genetic Programming, - application case studies on Genetic Algorithm on emerging fields.
Biomass measurements and relationships with Landsat-7/ETM+ and JERS-1/SAR data over Canada's western sub-arctic and low arctic
Authors: Wenjun Chen, D. Blain, Junhua Li, K. Keohler, R. Fraser, Yu Zhang, S. Leblanc, I. Olthof, Jixin Wang, M. McGovern
Venue: International Journal of Remote Sensing (2009)

Information on biomass distribution is needed to estimate GHG emissions and removals from land use changes in Canada's north for UNFCCC reporting. This paper reports aboveground biomass measurements along the Dempster Highway transect in 2004, and around Yellowknife and the Lupin Gold Mine in 2005. The measured aboveground biomass ranges are 10-100 t ha-1 for woodlands, 1-100 t ha-1 for shrub sites, and 0.5-10 t ha-1 for grass/herbs sites. The root mean squared error (RMSE) of measurements is 21%, and the median absolute percentage error (MedAPE) is 14%. The combination of JERS backscatter and Landsat TM4/TM5 gives the best biomass equation for the Dempster Highway transect, with r 2 = 0.72 when using a one-step approach (i.e. using all points) and 0.78 when using a two-step approach (i.e. stratifying data into three classes: grass, shrub, and woodlands). The two-step approach reduces the MedAPE from 53% to 33%. The validation against Yellowknife & Lupin data indicates that the equations have good transferability. The improvement of two-step approach over the one-step approach, however, is not significant for the validation dataset, suggesting that the one-step approach is as good as the two-step approach when applied over areas outside where the equations are developed. The relationships and error analysis of this study, as well as the final estimate of GHG emission/removal over Canada's north have been incorporated into Canada's 2006 UNFCCC report.
What Level of Tutor Interaction is Best?
Authors: Leena Razzaq, Neil T. Heffernan, Robert W. Lindeman
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Razzaq and Heffernan (2006) showed that scaffolding compared to hints on demand in an intelligent tutoring system could lead to higher averages on a middle school mathematics post-test. There were significant differences in performance by condition on individual items. For an item that proved to be difficult for all of the students on the pretest, an ANOVA showed that scaffolding helped significantly (p < 0.01). We speculated that the scaffolding had a greater positive effect on learning for this item because it was much more difficult for the students than the other items. We thought that this result warranted a closer look at the link between the difficulty of an item and the effectiveness of scaffolding. In this paper, we report on an experiment that examines the effect of math proficiency and the level of interaction on learning. We found an interesting interaction between the level of interaction and math proficiency where less-proficient students benefited from more tutor interaction and more-proficient students benefited from less interaction.
Extraction of Illumination-Invariant Features in Face Recognition by Empirical Mode Decomposition
Authors: Dan Zhang, Yuan Yan Tang
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

Two Empirical Mode Decomposition (EMD) based face recognition schemes are proposed in this paper to address variant illumination problem. EMD is a data-driven analysis method for nonlinear and non-stationary signals. It decomposes signals into a set of Intrinsic Mode Functions (IMFs) that containing multiscale features. The features are representative and especially efficient in capturing high-frequency information. The advantages of EMD accord well with the requirements of face recognition under variant illuminations. Earlier studies show that only the low-frequency component is sensitive to illumination changes, it indicates that the corresponding high-frequency components are more robust to the illumination changes. Therefore, two face recognition schemes based on the IMFs are generated. One is using the high-frequency IMFs directly for classification. The other one is based on the synthesized face images fused by high-frequency IMFs. The experimental results on the PIE database verify the efficiency of the proposed methods.
An Ontological Framework for Modeling Complex Cooperation Contexts in Organizations
Authors: Bendoukha Lahouaria
Venue: Proceeding of the 2008 conference on Information Modelling and Knowledge Bases XIX (2008)

In this paper we propose a cooperation modeling approach which aims to the alignment of system development with the organizational change where the system will operate. It consists in a semantic enrichment of cooperation documentation so that the intertwining interactions between organization, human and system views could be represented explicitly into the system development process. The proposed ontological framework plays crucial roles as a communication, learning and design artifact for different stakeholders.
Towards the mobile optimized future internet
Authors: Heeyoung Jung, Seokjoo Koh, Woogu Park
Venue: Proceedings of the 4th International Conference on Future Internet Technologies (2009)

It is envisioned that the future Internet environment consists of a variety of mobile/wireless networks with the rapidly increasing number of mobile users. This paper discusses some visions and issues on the design of the mobile optimized future Internet (MOFI). We present an architecture of mobility control for future Internet together with comparison of the existing mobility schemes. For further works, the MOFI needs to be designed more elaborately by keeping the congruency with the overall architecture of future Internet.
3-D force control on the human fingerpad using a magnetic levitation device for fingernail imaging calibration
Authors: Thomas Grieve, Yu Sun, John M. Hollerbach, Stephen A. Mascaro
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

This paper demonstrates fast, accurate, and stable force control in three axes simultaneously when a flat surface is pressed against the human fingerpad. The primary application of this force control is for the automated calibration of a fingernail imaging system, where video images of the human fingernail are used to predict the normal and shear forces that occur when the fingerpad is pressed against a flat surface. The system consists of a six degree-of-freedom magnetic levitation device (MLD), whose flotor has been modified to apply forces to the human fingerpad, which is resting in a passive restraint. The system is capable of taking simultaneous steps in normal force and two axes of shear forces with a settling time of less than 0.2 seconds, and achieves a steady-state error as small as 0.05 N in all three axes. The system is also capable of tracking error of less than 0.2 N when the shear force vector rotates with a frequency of 1 rad/s. This paper also demonstrates the successful tracking of a desired force trajectory in three dimensions for calibrating a fingernail imaging system.
Functional observers for linear descriptor systems
Authors: M. Darouach
Venue: Proceedings of the 2009 17th Mediterranean Conference on Control and Automation (2009)

This paper is concerned with the design of functional observers for linear time-invariant descriptor systems. The order of these observers is equal to the dimension r of the functional to be estimated. Sufficient conditions for the existence and stability of these observers are given. The obtained results extend those given in [8] for the standard systems. Continuous and discrete time systems are considered. 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
Communication: On complexity of Minimum Leaf Out-Branching problem
Authors: Peter Dankelmann, Gregory Gutin, Eun Jung Kim
Venue: Discrete Applied Mathematics (2009)

Given a digraph D, the Minimum Leaf Out-Branching problem (MinLOB) is the problem of finding in D an out-branching with the minimum possible number of leaves, i.e., vertices of out-degree zero. Gutin, Razgon and Kim [G. Gutin, I. Razgon, E.J. Kim, Minimum leaf out-branching problems, in: Proc. 4th International Conference on Algorithmic Aspects in Information and Management, AAIM'08, in: Lect. Notes Comput. Sci., vol. 5034 2008, pp. 235-246] proved that MinLOB is polynomial time solvable for acyclic digraphs which are exactly the digraphs of directed path-width (DAG-width, directed tree-width, respectively) 0. We investigate how much one can extend this polynomiality result. We prove that already for digraphs of directed path-width (directed tree-width, DAG-width, respectively) 1, MinLOB is NP-hard. On the other hand, we show that for digraphs of restricted directed tree-width (directed path-width, DAG-width, respectively) and a fixed integer k, the problem of checking whether there is an out-branching with at most k leaves is polynomial time solvable.
Trustworthy Voting: From Machine to System
Authors: Nathanael Paul, Andrew S. Tanenbaum
Venue: Computer (2009)

The authors describe an electronic voting approach that takes a system view, incorporating a trustworthy process based on open source software, simplified procedures, and built-in redundant safeguards that prevent tampering.